Introduction
Alzheimer’s disease (AD) is the leading cause of dementia, affecting more than 24 million people worldwide [
4] and with the incidence of AD increasing dramatically as the global population ages. The classical hallmarks of AD include the formation of amyloid-beta (Aβ) plaque deposits and neurofibrillary tangles (NFT) containing abnormal hyperphosphorylation of tau. Over the course of the disease, the deposition of Aβ and the formation of the NFTs appear to be correlated with the on-going hippocampal neurodegeneration [
6]. Hippocampal neurodegeneration has a negative effect on the cognitive ability of patients, especially in the formation of new memories. The mechanisms triggering the deposition of the Aβ or the formation of NFTs are currently under investigation. However, several mechanisms and factors have been suggested to be involved in the initiation and the progression of the disease, including activation of the innate immune system, environmental factors and lifestyle [
42]. The innate immune system has been widely studied and has been implicated in several neurodegenerative diseases [
19]. Over the last few years, several studies have suggested that inflammation plays a major role in the initiation and progression of AD [
17,
20,
62,
66].
The inflammatory process in the central nervous system (CNS) is generally referred to as neuroinflammation. Glial cells have a leading role in propagating neuroinflammation. Among glial cells, microglia are considered the main source of proinflammatory molecules within the brain [
18,
27,
63]. It is believed that sustained release of proinflammatory molecules such as cytokines, chemokines, nitrogen reactive species (NRS) or reactive oxygen species (ROS) can create a neurotoxic environment that drives the progression of AD [
1,
19,
46]. Moreover, there is strong evidence supporting the role of inflammatory molecules, such as iNOS, in the initiation of plaque formation due to post-translational modifications of Αβ, leading to a faster aggregation [
31,
32]. Indeed, counteracting the aggregation process has been proposed as a therapeutic strategy to alleviate the progression of the pathology [
58].
Conclusive evidence supporting a direct role of microglia in human neurodegeneration was revealed with the recent advent of massive genome analysis. Specifically, genetic-associated studies have identified several AD-risk genes strongly associated with the innate immune system, including, among others, triggering receptor on myeloid cells 2 (
trem2),
cd33,
cr1,
clu,
epha1 and
ms4a4a/ms4a6a [
7,
16]. Recent single-cell transcriptomic studies of microglia have pointed out galectin-3 (Lgals3; gal3) as one of the most attractive molecules in brain innate immunity associated with neurodegeneration (see recent review [
10]). Indeed, Holtman et al. [
22] analyzed transcriptional profiles of isolated microglia from different disease mouse models, including AD, amyotrophic lateral sclerosis and aging, and revealed a shared transcriptional network in all conditions, including a strong upregulation of gal3. Interestingly, the authors analyzed the most likely candidates to orchestrate the microglial activation phenotype and found four major hub genes:
Lgals3,
Igf1,
Csf1 and
Axl [
22]. Two other recent studies characterized the molecular signature of microglial cells associated with different disease conditions, including aging and AD [
27,
30]. Again, a common microglial neurodegenerative disease-associated phenotype was identified, supporting that (1) the microglial phenotype was driven by TREM2 and (2) gal3 was one of the most upregulated genes.
Recent findings from our group have demonstrated that gal3 is released by LPS-treated microglia and acts as a ligand of toll-like receptor 4 (TLR4), which is one of the canonical receptors involved in the microglial inflammatory response [
9]. Based on our previous studies and recent findings from the AD field related to the role of inflammation in the disease, we hypothesize that the reduction of microglial activation by removing/inhibiting gal3 will counteract the inflammatory response present in AD and slow the progression of the disease. In this work, we demonstrate (1) a significant upregulation of gal3 in human AD patients compared to age-matched healthy controls, (2) the preferential expression of gal3 in microglial cells in contact with Aβ plaques both in human and mouse, (3) a clear reduction of the inflammatory response in microglial cells challenged with Aβ fibrils (fAβ) following gal3 inhibition and gal3 deletion in microglial cultures, (4) a significant Aβ plaque reduction and better cognitive outcome in 5xFAD mice lacking gal3, and (5) the role of gal3 as a ligand of TREM2 through its carbohydrate recognition domain (CRD) domain, which confers the ability to bind glycans.
Materials and methods
Animals
5xFAD–Gal3 KO transgenic mice were generated by crossing heterozygous 5xFAD (±) mice with homozygous Gal3KO (−/−) mice to get 5xFAD (±)/Gal3 (±) mice. Subsequent crossings between animals expressing this genotype allowed for the generation of 5xFAD (±)–Gal3 (−/−) mice, hence referred to as 5xFAD/Gal3KO mice. 5xFAD/Gal3 mice are hence referred to as 5xFAD mice. Male and female mice were equally distributed between the experimental groups. No differences were found between male and female mice in any of the experiments performed. All animal experiments were performed in accordance with the animal research regulations (RD53/2013 and 2010/63/UE) in Spain and European Union and with the approval of the Committee of Animal Research at the University of Seville (Spain).
For primary microglial cultures, Gal3KO mice with a C57BL/6 background were obtained from Dr. K. Sävman at Gothenburg University. All procedures were carried out in accordance with the international guidelines on experimental animal research and were approved by the Malmö-Lund Ethical Committee for Animal Research in Sweden (M250-11, M30-16, Dnr 5.8.18-01107/2018).
Genotyping
The genotypes of Gal3−/− (KO) and Gal3+/+ (WT) mice were determined using an integrated extraction and amplification kit (Extract-N-Amp™, Sigma-Aldrich). First, the samples were incubated at 94 °C for 5 min, followed by 40 cycles with denaturation at 94 °C for 45 s, annealing at 55 °C for 30 s, and elongation at 72 °C for 1.5 min. The following primers (CyberGene, Solna, Sweden) were used: galectin-3 common (5′-CAC GAA CGT CTT TTG CTC TCT GG-3′), galectin-3−/− (5′-GCT TTT CTG GAT TCA TCG ACT GTG G-3′, single band of 384 bp) and galectin-3+/+ (5′-TGA AAT ACT TAC CGA AAA GCT GTC TGC-3′, single band of 300 bp) [
17]. For the 5xFAD mice, the primers (5′–3′) used are listed below: APP Forward AGGACTGACCACTCGACCAG, APP Reverse CGGGGGTCTAGTTCTGCAT, PSN1 Forward AATAGAGAACGGCAGGAGCA, PSN1 Reverse GCCATGAGGGCACTAATCAT, WT APP Forward CTAGGCCACAGAATTGAAAGATCT, WTT APP Reverse GTAGGTGGAAATTCTAGCATCATCC, RD1, RD2 and RD3 AAGCTAGCTGCAGTAACGCCATTT ACCTGCATGTGAACCCAGTATTCTATC, CTACAGCCCCTCTCCAAGGTTTATAG. The PCR products were labeled with SYBR
® Green (Sigma-Aldrich), separated by gel electrophoresis and visualized using a CCD camera (SONY, Tokyo, Japan).
