Variation of FMRP Expression in Peripheral Blood Mononuclear Cells from Individuals with Fragile X Syndrome

Abstract

Fragile X syndrome (FXS) is the most common heritable cause of intellectual disability and autism spectrum disorder. The syndrome is often caused by greatly reduced or absent protein expression from the fragile X messenger ribonucleoprotein 1 (FMR1) gene due to expansion of a 5′-non-coding trinucleotide (CGG) element beyond 200 repeats (full mutation). To better understand the complex relationships among FMR1 allelotype, methylation status, mRNA expression, and FMR1 protein (FMRP) levels, FMRP was quantified in peripheral blood mononuclear cells for a large cohort of FXS (n = 154) and control (n = 139) individuals using time-resolved fluorescence resonance energy transfer. Considerable size and methylation mosaicism were observed among individuals with FXS, with FMRP detected only in the presence of such mosaicism. No sample with a minimum allele size greater than 273 CGG repeats had significant levels of FMRP. Additionally, an association was observed between FMR1 mRNA and FMRP levels in FXS samples, predominantly driven by those with the lowest FMRP values. This study underscores the complexity of FMR1 allelotypes and FMRP expression and prompts a reevaluation of FXS therapies aimed at reactivating large full mutation alleles that are likely not capable of producing sufficient FMRP to improve cognitive function.

Full text

Citation: Randol, J.L.; Kim, K.;
Ponzini, M.D.; Tassone, F.; Falcon,
A.K.; Hagerman, R.J.; Hagerman, P.J.
Variation of FMRP Expression in
Peripheral Blood Mononuclear Cells
from Individuals with Fragile X
Syndrome. Genes 2024,15, 356.
https://doi.org/10.3390/
genes15030356
Academic Editor: Mariarosa Anna
Beatrice Melone
Received: 12 February 2024
Revised: 2 March 2024
Accepted: 8 March 2024
Published: 13 March 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
genes
G C A T
T A C G
G C A T
Article
Variation of FMRP Expression in Peripheral Blood Mononuclear
Cells from Individuals with Fragile X Syndrome
Jamie L. Randol 1, Kyoungmi Kim 2,3, Matthew D. Ponzini 2,3 , Flora Tassone 1,2 , Alexandria K. Falcon 1,
Randi J. Hagerman 2,4 and Paul J. Hagerman 1, 2, *
1Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis,
Davis, CA 95616, USA
2Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, UC Davis Health,
Sacramento, CA 95817, USA
3Department of Public Health Sciences, School of Medicine, University of California, Davis,
Davis, CA 95616, USA
4Department of Pediatrics, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA
*Correspondence: pjhagerman@ucdavis.edu
Abstract: Fragile X syndrome (FXS) is the most common heritable cause of intellectual disability
and autism spectrum disorder. The syndrome is often caused by greatly reduced or absent protein
expression from the fragile X messenger ribonucleoprotein 1 (FMR1) gene due to expansion of a 5
′
-non-
coding trinucleotide (CGG) element beyond 200 repeats (full mutation). To better understand the
complex relationships among FMR1 allelotype, methylation status, mRNA expression, and FMR1
protein (FMRP) levels, FMRP was quantified in peripheral blood mononuclear cells for a large cohort
of FXS (n= 154) and control (n= 139) individuals using time-resolved fluorescence resonance energy
transfer. Considerable size and methylation mosaicism were observed among individuals with FXS,
with FMRP detected only in the presence of such mosaicism. No sample with a minimum allele
size greater than 273 CGG repeats had significant levels of FMRP. Additionally, an association was
observed between FMR1 mRNA and FMRP levels in FXS samples, predominantly driven by those
with the lowest FMRP values. This study underscores the complexity of FMR1 allelotypes and FMRP
expression and prompts a reevaluation of FXS therapies aimed at reactivating large full mutation
alleles that are likely not capable of producing sufficient FMRP to improve cognitive function.
Keywords: FMR1; fragile X syndrome; autism; full mutation; TR-FRET; mosaicism; intellectual disability
1. Introduction
Fragile X syndrome (FXS) is an X-linked neurodevelopmental disorder caused by
reduced or absent expression of the protein product (FMRP) of the fragile X messenger
ribonucleoprotein 1 (FMR1) gene. In the vast majority of FXS cases, expansion of a CGG
trinucleotide, located in the 5
′
untranslated region of FMR1, to above 200 repeats (full
mutation, FM) triggers hypermethylation and silencing of the gene, with consequent re-
duction/absence of the gene product [
1
–
3
]. Such expansion mutations constitute the most
common heritable cause of intellectual impairment and of autism spectrum disorder [
4
–
9
].
FMRP is an RNA-binding protein with a plethora of binding partners and functions, both
in the CNS and in peripheral tissues. Within the CNS, FMRP regulates synaptic plasticity,
neural development, and cognitive function, primarily by regulating the translation, trans-
port, and stability of many mRNAs [
10
]. The loss of FMRP results in intellectual disability
and, often, attention-deficit/hyperactivity disorder (ADHD), anxiety, autism, and other
neurological and behavioral symptoms [
11
–
13
]. Predictably, decreasing FMRP levels are
associated with increasing severity of FXS [
14
], lowered IQ [
15
], and other neurological
disorders including bipolar disorder, depression, and schizophrenia [16–18].
Genes 2024,15, 356. https://doi.org/10.3390/genes15030356 https://www.mdpi.com/journal/genes

Genes 2024,15, 356 2 of 22
Increases in FMR1 CGG-repeat length beyond the normal range (<55 CGG repeats)
are known to cause various fragile X-associated conditions. CGG alleles with repeats
between 55 and 200 are termed “premutation” (PM), given their propensity to expand to
FM alleles within one generation. Unlike FM alleles, PM alleles are generally unmethylated
and produce excess mRNA and relatively normal to slightly reduced levels of FMRP [
19
].
Therefore, individuals with PM alleles usually exhibit normal cognitive function. However,
they are predisposed to develop other physical and psychiatric disorders. Chief among
them is the neurodegenerative disorder fragile X-associated tremor/ataxia syndrome
(FXTAS), which can cause intention tremor, cerebellar ataxia, and cognitive decline, similar
to symptoms seen in Parkinson disease and Alzheimer disease [19].
Often, individuals with FXS exhibit allelic mosaicism, possessing multiple alleles that
can distribute across FM–PM or PM–normal size boundaries. It is not uncommon for an
FXS individual to have unmethylated normal or PM alleles in addition to multiple FM
alleles [
19
]. Moreover, methylation mosaicism is also a frequent occurrence among those
with FM alleles. The large degree of mosaicism contributes to varying levels of FMRP
production and is a confounding factor in determining the relationship between CGG
repeat size, methylation, transcription, and translation at the FMR1 locus, particularly in
the upper PM and FM range.
Accurately measuring low FMRP levels in FXS is important for better understanding
the relationship between CGG-repeat size and methylation class and its effect on the
development of potential treatments for fragile X-associated disorders. Various methods
have been developed to measure FMRP. Some of the first methods, such as Western blotting,
immunocytochemistry, or immunohistochemistry, were low-throughput, semiquantitative,
and/or labor-intensive [
20
]. The introduction of assays using two unique antibodies to
simultaneously bind FMRP has dramatically improved sensitivity and specificity [
20
–
23
].
In a previous study, we used one of these methods, time-resolved fluorescence resonance
energy transfer (TR-FRET), to quantify the relationship between IQ and FMRP levels [
15
].
One advantage of TR-FRET is that it can occur in homogenous cell lysate, eliminating the
need for multiple washing or separation steps.
The current study extends the use of homogeneous TR-FRET to explore the relationship
between FMRP production and the complex size- and methylation-mosaic allelotypes found
in FXS individuals using a large sample size (293 individuals with and without FXS) in
an accessible cell type–peripheral blood mononuclear cells (PBMCs). A high degree of
size and methylation mosaicism is observed for the FXS cohort, with significant levels of
FMRP being produced only from size and/or methylation mosaics with alleles below or
just above the PM–FM size boundary. Additionally, a reduction in translation efficiency
was observed as the repeat size of the smallest allele increased in the FM range. Despite
evidence of excess mRNA production in the unmethylated FM range, no PBMCs with their
smallest allele above ~270 CGGs produced significant levels of FMRP.
2. Materials and Methods
2.1. Participants and Samples
Blood samples from males with FXS and male controls were used in this study. Males
with FXS were diagnosed clinically after assessing the behavioral, cognitive, and physical
phenotypes at the Fragile X Treatment and Research Center at the UC Davis MIND Institute
at UC Davis Health. Females were excluded from this study, eliminating the confounding,
variable contribution to FMRP levels from the unaffected X chromosome due to a broad
range of activation ratios. Blood was collected at the MIND Institute, under protocols
approved by the Institutional Review Board at the University of California, Davis. Between
2010 and 2017, blood was drawn from a total of 293 individuals (322 samples, including
biological replicates): 155 individuals (170 samples, including biological replicates) diag-
nosed with FXS and 138 typically developing controls (152 samples, including biological
replicates). From these individuals, 390 samples were processed, which included both
technical replicates (same blood draw) and biological replicates (same individual, different

