Abstract
Evidence suggests that there may be racial differences in risk factors
associated with the development of Alzheimer’s disease and related
dementia (ADRD). We used whole-genome sequencing analysis and
identified a novel combination of three pathogenic variants in the
heterozygous state (UNC93A: rs7739897 and WDR27: rs61740334; rs3800544)
in a Peruvian family with a strong clinical history of ADRD. Notably,
the combination of these variants was present in two generations of
affected individuals but absent in healthy members of the family. In
silico and in vitro studies have provided insights into the
pathogenicity of these variants. These studies predict that the loss of
function of the mutant UNC93A and WDR27 proteins induced dramatic
changes in the global transcriptomic signature of brain cells,
including neurons, astrocytes, and especially pericytes and vascular
smooth muscle cells, indicating that the combination of these three
variants may affect the neurovascular unit. In addition, known key
molecular pathways associated with dementia spectrum disorders were
enriched in brain cells with low levels of UNC93A and WDR27. Our
findings have thus identified a genetic risk factor for familial
dementia in a Peruvian family with an Amerindian ancestral background.
Keywords: unbiased gene discovery, whole genome sequencing,
family-specific genetic factor, Amerindian ancestral background,
Alzheimer’s disease
1. Introduction
Neurological disorders are an important cause of disability and death
around the world. Interestingly, Alzheimer’s disease, dementia,
Parkinson’s disease, epilepsy, schizophrenia, and autism spectrum
disorder share common anatomical alterations and cognitive defects
([45]Kochunov et al., 2021). Certainly, Alzheimer’s disease is the main
cause of dementia, contributes 50 to 75% of cases ([46]Report, 2021),
and can be presented in two forms as defined by age: early-onset
Alzheimer’s disease (EOAD), which occurs before 65 years of age, and
late-onset Alzheimer’s disease (LOAD), which is mostly present after
65 years of age ([47]Mendez, 2017). Both EOAD and LOAD have a family
origin and involve an inheritance mode of autosomal dominant
transmission ([48]Gatz et al., 2006). Genetic variations in three
genes, namely, amyloid precursor protein (APP), presenilin 1, and
presenilin 2 (PSEN1 and PSEN2), are nearly 100% penetrant and were
identified as causative of EOAD ([49]Van Cauwenberghe et al., 2016). On
the contrary, the expression of the apolipoprotein E (APOE) ε4 gene is
the major risk factor for LOAD in the Caucasian population
([50]Guerreiro et al., 2012). There has been evidence, however, that
the risk of developing Alzheimer’s disease in ε4 carriers differs among
ethnic groups. For instance, ε4 carriers of African descent have a low
risk of Alzheimer’s disease ([51]Farrer et al., 1997), while Amerindian
genetic ancestry seemed to be protected from cognitive decline
([52]Granot-Hershkovitz et al., 2021). Similarly, variants in the TREM2
(R47H, H157Y, and L211P) genes, which are closely associated with
Alzheimer’s disease in the Caucasian population ([53]Guerreiro et al.,
2013; [54]Li et al., 2021), were not replicated in Japanese descendants
([55]Miyashita et al., 2014). These epidemiological observations
indicate that genetic risk factors for neurological disorders have
different effects between ethnicities.
In recent years, genome-wide association studies (GWAS) have permitted
the identification and characterization of multiple genetic risk loci
associated with Alzheimer’s disease and related dementia (ADRD;
[56]Kunkle et al., 2019). Most of these genetic loci were not
associated with the APP processing, however, but rather with the immune
response (TREM2, CLU, CR1, CD33, EPHA1, and MS4A4A/MS4A6E), endosomal
trafficking (PICALM, BIN1, and CD2AP), or lipid metabolism (ABCA7;
[57]Bellenguez et al., 2020). An overlap between Alzheimer’s disease
pathogenic variants and other neurogenerative or neuropsychiatric
disorders has also been reported, indicating a shared genetic and
molecular origin. For example, an Alzheimer’s disease variant in the
TREM2 gene (rs75932628) was also correlated with amyotrophic lateral
sclerosis ([58]Cady et al., 2014), while a variant in the MARK2 gene
(rs10792421) was associated with Alzheimer’s disease and bipolar
disorder ([59]Drange et al., 2019).