Protein preparation
Aβ (M1–42) (i.e., with a starting Met0 to allow for production of otherwise untagged peptide) was expressed in E. coli (BL21 DE3 PLysS Star) and purified from inclusion bodies after repeated sonication using ion exchange in batch mode and size exclusion chromatography in column format. This was followed by lyophylization of monomer aliquots. Each day, before use in any experiment, Aβ42 monomer was again isolated from the aliquots by dissolving an aliquot in 1 mL of 6 M GuHCl, and, using gel filtration (Superdex 75, 10–300 column) with 20 mM sodium phosphate, 0.2 mM EDTA, pH 8.0 as running buffer, collected on ice in a low-binding tube (Genuine Axygen Quality, Microtubes, MCT-200-L-C, Union City, CA, USA). The concentration was determined by absorbance at 280 nm using ε280 = 1440 M−1 cm−1. The solution was diluted with buffer and supplemented with concentrated NaCl to achieve 10 µM monomer and 150 mM NaCl. The solution was placed in wells of a PEGylated polystyrene plate (Corning 3881) and sealed with a plastic film to avoid evaporation. Thioflavin T (ThT) was added to one well from a concentrated stock to obtain 6 µM ThT. Plates were incubated at 37 °C in a FLUOstar Omega plate reader under quiescent conditions (BMG Labtech, Offenburg, Germany), and the fibril formation of amyloid-beta was followed by reading the fluorescence (excitation 440 nm, emission 480 nm) through the bottom of the plate. The samples in wells without ThT were collected after reaching the plateau of the sigmoidal transition (after ca. 1 h).
Cryogenic transmission electron microscopy (cryo-TEM)
Specimens for electron microscopy were prepared in a controlled environment vitrification system (CEVS) to ensure stable temperature and to avoid the loss of solution during sample preparation. The specimens were prepared as thin liquid films, < 300 nm thick, on lacey carbon-filmed copper grids and plunged into liquid ethane at − 180 °C. This led to vitrified specimens, avoiding component segmentation and rearrangement and water crystallization, thereby preserving the original microstructures. The vitrified specimens were stored under liquid nitrogen until measured. An Oxford CT3500 cryoholder and its workstation were used to transfer the specimen into the electron microscope (Philips CM120 BioTWIN Cryo) equipped with a post-column energy filter (Gatan GIF100). The acceleration voltage was 120 kV. The images were recorded digitally with a CCD camera under low electron-dose conditions. The node-to-node distance was measured using the software Digital Graph (Gatan Inc.).
Endotoxin test
To further evaluate the properties of our protein preparation, we performed an endotoxin assay to ensure that BV2 microglial cell activation was not due to the presence of endotoxins such as LPS (suppl. Fig. 5, online resource 5). Fibril preparations used in the in vitro and in vivo experiments were tested for endotoxins using the Pierce® LAL Chromogenic Endotoxin Quantitation Kit, (ThermoScientific) according to the manufacturer’s instructions (suppl. Fig. 5a, online resource 5).
XTT (cell viability) assay
XTT assay was performed to measure mitochondrial activity (mitochondrial dehydrogenase) in living cells using XTT (2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxyanilide salt) (Sigma-Aldrich, Sweden). The assay was performed following the manufacturer’s protocol in a 96-well plate (Biochrom Asys Expert 96 micro plate reader, Cambridge, UK) (suppl. Fig. 5c, online resource 5).
Cell lines and primary cultures
BV2 microglial cells were cultured in 12-well plates, 250,000 cells/well and stimulated with Aβ monomers and fibrils for a range of time points (3, 6, 12 and 24 h) and concentrations (3 and 10 μM). Primary microglial cells were obtained from WT and Gal3KO mice. The primary cells were obtained from the cortex, as previously described [
15], and cultured for 14 days in T75 flask culture conditions before treatment. After 14 days, microglial cells were isolated, and 25,000 were cultured in 96-well plates and incubated for 12 h with fAβ. Cell viability and number of cells were assessed using a TC20 Bio-Rad Cell counter, Bio-Rad chambers slides and trypan blue 0.04% for BV2 and primary cultures. The same number of viable cells of each genotype (WT or Gal3KO) was plated per well. The cells were grown in DMEM (Invitrogen), FBS (Invitrogen) 10% (v/v) and penicillin–streptomycin (Invitrogen) 1% (v/v) in 5% CO
2 in air at 37 °C in a humidified incubator. After incubation, the media was collected to measure extracellular cytokines and proteins to evaluate the microglial activation profile.
TREM2–DAP12 reporter cell line
The ability of gal3 to activate TREM2–DAP12 signaling was assayed in a BWZ thymoma reporter cell line transfected with TREM2 and DAP12 as previously described [
23]. In these cells, TREM2/DAP12 signaling activates phospholipase C, leading to a calcium influx that activates calcineurin, which, in turn, leads to disinhibition of the nuclear import of NFAT (nuclear factor of activated T-cells). This, in turn, induces transcription of the LacZ β-galactosidase gene. The TREM2/DAP12 reporter cells or parental BWZ cells not expressing TREM2 or DAP12 (control) were incubated with the indicated concentrations of gal3 for 24 h at 37 °C. Afterwards, they were washed and then lysed in a buffer containing 100 mM 2-ME, 9 mM MgCl
2, 0.125% NP-40, and 30 mM chlorophenol red galactosidase (CPRG). Plates were developed for 24 h at 37 °C, and lacZ activity was measured as previously described [
23]. As a positive control, ionomycin (3 μM) was added.
Phagocytosis experiments
After 24 h of primary microglia culturing, 1 µM of gal3 was added for 30 min prior to incubation or simultaneously with labeled Aβ1-42 (HiLyte™ Fluor 647-labeled, Human; ANA64161) as monomers (mAβ) or fibrils (fAβ) for 1 h. fAβ samples were obtained by incubating them for 24 h at 37 °C. Cells were detached by brief incubation with trypsin with EDTA and analyzed by flow cytometry (Accuri C6, BD Biosciences). Mean fluorescence in the FL4 gate was used to plot results and is expressed as a percentage from Aβ1–42 uptake into primary microglia.