Genes 2024,15, 356 3 of 22
date of blood draw). See Table 1for descriptive statistics of all samples (technical replicates
were represented only once) by allele class, CGG repeat size, and age. See Supplementary
Information (Section S1) for methods of sample elimination.
Table 1. Descriptive statistics of participants’ age, allele size, and FMRP levels. Technical replicates
were randomly eliminated to generate one measurement per participant blood draw. Biological
replicates were maintained in the table to account for changes in an individual’s age and allelotype at
different blood draws. Subsequent analyses randomly eliminated biological and technical replicates,
so a single sample represented each individual. Smears were reduced to a single value represented
by the first quartile between the low and high end of a smear. This single-value representation was
added as a discrete allele to all other alleles in a sample to calculate the mean, median, and standard
deviation (SD) of CGG repeat size. Minimum and maximum CGG repeat size included the low
and high end of a smear. * FMRP
rel
values for the point mutation and the deletion, determined by
TR-FRET, are below the level of significance. The point mutation (position c.148 G>A) [
24
] prevented
detection of FMRP by the Cisbio TR-FRET assay and was eliminated from FMRP analyses; FMRP
was present according to Western blotting. The deletion encompasses the entire FMR1 gene [
25
].
Med. = Median.
Allele Class n
Age (Year) CGG Repeat Size FMRPrel
Min Max Mean Med.
SD Min Max
Mean Med.
SD Min Max
Mean Med.
SD
Control
152
1 80 33.9 30.5 21.4 19 46 29.7 30 4.8 0.39 1.87 0.99 0.98 0.26
Non-control
170
0 57 11.1 8.5 10.2 13 1400 542.5 505 282.4 −
0.05
0.49 0.09 0.07 0.09
Full mutation 97 1 44 10.7 9 9.2 200 1400 610 590 245.3 −
0.05
0.42 0.05 0.04 0.06
Methylation
mosaicism 22 1 32 10.6 5.5 10.2 200 1270 482.9 380 253.4 −
0.02
0.25 0.09 0.08 0.06
Size
mosaicism 4 9 53 29 27 18.4 170 1240 532.8 440 364.9 0.02 0.15 0.11 0.12 0.06
Size and
methylation
mosaicism
45 0 57 10.5 8 10.6 13 1540 477 440 314.7 0.01 0.49 0.17 0.14 0.12
Point
mutation * 1 - - 10 - - - - 25 - - - - 0.06 - -
Deletion * 1 - - 6 - - - - - - - - - 0.08 - -
2.2. PBMC Collection and Storage
PBMCs were collected by venipuncture in BD Vacutainer CPT tubes (BD, Franklin
Lakes, NJ, USA) and processed following the manufacturer’s protocol to obtain mostly
(~70–90%)
lymphocytes. Cells were resuspended in RPMI 1640 (Gibco, Grand Island,
NY, USA) with 10% dimethyl sulfoxide and partitioned into one to three aliquots for
cryopreservation in liquid nitrogen until needed.
2.3. CGG Genotyping and Methylation Status
Genomic DNA was isolated from PBMCs using standard procedures (Qiagen, Valen-
cia, CA, USA). CGG repeat sizing was carried out by a combination of PCR and Southern
blot analysis, as previously reported [
26
,
27
]. For Southern blot analysis, DNA was di-
gested with EcoRI and NruI, fixed on a nylon membrane and hybridized with the FMR1
genomic probe StB12.3, and labeled with Dig-11-dUTP by PCR (PCR Dig Synthesis Kit;
Roche Diagnostics) following the protocol as previously described [
27
]. PCR analysis was
performed using FMR1-specific primers (AmplideX PCR/CE, Asuragen, Austin, TX, USA);
amplicons were visualized by capillary electrophoresis and analyzed using Gene Mapper
software (Applied Biosystems, Waltham, MA, USA) [
26
]. Methylation status, including
the percentage of methylation (% of methylated alleles), was determined by densitometric
analysis of Southern blotting images, as described in [
28
]. See Table S1 for the molecular
data of each sample.
2.4. FMR1 mRNA Expression Levels
Total RNA was isolated from 2.5 mL of peripheral blood collected in PAXgene blood
RNA tubes using the PAXgene Blood RNA Kit (Qiagen, Valencia, CA, USA) and quantified
using the Agilent 2100 Bioanalyzer system (Agilent, Inc., Sana Clara, CA, USA). cDNA

Genes 2024,15, 356 4 of 22
synthesis and determination of FMR1 mRNA expression levels were performed using
real-time PCR (qRT-PCR). Three reference genes were used:
β
-glucuronidase (GUS) (probe
and primers as described in [
29
]), hydroxymethylbilane synthase (HMBS; TaqMan™ Assay
Hs0060927, ThermoFisher Scientific, Waltham, MA, USA), and hypoxanthine-guanine
phosphoribosyltransferase (HGPRT; TaqMan™ Assay Hs02800695, ThermoFisher Scientific,
Waltham, MA, USA). Details are as described in [
29
]. Relative RNA was calculated by
normalizing to the mean FMR1 mRNA value of control samples in this study. See Table S1
for molecular data by sample.
2.5. TR-FRET Assay
To avoid protein degradation, 200
µ
L of Dulbecco’s phosphate-buffered saline (DPBS;
Gibco, Grand Island, NY, USA) supplemented with Roche cOmpleteTM Ultra Protease
Inhibitor Tablets (MilliporeSigma, Burlington, MA, USA) was added to approximately
1 mL of frozen PBMCs. Cells were then thawed in a 37
◦
C dry bath with intermittent
gentle vortexing.
Cells were pelleted by centrifugation at 1500
×
gfor five minutes at 4
◦
C, washed
with 100–200
µ
L DPBS with protease inhibitor, then lysed in 85
µ
L of 1
×
Cisbio Human
FMRP lysis buffer (CisbioUS, Bedford, MA, USA) supplemented with cOmplete
TM
protease
inhibitor, 0.25 U/
µ
L Benzonase (Millipore Sigma, Burlington, MA, USA), and 2 mM MgCl
2
.
Pellets were disrupted by pipetting followed by rotation at room temperature for 2–3 h.
Lysates were then spun at 16,000
×
gfor 6 min to pellet any debris or unlysed cells. The
resulting supernatants were used to perform total protein concentration analysis using
PierceTM BCA Protein Assay (Thermo Fisher Scientific, Rockford, IL, USA).
The TR-FRET method was used to quantify FMRP using the Cisbio Human FMRP assay
(CisbioUS, Bedford, MA, USA) following the manufacturer’s protocol. Individual lysates
were diluted to two protein concentrations differing by a factor of two, each within the range
of 0.75 to 6.3
µ
g total protein in supplemented lysis buffer. Ten microliters of each total
protein concentration were loaded in quadruplicate in a 384-well Opti-Plate (Perkin Elmer,
Boston, MA, USA). Ten microliters of homogenous time-resolved fluorescence technology
pre-mixed antibodies were added to each well. The FRET plate was rocked overnight for
18 h
at room temperature and then read on the VictorX5 (PerkinElmer, Waltham, MA, USA).
TR-FRET measurements occurred over a 400
µ
s window after a
50 µs
delay to allow the decay
of short-lived (ns) background fluorescence, such as from direct excitation of the acceptor.
Readings at 615 nm (donor) and 665 nm (acceptor) were taken and ratios calculated as
Ratio = (fluorescence
at 665 nm/fluorescence at
615 nm
)
×
10
4
. The fractional change in this
ratio was computed by
∆F% = (Ratiosample −Ratiolysis buffer/Ratiolysis buffer)×100 and
used
to determined relative FMRP concentrations (below).
2.6. FMRP Quantification
2.6.1. Calculations
FMRP levels were quantified by interpolating
∆
F% on a standard curve using a fibrob-
last fiducial line run alongside PBMC samples from study participants. The same fibroblast
fiducial was used in the prior FRET analysis of Kim et al. [
15
]. Samples with
∆F% > 65 were
interpolated using a four-factor fit generated from 0 to 3.5
µ
g total protein of the fiducial
to account for the non-linearity of the model. However, for samples with
∆F% ≤65,
in-
terpolations used a linear fit generated for 0 to 0.4
µ
g of the fiducial. This allowed for
negative
∆
F% replicates to be interpolated and more accurate FMRP determination for
samples with FMRP at or near zero. A
∆
F% of 65 was chosen as the value to unite the two
models as the interpolated FMRP values for both models were approximately equal at this
∆
F% value. Next, interpolated FMRP values were corrected for PBMC total protein loaded
(FMRP/
µ
g). Finally, all corrected FMRP values were normalized to the mean corrected
FMRP for individuals with control alleles and known mRNA levels (FMRP
rel
). See Table S1
for molecular data by sample. See Supplementary Information (Section S2) for method of
extreme outlier removal.

Genes 2024,15, 356 5 of 22
2.6.2. Significance of the Presence of FMRP
FMRP significance was determined by correcting raw FRET ratios to two types of
negative controls. FRET ratios, rather than
∆
F%, were used to determine FMRP significance,
as these are the raw readings from the Victor X5 before further uncertainty is introduced
by interpolation at the low end of the standard curve. First, wells containing only lysis
buffer in the absence of total protein were used to determine background fluorescence
on a plate-to-plate basis. Second, a PBMC sample (195-13) from a participant carrying
a 300 Kb deletion encompassing the entire FMR1 gene [
25
] was included in the assay to
identify background fluorescence in the presence of total protein and the absence of FMRP.
Sample FRET ratios were corrected first to the median FRET ratio for its plate’s lysis buffer
control and then to the median lysis buffer-corrected FRET ratio of the deletion control.
This process was carried out separately for the two protein concentrations of each sample.
Significant presence of FMRP was then determined via one-sided, one-sample t-tests on the
doubly corrected FRET ratios, testing the hypothesis that the mean FMRP concentration
level is greater than zero. A sample was determined to have statistically significant FMRP
(FMRP(+)) if the higher concentration had a significant corrected FRET ratio greater than
0 (i.e.,
t-test p-value < 0.05). That is, samples whose lower concentration was significant
while the higher concentration was not significant were considered not significant for
FMRP overall (FMRP(
−
)), as the assay is more variable for lower concentrations of total
protein. See Table S1 for molecular data by sample.
2.7. Statistical Analyses
To assess the effects of FMR1 mRNA, unmethylated CGG repeat alleles, methylated
CGG repeat alleles, percentage of methylation, and/or age on FMRP, regression analy-
ses were performed using nested linear mixed-effects models to incorporate nested data
structures for FMRP (technical replicates nested within biological replicates). The mod-
els included FMR1 mRNA, age, unmethylated CGG repeats, methylated CGG repeats,
and/or fraction methylated as fixed effects, a random intercept for biological and technical
replicates, and a random slope for age-measured FMRP. The median CGG repeat size of
alleles—calculated using all CGG repeats, unmethylated and methylated CGG repeats sep-
arately, and the lower bound of smears plus one-quarter the range of the smears—was used
in the regression analyses. The first quartile of the smear range was used to more heavily
weight smaller alleles more likely to contribute to FMRP. We first performed a regression
analysis to assess the effect of FMR1 mRNA on FMRP, controlling for age. We then fitted
the following sequential models: (1) Model 1—assessing the effects of two parameters
(FMR1 mRNA and unmethylated CGG repeats) on FMRP, controlling for age; (2) Model
2—assessing the effects of two parameters (FMR1 mRNA and methylated CGG repeats)
on FMRP, controlling for age and fraction of methylated CGG repeats; and (3) Model
3—assessing the effects of three parameters (FMR1 mRNA, unmethylated CGG repeats,
and methylated CGG repeats) on FMRP, controlling for age and fraction of methylated
CGG repeats, as listed below.
Model 1: FMRP = FMR1 mRNA + Age + CGGunmethylated
Model 2: FMRP = FMR1 mRNA + Age + CGGmethylated + Fraction Methylated
Model 3: FMRP = FMR1 mRNA + Age + CGG
unmethylated
+ CGG
methylated
+
Fraction Methylated
Finally, likelihood ratio tests (LRTs) were performed to evaluate the difference between
two nested models. The first compared Model 1 and Model 3 in order to test the significance
of methylated CGG repeats and fraction of methylation on FMRP while accounting for
FMR1 mRNA, unmethylated CGG repeats, and age. The second compared Model 2 and
Model 3 in order to test the significance of unmethylated CGG repeats on FMRP while
accounting for FMR1 mRNA, methylated CGG repeats, and age. All the analyses were
conducted using open-source R software (version 4.2.1).