Identifying individuals at high risk of ADRD remains a global health
need and a major challenge for minority populations. Here, we performed
a whole-genome sequencing (WGS) analysis for a Peruvian family with a
strong clinical history of ADRD, including Alzheimer’s disease and
dementia. We also explored the effect of these variants on the
neurovascular unit of the brain through in silico and in vitro studies.
2. Materials and methods
2.1. Patient sample collection
A family (n = 14) originally from Peru, with five members diagnosed
with neurological and neuropsychiatry disorders, was enrolled in this
study. Non-familial patients with Alzheimer’s disease (n = 8) and
healthy individuals (n = 50) were recruited for the variant validation
study. The selection criteria for the healthy individuals were as
follows: age > 60 years, without signs of dementia, and no familial
history of Alzheimer’s disease. Probable Alzheimer’s disease was
diagnosed according to the guidelines of the National Institute of
Neurological and Communicative Disorders and the Stroke and Alzheimer
Disease and Related Disorders Association ([60]McKhann et al., 2011).
Whenever possible, the cognitive status of each family member was
diagnosed based on a neuropsychological test (MoCA blind test and the
clock drawing test). The cutoff for a normal MoCA score was 18, and for
a normal clock drawing test was 6.
2.2. Genetic analysis
Genomic DNA was extracted from saliva samples using the prepIT.L2P
reagent (Genotek, Cat. No PT-L2P-5) according to the manufacturer’s
instructions. The qualifying genomic DNA samples were randomly
fragmented using Covaris Technology, obtaining a fragment of 350 bp.
The DNA nanoballs (DNBs) were produced using rolling circle
amplification (RCA), and the qualified DNBs were loaded into the
patterned nanoarrays. The WGS was conducted on the BGISEQ-500 platform
(BGI Genomics, Shenzhen, China). Raw sequencing reads were aligned to
the human reference genome (GRCh38/HG38) with the Burrows–Wheeler
Aligner (BWA) software and variant calling was performed with the
Genome Analysis Toolkit (GATK v3.5) according to best practice. On
average, 88.10% mapped successfully and 93.23% mapped uniquely. The
duplicate reads were removed from the total mapped reads, resulting in
a duplicate rate of 2.48% and a 30.72-fold mean sequencing depth on the
whole genome, excluding gap regions. On average per sequencing
individual, 99.34% of the whole genome, excluding gap regions, were
covered by at least 1 × coverage, 98.78% had at least 4 × coverage, and
97.42% had at least 10 × coverage. The whole-genome sequencing analysis
pipeline is presented in [61]Supplementary Figure 1.
2.3. Sanger sequencing
Based on the results of the WGS, the variants that were present in
affected members of the family but absent in healthy individuals were
validated using Sanger sequencing. All PCR products were sequenced
using an ABI 3130 Genetic Analyzer. Sequence analysis was performed
with the Chromas program in the DNASTAR analysis package. The PCR and
sequencing primers are shown in [62]Table 1.
Table 1.
Polymerase chain reaction and sequencing primers.
Gene SNP ID PCR primers Sequencing primers
Forward primer sequence (5′–3′) Reverse primer sequence (5′–3′) Forward
primer sequence (5′–3′)
WDR27 rs3800544 GTTTGCGCTCCTAGTTTCATG GCATTCCGTACTTCCTTCCATC
TGTCCTACCGACCTCTCCACTG
WDR27 rs61740334 ACTGTGAATGTCTCCCGATCAC ACTTGAAGTTGCATGGCATGG
TTCCCTCAGGGAGGCATAC
UNC93A rsRS7739897 TACGGCGTTCTGTTTGAGAAG TCAACCAGGCAGAGGATGAAG
GCTGCCTTGTCGCCAATTAC
[63]Open in a new tab
2.4. Cell lines
Primary human vascular smooth muscle cells (VSMCs) from carotid of
healthy donors were purchased from Cell Applications Inc. (Cat. No
3514k-05a, neural crest origin). Human brain vascular pericytes were
purchased from ScienCell (Cat. No 1200). Human neurons (SH-SY5Y) were
purchased from ATCC (Cat. No CRL-2266). Human astrocytes were purchased
from Cell Applications Inc. (Cat. No 882A05f).