Soluble and insoluble protein fractions were obtained from the whole cortex (mice) and temporal cortex (human) using sequential protein extraction. For each sample, fractions were obtained by homogenization of the cortex with a Dounce homogenizer in the presence of PBS (1 mL/100 µg of tissue). The supernatant, the S1 fraction, was aliquoted and stored at − 80 °C. The S1 soluble fraction was obtained after centrifugation for 1 h at 40,000 rpm in special tubes for high-speed centrifugation by Beckman-Coulter. The pellet was dissolved in RIPA buffer (Sigma-Aldrich, Germany) and subsequently ultracentrifuged at 30,000 rpm. The resulting supernatant, the S2 fraction (intracellular particulate proteins), was aliquoted and stored. The pellet was re-dissolved in buffered-SDS (2% SDS in 20 mM Tris–HCl, pH 7.4, 140 mM NaCl) and then centrifuged as above. The supernatant, the S3 fraction (SDS releasable proteins), was stored. Finally, the remaining pellet (P3) was dissolved in SDS–urea (20 mM Tris–HCl, pH 7.4, 4% SDS and 8 M urea). PBS and RIPA solution were prepared with a protein inhibitor (Protein Inhibitor Cocktail, ThermoScientific) to prevent protein degradation and to inhibit the enzymatic activity of phosphatases (PhosphoStop, Roche).
Western blotting
Proteins were extracted from cell cultures with RIPA buffer (Sigma-Aldrich, Germany) along with proteinase and phosphatase inhibitors (Roche, Switzerland). Protein concentration was measured using a BCA kit according to the manufacturer’s protocol (BCA Protein Assay-Kit, ThermoScientific, Sweden). Protein extracts were then separated by SDS-PAGE using pre-cast gels (4–20%, Bio-Rad) in TGS buffer (Bio-Rad, Sweden). The proteins were transferred to nitrocellulose membranes (Bio-Rad, Sweden) using the TransBlot Turbo system from Bio-Rad. The membranes were subsequently blocked for 1 h with skim milk at 3% (w/v) in PBS, then washed 3 × 10 min in PBS supplemented with 0.1% (w/v) Tween 20 (PBS-T). Then, blots were incubated with primary antibodies in PBS-T, as mentioned above, overnight. Following this, we incubated the blots with secondary antibodies for 2 h. After the secondary antibody, we washed three times with PBS-T, and then the blots were developed using ECL Clarity (Bio-Rad) according to the manufacturer’s protocol and imaged using the ChemiBlot XRS+ system from Bio-Rad.
ELISA plates
MesoScale (MSD) plates were used to evaluate the cytokine levels (proinflammatory panels for IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12 and TNF-α) in culture media and in blood- and brain-soluble fractions from both human and mouse samples. To measure the cytokines in mouse and human soluble fractions, we pulled together 50 µg of the S1 and S2 fraction. MSD plates were also used to measure the levels of Aβ38/40/42, phosphorylated tau and total tau in the soluble and insoluble fraction from the brain extract from WT, Gal3KO, 5xFAD and 5xFAD/Gal3KO mice. Serial dilutions of the soluble and insoluble fractions were tested to obtain an accurate measure of the protein levels. 1 μg of protein from the soluble fraction was diluted to evaluate Aβ38/40/42 levels, and 0.3 µg of protein from the insoluble fraction was diluted to evaluate Aβ38/40/42 levels. The plates were developed using the 4 × reading buffer diluted to a factor of 1 × with distilled water, and the plates were read using the QuickPlex Q120 reader from Mesoscale. The detection ranges of the different cytokines measured were as follows: IL1β (1670–0.408 pg/mL), IL4 (1660–0.405 pg/mL), IL12 (32,200–7.86 pg/mL), IL10 (3410–0.833 pg/mL), IFN-γ (938–0.229 pg/mL), IL2 (2630–0.642 pg/mL), IL5 (967–0.236 pg/mL), IL6 (5720–1.40 pg/mL), KC/GRO (1980–0.483 pg/mL) and TNF-α (627–0.153 pg/mL). The Aβ40 detection range was 15,100–3.69 pg/mL, and the Aβ42 detection range was 2280–0.557 pg/mL. ELISA plates from R&D (DY008 and DY1197-05) were used to measure the levels of gal3 (detection range 1000–15.6 pg/mL) in culture media. The protocol was carried out according to the manufacturer’s protocol. FluoStar Optima reader (BMG Labtech) was used to read the gal3 assay.
Immunohistochemistry and immunofluorescence
Immunohistochemistry
Mice were transcardially perfused under deep anesthesia with 4% paraformaldehyde and PBS, pH 7.4. The brains were removed, cryoprotected in sucrose and frozen in isopentane at − 15 °C. They were then cut in 40-μm thick slices in the coronal plane on a freezing microtome. The slices were serially collected in wells containing cold PBS and 0.02% sodium azide. Free-floating sections from 5xFAD and 5xFAD/Gal3KO mice were first treated with 3% H2O2/10% methanol in PBS, pH 7.4 for 20 min to inhibit endogenous peroxidases and then with an avidin–biotin blocking kit (Vector Labs, Burlingame, CA, USA) for 30 min to block endogenous avidin, biotin and biotin-binding proteins. For single immunolabeling, sections were immunoreacted with one of the following primary antibodies: anti-Aβ42 rabbit polyclonal (1:5000 dilution, Abcam), anti-amyloid precursor protein (APP) rabbit polyclonal (1:20,000 dilution, Sigma), anti-CD45 rat monoclonal (clone IBL-3/16, 1:500 dilution, AbD Serotec), anti-galectin3 (gal3) goat polyclonal (1:3000 dilution, R&D) over 24 or 48 h at room temperature. The tissue-bound primary antibody was detected by incubating for 1 h with the corresponding biotinylated secondary antibody (1:500 dilution, Vector Laboratories) and then followed by incubating for 90 min with streptavidin-conjugated horseradish peroxidase (Sigma-Aldrich) (dilution 1:2000). The peroxidase reaction was visualized with 0.05% 3-3-diaminobenzidine tetrahydrochloride (DAB, Sigma-Aldrich), 0.03% nickel ammonium sulfate and 0.01% hydrogen peroxide in PBS. After using DAB, some sections immunolabeled for APP, CD45 or gal3 were incubated for 3 min in a solution of 0.2% of Congo red. Sections were mounted on gelatine-coated slides, air-dried, dehydrated in graded ethanol, cleared in xylene and covered with DPX (BDH) mounting medium, omitting the primary antisera controlled for specificity of the immune reactions.
