Innate Immunity Protein IFITM3 in Alzheimer's Disease

Abstract

Several genes in innate immunity have been implicated in Alzheimer's disease (AD). However, the effect of innate immunity on amyloid β (Aβ) production, which makes amyloid plaques in AD brains, was previously not known. Recently, the antiviral protein interferon-induced transmembrane protein 3 (IFITM3) has been identified as a novel γ-secretase modulatory protein for Aβ production. In this review, the mechanisms of how innate immunity modulates Aβ production via IFITM3-γ-secretase complexes and contributes to AD pathogenesis are discussed.

Alzheimer's disease (AD) is the most common form of dementia and there are two pathological hallmarks of AD, neurofibrillary tangles and amyloid plaques (Terry and Davies, 1980; Selkoe, 1999). Neurofibrillary tangles are the intracellular accumulation of hyperphosphorylated tau proteins, while amyloid plaques are from the extracellular aggregation of amyloid β (Aβ) (Terry and Davies, 1980; Selkoe, 1999). Neuroinflammation with activated astrocytes and microglia also plays a critical role in AD pathogenesis (Griciuc and Tanzi, 2021). Interestingly, some “resilient” individuals with amyloid plaques and tangles, but without neuroinflammation, were nondemented (Perez-Nievas et al., 2013).

Genetically inherited AD (Early onset AD) includes mutations in the genes of APP, PSEN1, and PSEN2, which are important for Aβ production (Holtzman et al., 2011). However, most of AD cases are late onset AD (LOAD) and LOAD is due to the mixture of genetic components and environmental factors (Bettens et al., 2010). Genome-wide associated studies and sequencing studies showed that innate immunity genes involved in Aβ clearance such as CD33 and TREM2 in microglia are implicated in LOAD (Karch and Goate, 2015; Huang and Xu, 2019). Impaired phagocytosis can accumulate Aβ, which eventually leads to neuroinflammation (Griciuc and Tanzi, 2021). Gene-regulatory networks constructed from LOAD patients and control subjects highlighted an immune- and microglia-specific module, mainly including genes in pathogen phagocytosis in LOAD (Zhang et al., 2013). TYROBP was a key regulator and was upregulated in LOAD brains (Zhang et al., 2013). TYROBP (also known as DAP12) is directly involved in TREM2 signaling (Griciuc and Tanzi, 2021).

However, it has not been shown how innate immunity might be involved in Aβ production. Recently, Hur et al. (2020) have identified that an innate immunity protein, interferon-induced transmembrane protein 3 (IFITM3), as a novel γ-secretase modulatory protein (GSMP) and demonstrated its physiological interaction with γ-secretase complexes for APP processing, which in turn contributes to Aβ production and the pathogenesis of AD.

Infections and AD

IFITM3 mRNA expression correlates with the expression level of human herpes virus 6B (HHV-6B) and hepatitis C virus genotype 4 (HCV-4) in AD brains (Hur et al., 2020). It has been a long-standing debate whether infections can lead to the development of AD (the “infection hypothesis”) (Seaks and Wilcock, 2020) and it is still controversial. It has also been an interest whether to find physical evidence of the virus in AD brain to support the “infection hypothesis.” There are various reports indicating that bacterial or viral infections might cause AD. Herpes simplex virus type 1 (HSV-1), human herpesvirus 6A (HHV-6A), and human herpesvirus 7 (HHV-7) were associated with AD brains (Itzhaki et al., 1997; Readhead et al., 2018; Tzeng et al., 2018), while others could not find the association between viruses (HSV-1, HHV-6 [human herpesvirus 6], and VZV [varicella-zoster virus]) and AD (Hemling et al., 2003). Interestingly, the presence of HSV-1 in the brain was shown higher in APOE-ɛ4 allele carriers (Itzhaki et al., 1997), but the study by Hemling et al. (2003) did not see the same correlation. HSV-1 DNA was also found in amyloid plaques of AD brains (Wozniak et al., 2009). For bacterial infection, gum disease was found to associate with AD (Beydoun et al., 2020).