2.5. RNA sequencing and qPCR
Total RNA was extracted using a miRNeasy kit (Qiagen, Cat. No 217084)
following the manufacturer’s protocol instructions. The BGISEQ platform
was used for RNA-seq, generating some 4.28G Gb bases per sample, on
average. The average mapping ratio with the reference genome was 97.01%
and the average mapping ratio with the gene was 74.05%; 17,029 genes
were identified. We used HISAT to align the clean reads to the
reference genome and Bowtie2 to align the clean reads to the reference
genes. A total of 100 ng of total RNA was used for qPCR as the starting
template for cDNA synthesis. The cDNA was prepared by reverse
transcription (RT), and gene expression was analyzed by quantitative
PCR (qPCR) on an SYBR green system (Applied Biosystems). Expression
results were analyzed using the DDCT method, and GAPDH (encoding
glyceraldehyde-3-phosphate dehydrogenase) was used as a housekeeping
gene. Fold changes were calculated as the average relative to the
control carotid as the baseline.
2.6. Computational details
2.6.1. System building, structural refinements, and molecular dynamic
simulations (MDS)
The [64]Q86WB7–1 (UNC93A, 457 aa) and A2RRH5-4 (WDR27, 827 aa)
sequences ([65]WDR27, 2020; [66]Wang et al., 2021) were used to build
the 3D wild-type protein structures using the I-TASSER server
([67]Zheng et al., 2021). The mutant variants (UNC93A V409I, and WDR27
R467H-T542S) were built based on these 3D models by performing
site-direct mutagenesis using UCSF Chimera software ([68]Pettersen et
al., 2004). To avoid the residue overlapping in all protein systems, a
structural refinement was carried out using the ModRefiner server
([69]Xu and Zhang, 2011). Classical MD simulations were performed using
the GROMACS 2020.4 package with the OPLS-AA force field parameters
([70]Jorgensen et al., 1996; [71]Kutzner et al., 2019). All protein
systems were built in a triclinic simulation box with periodic boundary
conditions (PBC) in all directions (x, y, and z). They were then
solvated using the TIP4P water model ([72]Jorgensen et al., 1983), and
Cl^– or Na^+ ions were used to neutralize the total charge in the
simulation box. The mimicking of physiological conditions was performed
by ionic strengthening, with the addition of 150 mM NaCl. The distance
of the protein surfaces to the edge of the periodic box was set at
1.5 nm, and a 1 fs step was applied to calculate the motion equations
using the Leapfrog integrator ([73]Hockney et al., 1974). The
temperature for proteins and water-ions in all simulations was set at
309.65 K using the modified Berendsen thermostat (V-rescale algorithm),
with a coupling constant of 0.1 ps ([74]Berendsen et al., 1984). The
pressure was maintained at 1 bar using the Parrinello–Rahman barostat
with a compressibility of 4.5 × 10^−5 bar^−1 and coupling constant of
2.0 ps ([75]Bussi et al., 2007). The particle mesh Ewald method was
applied to long-range electrostatic interactions with a cutoff equal to
1.1 nm for nonbonded interactions, with a tolerance of 1×10^5 for
contribution in the real space of the 3D structures. The Verlet
neighbor searching cutoff scheme was applied with a neighbor-list
update frequency of 10 steps (20 fs). Bonds involving hydrogen atoms
were constrained using the linear constraint solver (LINCS) algorithm
([76]Hess, 2008). The energy was minimized in all simulations with the
steepest descent algorithm for a maximum of 100,000 steps. We performed
two steps for the equilibration process; one step of dynamics (1 ns) in
the NVT (isothermal-isochoric) ensemble, followed by 2 ns of dynamics
in the NPT (isothermal-isobaric) ensemble. The final simulation was
then performed in the NPT ensemble for 500 ns followed by the analysis
of the structures and their energy properties.
2.6.2. Structural and energetic analysis of 3D protein structures
All MD trajectories were corrected, and the 3D structures were
recentered in the simulation boxes. RMSD, RMSF, the radius of
gyrations, H-bonds, residue distances, and solvent-accessible surface
area analyses were performed using the Gromacs tools, and the results
were plotted using XMGrace software. We used the UCSF Chimera, VMD
software packages, to visualize the structures. Atomic interactions and
2D plots were analyzed using the LigPlot software packages ([77]Wallace
et al., 1995). Electrostatic potential (ESP) surfaces were calculated
using the APBS (Adaptive Poisson-Boltzmann Solver) software, and the
PDB2PQR software was used to assign the charges and radii to protein
atoms ([78]Baker et al., 2001).