Immunofluorescence
For immunofluorescence, free-floating sections were first incubated with primary antibody followed by the corresponding Alexa secondary antibody (1:500 dilution, Invitrogen). Sections were embedded in autofluorescence eliminator reagent (Merck Millipore), following the manufacturer’s recommendations, to eliminate fluorescence emitted by intracellular lipofuscin accumulation. Tissue was incubated with the specific primary antibody for 24 h, and, the following day, the brain sections were rinsed for 1 h in PBS containing 0.1% Triton X-100. After incubating for 1 h with the corresponding secondary antibody (1:500, Alexa antibodies, Invitrogen), the sections were rinsed again with PBS containing 0.1% Triton X-100 for 60 min. Then, brain sections were mounted in glycerol 50% for visualization. For staining with thioflavin-S (Sigma-Aldrich), sections were first washed in PBS containing 0.1% Triton X-100 and then incubated for 5 min with 0.5% thioflavin-S. Next, we washed the sections for 5x10 min in PBS containing 0.1% Triton X-100. The brain sections were mounted in glycerol 50% for visualization. The fixed tissue was examined in a confocal laser microscope (Leica SP5 II), under a Olympus BX-61 epifluorescent microscope and in an inverted ZEISS LSM 7 DUO confocal laser-scanning microscope using a 20x air objective with a numerical aperture of 0.5. All images were obtained under similar conditions (laser intensities and photomultiplier voltages) and usually on the same day. Morphometric analysis of the fluorescently labeled structures was performed offline with Fiji ImageJ software (W. Rasband, National Institutes of Health). Areas for the specific antibodies were determined automatically by defining outline masks based on brightness thresholds from maximal projected confocal images [
3]. Finally, the phagosome area and the circularity of plaques (suppl. Fig. 6c, d, online resource 6) were measured as described previously [
65].
Plaque loading quantification
Plaque loading was defined as the percentage of hippocampal CA1 region stained for Aβ (with anti-Aβ42). Quantification of extracellular Aβ content in neurites was done as previously described [
51,
55]. Images were acquired with a Nikon DS-5 M high-resolution digital camera connected to a Nikon Eclipse 80i microscope. The camera settings were adjusted at the start of the experiment and maintained for uniformity. Digital 4 × (plaques) images from 6- and 18-month-old 5xFAD and 5xFAD/Gal3KO mice (4 sections/mouse;
n = 6–7/age/genotype) were analyzed using Visilog 6.3 analysis program (Noesis, France). The hippocampal area (CA1 or thalamus) in each image was manually outlined, leaving out the pyramidal and granular layers in the case of APP quantification. Then, plaque areas within the hippocampal regions were identified by level threshold that was maintained throughout the experiment for uniformity. The color images were converted to binary images with plaques. The loading (%) for each transgenic mouse was estimated and defined as (sum plaque area measured/sum hippocampal area analyzed) × 100. The sums were taken over all slides sampled, and a single burden was computed for each mouse. The mean and standard deviation (SD) of the loadings were determined using all the available data. Quantitative comparisons were carried out on sections processed at the same time with the same batches of solutions.
Hippocampal Aβ injections in wild-type mice
Aβ monomers (10 μM) and Aβ monomers together with gal3 (10 μM) were pre-incubated for 1 h at 37 °C prior to the intracerebral injections. A volume of 2 μl was injected (0.5 μl/min) in the dentate gyrus. In the left hemisphere, we injected only Aβ monomers, and, in the right hemisphere, we injected Aβ monomers with gal3. Two months after injections, the mice were sacrificed and transcardially perfused with 4% PFA. The brains were postfixed in 4% PFA overnight followed by sucrose saturation (25% in PBS). Brains were sectioned by a microtome (30 µm) and stained for ThioS, 6E10, GFAP, NeuN and Iba1.
Stochastic optical reconstruction microscopy (STORM)
The samples were the same that we used for the confocal images (see the protocol in immunofluorescence staining section in the “
Materials and methods” section), except that we changed the buffer and the detection device. Images were acquired as previously described by Van der Zwaag et al. [
56]. Briefly, images were acquired using a Nikon N-STORM system configured for total internal reflection fluorescence (TIRF) imaging. STORM buffer contains 10 mM Tris pH 8, 50 mM NaCl an oxygen scavenging system (0.5 mg/mL glucose oxidase, Sigma-Aldrich), 34 μg/mL catalase (Sigma), 5% (w/v) glucose and 100 mM cysteamine (Sigma-Aldrich). Excitation inclination was tuned to adjust the focus and to maximize the signal-to-noise ratio. Fluorophores were excited by illuminating the sample with the 647 nm (~ 125 mW) and 488 nm (~ 50 mW) laser lines built into the microscope. Fluorescence was detected by means of a Nikon APO TIRF 100 ×/1.49 Oil W.D. 0.12 mm. Images were recorded onto a 256 × 256 pixel region of a EMCCD camera (Andor Ixon3 897). Single-molecule localization movies were analyzed with NIS element Nikon software.
Immunofluorescence of human sections
Endogenous peroxidases were deactivated by incubating the samples in a peroxidase block for 15 min with gentle agitation. The sections were then washed (3 × 15 min) in 0.1 M KPBS, after which they were incubated in blocking buffer (5% goat serum blocking with 0.1 M KPBS and 0.025% Triton-X) for at least 1 h with gentle agitation. The sections were then washed (3 × 15 min) in 0.1 M KPBS, and the primary antibody was added (1:300). The sections were then incubated at 4 °C overnight with gentle agitation. The sections were washed (3 × 15 min) in 0.1 M KPBS, after which poly-HRP secondary antibody was added. The sections were then incubated for 1 h at room temperature. For triple Iba1/gal3/Aβ immunofluorescence, sections were first incubated with the primary antibodies followed by the corresponding Alexa 647/488/555 secondary antibodies (1:1000 dilution, AlexaFluor, Life Technologies). Sections were embedded in 0.6 g Sudan Black (Sigma) dissolved in 70% ethanol. The sections, after mounting and drying on slides, were incubated in the sudden black solution for 5 min. Subsequently, the sections were washed in PBS and mounted with mounting medium. The camera settings were adjusted at the start of the experiment and maintained for uniformity. A Nikon Eclipse Ti confocal microscope (Nikon, Japan) and NIS elements software (Nikon, Japan) were used to take 20x magnification pictures and for the final collage.
Fluorescent anisotropy
Production of recombinant human galectins
Recombinant human galectins (i.e., gal3 wild-type and gal3 R186S mutant) were produced in
E. coli BL21 Star (DE3) cells and purified by affinity chromatography on lactosyl-sepharose columns, which has been previously described by Salomonsson et al. [
49].