Genes 2024,15, 356 6 of 22
3. Results
3.1. PBMCs Express Lower Levels of FMRP Relative to Fibroblasts
A control dermal fibroblast line was used as a fiducial to generate a standard curve
for relative FMRP levels, both for PBMC samples in this study and for dermal fibroblasts
in [
15
]. Interpolation on this standard curve followed by correction for total protein loaded
produced a mean of 0.26 FMRP/
µ
g for PBMCs with control alleles. That is, control PBMCs
produce approximately 4-fold less FMRP for the same total protein than the control fibrob-
last fiducial line. Subsequent analyses were performed on FMRP values normalized to the
mean of control PBMCs (0.26 FMRP/
µ
g was normalized to 1.0 as the control
PBMC mean
).
3.2. FMRP Levels Are Independent of Year of Blood Draw and Age
One sample among biological and technical replicates per individual was randomly
selected to generate a sample set with one sample per individual. Relative FMRP was
plotted against the date of sample collection for research participants with known blood
draw dates (Figure 1) to see if storage duration affected results. Linear regression analysis
showed that FMRP levels are not significantly correlated with the date of the blood draw
in individuals with control (n= 135 unique individuals with known blood draw dates,
p= 0.07213)
and non-control (n= 143 unique individuals with known blood draw dates,
p= 0.143)
alleles, suggesting that measurements of FMRP in cryopreserved whole PBMCs
stored in liquid nitrogen were not sensitive to the number of years of sample storage
(samples collected from January 2010 through March 2017) prior to assay of PBMC-isolated
FMRP. Therefore, year of draw was not considered as a source of variation that would bias
results in subsequent statistical analyses. Relative FMRP was also plotted by age at time of
draw (Figure 2). Linear regression analysis showed no association between FMRP levels
and age of individual at time of blood draw (control p= 0.4733, non-control p= 0.1277).
3.3. Individuals with FM Alleles Generally Have a Complex Genotype
Individuals with FM alleles generally are mosaic in terms of number, size, and/or
methylation of alleles. That is, individuals with nominally FM alleles tend to have multiple
alleles whose CGG repeats can span allele classes and be distinctly methylated (Table 1,
Figure 3). Of 153 individuals with expanded CGG repeat alleles covering the FM range,
only 26 (~17%) possessed a single detectable methylated allele; thus, 83% were mosaics of
size (inter- or intra-class) and/or methylation status. Of these 127 individuals, there were
19 with methylation mosaicism, 1 who was mosaic for allele class, 41 who were mosaic
for both methylation and allele class, and 66 with up to 8 discrete FM methylated alleles.
Discrete allele sizes ranged from 13 to 1400 CGGs (Table 1).
Additional complexity was created by the presence of smears and degree of methyla-
tion. A smear is a quasi-continuous series of PCR products that differ in length by only a
few CGG repeats, such that they appear as a smear rather than a discrete band when run on
a gel or an electrophoretogram. Of the 153 individuals with nominally FM alleles, 20 had
unmethylated smears and 4 had methylated smears. In 61 samples with unmethylated alle-
les, the fraction of unmethylated alleles ranged from 5% to 95% of all alleles present. Smear
allele sizes ranged between 30 and 1540 CGGs (Table 1). Unsurprisingly, expanded-repeat
samples with the largest significant FMRP levels were those with size and/or methylation
mosaicism. Interestingly, not all samples with detectable RNA (those with larger FM CGG
repeats) produced detectable FMRP (Figure 3).
3.4. Large Unmethylated and Methylated Alleles Produce mRNA
Methylation mosaics with CGG repeat alleles only in the FM range produced mRNA
levels 0 to 2.28-fold that of the mean of control samples (Figure 4). Sample P03-10, with
an unmethylated allele at 250 CGGs, produced the highest relative FMR1 mRNA levels at
2.28 ±0.085 (mean ±SEM)
, suggesting that smaller FM alleles can produce large quantities
of mRNA, even above the range for samples with control alleles (0.47–1.43) and similar to
the excess mRNA production in the PM range [
30
]. Larger FM alleles can produce mRNA as

Genes 2024,15, 356 7 of 22
well, though to a lesser degree. For example, sample P06-32 had an unmethylated
500 CGG
repeat plus a methylated 860 repeat and still produced some mRNA (
0.37 ±0.046 relative
to control mean).
Genes 2024, 15, x FOR PEER REVIEW 7 of 23
Figure 1. Relative FMRP levels by date of draw. Biological (same individual, different blood draw)
and technical (same individual, same blood draw) replicates were removed at random to generate
one sample per individual with known date of blood draw. Relative FMRP (mean ± SEM) from
PBMCs was ploed against the date of blood draw and a linear regression was fied, showing
independence of FMRP on date of draw and thus length of time stored in liquid nitrogen. Control:
n = 135, estimate/slope = −0.000067 relative FMRP units/day, p-value = 0.072. Non-control: n = 143,
estimate/slope = 0.000014 relative FMRP units/day, p-value = 0.14.
Figure 1. Relative FMRP levels by date of draw. Biological (same individual, different blood draw)
and technical (same individual, same blood draw) replicates were removed at random to generate
one sample per individual with known date of blood draw. Relative FMRP (mean
±
SEM) from
PBMCs was plotted against the date of blood draw and a linear regression was fitted, showing
independence of FMRP on date of draw and thus length of time stored in liquid nitrogen. Control:
n= 135
, estimate/slope =
−
0.000067 relative FMRP units/day, p-value = 0.072. Non-control: n= 143,
estimate/slope = 0.000014 relative FMRP units/day, p-value = 0.14.
Additionally, in the absence of detected unmethylated alleles, some FM alleles are
still capable of producing detectable mRNA, 0- to 0.30-fold relative to the control mean
(Figure 4). Sample P08-19 had large, methylated alleles that were 760 and 870 CGG repeats
in size, yet produced 0.08
±
0.0046 relative mRNA. However, it is not known whether this
residual mRNA is produced by a small number of undetected FMR1 alleles or whether
methylated alleles are capable of producing low levels of mRNA.

Genes 2024,15, 356 8 of 22
Genes 2024, 15, x FOR PEER REVIEW 8 of 23
Figure 2. Relative FMRP levels by age at time of blood draw. Biological (same individual, different
blood draw) and technical (same individual, same blood draw) replicates were removed at random
to generate one sample per individual. Relative FMRP (mean ± SEM) from PBMCs was ploed
against age of individual in years at the time of blood draw, and a linear regression was fied,
showing independence of FMRP on age. Control: n = 138, estimate/slope = −0.00075 FMRP units/day,
p-value = 0.47. Non-control: n = 155, estimate/slope = 0.0010 FMRP units/day, p-value = 0.13.
3.3. Individuals with FM Alleles Generally Have a Complex Genotype
Individuals with FM alleles generally are mosaic in terms of number, size, and/or
methylation of alleles. That is, individuals with nominally FM alleles tend to have multiple
alleles whose CGG repeats can span allele classes and be distinctly methylated (Table 1,
Figure 3). Of 153 individuals with expanded CGG repeat alleles covering the FM range,
only 26 (~17%) possessed a single detectable methylated allele; thus, 83% were mosaics of
size (inter- or intra-class) and/or methylation status. Of these 127 individuals, there were
19 with methylation mosaicism, 1 who was mosaic for allele class, 41 who were mosaic for
both methylation and allele class, and 66 with up to 8 discrete FM methylated alleles.
Discrete allele sizes ranged from 13 to 1400 CGGs (Table 1).
Additional complexity was created by the presence of smears and degree of
methylation. A smear is a quasi-continuous series of PCR products that differ in length by
only a few CGG repeats, such that they appear as a smear rather than a discrete band
when run on a gel or an electrophoretogram. Of the 153 individuals with nominally FM
alleles, 20 had unmethylated smears and 4 had methylated smears. In 61 samples with
unmethylated alleles, the fraction of unmethylated alleles ranged from 5% to 95% of all
alleles present. Smear allele sizes ranged between 30 and 1540 CGGs (Table 1).
Unsurprisingly, expanded-repeat samples with the largest significant FMRP levels were
those with size and/or methylation mosaicism. Interestingly, not all samples with
detectable RNA (those with larger FM CGG repeats) produced detectable FMRP (Figure
3).
Figure 2. Relative FMRP levels by age at time of blood draw. Biological (same individual, different
blood draw) and technical (same individual, same blood draw) replicates were removed at random to
generate one sample per individual. Relative FMRP (mean
±
SEM) from PBMCs was plotted against
age of individual in years at the time of blood draw, and a linear regression was fitted, showing
independence of FMRP on age. Control: n= 138, estimate/slope =
−
0.00075 FMRP units/day,
p-value = 0.47. Non-control: n= 155, estimate/slope = 0.0010 FMRP units/day, p-value = 0.13.
3.5. Large FM Alleles Produce Little to No FMRP, Rarely Approaching Control Levels despite
Excess mRNA
Despite the ability of FM alleles to produce FMR1 mRNA, they produce little to no
detectable levels of FMRP. That is, samples with any form of FM allele have low levels of
FMRP, thus forming a distinct group from samples with control alleles (Figure 5). However,
rarely, size- and methylation-mosaic samples approached the lower bound of FMRP levels
produced from control alleles; again, these observations could be due to multiple low-
abundance alleles that extend into the PM range or that remain unmethylated in the
low-FM range. These cases create a small range of overlap between the highest FMRP
levels for size and methylation mosaics (max = 0.49) and the lower bound of control samples
(
min = 0.39
). Notably, excess mRNA did not guarantee higher levels of FMRP, indicating
the importance of the CGG repeat allele size, likely reflecting the translation efficiency, in
ultimately determining the FMRP expression levels.

Genes 2024,15, 356 9 of 22
Genes 2024, 15, x FOR PEER REVIEW 9 of 23
Figure 3. Cont.

Genes 2024,15, 356 10 of 22
Genes 2024, 15, x FOR PEER REVIEW 10 of 23
Figure 3. Cont.