2.6.3. Calculation of binding free energy
The Molecular Mechanics Poisson–Boltzmann Surface Area (MM/PBSA) of
free energies and energy contribution by individual residues was
calculated to analyze the effect of amino-acid substitutions on the
different structures using the last 100 ns of MD trajectories and the
g_mmpbsa package ([79]Kumari et al., 2014). The interacting energy was
calculated using the following equation:
[MATH:
ΔGint=GProt−G1 :MATH]
where the term G[1] is the free energy of the different sites of the
protein, and G[Prot] is the free energy of the entire 3D structure. In
this context, the free energy of each term was calculated as follows:
[MATH:
Gx=E
MM+Gsolv−TS :MATH]
where E[MM] is the standard mechanical energy (MM) produced from bonded
interactions, electrostatic interactions, and van der Waals
interactions. G[solv] is the solvation energy that includes the free
energy contributions of the polar and nonpolar terms. The TS term
refers to the entropic contribution and was not included in this
calculation due to the computational costs ([80]Rastelli et al., 2010;
[81]Kumari et al., 2014). Finally, 309 Kelvin (K) of temperature was
used as the default parameter in all our calculations.
3. Results
3.1. Genetic analyses
Genealogic investigations allowed us to identify five members of a
Peruvian family with a strong clinical history of ADRD, including
dementia, Alzheimer’s disease, and schizophrenia across two generations
([82]Figure 1A; [83]Supplementary material Table 1). To detect genetic
risk factors associated with the development of the neurologic
disorders observed in this family, we performed a WGS analysis on
affected and healthy members (n = 14) of the family. By using the
BGISEQ-500 platform, we obtained an average of 113,895.38 Mb of raw
bases. After removing low-quality reads, we obtained an average of
106,676.25 Mb clean reads, identifying a total of 3,933,470 SNPs. We
then selected coding variants that met the following two criteria:
first, candidate variants that harbored at least one “disruptive” or
missense variant, and second, variants that were present in affected
probands, but not in unaffected members of the family. As a result, we
identified three coding variants that segregated across two generations
of affected individuals ([84]Supplementary material Table 2). These
variants were found to be located at chr6:167728791 (UNC93A;
rs7739897), at chr6:170047902 (WDR27; rs61740334), and at
chr6:170058374 (WDR27; rs3800544; [85]Figures 1B,[86]C). Two different
Sanger PCR sequencing platforms were used to validate the presence of
these SNPs ([87]Figure 1D). Several studies have also found SNPs in
genes located on chromosome 6q with a significant connection to
neurological diseases ([88]Kohn and Lerer, 2005; [89]Naj et al., 2010);
however, the UNC93A and WDR27 variants have not yet been associated
with ADRD. It is worthy of note that none of the currently known
Alzheimer’s disease-associated variants were present in this Peruvian
family.
Figure 1.
[90]Figure 1
[91]Open in a new tab
Family pedigrees and variants identified in affected members of family.
(A) Pedigree of the Peruvian family. (B) The chromosomal position of
UNC93A and WDR27 genes. (C) Location of a mutation in the protein
structure. (D) An electropherogram from affected probands showing a
base pair change in the UNC93A gene (Val409Ile/c.1225G > A) and in the
WDR27 gene (Thr542Ser/c.1624A > T; Arg467His/c.1400G > A), compared to
healthy controls.
To further confirm an association between the UNC93A and WDR27 variants
and familial genetics risk for neurologic disorders, we analyzed their
presence in unrelated healthy Peruvians (n = 50) and unrelated
individuals with neurological disorders (probable Alzheimer’s disease,
n = 8). As shown in [92]Supplementary material Table 3, the UNC93A
variant (V409I) was present in 1/50 of the healthy group, and the WDR27
variants (Thr542S and Arg467His) were present in 2/50 of the healthy
group. Interestingly, the three variants did not co-exist in any
healthy individuals and were absent in volunteers diagnosed with
probable Alzheimer’s disease with no familial history of ADRD. These
findings suggest that the co-occurrence of these three variants may be
related to neurological disorders in a Peruvian family.