Establishment of the affinity between galectins and TREM2
A fluorescence anisotropy (FA) assay was used to determine the affinity of recombinant TREM2 for wild-type or mutant gal3 in solution. The method has previously been described in detail by Sörme et al. for saccharides and synthetic small-molecule galectin inhibitors [
53]. In short, increasing concentrations of galectins are first tittered against a fixed concentration of saccharide probe (0.02 µM). When this is done, the anisotropy value increases from a value when probe is free in solution (
A0) to a value when all probe molecules are bound to galectins (
Amax). To establish the dissociation constant (
Kd) values between TREM2 and gal3 wild-type or R186S mutant, a competitive variant of the FA assay was used. In this assay, increasing concentrations of TREM2 were tittered against fixed concentrations of galectin and probe (see below for details). By obtaining the anisotropy values for the different TREM2 concentrations, together with the values for
Amax and
A0, the
Kd values could be calculated according to the equations presented in Sörme et al. [
53].
The FA of the fluorescein-conjugated probes was measured using a PheraStarFS plate reader and PHERAstar Mars version 2.10 R3 software (BMG, Offenburg, Germany). The excitation wavelength used was 485 nm, and the emission was read at 520 nm. All experiments were performed in PBS at room temperature (~ 20 °C). The anisotropy values for each data point were read in duplicate wells of 386-well plates (at a total volume of 20 µl). Kd values were calculated as weighted mean values from concentrations of TREM2 that generated between 20 and 80% inhibition, where inhibition values of approximately 50% had the highest impact on the mean value.
Gal3 (wild type) affinities Experiments were performed with gal3 at a concentration of 0.30 µM and the fluorescent probe 3,3′-dideoxy-3-[4-(fluorescein-5-yl-carbonylaminomethyl)-1
H-1,2,3-triazol-1-yl]-3′-(3,5-dimethoxybenzamido)-1,1′-sulfanediyl-di-β-
d-galactopyranoside at 0.02 µM [
50].
Gal3 (R186S mutant) affinities Experiments were performed with gal3 R186S at a concentration of 2 µM and the fluorescent probe 2-(fluorescein-5/6-yl-carbonyl)aminoethyl-2-acetamido-2-deoxy-α-
d-galactopyranosyl-(1–3)-[α-
l-fucopyranosyl-(1–2)]-β-
d-galactopyranosyl-(1–4)-β-
d-glucopyranoside at 0.02 µM [
13].
TREM2 and galectin-3 3D modeling
The extracellular domain of TREM2 (white) with tetratennary N-glycan (stick-model) and gal3 CRD (yellow) was modeled. Mutations in TREM2 leading to an increased risk of AD are blue, and those leading to Nasu–Hakola disease (NHD) are red. The C-terminus of the extracellular fragment that normally links further to the transmembrane domain is green (suppl. Fig. 7c).
The TREM2 model is from pdb 5 ELI, which was published by Ref. [
29].
A tetranatennary N-glycan was modeled in at the single N-glycosylation site using the GlyCam server, Woods Group (2005–2018) GLYCAM Web. Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA (
http://glycam.org). The model of bound gal3 CRD was from a structure in complex with LacNAc (pdb IKJL, [
52], which was superimposed on the terminal LacNAc of the N-glycan. The picture was made with The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.
Gene array
Hippocampal samples from 5xFAD, 5xFAD/Gal3KO, WT and Gal3KO mice at 6 and 18 months were collected and snap-frozen in dry ice to carry out the mRNA evaluation. mRNA was extracted using the RNAeasy Mini Kit (Qiagen) according to the manufacturer’s protocol. The extraction was performed automatically using the QIAcube device from Qiagen. RNA concentration was subsequently quantified using a NanoDrop 2000C. Samples with a RIN value under 8.7 were not included. cDNA synthesis was performed using Superscript Vilo cDNA Synthesis (ThermoScientific) according to the manufacturer’s protocol. TaqMan® OpenArray® Mouse Inflammation, TaqMan® OpenArray® Real-Time PCR Master and TaqMan® OpenArray® Real-Time PCR were used to perform the qPCR. Real-Time PCR Open Array from Applied Biosystems was used to read the Open Array 384-well plates, which were used to perform the qPCR.
Gene array analysis
Differently expressed genes were identified using the data from the HTqPCR assay assessed in the Openarray platform (Qiagen). Statistical analysis was performed using the software DataAssist v3.01. The maximum CT permitted was 35. First, we sorted the data based on the gene fold change and then we converted the data to Log2FC. We compared 5xFAD mice at 6 and 18 months to WT mice and also compared the 5xFAD mice with the 5xFAD Gal3KO mice at 6 and 18 months. We selected genes with a Log2FC value between ± 2 at 6 months and ± 4 at 18 months. Out of 629 genes, 95 were selected at 6 months and 106 genes at 18 months. For the analysis of the main pathways affected by the lack of gal3 at 6 and 18 months in our 5xFAD mouse model, we used network analysis along with KEGG and the Reactome database.
Behavioral tests
The Morris water maze test tests spatial acquisition memory and was conducted in a pool consisting of a circular tank (180 cm diameter) filled with opaque water at 20 °C ± 1 °C. A platform (15 cm diameter) was submerged 10 mm under the water surface. A white curtain with specific distal visual cues surrounded the water maze. White noise was produced from a radio centrally positioned above the pool to avoid the use of auditory cues for navigation. Spatial learning sessions were conducted over ten consecutive days with four trials per day. Each trial was started by introducing the mouse, facing the pool wall, at one of four starting points in a quasi-random fashion to prevent strategy learning. Each mouse remained on the platform for 30 s before transfer to a heated waiting cage. During all acquisition trials, the platform remained in the same position. On the day following the last learning trial, a 60 s probe test was conducted, during which the platform was removed from the pool. All mouse movements were recorded using a computerized tracking system that calculated distances moved and latencies required for reaching the platform (ANY-maze 5.2).
Total RNA extraction and qPCR
Total RNA and proteins were extracted using TriPure Isolation Reagent (Roche). RNA integrity (RIN) was determined using RNA Nano 6000 (Agilent). The RIN was 8.5 ± 0.5. RNA was quantified using a NanoDrop 2000 spectrophotometer (ThermoFischer, Spain).