Genes 2024,15, 356 11 of 22
Genes 2024, 15, x FOR PEER REVIEW 11 of 23
Figure 3. Genetic and molecular characteristics of non-control samples. All observations of each
individual were included. Samples that had a technical replicate are denoted by the letter “R” and are

Genes 2024,15, 356 12 of 22
shown in a lighter hue (light grey for mRNA level and light green for significant presence of FMRP).
Samples were arranged first by FMRP significance then by mRNA level. Sample P08-15 contains a
control allele with a point mutation. It produces mRNA, but its protein is not detected by the Cisbio
FRET assay. Sample P06-24 is a deletion sample. FMR1 is not present. Therefore, no methylation
analysis was performed. Allelic complexity. All alleles identified via capillary electrophoresis are
plotted by CGG repeat size and connected via a dashed line for each sample. Black circles: methylated
alleles; white circles: unmethylated alleles; dark gray highlights: methylated smear range; light gray
highlights: unmethylated smear range. Unmethylated alleles. Teal: fraction of unmethylated alleles;
salmon: fraction of methylated alleles in a sample. mRNA
rel
.Relative mRNA (mean
±
SE) was
determined via RT-qPCR and normalized to the mean of samples with control alleles. Samples whose
mRNA was not evaluated are denoted by “NA”. FMRP status. One-sample t-tests were performed
on FRET ratios corrected to lysis buffer and deletion controls. A sample had significant FMRP when
p< 0.05 for the highest concentration assayed. Only 27 samples with extended CGG repeats had
significant levels of FMRP. All were mosaic for methylation and/or allele class.
Genes 2024, 15, x FOR PEER REVIEW 12 of 23
Figure 3. Genetic and molecular characteristics of non-control samples. All observations of each
individual were included. Samples that had a technical replicate are denoted by the leer “R” and
are shown in a lighter hue (light grey for mRNA level and light green for significant presence of
FMRP). Samples were arranged first by FMRP significance then by mRNA level. Sample P08-15
contains a control allele with a point mutation. It produces mRNA, but its protein is not detected by
the Cisbio FRET assay. Sample P06-24 is a deletion sample. FMR1 is not present. Therefore, no
methylation analysis was performed. Allelic complexity. All alleles identified via capillary
electrophoresis are ploed by CGG repeat size and connected via a dashed line for each sample.
Black circles: methylated alleles; white circles: unmethylated alleles; dark gray highlights:
methylated smear range; light gray highlights: unmethylated smear range. Unmethylated alleles. Te al:
fraction of unmethylated alleles; salmon: fraction of methylated alleles in a sample. mRNArel.
Relative mRNA (mean ± SE) was determined via RT-qPCR and normalized to the mean of samples
with control alleles. Samples whose mRNA was not evaluated are denoted by “NA.” FMRP status.
One-sample t-tests were performed on FRET ratios corrected to lysis buffer and deletion controls. A
sample had significant FMRP when p < 0.05 for the highest concentration assayed. Only 27 samples
with extended CGG repeats had significant levels of FMRP. All were mosaic for methylation and/or
allele class.
3.4. Large Unmethylated and Methylated Alleles Produce mRNA
Methylation mosaics with CGG repeat alleles only in the FM range produced mRNA
levels 0 to 2.28-fold that of the mean of control samples (Figure 4). Sample P03-10, with an
unmethylated allele at 250 CGGs, produced the highest relative FMR1 mRNA levels at
2.28 ± 0.085 (mean ± SEM), suggesting that smaller FM alleles can produce large quantities
of mRNA, even above the range for samples with control alleles (0.47–1.43) and similar to
the excess mRNA production in the PM range [30]. Larger FM alleles can produce mRNA
as well, though to a lesser degree. For example, sample P06-32 had an unmethylated 500
CGG repeat plus a methylated 860 repeat and still produced some mRNA (0.37 ± 0.046
relative to control mean).
Figure 4. Distribution of relative FMR1 mRNA within each allele type. Technical replicates were
randomly removed to keep one observation per blood draw. Deletion: FMR1 not present. Full
mutation: only full-mutation methylated alleles. Methylation mosaics: some methylated and some
unmethylated full-mutation alleles in the same individual. Size mosaic: some alleles under 200 CGG
repeats and some full-mutation alleles. Size and methylation mosaics: some alleles smaller than 200
CGG repeats and some alleles of varying methylation status above 200 CGG repeats. Control:
control alleles. Control point mutation: control allele with a point mutation that prevents detection
by Cisbio FRET assay.
Figure 4. Distribution of relative FMR1 mRNA within each allele type. Technical replicates were
randomly removed to keep one observation per blood draw. Deletion: FMR1 not present. Full
mutation: only full-mutation methylated alleles. Methylation mosaics: some methylated and some
unmethylated full-mutation alleles in the same individual. Size mosaic: some alleles under 200 CGG
repeats and some full-mutation alleles. Size and methylation mosaics: some alleles smaller than
200 CGG
repeats and some alleles of varying methylation status above 200 CGG repeats. Control:
control alleles. Control point mutation: control allele with a point mutation that prevents detection
by Cisbio FRET assay.
FRET ratios in only 27 of 199 (~14%) non-control samples showed significant FMRP
(Figure 3). Non-control FMRP-positive samples were exclusively size and/or methylation
mosaics. Twenty were both size and methylation mosaics, many of which contained
unmethylated PM alleles. Four had methylated PM alleles. Only three samples with purely
FM alleles had evidence of FMRP: P03-10, P10-04, and P12-30 (Figure 3). Notably, all
three contain unmethylated FM alleles. No sample containing only methylated FM alleles
produced significant FMRP. P03-10 had methylated alleles between 470 and
800 CGGs
and one unmethylated allele with approximately 250 CGGs; this sample produced FMR1
mRNA and FMRP at levels of 2.28
±
0.085 and 0.15
±
0.032, respectively, relative to control
means. P10-04 had a methylated 220 CGG repeat and an unmethylated 200 CGG repeat. It
produced 1.6
±
0.031 FMR1 mRNA but only 0.09
±
0.036 FMRP compared to the control
means. P12-30 had methylated alleles between 273 and 810 CGGs and one unmethylated
allele at 340 CGGs. Its relative FMR1 mRNA was 0.25
±
0.015 and relative FMRP levels

Genes 2024,15, 356 13 of 22
were 0.25
±
0.037. See Supplementary Information (Section S3) for an analysis of the
accuracy of the TR-FRET assay, including Figures S1–S4.
Genes 2024, 15, x FOR PEER REVIEW 13 of 23
Additionally, in the absence of detected unmethylated alleles, some FM alleles are
still capable of producing detectable mRNA, 0- to 0.30-fold relative to the control mean
(Figure 4). Sample P08-19 had large, methylated alleles that were 760 and 870 CGG repeats
in size, yet produced 0.08 ± 0.0046 relative mRNA. However, it is not known whether this
residual mRNA is produced by a small number of undetected FMR1 alleles or whether
methylated alleles are capable of producing low levels of mRNA.
3.5. Large FM Alleles Produce Lile to No FMRP, Rarely Approaching Control Levels Despite
Excess mRNA
Despite the ability of FM alleles to produce FMR1 mRNA, they produce lile to no
detectable levels of FMRP. That is, samples with any form of FM allele have low levels of
FMRP, thus forming a distinct group from samples with control alleles (Figure 5).
However, rarely, size- and methylation-mosaic samples approached the lower bound of
FMRP levels produced from control alleles; again, these observations could be due to
multiple low-abundance alleles that extend into the PM range or that remain
unmethylated in the low-FM range. These cases create a small range of overlap between
the highest FMRP levels for size and methylation mosaics (max = 0.49) and the lower
bound of control samples (min = 0.39). Notably, excess mRNA did not guarantee higher
levels of FMRP, indicating the importance of the CGG repeat allele size, likely reflecting
the translation efficiency, in ultimately determining the FMRP expression levels.
Figure 5. FMRP levels by mRNA level. Relative FMR1 mRNA was ploed against relative FMRP
(mean ± SE) for all allele types without controlling for subject age, CGG size, or CGG methylation
status. Relative levels are normalized to that of the mean for control samples. All samples from each
subject were included. Relative FMRP levels for samples with extended CGG repeats rarely
approach those of control samples, regardless of mRNA level. Dashed line: maximum FMRP level
of non-control samples. Solid line: minimum FMRP level of control samples.
FRET ratios in only 27 of 199 (~14%) non-control samples showed significant FMRP
(Figure 3). Non-control FMRP-positive samples were exclusively size and/or methylation
mosaics. Twenty were both size and methylation mosaics, many of which contained
unmethylated PM alleles. Four had methylated PM alleles. Only three samples with
Figure 5. FMRP levels by mRNA level. Relative FMR1 mRNA was plotted against relative FMRP
(mean
±
SE) for all allele types without controlling for subject age, CGG size, or CGG methylation
status. Relative levels are normalized to that of the mean for control samples. All samples from
each subject were included. Relative FMRP levels for samples with extended CGG repeats rarely
approach those of control samples, regardless of mRNA level. Dashed line: maximum FMRP level of
non-control samples. Solid line: minimum FMRP level of control samples.
3.6. Significant Association between FMR1 mRNA and FMRP in Non-Control PBMCs
To further characterize the effect of FMR1 mRNA on FMRP production, univariate
linear regression analysis was performed using a set of representative samples, with one
sample randomly chosen among biological and technical replicates per individual (sepa-
rately on control and non-control samples) (Figure 6). No significant association between
FMR1 mRNA and FMRP was found in samples with control alleles (
n= 134 unique
indi-
viduals with both mRNA and FMRP values; slope =
−
0.041, p= 0.73). However, there
was a significant association between relative FMR1 mRNA and FMRP in non-control sam-
ples (
n= 148 unique
individuals with both mRNA and FMRP values;
slope = 0.059 relative
FMRP units per relative mRNA unit, p= 1.21 ×10−9).
The association between relative FMR1 mRNA and FMRP in non-control samples
was driven by those with the lowest FMRP values. Loess regression on mRNA versus
FMRP in all non-control samples with available mRNA data (n= 191) shows that between
0 and ~0.5 relative mRNA, increasing mRNA leads to increasing protein. However, above
~0.5 relative
mRNA, relative FMRP levels plateau at ~0.25, at which point further increases
in FMR1 mRNA no longer influence FMRP levels (Figure S5); however, FMRP levels will
always depend on the RNA’s expanded CGG repeat length. Non-control samples were
further analyzed separately by FMRP significance (Figure 7). FMRP(+) samples showed no
significant association between FMR1 mRNA and FMRP (n= 18, slope =
−
0.0024, p= 0.93).
However, levels of FMRP(
−
) samples were positively associated with FMR1 mRNA levels
(n= 131, slope = 0.028, p= 0.011), suggesting that samples with non-significant FMRP (upon