3.2. Structural analysis of the WDR27 and UNC93A variants
UNC93A genes encode a transmembrane protein (457 amino acids) that has
11 alpha-helices and is mainly expressed in the brain, kidney, and
liver ([93]Ceder et al., 2017). The WDR27 gene encodes a scaffold
protein with multiple WD repeat domains and is ubiquitously expressed
in the human body. We used in silico approaches to provide insights
into the molecular and structural effect of the UNC93A (V409I) and
WDR27 (Arg467His and T542S) variants associated with familial ADRD. We,
therefore, built the human structure of the UNC93A and WDR27 proteins
by homology modeling and performed site-direct mutagenesis to generate
the mutated proteins using the UCSF Chimera software ([94]Figures
2A,[95]B). Molecular dynamics simulations (MDS) for 500 ns were
performed to stabilize the physical motions of atoms in both proteins
to mimic physiological conditions. Importantly, we observed that at the
beginning of the MDS (0–50 ns), the residue V409 of the wild-type UNC93
protein interacts with the lipid bilayer of the cell membrane. After
500 ns of MDS, the V409 is internalized toward the protein-active
transmembrane conduct region in which the subsequent interactions with
ligands or ionic exchanges occur. These movements were characterized by
high structural vibration and epitope exposure of the UNC93A’s
ectodomain (aa 200–300) of the protein toward the surface of the cell
membrane ([96]Figure 2C). In contrast, the mutant I409 blocks the
internalization of this residue and loses its capacity to move toward
the protein-active transmembrane conduct region due to the loss of
about 50% of the global residual vibration ([97]Figure 2D). As a result
of this change in the amino acid, the UNC93A loses its capacity for ion
exchange and interaction with potential ligands or partners. Regarding
the WDR27 protein, both His467 and S542 variants induce the
internalization of these residues in the part of the hinge domain of
the protein predicting the loss of function and capacity to interact
with other partners ([98]Figure 2E). We also observed that the
wild-type and mutant proteins are very stable due to their close
residual vibration and epitope exposure patterns ([99]Figure 2F). The
solvent accessible surface area (SASA) analysis demonstrated that the
UNC93A (I409) protein increased the surface area of the mutated amino
acid and its environment ([100]Figure 2G), while the WDR27 variants
reduced its SASA ([101]Figure 2H). The effect on the amino acid
substitutions for both proteins was determined using the Molecular
Mechanics Poisson–Boltzmann Surface Area (MM/PBSA) calculation of free
energies and energy contribution using the last 150 ns of MD
trajectories. Remarkably, the I409 increased the affinity of the
protein to the membrane, indicating a reduced capacity for
internalization, while the WDR27 variants reduced the protein
stabilization of both His467 and S542 mutated amino acids. Together,
these findings indicate that the UNC93A and WDR27 variants have a
strong effect on the functionality and ability to interact with their
environment, and thus potentially affect brain homeostasis.
Figure 2.
[102]Figure 2
[103]Open in a new tab
In silico analysis of UNC93A and WDR27 amino acid substitutions predict
loss of protein function. (A,B) show the UNC93A and WDR27 wild-type and
mutant proteins. (C,E) show the MM-PBSA calculation of the main
energetic interactions of residues at mutation sites. Blue indicates
favorable energies and red unfavorable energies. (D,F) show a Circos
plot of the wild-type and mutant full-length protein. The heat map
represents the vibrational movement of each residue throughout MD
simulations at the scale bar values. The outer histograms show the
regions most likely (> 50%) to be epitopes. (G,H) show the
solvent-accessible surface area (SASA) average values of the wild-type
and mutation residues and their neighbors.
3.3. Functional analysis of loss of WDR27 and UNC93A gene expression in vitro
We next investigated how the co-occurring inhibition of UNC93A and
WDR27 gene expression affects the cellular homeostasis of brain cell
lines, including neurons, pericytes, astrocytes, and VSMCs. To achieve
this goal, we simultaneously silenced both UNC93A and WDR27 genes using
a siRNA approach and performed a high throughput RNA sequencing
([104]Supplementary material Figure 2). Differential expression
analysis demonstrates that VSMCs had the highest numbers of
differentially expressed genes (DEG = 2,231), followed by pericytes
(DEG = 2091), while astrocytes (DEG = 226) and neurons (DEG = 191)
showed a modest change in gene expression compared with control groups
([105]Figure 3A), suggesting that mutations promoting a loss of
function of the UNC93A and WDR27 genes affect the neurovascular unit of
the brain. Pathway analysis of VSMC and pericyte gene signatures
identified enrichment for multiple known molecular pathways associated
with neurological disorders, such as impaired autophagy pathways
signaling ([106]Li et al., 2016), ubiquitin-mediated proteolysis
([107]Upadhya and Hegde, 2007), unproductive metabolism signaling
([108]Van Der Velpen et al., 2019) inflammation, and necroptosis
([109]Zhang et al., 2021; [110]Figure 3B), while neurons showed a gene
signature enriched for cellular senescence, cytokine–cytokine
interactions, and apoptosis. Astrocytes showed only modest enrichment
for necroptosis and the proteasomal degradation pathway ([111]Figure
3B). Similar to our results, previous in vivo studies have reported a
direct connection between autophagy activation and UNC93A levels in the
healthy brains of mice under starvation, indicating the potential role
of UNC93A in metabolic stability, energy uptake, and nutrient transport
in the brain ([112]Ceder et al., 2017). Interestingly, several
neurological diseases have been associated with defects of autophagy
and metabolism homeostasis, including Alzheimer’s, Parkinson’s, and
Huntington’s diseases ([113]Wang et al., 2016; [114]Croce and Yamamoto,
2019; [115]Xu et al., 2021). We also observed that multiple genes
previously related to Alzheimer’s disease, such as CLU ([116]Jun et
al., 2010) SQSTM1 ([117]Cuyvers et al., 2015) GPC6 ([118]Kunkle et al.,
2021), and ABCA7 ([119]De Roeck et al., 2019), were dysregulated in
brain cells deficient in UNC93A and WDR27 ([120]Figure 3C).