Retrotranscription and quantitative real-time RT-PCR
Retrotranscription (RT) (4 μg of total RNA) was performed with the High-Capacity cDNA Archive Kit (Applied Biosystems). For real-time qPCR, 40 ng of cDNA was mixed with 2 × Taqman Universal Master Mix (Applied Biosystems) and 20x Taqman Gene Expression assay probes (Applied Biosystems, supplemental). Quantitative PCR reactions (qPCR) were done using an ABI Prism 7900HT (Applied Biosystems). The cDNA levels were determined using GAPDH as the housekeeping gene. Results were expressed using the comparative double-delta Ct method (\(2^{{ - \Delta \Delta C_{\text{t}} }}\)). ΔCt values represent GAPDH normalized expression levels. ΔΔCt was calculated using 6-month-old WT mice samples.
Taqman probes
Iba1 (Ref. Mm00479862_g1), CD45 (Ref. Mm01293577_m1), CD68 (Ref. Mm03047343_m1), TREM2 (Ref. Mm04209424_g1), Cx3Cr1 (Ref. Mm02620111_s1), GAPDH (Ref. Mm99999915_g1), IL-6 (Ref. Mm00446190_m1), TNFa (Ref. Mm00443258_m1), GFAP (Ref. Mm01253033_m1).
Genetic association analysis
Datasets
Genotypic datasets from four genome-wide association studies (GWAS) were used in this study: (a) the Murcia study [
2]; (b) the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study [
41]; (c) the GenADA study [
36]; and (d) the NIA study [
60] (for GWAS dataset details, see supplementary information). The Murcia study was previously performed by researchers from our group. Datasets from the ADNI, GenADA, and NIA studies were obtained from dbGAP (
http://www.ncbi.nlm.nih.gov/gap), Coriell Biorepositories (
http://www.coriell.org/) or ADNI (
http://adni.loni.ucla.edu/). Prior to the genetic association analysis, each dataset (Murcia, ADNI, GenADA, NIA and TGEN) was subjected to both an extensive quality control analysis and a principal component analysis. In addition, since different platforms were used in the five GWAS analyzed, we imputed genotypes using HapMap phase 2 CEU founders (
n = 60) as the reference panel. This approach has been previously described [
2,
8,
37]. Overall, a total of 2252 cases and 2538 controls were included in the meta-analysis.
SNP selection
To select single-nucleotide polymorphisms (SNPs) within the
LGALS3 gene, including 1000 pb upstream and downstream of that genetic region, we used the UCSC Table Browser data retrieval tool [
26], release genome assembly: Mar. 2006 (NCBI36/hg18), from the UCSC Genome Browser database (
http://genome.ucsc.edu/) [
21]. Selected SNPs were extracted from GWAS datasets using Plink v1.06 software [
47].
Linkage disequilibrium blocks
Linkage disequilibrium (LD) blocks were determined along the genomic regions studied using Haploview software [
5] and genotyping data from the largest dataset used (NIA dataset).
Association analyses
Unadjusted single-locus allelic (1 df) association analysis within each independent GWAS dataset was carried out using Plink software. We combined data from these four GWAS datasets using the meta-analysis tool in Plink selecting only those markers common to, at least, three studies. For all single-locus meta-analyses, fixed effect models were employed when no evidence of heterogeneity was found, otherwise random effect models were employed.
All selected SNPs were located close to 3′end of the LGALS3 gene. Because all SNPs belonged to the same linkage disequilibrium block, multiple test correction was not applied. Thus, the p value threshold was established as 0.05.
The Murcia study was designed as a new case–control GWAS of the Spanish population. In this study, 1128 individuals were genotyped using an Affymetrix NspI 250K chip. A sample of 327 sporadic Alzheimer’s disease (AD) patients diagnosed as possible or probable AD in accordance with NINCDS–ADRDA criteria by neurologists at the Virgen de Arrixaca University Hospital in Murcia (Spain) and 801 controls with unknown cognitive status from the Spanish general population were included. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) longitudinal study was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations as a $60 million, 5-year public–private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians in developing new treatments and monitoring their effectiveness, as well as to lessen the time and cost of clinical trials. The principal investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California, San Francisco. ADNI is the result of efforts of many co-investigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the US and Canada. The initial goal of ADNI was to recruit 800 adults, aged 55–90, to participate in the research, approximately 200 cognitively normal older individuals to be followed for 3 years, 400 people with MCI to be followed for 3 years and 200 people with early AD to be followed for 2 years. For up-to-date information, see
http://www.adni-info.org. The GenADA study included 801 cases that met the NINCDS–ADRDA and DSM-IV criteria for probable AD and 776 control subjects without a family history of dementia that were genotyped using the Affymetrix 500K GeneChip Array set. The NIA Genetic Consortium for Late Onset Alzheimer’s Disease (LOAD) study originally included 1985 cases and 2059 controls genotyped with the Illumina Human 610Quad platform. Using family trees provided in the study, we excluded all related controls and kept one case per family, resulting in a total of 1077 cases and 876 controls.
Human material
All the human material used was obtained from the Lund University Hospital, Neuropathology Unit (suppl. Table 1, online resource 8; Elisabet Englund, elisabet.englund@med.lu.se) and The Netherlands Institute for Neuroscience, Amsterdam, The Netherlands (Inge Huitinga, i.huitinga@nin.knaw.nl; suppl. Table 2, online resource 9). Written informed consent for the use of brain tissue and clinical data for research purposes was obtained from all patients or their next of kin in accordance with the International Declaration of Helsinki. Medisch Ethische Toetsingscommissie (METc) of VU University has approved the procedures for brain tissue collection, and the regional ethical review board in Lund has approved the study. All human data were analyzed anonymously.
Antibodies
Antibodies used for this study: anti-rabbit iNOS primary antibody (1:5000, Santa Cruz), anti-rat gal3 antibody (1:3000, M38 clone from Hakon Leffler’s lab, in-house antibody), anti-goat gal3 antibody (1:1000, R&D Systems), anti-mouse actin antibody 1:10,000 (Sigma-Aldrich), anti-human Aβ antibody (1:5000, Covance), anti-rabbit Iba-1 antibody (1:500, Wako), anti-mouse TLR4 antibody (1:1000, Santa Cruz), anti-mouse NLRP3 antibody (1:5000, Adipogen), anti-rabbit C83 antibody (369) (1:1000, Gunnar Gouras Laboratory, BMC, Lund, Sweden), TREM2 antibody 1:500 (AF1729, anti-sheep) anti-rabbit IDE-1 antibody (1:1000, Calbiochem), anti-Rabbit p-Tau (pTau181, 1:1000, Santa Cruz), anti-mouse Aβ (1:1000, Sigma-Aldrich), anti-CD45 rat monoclonal (clone IBL-3/16, 1:500, AbD Serotec), anti-CD68 (1:500, eBiosciences) and anti-Clec7a rabbit monoclonal (1:500, abcam). Secondary antibodies used for western blot were anti-rabbit, anti-mouse, anti-goat and anti-rat from Vector Labs. Secondary antibodies used for immunofluorescence were raised in donkey and were anti-rabbit, anti-goat, anti-mouse and anti-rat from Life Technology (AlexaFluor).