Genes 2024,15, 356 14 of 22
FRET analysis) do produce some FMRP, albeit at levels too low for significance testing on
the basis of individual samples.
Genes 2024, 15, x FOR PEER REVIEW 14 of 23
purely FM alleles had evidence of FMRP: P03-10, P10-04, and P12-30 (Figure 3). Notably,
all three contain unmethylated FM alleles. No sample containing only methylated FM
alleles produced significant FMRP. P03-10 had methylated alleles between 470 and 800
CGGs and one unmethylated allele with approximately 250 CGGs; this sample produced
FMR1 mRNA and FMRP at levels of 2.28 ± 0.085 and 0.15 ± 0.032, respectively, relative to
control means. P10-04 had a methylated 220 CGG repeat and an unmethylated 200 CGG
repeat. It produced 1.6 ± 0.031 FMR1 mRNA but only 0.09 ± 0.036 FMRP compared to the
control means. P12-30 had methylated alleles between 273 and 810 CGGs and one
unmethylated allele at 340 CGGs. Its relative FMR1 mRNA was 0.25 ± 0.015 and relative
FMRP levels were 0.25 ± 0.037. See Supplementary Information (Section S3) for an analysis
of the accuracy of the TR-FRET assay, including Figures S1–S4.
3.6. Significant Association between FMR1 mRNA and FMRP in Non-Control PBMCs
To further characterize the effect of FMR1 mRNA on FMRP production, univariate
linear regression analysis was performed using a set of representative samples, with one
sample randomly chosen among biological and technical replicates per individual
(separately on control and non-control samples) (Figure 6). No significant association
between FMR1 mRNA and FMRP was found in samples with control alleles (n = 134
unique individuals with both mRNA and FMRP values; slope = −0.041, p = 0.73). However,
there was a significant association between relative FMR1 mRNA and FMRP in non-
control samples (n = 148 unique individuals with both mRNA and FMRP values; slope =
0.059 relative FMRP units per relative mRNA unit, p = 1.21 × 10
−9
).
Figure 6. Relative FMRP positively associates with relative FMR1 mRNA for individuals with non-
control alleles. Biological and technical replicates were removed at random to generate one
measurement per individual. Relative mRNA (mean ± SE) from PBMCs was ploed against relative
FMRP (mean ± SE) and a linear regression was fit. No association between FMR1 mRNA and FMRP
was found for individuals with control alleles. However, FMR1 mRNA significantly associated with
FMRP in individuals with non-control alleles. Control: n = 134, estimate/slope = −0.041, p-value =
0.727. Non-control: n = 148, estimate/slope = 0.059, p-value = 1.21 × 10
−9
.
Figure 6. Relative FMRP positively associates with relative FMR1 mRNA for individuals with
non-control alleles. Biological and technical replicates were removed at random to generate one
measurement per individual. Relative mRNA (mean
±
SE) from PBMCs was plotted against relative
FMRP (mean
±
SE) and a linear regression was fit. No association between FMR1 mRNA and FMRP
was found for individuals with control alleles. However, FMR1 mRNA significantly associated
with FMRP in individuals with non-control alleles. Control: n= 134, estimate/slope =
−
0.041,
p-value = 0.727. Non-control: n= 148, estimate/slope = 0.059, p-value = 1.21 ×10−9.
3.7. Translation Efficiency Is Negatively Associated with the Smallest CGG Repeat Size in Samples
with FM Alleles
Considering the ability of FM alleles to produce mRNA, but little protein, we next
examined the ratio of relative FMRP to relative FMR1 mRNA to approximate the efficiency
of protein production in FMRP(+) individuals. The ratio of relative FMRP to mRNA was
plotted against the smallest CGG repeat size regardless of methylation status (Figure 8).
The smallest CGG repeat size was chosen to represent an individual’s allele most likely
to contribute to protein production. A linear regression model was fitted for individu-
als with either control or non-control (expanded CGG repeat) alleles, respectively. No
association was detected for samples with control alleles (n= 134 unique individuals
with both FMRP and mRNA data, slope = 0.0065 relative efficiency units per CGG repeat,
p-value = 0.31
). However, a significant negative association was observed for non-control
samples
(n= 18 unique
FMRP(+) individuals, slope =
−
0.006 relative efficiency units per
CGG repeat, p-value = 0.045). That is, as the minimum CGG repeat size increased, less
FMRP was detected for the same quantity of mRNA, suggesting a decrease in translation
efficiency for alleles with larger repeats, in agreement with earlier studies [29,31].
3.8. No Significant FMRP Production Was Detected for Alleles Greater Than ~270 CGG Repeats
To estimate the largest allele capable of producing FMRP
in vivo
in the current study,
each sample was represented by its smallest CGG repeat size regardless of methylation
status and plotted against relative FMRP (Figure 9). The smallest CGG repeat size was

Genes 2024,15, 356 15 of 22
chosen to represent an individual’s allele most likely to contribute to protein production.
Sample P12-30 had the largest value for the lower bound CGG repeat, at 273 CGGs, among
cells producing significant FMRP. That is, no sample with its smallest allele larger than
273 CGG
repeats (from sample P12-30) produced significant levels of protein. Notably, the
273 CGG repeat allele of P12-30 was methylated. P12-30 also possessed an unmethylated
340 CGG repeat. It is unclear from these data which allele or combination of alleles
produced the FMR1 mRNA and subsequent FMRP.
Genes 2024, 15, x FOR PEER REVIEW 15 of 23
The association between relative FMR1 mRNA and FMRP in non-control samples
was driven by those with the lowest FMRP values. Loess regression on mRNA versus
FMRP in all non-control samples with available mRNA data (n = 191) shows that between
0 and ~0.5 relative mRNA, increasing mRNA leads to increasing protein. However, above
~0.5 relative mRNA, relative FMRP levels plateau at ~0.25, at which point further increases
in FMR1 mRNA no longer influence FMRP levels (Figure S5); however, FMRP levels will
always depend on the RNA’s expanded CGG repeat length. Non-control samples were
further analyzed separately by FMRP significance (Figure 7). FMRP(+) samples showed
no significant association between FMR1 mRNA and FMRP (n = 18, slope = −0.0024, p =
0.93). However, levels of FMRP(−) samples were positively associated with FMR1 mRNA
levels (n = 131, slope = 0.028, p = 0.011), suggesting that samples with non-significant FMRP
(upon FRET analysis) do produce some FMRP, albeit at levels too low for significance
testing on the basis of individual samples.
Figure 7. The positive association between relative FMR1 mRNA and relative FMRP in non-control
samples is driven by those with non-significant FMRP. Biological and technical replicates were
removed at random to generate one measurement per subject. Relative mRNA (mean ± SE) from
PBMCs was ploed against relative FMRP (mean ± SE) for non-control samples. A linear regression
was fit for FMRP(+) (significant) and FMRP(−) (non-significant) samples, respectively. No
association between FMR1 mRNA and FMRP was found for FMRP(+) individuals. However, FMR1
mRNA was significantly associated with FMRP in FMRP(−) individuals, suggesting the presence of
FMRP below the level of detection via one-sample t-tests. FMRP(−): n = 131, estimate/slope = 0.028,
p-value = 0.011. FMRP(+): n = 18, estimate/slope = −0.0024, p-value = 0.93.
3.7. Translation Efficiency Is Negatively Associated with the Smallest CGG Repeat Size in
Samples with FM Alleles
Considering the ability of FM alleles to produce mRNA, but lile protein, we next
examined the ratio of relative FMRP to relative FMR1 mRNA to approximate the efficiency
of protein production in FMRP(+) individuals. The ratio of relative FMRP to mRNA was
ploed against the smallest CGG repeat size regardless of methylation status (Figure 8).
The smallest CGG repeat size was chosen to represent an individual’s allele most likely to
contribute to protein production. A linear regression model was fied for individuals with
either control or non-control (expanded CGG repeat) alleles, respectively. No association
was detected for samples with control alleles (n = 134 unique individuals with both FMRP
Figure 7. The positive association between relative FMR1 mRNA and relative FMRP in non-control
samples is driven by those with non-significant FMRP. Biological and technical replicates were
removed at random to generate one measurement per subject. Relative mRNA (mean
±
SE) from
PBMCs was plotted against relative FMRP (mean
±
SE) for non-control samples. A linear regression
was fit for FMRP(+) (significant) and FMRP(
−
) (non-significant) samples, respectively. No association
between FMR1 mRNA and FMRP was found for FMRP(+) individuals. However, FMR1 mRNA
was significantly associated with FMRP in FMRP(
−
) individuals, suggesting the presence of FMRP
below the level of detection via one-sample t-tests. FMRP(
−
): n= 131, estimate/slope = 0.028,
p-value = 0.011. FMRP(+): n= 18, estimate/slope = −0.0024, p-value = 0.93.
3.9. FMR1 mRNA Significantly Affects FMRP and Unmethylated CGG Repeat Size Trends toward
Significance in Nested Mixed-Effects Models
Three nested mixed-effects models were fitted to examine the effects of FMR1 mRNA
level, median unmethylated CGG repeat size, median methylated CGG repeat size, and
fraction of methylation to evaluate their respective effects while accounting for the other
parameters and age (see Section 2). All three models showed a significant effect of FMR1
mRNA level on FMRP for non-control samples with median allele sizes in the FM range
(p< 0.02 for all; Table S2, Figure S6). An LRT comparing the models with and without
the unmethylated CGG repeat size (Model 2 vs. Model 3) suggested that the size of the
median unmethylated CGG repeat significantly affects FMRP levels when accounting for
the median methylated CGG repeat size, fraction of methylated alleles, participant’s age,
and FMR1 mRNA level (p= 0.0368). An LRT comparing Models 1 and 3 with and without
the methylated CGG repeat and the fraction of methylated alleles suggested that neither
had significant impact on FMRP levels when accounting for the unmethylated CGG repeat,
participant’s age, and mRNA level (p= 0.6079). This finding is likely because total mRNA

Genes 2024,15, 356 16 of 22
level encompasses the effect of the fraction of methylated (and thus unmethylated) alleles.
Moreover, methylated CGGs likely contribute little to protein production, as evidenced by
the fact that no sample containing only methylated FM alleles had significant levels of FMRP
(Figure 3). Therefore, LRTs suggested that when FMR1 mRNA and the unmethylated CGG
repeat allele size are already accounted for, the methylated CGG repeat and the fraction of
methylated alleles are no longer influential factors that could significantly contribute to the
variation in FMRP production, controlling for age.
Genes 2024, 15, x FOR PEER REVIEW 16 of 23
and mRNA data, slope = 0.0065 relative efficiency units per CGG repeat, p-value = 0.31).
However, a significant negative association was observed for non-control samples (n = 18
unique FMRP(+) individuals, slope = −0.006 relative efficiency units per CGG repeat, p-
value = 0.045). That is, as the minimum CGG repeat size increased, less FMRP was
detected for the same quantity of mRNA, suggesting a decrease in translation efficiency
for alleles with larger repeats, in agreement with earlier studies [29,31].
Figure 8. Translation efficiency by smallest CGG repeat size for samples with significant FMRP.
Biological and technical replicates were removed at random to generate one measurement per
individual with significant levels of FMRP. The ratio of relative FMRP to relative mRNA was used
as a measure of translation efficiency and ploed against the smallest CGG repeat of a sample,
regardless of methylation status. A linear regression was fit separately for control and non-control
samples. Translation efficiency was independent of CGG repeat size for control alleles but showed
a significantly negative correlation with repeat size for samples with non-control alleles. Control: n
= 134, estimate/slope = 0.0065 relative efficiency units per CGG, p-value = 0.31. Non-control: n = 18,
estimate/slope = −0.0060 relative efficiency units per CGG, p-value = 0.045.
3.8. No Significant FMRP Production Was Detected for Alleles Greater Than ~270 CGG Repeats
To estimate the largest allele capable of producing FMRP in vivo in the current study,
each sample was represented by its smallest CGG repeat size regardless of methylation
status and ploed against relative FMRP (Figure 9). The smallest CGG repeat size was
chosen to represent an individual’s allele most likely to contribute to protein production.
Sample P12-30 had the largest value for the lower bound CGG repeat, at 273 CGGs, among
cells producing significant FMRP. That is, no sample with its smallest allele larger than
273 CGG repeats (from sample P12-30) produced significant levels of protein. Notably,
the 273 CGG repeat allele of P12-30 was methylated. P12-30 also possessed an
unmethylated 340 CGG repeat. It is unclear from these data which allele or combination
of alleles produced the FMR1 mRNA and subsequent FMRP.
Figure 8. Translation efficiency by smallest CGG repeat size for samples with significant FMRP.
Biological and technical replicates were removed at random to generate one measurement per
individual with significant levels of FMRP. The ratio of relative FMRP to relative mRNA was used
as a measure of translation efficiency and plotted against the smallest CGG repeat of a sample,
regardless of methylation status. A linear regression was fit separately for control and non-control
samples. Translation efficiency was independent of CGG repeat size for control alleles but showed
a significantly negative correlation with repeat size for samples with non-control alleles. Control:
n= 134,
estimate/slope = 0.0065 relative efficiency units per CGG, p-value = 0.31. Non-control:
n= 18,
estimate/slope = −0.0060 relative efficiency units per CGG, p-value = 0.045.
Although Model 1 showed that the median unmethylated CGG repeat size is not
independently a significant contributor to FMRP (p= 0.0596) after accounting for FMR1
mRNA, it suggests that doubling the length of the median unmethylated CGG repeat
resulted in a 0.04% (2
×
(
−
0.0002)
×
100% =
−
0.04%) reduction, on average, in relative
FMRP in individuals with alleles in the FM range, while fixing an individual’s age and
their relative FMR1 mRNA level at average values (see effect of CGG
unmethylated
in Model 1
of Table S2).