Soscia et al. (2010) reported Aβ as an antimicrobial peptide (AMP). AMPs are the first line of defense against bacterial/fungal/viral infections and they are evolutionally conserved (Wiesner and Vilcinskas, 2010). AMP and Aβ share great deals in the following features: less than 50 amino acids in length, oligomerization, and fibrilization (Soscia et al., 2010). According to the “antimicrobial protection hypothesis of AD,” Aβ antimicrobial pathway steps are via Aβ oligomerization. Soluble Aβ oligomers bind the surface of microbes and disrupt membrane integrity (“Classical AMP pathway”) (Kumar et al., 2016b; Moir et al., 2018). In another pathway, soluble Aβ oligomers target microbes and Aβ fibrilization captures, agglutinates microbes, and entraps microbes in Aβ network (Kumar et al., 2016b; Moir et al., 2018). When Salmonella Typhimurium bacteria or herpesviridae was infected to brains of 5XFAD transgenic mice harboring APP and PSEN1 mutations, Aβ worked as AMP, Aβ deposition was accelerated, and Aβ was colocalized with bacteria and viruses (Kumar et al., 2016a; Eimer et al., 2018). However, the mechanisms between bacterial and viral infections to Aβ production in AD brains are poorly understood.

γ-Secretase

γ-Secretase is a transmembrane protein complex comprising of at least four obligatory subunits presenilin (PS), nicastrin (Nct), anterior pharynx-defective 1 (Aph-1), and presenilin enhancer 2 (Pen-2) (Kimberly et al., 2003). It is important to note that only a small fraction of PS1 (<14%) is engaged in functionally active γ-secretase complexes and PS1 in active complexes can be targeted by the active site directed γ-secretase inhibitors (GSIs) (Lai et al., 2003). Since γ-secretase cleaves APP-CTFs (C99) before Aβ production (Fig. 1.) (Selkoe, 2001), clinical trials using GSIs such as Semagacestat (LY450139, Eli Lilly), Begacestat (GSI-953, Wyeth/Pfizer), and Avagacestat (BMS-708163, Bristol-Myers Squibb) were tried to reduce Aβ production in AD patients (De Strooper et al., 2010). However, the notch signaling related side effects in guts and the accumulation of APP-CTFs led to the pause of clinical trials (Coric et al., 2012; Doody et al., 2013).

FIG. 1.

APP processing by IFITM3-γ-secretase complexes in AD. In amyloidogenic pathway, APP is cleaved by β-secretase to release sAPPβ and leave a membrane bound APP-CTF (C99). C99 is subsequently cleaved by γ-secretase to produce Aβ and AICD. Inflammatory conditions such as aging, infection, proinflammatory cytokines, and familial AD mutations induce IFITM3 expression. IFITM3 forms active IFITM3-γ-secretase complexes and processes APP to produce more Aβ. Thereafter, Aβ accumulates to amyloid plaques, which contributes to AD pathogenesis. Aβ, amyloid β; AD, Alzheimer's disease; IFITM3, interferon-induced transmembrane protein 3.

Since the failure of GSI clinical trials in AD, the newer approach to inhibit Aβ production to a certain extent by using γ-secretase modulators (GSMs) or to modulate γ-secretase activity by GSMPs has emerged. Unlike GSIs, which inhibit Aβ40 and 42 productions, GSMs inhibit longer Aβ42 species and increase short Aβ37 or 38 species (Crump et al., 2013). GSMs also spare Notch processing and have no effect on the accumulation of APP-CTFs (Crump et al., 2013). To improve in vivo potency and blood-brain barrier penetrance after the first generation nonsteroidal anti-inflammatory drugs (NSAID) GSMs, the second generation GSMs were developed: non-NSAID-derived imidazole GSMs, NSAID-derived acid GSMs, and natural products-derived GSMs (Crump et al., 2013). To understand the mode of action by GSIs and GSMs on γ-secretase, identifying target proteins of GSIs and GSMs has shown that both compounds target PS1-NTF in active γ-secretase complexes (Crump et al., 2012; Pozdnyakov et al., 2013). Unlike the essential four γ-secretase complex components, the nature of GSMPs can be transient, modulatory, and cellular context-dependent (Villa et al., 2014; Hur et al., 2020). To capture GSMPs in active γ-secretase, affinity pulldown and photolabeling by GSIs or GSMs have been developed (Teranishi et al., 2010; Pozdnyakov et al., 2013).