Interestingly, the strongest genetic risk factor for Alzheimer’s
disease, APOE, was also modulated in pericytes and neurons ([121]Figure
3C).
Figure 3.
[122]Figure 3
[123]Open in a new tab
Loss of UNC93A and WDR27 affects the global transcriptomic signature of
brain cell lines in vitro. (A) Volcano plot of dysregulated genes in
four different brain cell types. (B) Shows the KEGG pathways analysis.
(C) Heatmap of dysregulated genes previously associated with
neurodegenerative disease.
4. Discussion
Recent studies have shown racial disparities in ADRD diagnosis between
white and minority groups ([124]Lennon et al., 2022; [125]Suran, 2022).
Diverse evidence suggests that there may be racial differences in risk
factors associated with the development of ADRD ([126]Mayeda et al.,
2017; [127]Brewster et al., 2019; [128]Barnes, 2022). Risk factors such
as genetics, age, lifestyle, and co-morbid cardiovascular disease can
be useful to understand the incidence, prevalence, and predisposition
of an individual to ADRD. Despite the research progress on racial
differences in ADRD in developed countries, the diagnosis of ADRD in
developing countries (e.g., Asian, African, and South American
countries) deserves more recognition for its contribution to the global
burden of Alzheimer’s disease ([129]Chávez-Fumagalli et al., 2021). The
limited resources to address mental health issues, the lack of adequate
technology to diagnose ADRD, and the few funding agencies that support
research studies are also major challenges faced by public health
systems in developing countries. Indeed, less than 10% of people living
with dementia in low-and middle-income countries are diagnosed
([130]Prince et al., 2011). Notably, the Peruvian population has a
strong Amerindian ancestral background (approximately 80%), compared to
other Latin American populations ([131]Norris et al., 2017, [132]2020),
indicating an opportunity to identify ancestry-specific genetic
modifiers associated with the development of ADRD.
Here, we report an inheritance risk factor for ADRD in a Peruvian
family with an Amerindian ancestral background. We identified a novel
combination of three pathogenic variants in the heterozygous state
(UNC93A: rs7739897 and WDR27: rs61740334; rs3800544) which segregated
across two generations in a family with a strong clinical history of
ADRD. Notably, the combination of these variants was present in members
with neurological disorders but absent in healthy individuals.
Importantly, although these three SNPs are fairly common in European
American and African American ancestry populations
(MAF = 1.78–17.09),[133]^1 the combined effect of these variants was
not previously studied. Our findings thus suggest that the combination
of these variants is necessary to manifest the disease. Supporting our
hypothesis, it has been reported that variants that have no impact on
health when found individually cause severe disease when in combination
with other genetic variants ([134]Gifford et al., 2019).