Genes 2024,15, 356 17 of 22
Genes 2024, 15, x FOR PEER REVIEW 17 of 23
Figure 9. Relative FMRP by smallest CGG repeat size. Relative FMRP (mean ± SE) was ploed
against the CGG repeat size of the smallest allele of each sample, regardless of methylation status.
Samples were color-coded by FMRP status. No sample with a minimum allele size above 273 CGGs
(solid black line) produced significant FMRP.
3.9. FMR1 mRNA Significantly Affects FMRP and Unmethylated CGG Repeat Size Trends
toward Significance in Nested Mixed-Effects Models
Three nested mixed-effects models were fied to examine the effects of FMR1 mRNA
level, median unmethylated CGG repeat size, median methylated CGG repeat size, and
fraction of methylation to evaluate their respective effects while accounting for the other
parameters and age (see Section 2). All three models showed a significant effect of FMR1
mRNA level on FMRP for non-control samples with median allele sizes in the FM range
(p < 0.02 for all; Table S2, Figure S6). An LRT comparing the models with and without the
unmethylated CGG repeat size (Model 2 vs. Model 3) suggested that the size of the median
unmethylated CGG repeat significantly affects FMRP levels when accounting for the
median methylated CGG repeat size, fraction of methylated alleles, participant’s age, and
FMR1 mRNA level (p = 0.0368). An LRT comparing Models 1 and 3 with and without the
methylated CGG repeat and the fraction of methylated alleles suggested that neither had
significant impact on FMRP levels when accounting for the unmethylated CGG repeat,
participant’s age, and mRNA level (p = 0.6079). This finding is likely because total mRNA
level encompasses the effect of the fraction of methylated (and thus unmethylated) alleles.
Moreover, methylated CGGs likely contribute lile to protein production, as evidenced
by the fact that no sample containing only methylated FM alleles had significant levels of
FMRP (Figure 3). Therefore, LRTs suggested that when FMR1 mRNA and the
unmethylated CGG repeat allele size are already accounted for, the methylated CGG
Figure 9. Relative FMRP by smallest CGG repeat size. Relative FMRP (mean
±
SE) was plotted
against the CGG repeat size of the smallest allele of each sample, regardless of methylation status.
Samples were color-coded by FMRP status. No sample with a minimum allele size above 273 CGGs
(solid black line) produced significant FMRP.
4. Discussion
A main finding of this study is the large degree of mosaicism observed in individuals
with FXS. Mosaicism was observed in three categories: (1) the number of alleles within
a mutation class, (2) the number of alleles across mutation classes (size mosaicism), and
(3) methylation status of each allele (methylation mosaicism). Size and methylation mo-
saicism for samples with FM alleles from various tissues, especially in individuals variably
affected by FXS, have been observed in many studies [
14
,
22
,
32
–
37
]. In 2022, Meng and
colleagues [
38
] also observed mosaicism in blood samples, but to a lesser degree. In the
current study, only 17% of FXS cases possessed a single, fully methylated allele. All other
individuals with non-control alleles had a combination of methylation or size mosaicism,
including multiple FM alleles. Considering the stricter definition of size mosaicism as
crossing allele class boundaries, 60% of research subjects in this study showed no size
mosaicism, which closely mirrors the 69% of FXS subjects that lacked mosaicism in blood
in
Meng et al. [38]
. Moreover, both Meng et al. [
38
] and the current study found similar de-

Genes 2024,15, 356 18 of 22
grees of methylation mosaicism, at approximately 12%. In contrast,
Budimirovic et al. [14]
found methylation mosaicism in more than half of their blood and buccal samples. Indi-
viduals with size mosaicism alone represented a smaller proportion of this study (<1%)
compared to Meng et al. [38] (11%).
Size mosaicism is likely due to the instability of the repeat tract outside of the control
allele range in both meiotically and mitotically dividing cells [
39
,
40
]. The mechanisms
for repeat instability at the FMR1 locus are not well understood, but likely result from
secondary structures of expanded repeats that disrupt DNA replication, repair, and/or
recombination [
41
]. While expansions and, to a lesser degree, contractions, occur in any
allele class, PM alleles are particularly meiotically unstable, often expanding into FM alleles
in a single generation [
40
,
42
]. More recently, it has been shown that somatic mosaicism
is also common in PM alleles and that genes involved in DNA repair may play a role in
somatic expansion risk, as reported in other repeat-expansion disorders [43].
How mosaicism, both in size and methylation, affects FMR1 mRNA expression
has been surveyed. A correlation between repeat size (especially in the PM range) and
(1) percent
of methylation, (2) FMR1 mRNA levels, and (3) more clinical involvement has
been found [
14
,
44
], with the level of FMRP directly related to the degree of cognitive and
neurodevelopmental impairment [
15
,
45
,
46
]. Therefore, it is unsurprising that mosaicism
in individuals with FM alleles is also associated with increased expression and improved
cognitive function [
14
,
15
,
38
,
47
,
48
]. Indeed, there is increasing evidence that unmethylated
FM alleles are actively transcribed [
30
,
36
]. However, more details are needed about which
specific alleles can express FMR1 mRNA and FMRP and to what degree.
The main finding of the current study was that only samples with size and/or methy-
lation mosaicism produced significant levels of FMRP (Figure 3). Indeed, some methylation
mosaics produced excess FMR1 mRNA, ~1.6-fold higher than the highest-producing con-
trol (Figure 4). This finding is consistent with observations of increased FMR1 mRNA
levels, sometimes 5- to 10-fold, in carriers of PM alleles [
29
,
31
] and with a case study of an
unmethylated FM allele producing 7-fold excess FMR1 mRNA [49].
Furthermore, FMRP was detectable in many FM-only samples but did not achieve
statistical significance. Despite this, we found that in the absence of evident unmethylated
alleles, some (apparently) fully methylated FM alleles are still capable of producing up
to 0.30-fold detectable FMR1 mRNA relative to the control mean (Figure 4). For example,
sample P08-19 had large, methylated alleles of sizes 760 and 870 CGG repeats, yet pro-
duced 0.06
±
0.0046 relative mRNA. Conceivably, unmethylated alleles present below the
level of detection could contribute to FMR1 mRNA levels. Furthermore, the fraction of
unmethylated alleles is determined by Southern blotting and is based on the methylation
status of a single CpG site (See Section 2); however, the degree of methylation along the
length of each allele is unknown. Therefore, the degree of methylation may be important to
identify which alleles can be transcribed and, potentially, which isoforms thereof.
No association between FMR1 mRNA and FMRP in control samples was observed
(Figure 6). This observation is consistent with previous slot-blot [
31
], Luminex immunoas-
say [
21
], and TR-FRET measurements [
15
], and suggests that there is a limit to FMRP
production beyond which there is no further metabolic drive. However, we did find an asso-
ciation between FMR1 mRNA and FMRP in samples with non-control alleles as assessed via
linear regression and nested mixed-effects models in the current study (
Figures 6and S6)
.
Interestingly, LOESS regression showed that FMRP increased with increasing mRNA up
to ~0.5-fold relative mRNA, after which no association between mRNA and FMRP was
observed (Figure S5). Therefore, the dependence of FMRP on mRNA level in non-control
samples was driven by those with the lowest FMRP levels, which generally failed signifi-
cance testing (Figure 7). Likely, these samples produced small amounts of FMRP below the
level of significance for an individual sample, which nevertheless uncovers an association
between mRNA and FMRP when taken as whole.
Despite the capacity to produce excess FMR1 mRNA, FMRP levels remain low in FXS
samples [
49
] (Figure 5), which is likely due to the difficulty of translation machinery in

Genes 2024,15, 356 19 of 22
traversing secondary structure in the FMR1 mRNA during ribosomal scanning as CGG
repeats expand [
31
,
50
].
In vitro
transcribed CGG repeats have been shown to produce
hairpin-like structures with both CG and GG base-pair bonding and even tetraplex struc-
tures resulting from guanine quartets between two such hairpins, which would impede
translation [
41
,
51
]. Indeed, the ratio of relative FMRP to relative mRNA, a measure of
translation efficiency, decreased as the size of the smallest CGG repeat allele in a sample
increased in the current study (Figure 8). Others have also found decreased translation
efficiency in expanded repeats starting in the intermediate and PM range [
31
,
52
]. Therefore,
it is unsurprising that translation efficiency would continue to decrease in the FM range.
Still, the question as to the largest CGG repeat capable of producing FMRP remains
unanswered. In the current study, no sample with a minimum allele size above 273 CGG
repeats produced significant levels of FMRP (Figure 9). Similarly, Feng and colleagues [
50
]
identified low, but detectable levels of FMRP for a fibroblast clone with 285 CGG repeats
via Western blotting. However, examining large repeat sizes in the absence of methylation
and/or size mosaicism will more directly answer this question. This apparent threshold
is of great relevance to previous and ongoing efforts to reactivate the FMR1 gene [
53
–
55
],
since reactivation in the absence of the ability to produce FMRP would not be a productive
approach to the treatment of FXS; moreover, the production of expanded CGG repeat
RNA would increase the risk of developing the late-onset neurodegenerative disorder,
FXTAS [19].
Study Strengths and Limitations
A notable strength of the current study is our use of a large sample size (138 control
individuals; 155 individuals with FXS), which allowed us to examine FMRP in the FM
range via linear regression and mixed-effects modeling independent of patterns in the
control or PM range. For comparison, other recent studies using two-antibody detection
methods for FMRP measured fewer individuals with FM alleles: n=9[
22
], n= 31 [
14
],
n= 103 [32].
A fundamental limitation of studies of this type is the underdetermination of the com-
plex allelotypes, particularly for FM and methylation- and size-mosaic patterns. Identifying
all alleles in a sample is challenging given the presence of smears or cryptic alleles that
represent a minor but contributing fraction. Compounding this challenge is the absence of
direct correspondence between capillary electrophoresis (CE) peak intensities and allele
abundance, which is more pronounced for larger alleles. Due to this complexity, smears
have typically been ignored in previous studies, with alleles simplified to the smallest or
most prevalent allele. For modeling, this study calculated the lower and upper limit of
smears and represented them as a unique allele by using the first quartile of the smear
range. While it is important to include smears in our analyses, how we might best represent
them is still unclear. Furthermore, total FMR1 mRNA levels were assessed in this study.
The contribution and impact of different FMR1 mRNA isoforms was not addressed.
Finally, it remains unclear how well FMRP levels in PBMCs reflect those in the brain
and therefore how useful PBMC data are for understanding neurodevelopmental disorders
like FXS. Few studies have examined the correlation between blood FMRP and postmortem
brain tissue FMRP in the same subject, including Pretto and colleagues [
44
], who showed
differences in methylation status in the blood versus the brain in the PM range, and Tassone
and colleagues [
28
], who showed tissue-specific methylation differences in PM carriers even
when the size of alleles remained the same. However, our previous study did demonstrate a
strong correlation between FMRP levels in peripheral tissue and IQ. Accordingly, the impor-
tance of the current work lies more in commenting on the relationships between allelotype
and epiallelotypes for expanded alleles and the ability of those alleles to produce FMRP.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/genes15030356/s1, Supplementary Information: Descrip-
tion of additional methods for sample elimination and outlier removal and results for assessing the
accuracy of the TR-FRET FMRP assay; Figure S1: Coefficient of variance by FMRP level;
Figure S2:
Deviation in relative FMRP among biological replicates; Figure S3: Deviation in relative FMRP

