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APP mouse models for Alzheimer's disease preclinical studies

Abstract Animal models of human diseases that accurately recapitulate clinical pathology are indispensable for understanding molecular mechanisms and advancing preclinical studies. The Alzheimer's disease (AD) research community has historically used first‐generation transgenic (Tg) mouse models that overexpress proteins linked to familial AD (FAD), mutant amyloid precursor protein (APP), or APP and presenilin (PS). These mice exhibit AD pathology, but the overexpression paradigm may cause additional phenotypes unrelated to AD. Second‐generation mouse models contain humanized sequences and clinical mutations in the endogenous mouse App gene. These mice show Aβ accumulation without phenotypes related to overexpression but are not yet a clinical recapitulation of human AD. In this review, we evaluate different APP mouse models of AD, and review recent studies using the second‐generation mice. We advise AD researchers to consider the comparative strengths and limitations of each model against the scientific and therapeutic goal of a prospective preclinical study.

major research topic (De Strooper & Karran, 2016). Ab pathology is initiated at least two decades before cortical tau pathology and the onset of clinical symptoms (Bateman et al, 2012;Maruyama et al, 2013). After disease onset, it is increasingly difficult to treat symptoms after postmitotic neurons start to degenerate (Fig 1) and finely tuned neuronal circuits and cognitive skills are not easily recovered at later stages. Thus, the development of accurate preclinical animal models of AD for studies of disease mechanisms and the development of medications for early prevention and treatment are considered vital research goals in accord with the global epidemiological status.
Sporadic late-onset AD (LOAD) accounts for more than 99% of all cases (Campion et al, 1999), and the ratio of LOAD patients to all AD patients continues to increase because aging is a primary risk factor aligned with aging of the world population. Early-onset AD (EOAD), in contrast, is predominately familial and caused by mutations in genes that encode amyloid precursor protein (App), presenilin-1 (PSEN1 or PS1) and presenilin-2 (PS2). Proteolytic processing of APP by b-secretase (b-site APP cleaving enzyme 1 or BACE1) and c-secretase generates soluble Ab fragments. c-Secretase is a protein complex composed of PS1 or PS2, nicastrin, Aph1 and presenilin enhancer 2 (PEN2). Most familial AD (FAD) mutations affect processivity of c-secretase resulting in the release of longer Ab peptides and a shift in the relative ratios of the different peptides, including the Ab 42 /Ab 40 ratio (Welander et al, 2009;Chávez-Gutiérrez et al, 2012). Similarly, mutations in the App gene result in the production of longer Ab peptides that aggregate more easily (Rosenberg et al, 2016). Interestingly, an App A673T mutation was claimed to reduce the risk of sporadic AD (SAD) and age-related cognitive decline by decreasing the production of Ab (Jonsson et al, 2012), although these findings require confirmation (Wang et al, 2015) and the mutation appears to affect the biophysical properties of Ab peptides (Benilova et al, 2014;Maloney et al, 2014). The existence of familial mutations that directly affect Ab production and influence AD risk is often cited as evidence that Ab accumulation is central to AD pathogenesis (amyloid cascade hypothesis: Selkoe & Hardy, 2016).
In general, Ab and tau pathology in sporadic and familial cases are morphologically similar, rationalizing the use of mouse models with genetically engineered FAD mutations for understanding SAD. However, the extent to which these models actually reproduce SAD remains unknown. A critical factor to consider in developing and using mouse APP models is the potential mechanism of Ab accumulation. In FAD, Ab deposition is primarily caused by the increased production of Ab >40 except for intra-Ab sequence mutations that alter its structural properties (Selkoe & Hardy, 2016) or the Swedish mutation that increases all Ab species by increasing cleavage at the b-site. Whether the Iceland mutation App A673T is protective by decreasing Ab production (Jonsson et al, 2012) remains controversial. However, because Ab degradation declines with aging potentially due to a decrease in the major Ab-degrading enzyme neprilysin (Iwata et al, 2001(Iwata et al, , 2002Hellström-Lindahl et al, 2008) and because Ab clearance is decreased in SAD patients (Mawuenyega et al, 2010), Ab deposition in SAD is likely partially caused by an aging-associated decrease in degradation/clearance of Ab (Fig 2). In accord, increased production of Ab or its decreased degradation/clearance might contribute to Ab accumulation in AD (Saido & Iwata, 2006). Recent studies on the ubiquitin-proteasome system and autophagy (Nilsson et al, 2013;Ciechanover & Kwon, 2015;Khaminets et al, 2016) point to the essential significance of protein degradation in many diseases including AD. While decreased Ab degradation may be dominant in SAD, most APP mouse models have increased FAD-like production. The selection of a mouse model for preclinical studies should consider this issue.
Various AD mouse models (Onos et al, 2015;Puzzo et al, 2015;Drummond & Wisniewski, 2017) as well as other animal models including rats, non-human primates, Drosophila, and Caenorhabditis elegans (Drummond & Wisniewski, 2017) have been recently reviewed. In this review, we will focus on genetically modified APP mouse models of AD as they are the most practical approach for in vivo screening and validation of preventive medications at this There are three neurological phases leading to the onset of AD and associated cortical pathology. The first phase is preclinical AD, where Ab accumulates in cortex without neurological symptoms. The second phase is mild cognitive impairment (MCI), where tauopathy and neurodegeneration proceed with predementia symptoms. The third phase is AD, where neurodegeneration eliminates neurons and neuronal circuits in an irreversible manner with progressively serious symptoms of dementia. As models of preclinical AD, APPoverexpressing mice or App knock-in mice exhibit extensive Ab pathology without tauopathy and neurodegeneration, for which there is a preventive window of approximately two decades. Modified from Ihara and Arai (2007). The pathology shown is from the cortex of a 9-month-old App NL-G-F/NL-G-F mouse. Blue: Ab; red: microglia (Iba-1); green: astrocyte (GFAP) .
The EMBO Journal Alzheimer's disease mouse models Hiroki Sasaguri et al time (Zahs & Ashe, 2010). In the absence of gene manipulations, no small animal models exist at present that sufficiently or consistently mimic clinical disease pathology for experimental and preclinical studies of AD. Furthermore, we will focus on the preclinical stage when the time window for effective prevention and treatment is wider. Preclinical AD patients are cognitively normal (Bateman et al, 2012), and likewise, model mice in parallel preclinical stages having Ab pathology without tauopathy and neurodegeneration should not exhibit strong cognitive impairment. However, at this early stage, pathological alterations in Ab or tau are thought to initiate disease processes including synaptic dysfunction, local damage to spines and dendrites, and vascular pathology that are observed in AD mouse models and presumably in presymptomatic humans (Ashe & Zahs, 2010). There are other potential points for prevention at this stage, such as mechanism(s) by which Ab amyloidosis affects tauopathy or by which TREM2 (Guerreiro et al, 2013;Jonsson & Stefansson, 2013) influences pathogenesis and these may also be studied in mouse models for Ab pathology.