Our in silico analysis of the 3D structure of the mutant UNC93A (V409I)
and WDR27 (Arg467His and T542S) proteins demonstrates that changes in
the amino acid sequences have a dramatic effect on the conformational
structure, predicting the loss of function of both proteins. However,
the exact biological role of the UNC93A protein remains unknown. For
instance, some studies have identified the potential role of UNC93A as
a solute carrier and in ion homeostasis ([135]Ceder et al., 2020). Its
expression seemed to be associated with increased metabolic activity in
organs such as the brain and kidneys ([136]Ceder et al., 2017). In this
context, we observed that amino acid substitution (I409) in the UNC93A
protein reduced its capacity for ion exchange and interaction with
potential ligands or partners, indicating a negative effect on UNC93A
bioactivity in the brain. Our in silico analysis of the mutant WDR27
proteins demonstrated that both amino acid substitutions induced the
internalization of the hinge domain of the protein, affecting its
segmental flexibility and ability to clamp down on its substrates or
ligands. Similarly, little is known about the WDR27 biological
functions in the brain; however, an SNP in the intergenic region
adjoining WDR27 (rs924043) was associated with type 1 diabetes
([137]Bradfield et al., 2011), and its duplication has been seen in
obese patients ([138]D’Angelo et al., 2018). These results suggest the
involvement of UNC93A and WRD27 in metabolic syndrome and related
diseases.
Importantly, the brain is the most complex and metabolically active
organ, being equipped with a sophisticated network of specialized cell
types such as neurons, microglia, astrocytes, pericytes, and VSMCs. In
recent years, diverse studies have demonstrated the contribution of
these cells to Alzheimer’s disease pathogenesis ([139]Zenaro et al.,
2017; [140]Aguilar-Pineda et al., 2021). To investigate the potential
effects of a loss of function of UNC93A and WDR27, we used gene
silencing technologies to simultaneously reduce the expression of both
proteins in four brain cell types to mimic the clinical phenotype of
members of the family with ADRD. Our KEGG pathway enrichment analysis
showed that autophagy, mitophagy, and metabolic pathways are the most
affected in both UNC93A and WDR27 inhibitory conditions. Interestingly,
these pathways play an important role in Aβ clearance, and thus
dysfunction may lead to the development of Alzheimer’s disease
([141]Zeng et al., 2022). As reduced autophagy activity was related to
increased cell death in response to intracellular stress ([142]Galati
et al., 2019), these variants could have a negative effect on BBB
integrity.
Our study has several limitations. First, a lack of access to
imagological studies meant that we were not able to correlate the
variants with damaged areas of the brain; however, the MoCA test
([143]Supplementary material Table 1) corroborated that brain areas
associated with cognitive domains, predominantly temporal and frontal
lobe areas, are damaged. Second, we could not find a validation family
for the combination of these variants. However, these combinatory
variants may only be present in the reported family. This could be a
similar case to that reported for the PSEN1 (E280A) mutation, which
only affects the Colombian family descendant of a Spanish conquistador
([144]Lall et al., 2014), or the mutation in the PSEN2 gene (N141I)
that is only present in families with German descendants who emigrated
to a southern Volga region in Russia in the 1760s ([145]Tomita et al.,
1997). Despite these limitations, this study reports for the first time
a new genetic risk locus associated with ADRD and the great importance
of the UNC93A and WDR27 genes in brain biology.
Data availability statement
The original contributions presented in the study are publicly
available in the Mendeley Data repository. This data can be found here:
[146]https://data.mendeley.com/datasets/wsz875f5hs/1, doi:
[147]10.17632/wsz875f5hs.1.
Ethics statement
The studies involving human participants were reviewed and approved by
Comité Institucional de Ética en Investigación Red Asistencial
Arequipa—ESSALUD. The patients/participants provided their written
informed consent to participate in this study.
Author contributions
CLLC conceived the work with KLFA and GD-D-C. CLLC and KLFA designed
the work. CLLC and KLFA performed the experiments. KLFA, MMO-M, LDG-M,
MAC-F, BCC-Q, and KJV-L collected the samples. MMO-M and BCC-Q
collected the medical records. JAAP performed the in silico analysis.
MFP-C performed and analyzed the neurological tests. PLM analyzed the
neurological tests. CLLC and KLFA analyzed and interpreted the data of
the study. AP-M provided the clinical samples, and made a substantial
contribution to the design of the study. GD-D-C and CLLC supervised the
study. CLLC and KLFA wrote the paper. All authors read and approved the
final manuscript.
Funding
This research was funded by the Consejo Nacional de Ciencia, Tecnologia
e Innovacion Tecnologica de Peru (grant no
024-2019-Fondecyt-BM-INC.INV). CLLC was supported by NIH (K01HL164687),
the MGH Physician–Scientist Development Award, the Ruth L. Kirschstein
National Research Service Award (5T32HL007208-43), and the
Physician-Scientist Development Award (PSDA-MGH).
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Acknowledgments