Genes 2024,15, 356 20 of 22
among technical replicates; Figure S4: Variance among biological and technical replicates;
Figure S5:
Relative mRNA levels by relative FMRP levels for non-control samples; Figure S6: Nested linear
mixed-effects models showing that relative FMRP is significantly dependent on relative FMR1 mRNA
for individuals with non-control alleles; Table S1: Molecular dataset; Table S2: FMR1 mRNA statistics
for nested mixed-effects modeling.
Author Contributions: Conceptualization, J.L.R. and P.J.H.; Methodology, J.L.R., F.T. and P.J.H.;
Software, J.L.R., K.K. and M.D.P.; Validation, J.L.R., K.K. and M.D.P.; Formal Analysis, J.L.R., K.K.,
M.D.P., F.T. and P.J.H.; Investigation, J.L.R., F.T. and A.K.F.; Resources, J.L.R., K.K., F.T. and P.J.H.;
Data curation, J.L.R., K.K., M.D.P., F.T. and P.J.H.; Writing—Original Draft Preparation, J.L.R., K.K.,
M.D.P., F.T. and P.J.H.; Writing—review and editing, J.L.R., K.K., M.D.P., F.T., A.K.F., R.J.H. and P.J.H.;
Visualization, J.L.R., K.K., M.D.P. and P.J.H.; Supervision, J.L.R. and P.J.H.; Project Administration,
P.J.H.; Funding Acquisition, K.K., F.T., R.J.H. and P.J.H. All authors have read and agreed to the
published version of the manuscript.
Funding: Research reported in this publication was supported by the Eunice Kennedy Shriver
National Institute Of Child Health & Human Development of the National Institutes of Health under
Award Number R01 HD036071 (R.J.H., P.J.H., F.T.), the Azrieli Foundation (P.J.H., R.J.H.), and the
MIND Institute Intellectual and Developmental Disabilities Research Center (NIH P50 HD103526;
K.K.). The project described was also supported by the National Center for Advancing Translational
Sciences, National Institutes of Health, through grant number UL1 TR001860. The content is solely
the responsibility of the authors and does not necessarily represent the official views of the NIH. The
funders had no role in study design, data collection and analysis, decision to publish, or preparation
of the manuscript.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Institutional Review Board of the University of California, Davis
(protocol code 215292, 23 July 2023).
Informed Consent Statement: Informed consent was obtained from all subjects involved in
the study.
Data Availability Statement: We have provided a table of all data used to perform the current
analysis. This table is presented as Table S1.
Acknowledgments: The authors wish to thank all the families who have participated in this research,
and to Glenda Espinal who shared her expertise with TR-FRET and its use in quantifying FMRP
in fibroblasts.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
Bassell, G.J.; Warren, S.T. Fragile X syndrome: Loss of local mRNA regulation alters synaptic development and function. Neuron
2008,60, 201–214. [CrossRef] [PubMed]
2.
De Rubeis, S.; Bagni, C. Regulation of molecular pathways in the Fragile X Syndrome: Insights into Autism Spectrum Disorders.
J. Neurodev. Disord. 2011,3, 257–269. [CrossRef] [PubMed]
3. Deng, P.Y.; Klyachko, V.A. Channelopathies in fragile X syndrome. Nat. Rev. Neurosci. 2021,22, 275–289. [CrossRef] [PubMed]
4.
McCary, L.M.; Roberts, J.E. Early identification of autism in fragile X syndrome: A review. J. Intellect. Disabil. Res. 2013,57,
803–814. [CrossRef] [PubMed]
5.
Kaufmann, W.E.; Kidd, S.A.; Andrews, H.F.; Budimirovic, D.B.; Esler, A.; Haas-Givler, B.; Stackhouse, T.; Riley, C.; Peacock, G.;
Sherman, S.L.; et al. Autism Spectrum Disorder in Fragile X Syndrome: Cooccurring Conditions and Current Treatment. Pediatrics
2017,139, S194–S206. [CrossRef] [PubMed]
6.
Kosinovsky, B.; Hermon, S.; Yoran-Hegesh, R.; Golomb, A.; Senecky, Y.; Goez, H.; Kramer, U. The yield of laboratory investigations
in children with infantile autism. J. Neural Transm. 2005,112, 587–596. [CrossRef] [PubMed]
7.
Iossifov, I.; Ronemus, M.; Levy, D.; Wang, Z.; Hakker, I.; Rosenbaum, J.; Yamrom, B.; Lee, Y.H.; Narzisi, G.; Leotta, A.; et al. De
novo gene disruptions in children on the autistic spectrum. Neuron 2012,74, 285–299. [CrossRef]
8. Muhle, R.; Trentacoste, S.V.; Rapin, I. The genetics of autism. Pediatrics 2004,113, e472–e486. [CrossRef]
9.
Schaefer, G.B.; Mendelsohn, N.J. Clinical genetics evaluation in identifying the etiology of autism spectrum disorders: 2013
guideline revisions. Genet. Med. 2013,15, 399–407. [CrossRef]
10.
Richter, J.D.; Zhao, X. The molecular biology of FMRP: New insights into fragile X syndrome. Nat. Rev. Neurosci. 2021,22, 209–222.
[CrossRef]
11.
Kaufmann, W.E.; Raspa, M.; Bann, C.M.; Gable, J.M.; Harris, H.K.; Budimirovic, D.B.; Lozano, R. Latent Class Analysis Identifies
Distinctive Behavioral Subtypes in Children with Fragile X Syndrome. J. Autism. Dev. Disord. 2024,54, 725–737. [CrossRef]