In the recent study by Hur et al. (2020), as an unbiased proteomic approach to search for the imidazole GSM (E2012) binding proteins, a photoaffinity probe (E2012-BPyne) was utilized to photo-crosslink transient E2012-binding proteins around active γ-secretase complexes. The photolabeling by E2012-BPyne followed by LC-MS/MS identified proteins around 30 and 15 kDa, which were selectively inhibited by an excessive amount of the parent compound, E2012 (Hur et al., 2020). The 30 kDa protein was PS1-NTF. which was reported previously as a target protein for E2012 (Pozdnyakov et al., 2013) and confirmed by the γ-secretase-based western blotting approach (Pozdnyakov et al., 2013; Hur et al., 2020). The 15 kDa protein was identified as IFITM3, which is a novel GSMP (Hur et al., 2020).

Antiviral Protein, IFITM3

IFITM3 is involved in innate immunity to defend against viral infections in our body (Zhao et al., 2018). There are five different isoforms of IFITMs in humans: IFITM1, IFITM2, and IFITM3 are implicated in viral infections, while IFITM5 and IFITM10 are not (Zhao et al., 2018). IFITMs have been studied against many different types of viruses: influenza A virus, dengue virus, West Nile virus, Zika virus, hepatitis C virus, Ebola virus, severe acute respiratory syndrome coronavirus (SARS-CoV), human immunodeficiency virus (HIV), and many more (Bailey et al., 2014; Zhao et al., 2018). Most recently, it was reported that SARS-CoV-2 increased IFITM3 protein (Hachim et al., 2020) and IFITM3 levels were increased in the frontal cortex and the choroid plexus of severe Covid-19 cases (Yang et al., 2021). The subcellular localization of IFITMs determines their antiviral activity. While IFITM1 is active in the plasma membrane or early endosomes, IFITM2 and IFITM3 inhibit viruses in late endocytic compartments (Zhao et al., 2018; Spence et al., 2019). IFITM3 knockout (KO) mice showed susceptibility to certain viral infection (Everitt et al., 2012), but not to bacterial infections (Salmonella typhimurium, Citrobacter rodentium, and Mycobacterium tuberculosis) and protozoan (Plasmodium berghei) (Everitt et al., 2013). Different IFITM3 single-nucleotide polymorphisms such as rs12252 and rs34481144 have been linked to the susceptibility to disease severity and/or the IFITM3 protein expression level (Everitt et al., 2012; Allen et al., 2017; Zhao et al., 2018). Recently, the C allele of rs12252 was linked to disease severity in Covid-19 patients (Zhang et al., 2020). In association with AD, RT-PCR showed that IFITM3 levels increased in LOAD brains previously (Ricciarelli et al., 2004). A recent study by Hur et al. (2020) showed that IFITM3 mRNA expression correlates with Aβ load in AD brains.

IFITM3-γ-Secretase Complexes in AD

Hur et al. have shown the newly identified role of IFITM3 in AD through forming active complexes with γ-secretase (Hur et al., 2020) and this showed the direct link between innate immunity to Aβ production (Yao and Yan, 2020; Wang, 2021). Pulldown and photolabeling assays by using various GSIs or GSMs showed that IFITM3 is near the active site of γ-secretase and is a part of active γ-secretase complexes (Hur et al., 2020). In addition, E2012-BPyne photolabeling in wild-type (WT) or PS1/PS2 double-KO mouse embryonic fibroblast cells showed that IFITM3 is dependent on PS1/PS2 protein in active γ-secretase complexes (Hur et al., 2020). Within PS1, IFITM3 crosslinked with PS1-NTF, not with PS1-CTF, in the γ-secretase complexes, shown by the dual probe L631 crosslinking (Hur et al., 2020).

More importantly, IFITM3-γ-secretase complexes also have active γ-secretase activity. Aβ42 and Aβ40 levels were decreased upon knockdown or KO of IFITM3, and Aβ levels were rescued when IFITM3 was overexpressed in the KO cells (Hur et al., 2020). In vivo, IFITM3 protein levels were increased in the 5XFAD transgenic mouse brains (Hur et al., 2020). IFITM3 protein levels and Aβ levels (Aβ42 and Aβ40) were decreased in IFITM3 KO × 5XFAD mice (Hur et al., 2020). Moreover, thioflavin S-stained Aβ from amyloid plaques were decreased in the cortex and hippocampus of IFITM3 KO × 5XFAD mice (Hur et al., 2020). Therefore, Hur et al. have shown that active IFITM3-γ-secretase complexes regulate APP processing for Aβ production in cells and in vivo.