Inhibitor used in our study
The inhibitor used for the experiments in suppl. Fig. 5 (online resource 5) was 1,1′-sulfanediyl-bis-3-deoxy-3-4-3-fluorophenyl-1
H-1,2,3-triazol-1-yl-β-
d-galactopyranoside [
45] (inhibitor 1). It was synthesized and characterized as reported previously [
16]. The purity was determined to be 97.3%, according to UPLC-analysis (Waters Acquity UPLC system, column Waters Acquity CSH C18, 0.5 ml/min H
2O-MeCN gradient 5-95% 10 min with 0.1% formic acid).
Discussion
Recent findings suggest an important role of neuroinflammation in neurodegenerative diseases [
18]. Thus, selective mutations in microglia/myeloid-specific genes, including the gene encoding TREM2, have been associated with AD [
16]. Experimental AD studies have suggested that TREM2 is instrumental in neuroinflammation [
24,
35] and drives DAM microglia [
27,
30]. Gal3, a carbohydrate-binding protein, is one of the most upregulated genes associated with DAM [
30,
34,
38]. Holtman et al. (2015) anticipated gal3 as a gene strongly related to microglial DAM phenotype [
22]. To study the role of gal3 in AD pathogenesis, we have analyzed human brains from AD patients and 5xFAD mice lacking gal3. The main conclusions from our study are (1) gal3 protein is increased tenfold in human AD brains and is mostly restricted to plaque-associated microglia in humans and 5xFAD mice, (2) certain SNPs linked to the
LGALS3 gene are associated with AD, (3) gal3 deficiency reduces Aβ plaque burden and the overall proinflammatory response and plaque size and improves cognitive performance in 5xFAD/Gal3KO mice, (4) gal3 is an endogenous TREM2 ligand, (5) gal3 is released in response to fAβ and (6) gal3 interacts with Aβ, affecting amyloid plaque morphology. Hence, gal3 emerges as a central upstream regulator of AD-associated pathology.
Given the relationship between innate immune system-related genes and AD incidence [
18,
19], deciphering how Aβ aggregates trigger neuroinflammation is of critical importance. For this purpose, we first analyzed the role of gal3 in the Aβ-induced inflammatory response in microglial cells. To this end, we took advantage of both chemical inhibition and gene deletion. Gal3 inhibition robustly reduced fAβ-induced iNOS expression in BV2 cells, an effect that was extended to proinflammatory cytokines, including TNFα, IL6, IL8 and IL12, which we confirmed in primary microglia cultures from WT and Gal3KO mice. These data suggest that gal3 is a critical alarmin that amplifies the Aβ-induced proinflammatory response. Since inefficient clearance of Aβ may play a determinant role in AD pathogenesis [
67], we analyzed the effect of gal3 in two major mechanisms involved in Aβ clearance: Aβ phagocytosis and IDE-1 levels, a key metalloprotease involved in Aβ degradation by microglia [
48]. Gal3 was able to alter Aβ phagocytosis in vitro showing a complex role of gal3 in Aβ phagocytosis depending on Aβ species (monomers or fibrils) or if pretreated or not (see suppl. Fig. 2e–f, online resource 2). Hence, further experiments are needed to clarify the exact role of gal3 in the whole dynamic process of Aβ phagocytosis and clearance. We found gal3 deficiency to increase IDE-1 levels in vitro and in vivo, suggesting a detrimental role of gal3 in Aβ clearance. Supporting this, CSF levels of Aβ42 were significantly higher in 5xFAD/Gal3KO as compared with 5xFAD. Overall, our study demonstrates an instrumental role of gal3 in driving the fAβ-induced proinflammatory response and in contributing to a deleterious effect in Aβ clearance.
Recent transcriptomic analysis of microglia at the single-cell level has identified a common disease-associated microglia phenotype, which has been suggested to be driven by TREM2 and apoE [
27,
30,
38]. Interestingly, Krasemann et al. (2017) identified gal3 as one of the most upregulated genes in plaque-associated microglia, supporting our findings in human AD brains and 5xFAD mice [
30]. Recently, Yang et al. overexpressed TREM2 in 5xFAD mice (5xFAD/TREM2) and found gal3 as one of the main genes affected by TREM2 overexpression [
34], suggesting that gal3 plays an important role in microglial function under disease conditions. We generated 5xFAD/Gal3KO mice to answer whether gal3 plays a role in microglia-associated AD pathogenesis and to test if gal3 signaling is associated with TREM2. To answer this question, we performed an inflammatory gene array, demonstrating an age-dependent inflammatory response, involving complement components, chemokines, interleukin receptors, toll-like receptors and DAM genes in 5xFAD mice. This inflammatory response was highly attenuated in 5xFAD/Gal3KO mice, thus confirming gal3 as a master regulator of AD-associated brain immune responses. Pathway analysis in mice at 6 and 18 months of age identified TLR and DAP12, a TREM2 signaling adaptor protein, as one of the most significant pathways associated with gal3 in the 5xFAD mice. This gene expression data suggest an important role of gal3 in the AD neuroinflammatory response, perhaps by stimulating NFκb-related regulators whereby TLR4, and/or other TLRs [
9,
25] or glycoproteins, such as TREM2 [
14,
65], are involved. Despite the clear connection between gal3-, TLR- and TREM2-related pathways, the regulation of these pathways is not known, and further experimentation is needed.
Consequently, we investigated whether gal3 interacts with TREM2, a key receptor that has been suggested as a driver of the DAM phenotype. Our confocal microscopy study demonstrated a remarkable cellular colocalization of TREM2 and gal3 in microglial cells around Aβ plaques and a near 100% correspondence between both microglial markers, an indication that gal3 specifically labels DAM. This view was further supported by Clec7a immunostaining, a recognized DAM marker. STORM microscopy demonstrated that TREM2 and gal3 physically interact on microglial processes, most likely at the cell membranes of DAM microglia. We also incubated sections from 5xFAD/Gal3KO mice with recombinant gal3 to analyze the potential binding between TREM2 and extracellular gal3. This assay demonstrated the ability of gal3 to selectively bind to TREM2
+ microglia but not to homeostatic microglia. Direct interaction between gal3’s carbohydrate recognition domain and TREM2 was demonstrated by fluorescent anisotropy with a
Kd value of 450 ± 90 nM, which was in the same range as other preferred glycoproteins [
11,
12]. The ability of gal3 to stimulate TREM2 was finally confirmed by a TREM2–DAP12 reporter cell line. Overall, our data suggest that the switch from homeostatic microglia to DAM is accompanied by significant upregulation of both TREM2 and gal3. Gal3 may thus bind to and activate different microglial receptors including TREM2 (present study), TLR4 [
9], IGFR [
33] and MerTK [
43]. In fact, because gal3 is relatively promiscuous in its interactions with glycoproteins, it may be behind the chronic and detrimental activation of microglia in AD. Regardless of this possibility, what our study demonstrates is that the pleiotropic activity of gal3 drives important amyloid-associated immune responses.