Genes 2024,15, 356 21 of 22
12.
Bailey, D.B., Jr.; Raspa, M.; Bishop, E.; Mitra, D.; Martin, S.; Wheeler, A.; Sacco, P. Health and economic consequences of fragile X
syndrome for caregivers. J. Dev. Behav. Pediatr. JDBP 2012,33, 705–712. [CrossRef]
13.
Wheeler, A.C.; Gwaltney, A.; Raspa, M.; Okoniewski, K.C.; Berry-Kravis, E.; Botteron, K.N.; Budimirovic, D.; Hazlett, H.C.;
Hessl, D.;
Losh, M.; et al. Emergence of Developmental Delay in Infants and Toddlers with an FMR1 Mutation. Pediatrics 2021,
147, e2020011528. [CrossRef]
14.
Budimirovic, D.B.; Schlageter, A.; Filipovic-Sadic, S.; Protic, D.D.; Bram, E.; Mahone, E.M.; Nicholson, K.; Culp, K.;
Javanmardi, K.;
Kemppainen, J.; et al. A Genotype-Phenotype Study of High-Resolution FMR1 Nucleic Acid and Protein Analyses in Fragile X
Patients with Neurobehavioral Assessments. Brain Sci. 2020,10, 694. [CrossRef]
15.
Kim, K.; Hessl, D.; Randol, J.L.; Espinal, G.M.; Schneider, A.; Protic, D.; Aydin, E.Y.; Hagerman, R.J.; Hagerman, P.J. Association
between IQ and FMR1 protein (FMRP) across the spectrum of CGG repeat expansions. PLoS ONE 2019,14, e0226811. [CrossRef]
[PubMed]
16.
Fatemi, S.H.; Folsom, T.D. The role of fragile X mental retardation protein in major mental disorders. Neuropharmacology 2011,60,
1221–1226. [CrossRef]
17.
Fatemi, S.H.; Kneeland, R.E.; Liesch, S.B.; Folsom, T.D. Fragile X mental retardation protein levels are decreased in major
psychiatric disorders. Schizophr. Res. 2010,124, 246–247. [CrossRef]
18.
Kelemen, O.; Kovacs, T.; Keri, S. Contrast, motion, perceptual integration, and neurocognition in schizophrenia: The role of
fragile-X related mechanisms. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2013,46, 92–97. [CrossRef] [PubMed]
19.
Tassone, F.; Protic, D.; Allen, E.G.; Archibald, A.D.; Baud, A.; Brown, T.W.; Budimirovic, D.B.; Cohen, J.; Dufour, B.;
Eiges, R.; et al
.
Insight and Recommendations for Fragile X-Premutation-Associated Conditions from the Fifth International Conference on
FMR1 Premutation. Cells 2023,12, 2330. [CrossRef] [PubMed]
20.
LaFauci, G.; Adayev, T.; Kascsak, R.; Brown, W.T. Detection and Quantification of the Fragile X Mental Retardation Protein 1
(FMRP). Genes 2016,7, 121. [CrossRef]
21.
LaFauci, G.; Adayev, T.; Kascsak, R.; Kascsak, R.; Nolin, S.; Mehta, P.; Brown, W.T.; Dobkin, C. Fragile X screening by quantification
of FMRP in dried blood spots by a Luminex immunoassay. J. Mol. Diagn. 2013,15, 508–517. [CrossRef]
22.
Roth, M.; Ronco, L.; Cadavid, D.; Durbin-Johnson, B.; Hagerman, R.J.; Tassone, F. FMRP Levels in Human Peripheral Blood
Leukocytes Correlates with Intellectual Disability. Diagnostics 2021,11, 1780. [CrossRef]
23.
Schutzius, G.; Bleckmann, D.; Kapps-Fouthier, S.; di Giorgio, F.; Gerhartz, B.; Weiss, A. A quantitative homogeneous assay for
fragile X mental retardation 1 protein. J. Neurodev. Disord. 2013,5, 8. [CrossRef]
24.
Sitzmann, A.F.; Hagelstrom, R.T.; Tassone, F.; Hagerman, R.J.; Butler, M.G. Rare FMR1 gene mutations causing fragile X syndrome:
A review. Am. J. Med. Genet. A 2018,176, 11–18. [CrossRef] [PubMed]
25.
Jiraanont, P.; Hagerman, R.J.; Neri, G.; Zollino, M.; Murdolo, M.; Tassone, F. Germinal mosaicism for a deletion of the FMR1 gene
leading to fragile X syndrome. Eur. J. Med. Genet. 2016,59, 459–462. [CrossRef] [PubMed]
26.
Filipovic-Sadic, S.; Sah, S.; Chen, L.; Krosting, J.; Sekinger, E.; Zhang, W.; Hagerman, P.J.; Stenzel, T.T.; Hadd, A.G.;
Latham, G.J.; et al
. A novel FMR1 PCR method for the routine detection of low abundance expanded alleles and full mutations in
fragile X syndrome. Clin. Chem. 2010,56, 399–408. [CrossRef] [PubMed]
27.
Tassone, F.; Pan, R.; Amiri, K.; Taylor, A.K.; Hagerman, P.J. A rapid polymerase chain reaction-based screening method for
identification of all expanded alleles of the fragile X (FMR1) gene in newborn and high-risk populations. J. Mol. Diagn. 2008,10,
43–49. [CrossRef] [PubMed]
28.
Tassone, F.; Longshore, J.; Zunich, J.; Steinbach, P.; Salat, U.; Taylor, A.K. Tissue-specific methylation differences in a fragile X
premutation carrier. Clin. Genet. 1999,55, 346–351. [CrossRef]
29.
Tassone, F.; Hagerman, R.J.; Taylor, A.K.; Gane, L.W.; Godfrey, T.E.; Hagerman, P.J. Elevated levels of FMR1 mRNA in carrier
males: A new mechanism of involvement in the fragile-X syndrome. Am. J. Hum. Genet. 2000,66, 6–15. [CrossRef]
30.
Tassone, F.; Hagerman, R.J.; Chamberlain, W.D.; Hagerman, P.J. Transcription of the FMR1 gene in individuals with fragile X
syndrome. Am. J. Med. Genet. 2000,97, 195–203. [CrossRef]
31.
Kenneson, A.; Zhang, F.; Hagedorn, C.H.; Warren, S.T. Reduced FMRP and increased FMR1 transcription is proportionally
associated with CGG repeat number in intermediate-length and premutation carriers. Hum. Mol. Genet. 2001,10, 1449–1454.
[CrossRef]
32.
Boggs, A.E.; Schmitt, L.M.; McLane, R.D.; Adayev, T.; LaFauci, G.; Horn, P.S.; Dominick, K.C.; Gross, C.; Erickson, C.A.
Optimization, validation and initial clinical implications of a Luminex-based immunoassay for the quantification of Fragile X
Protein from dried blood spots. Sci. Rep. 2022,12, 5617. [CrossRef]
33.
Genc, B.; Muller-Hartmann, H.; Zeschnigk, M.; Deissler, H.; Schmitz, B.; Majewski, F.; von Gontard, A.; Doerfler, W. Methylation
mosaicism of 5
′
-(CGG)(n)-3
′
repeats in fragile X, premutation and normal individuals. Nucleic Acids Res. 2000,28, 2141–2152.
[CrossRef] [PubMed]
34.
McConkie-Rosell, A.; Lachiewicz, A.M.; Spiridigliozzi, G.A.; Tarleton, J.; Schoenwald, S.; Phelan, M.C.; Goonewardena, P.;
Ding, X.;
Brown, W.T. Evidence that methylation of the FMR-I locus is responsible for variable phenotypic expression of the
fragile X syndrome. Am. J. Hum. Genet. 1993,53, 800–809.
35.
Nolin, S.L.; Glicksman, A.; Houck, G.E., Jr.; Brown, W.T.; Dobkin, C.S. Mosaicism in fragile X affected males. Am. J. Med. Genet.
1994,51, 509–512. [CrossRef]

Genes 2024,15, 356 22 of 22
36.
Pretto, D.; Yrigollen, C.M.; Tang, H.T.; Williamson, J.; Espinal, G.; Iwahashi, C.K.; Durbin-Johnson, B.; Hagerman, R.J.;
Hagerman, P.J.;
Tassone, F. Clinical and molecular implications of mosaicism in FMR1 full mutations. Front. Genet. 2014,
5, 318. [CrossRef] [PubMed]
37.
Tassone, F.; Hagerman, R.J.; Loesch, D.Z.; Lachiewicz, A.; Taylor, A.K.; Hagerman, P.J. Fragile X males with unmethylated, full
mutation trinucleotide repeat expansions have elevated levels of FMR1 messenger RNA. Am. J. Med. Genet. 2000,94, 232–236.
[CrossRef] [PubMed]
38.
Meng, L.; Kaufmann, W.E.; Frye, R.E.; Ong, K.; Kaminski, J.W.; Velinov, M.; Berry-Kravis, E. The association between mosaicism
type and cognitive and behavioral functioning among males with fragile X syndrome. Am. J. Med. Genet. A 2022,188, 858–866.
[CrossRef]
39.
Usdin, K.; Hayward, B.E.; Kumari, D.; Lokanga, R.A.; Sciascia, N.; Zhao, X.N. Repeat-mediated genetic and epigenetic changes at
the FMR1 locus in the Fragile X-related disorders. Front. Genet. 2014,5, 226. [CrossRef]
40.
Zhao, X.N.; Usdin, K. Ups and Downs: Mechanisms of Repeat Instability in the Fragile X-Related Disorders. Genes 2016,7, 70.
[CrossRef]
41.
Tabolacci, E.; Nobile, V.; Pucci, C.; Chiurazzi, P. Mechanisms of the FMR1 Repeat Instability: How Does the CGG Sequence
Expand? Int. J. Mol. Sci. 2022,23, 5425. [CrossRef] [PubMed]
42.
Nolin, S.L.; Glicksman, A.; Tortora, N.; Allen, E.; Macpherson, J.; Mila, M.; Vianna-Morgante, A.M.; Sherman, S.L.; Dobkin, C.;
Latham, G.J.; et al. Expansions and contractions of the FMR1 CGG repeat in 5,508 transmissions of normal, intermediate, and
premutation alleles. Am. J. Med. Genet. A 2019,179, 1148–1156. [CrossRef] [PubMed]
43.
Hwang, Y.H.; Hayward, B.E.; Zafarullah, M.; Kumar, J.; Durbin Johnson, B.; Holmans, P.; Usdin, K.; Tassone, F. Both cis and
trans-acting genetic factors drive somatic instability in female carriers of the FMR1 premutation. Sci. Rep. 2022,12, 10419.
[CrossRef] [PubMed]
44.
Pretto, D.I.; Mendoza-Morales, G.; Lo, J.; Cao, R.; Hadd, A.; Latham, G.J.; Durbin-Johnson, B.; Hagerman, R.; Tassone, F. CGG
allele size somatic mosaicism and methylation in FMR1 premutation alleles. J. Med. Genet. 2014,51, 309–318. [CrossRef] [PubMed]
45.
Loesch, D.Z.; Huggins, R.M.; Bui, Q.M.; Taylor, A.K.; Pratt, C.; Epstein, J.; Hagerman, R.J. Effect of fragile X status categories and
FMRP deficits on cognitive profiles estimated by robust pedigree analysis. Am. J. Med. Genet. A 2003,122A, 13–23. [CrossRef]
46.
Menon, V.; Leroux, J.; White, C.D.; Reiss, A.L. Frontostriatal deficits in fragile X syndrome: Relation to FMR1 gene expression.
Proc. Natl. Acad. Sci. USA 2004,101, 3615–3620. [CrossRef] [PubMed]
47.
de Vries, B.B.; Jansen, C.C.; Duits, A.A.; Verheij, C.; Willemsen, R.; van Hemel, J.O.; van den Ouweland, A.M.; Niermeijer, M.F.;
Oostra, B.A.; Halley, D.J. Variable FMR1 gene methylation of large expansions leads to variable phenotype in three males from
one fragile X family. J. Med. Genet. 1996,33, 1007–1010. [CrossRef]
48.
Jacquemont, S.; Pacini, L.; Jonch, A.E.; Cencelli, G.; Rozenberg, I.; He, Y.; D’Andrea, L.; Pedini, G.; Eldeeb, M.; Willemsen, R.; et al.
Protein synthesis levels are increased in a subset of individuals with fragile X syndrome. Hum. Mol. Genet. 2018,27, 2039–2051.
[CrossRef]
49.
Santa Maria, L.; Pugin, A.; Alliende, M.A.; Aliaga, S.; Curotto, B.; Aravena, T.; Tang, H.T.; Mendoza-Morales, G.; Hagerman, R.;
Tassone, F. FXTAS in an unmethylated mosaic male with fragile X syndrome from Chile. Clin. Genet. 2014,86, 378–382. [CrossRef]
50.
Feng, Y.; Zhang, F.; Lokey, L.K.; Chastain, J.L.; Lakkis, L.; Eberhart, D.; Warren, S.T. Translational suppression by trinucleotide
repeat expansion at FMR1.Science 1995,268, 731–734. [CrossRef]
51.
Weisman-Shomer, P.; Cohen, E.; Fry, M. Interruption of the fragile X syndrome expanded sequence d(CGG)(n) by interspersed
d(AGG) trinucleotides diminishes the formation and stability of d(CGG)(n) tetrahelical structures. Nucleic Acids Res. 2000,28,
1535–1541. [CrossRef]
52.
Peprah, E.; He, W.; Allen, E.; Oliver, T.; Boyne, A.; Sherman, S.L. Examination of FMR1 transcript and protein levels among 74
premutation carriers. J. Hum. Genet. 2010,55, 66–68. [CrossRef]
53.
Kumari, D.; Gazy, I.; Usdin, K. Pharmacological Reactivation of the Silenced FMR1 Gene as a Targeted Therapeutic Approach for
Fragile X Syndrome. Brain Sci. 2019,9, 39. [CrossRef] [PubMed]
54.
Tabolacci, E.; Palumbo, F.; Nobile, V.; Neri, G. Transcriptional Reactivation of the FMR1 Gene. A Possible Approach to the
Treatment of the Fragile X Syndrome. Genes 2016,7, 49. [CrossRef] [PubMed]
55.
Liu, X.S.; Wu, H.; Krzisch, M.; Wu, X.; Graef, J.; Muffat, J.; Hnisz, D.; Li, C.H.; Yuan, B.; Xu, C.; et al. Rescue of Fragile X Syndrome
Neurons by DNA Methylation Editing of the FMR1 Gene. Cell 2018,172, 979–992.e976. [CrossRef] [PubMed]
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