Aging is the major risk factor for AD (Hou et al., 2019). In aging WT mouse brains, IFITM3 protein levels, Aβ production (Aβ42 and Aβ40), and the amount of active IFITM3-γ-secretase were increased in the brain of 28-month-old compared to 4-month-old (Hur et al., 2020). These results were not due to different subcellular localizations of IFITM3 and γ-secretase components by aging (Hur et al., 2020). In humans, the Genotype-Tissue Expression cohort also showed significant positive correlations between ages (ranging from 20 to 70 s) and IFITM3 mRNA expression in cortex and hippocampus (Hur et al., 2020).

In AD, LOAD human brain samples had variations in the IFITM3 protein expression levels unlike in cells or mice (Hur et al., 2020). Hur et al. (2020) divided LOAD samples into LOAD-low IFITM3 protein (LOAD-L) and LOAD-high IFITM3 protein (LOAD-H) groups based on two standard deviations from the mean of the control group for IFITM3 levels. LOAD-H had significantly higher IFITM3 protein expression levels, γ-secretase activity levels (Aβ42 and Aβ40), and the active IFITM3-γ-secretase complex formation compared to control and LOAD-L (Hur et al., 2020). LOAD-L was similar to control (Hur et al., 2020). This might suggest that there are different subgroups in LOAD patients. In thirty-two AD cases, patient-to-patient heterogeneity was also reported for hyperphosphorylated tau (Dujardin et al., 2020).

IFITM3 expression was upregulated in astrocytes and microglia of 5XFAD mouse brains and Ifitm3 mRNA and IFITM3 protein were expressed in neurons (Hur et al., 2020). To investigate that the induction of cytokines can increase the γ-secretase activity via IFITM3 protein in neurons, proinflammatory cytokines Type I IFN (IFN-α) or Type II IFN (IFN-γ) was treated on mouse cortical primary neurons. Proinflammatory cytokines induced IFITM3 protein expression, increased active γ-secretase complex formation levels, and increased the γ-secretase activity (Aβ production) (Hur et al., 2020). Thus, proinflammatory cytokines increase Aβ production in neurons via the increase of IFITM3 protein and active IFITM3-γ-secretase complex formation. This proves a direct link of how innate immunity increases Aβ production in neurons. Proinflammatory cytokines, interleukin (IL)-6, and IL-1β also showed similar results in human primary astrocytes (Hur et al., 2020).

In summary, Hur et al. (2020) have demonstrated that different inflammatory conditions such as viral infection and aging can induce proinflammatory cytokine releases by astrocytes and microglia, which in turn elevate IFITM3 expression in neurons and astrocytes. IFITM3 binds to the active γ-secretase complexes and increase Aβ production (Hur et al., 2020). This pathway could pose a risk in developing AD (Hur et al., 2020). In part, this pathway might also explain Aβ as an innate immune response against infection (the “antimicrobial protection hypothesis of AD”) in brains, and as a result, Aβ production might contribute to AD as a “double-edged sword” effect.

Conclusion and Perspectives

The activity of γ-secretase is regulated by neuroinflammation via IFITM3, leads to Aβ production, and contributes to AD development (Fig. 1). Further studies to identify the precise molecular interactions between IFITM3 and γ-secretase complex components within active γ-secretase complexes can suggest how to modulate γ-secretase as therapeutic targets for AD. Moreover, identifying subgroups of LOAD patients by using IFITM3 as a biomarker could dissect heterogeneous LOAD patient populations for more precision medicine development in the future.

Footnotes

Acknowledgments

Author thanks Dr. Yue-Ming Li for valuable comments on the article and Dr. Yujia Zhai for critical reading of the article.

Disclosure Statement

No competing financial interests exist.

Funding Information

Author acknowledges the MSK Cancer Center Support Grant/Core Grant (Grant P30 CA008748).

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