Additionally, our study has uncovered an unexpected role of gal3 as a powerful Aβ binding agent. We have previously demonstrated the ability of LPS-induced reactive microglia to release gal3 [
9,
64]. More recently, we have demonstrated a significant increase of gal3 levels in CSF from mice exposed to traumatic brain injury [
64], an indication that gal3 is released by reactive microglia. In this study, we provide evidence that gal3 is actively released by microglia in response to a fAβ challenge. In APP mice, gal3 is present in the extracellular space and associated with amyloid fibrils (unpublished). Taken together, the possibility that gal3 directly interacts with Aβ to affect aggregation becomes plausible. To test this possibility, we incubated Aβ monomers with or without gal3 for 1 h at 37 °C and injected 2 µl of each condition into either the left or right hippocampi of WT mice. A ThT assay demonstrated fAβ as the predominant form of Aβ present in both injections. Aβ deposition was analyzed 2 months after injections. While no Aβ deposition was found in animals injected with Aβ monomers alone, co-injection of Aβ monomers and gal3 resulted in evident insoluble Aβ aggregates, suggesting that gal3 is directly involved in Aβ plaque formation. To our knowledge, this is the first report showing insoluble Aβ aggregates long after Aβ brain injections in WT mice. Thus, Meyer-Luehmann et al. [
40] injected brain homogenates from aged APP mice in the hippocampus of WT mice and found no evidence of Aβ aggregates 4 months after injection. In contrast, injections of the same brain extracts in AD transgenic mice led to robust deposition of Aβ [
15,
28,
40,
44]. Further, repetitive Aβ injections into the hippocampus of WT mice demonstrated that Aβ deposits are drastically reduced from day 1 to day 7 after injections [
15].
Recently, Heneka et al. have demonstrated that ASC specks released from reactive microglia physically interact with Aβ, acting as an Aβ cross-seeding agent [
57]. Eliminating microglia has been shown to prevent plaque formation in APP transgenic mice [
54], suggesting that factors released from microglia may seed amyloid plaques. Our study reinforces the view that reactive microglia play a critical role in Aβ plaque dynamics and associated immune responses by releasing Aβ cross-seeding agents (i.e., ASC specks) and gal3. Aβ plaque formation is believed to precede the appearance of clinical symptoms, so elucidating the earlier molecular mechanism involved in Aβ plaque formation appears critical for the establishment of early promising therapeutic strategies aiming at halting the development of AD early. Gal3-inhibition appears to be a promising Aβ therapeutic target. The ability of gal3 to drive proinflammatory fAβ-associated immune responses and hinder Aβ clearance makes this protein a strategic upstream regulator of AD pathology. Indeed, the pathogenic role of gal3 was confirmed in adult 5xFAD mice lacking gal3 as those mice had a significantly lower Aβ load and ameliorated cognitive/spatial memory deficits, which was observed in the Morris water maze test.
Our results suggest that
LGALS3 gene variants affect the risk of developing AD, as indicated by the 5 SNPs in the
LGALS3 gene that we found to be related to increased AD frequency. However, according to the GWAS catalogue (
https://www.ebi.ac.uk/gwas/), none of the variants reported in suppl. Table 3 (online resource 10) or those in linkage disequilibrium with them (suppl. Fig. 3b, online resource 3), have been previously associated with AD. This could be due to the fact that the GWAS approach requires large samples to detect modest effects, partly due to the multiple testing corrections applied. However, the impact of these
LGALS3 SNPs appears to be similar to other SNPs included in the GWAS, supporting a role for this gene in the development of AD. Our meta-analysis only comprised those SNPs belonging to a linkage disequilibrium block located at the 5′-end of the
LGALS3 gene, therefore, we do not know if other regions have genetic variants that could lead to stronger effects. At present it is difficult to speculate in what way the SNPs of gal3 is altering the function or expression of the protein. Further replication studies covering the entire genetic region of this locus will be necessary to confirm our results.
Acknowledgements
This work was supported by Grants from the Swedish Research Council, and the Strong Research Environment MultiPark (Multidisciplinary Research in Parkinson’s and Alzheimer’s Disease at Lund University), Bagadilico (Linné consortium sponsored by the Swedish Research Council), the Swedish Alzheimer’s Foundation, Swedish Brain Foundation, A.E. Berger Foundation, Gyllenstiernska Krapperup Foundation, the Royal Physiographic Society, Crafoord Foundation, Olle Engkvist Byggmästare Foundation, Wiberg Foundation, G&J Kock Foundation, Stohnes Foundation, Swedish Dementia Association and the Medical Faculty at Lund University. This work was supported by Grant SAF2015-64171R (Spanish MINECO/FEDER, UE), by Instituto de Salud Carlos III (ISCiii) of Spain, co-financed by FEDER funds from European Union through grants PI15/00796 and PI18/01557 (to AG), PI15/00957 and PI18/01556 (to JV), and CIBERNED (to AG and JV), by Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucia Proyecto de Excelencia (CTS-2035) (to JV and AG), and by Malaga University grant PPIT.UMA.B1.2017/26 (to RSV). AV and GCB received funding from the Innovative Medicines Initiative 2 Joint Undertaking under Grant agreement no. 115976 (PHAGO). CIBERNED “Centro de Investigacion Biomedica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid (Spain)”. HL and AF were supported by the Swedish Research Council, the Swedish Brain Foundation, the Alzheimer Foundation and the Åhlén Foundation. UJN was supported by Grants from the Knut and Alice Wallenberg Foundation (KAW 2013.0022) and the Swedish Research Council (Grant no. 621-2012-2978). We thank Barbro-Kahl Knutson for help with producing and analyzing galectins, and Fredrik Zetterberg and Hans Schambye at Galecto Biotech AB, Sweden, for providing galectin inhibitors.
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