First-generation mouse models
First, a note on terminology: The term "transgenic (Tg) mice" could be confusing because in a wide sense it means "genetically modified mice" and because it also means mice into the host genome of which transgene is inserted in single or multicopy number. We use the second definition for the Tg mice in this review because knockin and knockout mice are different from Tg mice in that they maintain the original murine genomic structure except for the introduced mutations.
Several groups generated Tg mice that overexpress APP with or without FAD mutations using various promoters (Table 1), such as platelet-derived growth factor-b (PDGF-b), prion protein (PrP), and Thy1. Frequently used models include PDAPP (Games et al, 1995), Tg2576 (Hsiao et al, 1996), APP23 (Sturchler-Pierrat et al, 1997), J20 (Mucke et al, 2000), and TgCRND8 (Chishti et al, 2001). The APP constructs differ among the lines: They include APP695, APP770, and minigenes. Some mice carry more than one mutation in the transgene, and the most commonly used mutation is the Swedish mutation (K670N/M671L; Citron et al, 1992), which causes the overproduction of total Ab from APP. These mice exhibit extracellular Ab deposits in the brain, which are reminiscent of plaques in human patients with some differences (refer to section "Limitations of first-generation mouse models"). In addition, these mice develop cognitive dysfunction before the appearance of amyloid plaques in many cases. However, they are unable to recapitulate neurofibrillary tangle (NFT) formation or neuronal loss.
APP-Tg mice recapitulate only a part of AD pathology, and efforts were made to combine them with other mutant mice to further reconstitute the remaining pathological hallmarks. PS1 is a constituent of the c-secretase complex that cleaves the C terminal fragment of APP generated by b-secretase (CTF-b) to produce Ab (De Strooper et al, 1998). PS1 mutations cause the majority of FAD cases (Karch et al, 2014). The overexpression of mutant PS1 or a knock-in pathogenic PSEN1 gene mutation alone did not induce Ab pathology, presumably because the absolute amount of pathogenic longer Ab such as Ab 42 and Ab 43 generated from mouse APP was insufficient (De Strooper et al, 1995). Alternately, mouse Ab might have low amyloidogenic potential that might be caused by the existence of three different amino acids compared to human Ab (Chui et al, 1999;Guo et al, 1999;Schmitz et al, 2004;Xu et al, 2015). However, the combination of these mice with human APP-overexpressing Tg mice increased pathogenic Ab production and conferred amyloidogenicity, which resulted in accelerated Ab deposition, behavioral deficits, and neuronal loss. These combinations include Tg2576 and PS1 M146L Tg (Holcomb et al, 1998), APP KM670/671NL Tg and PS A246E Tg (Borchelt et al, 1996, APP751 KM670/671NL-V717I Tg and PS M146L Tg , and APP KM670/671NL-V717I and PSEN1 M233T/L235P knockin (Casas et al, 2004). Oakley et al (2006) generated 5XFAD mice carrying five FAD mutations in APP and PS1 transgenes (APP K670N/M671L/I716V/V717I Tg and PSEN1 M146L/L286V Tg) driven by the Thy-1 promoter. These mice exhibited cerebral Ab pathology and gliosis as early as 2 months of age, synaptic degeneration and neuronal loss, and developed progressive cognitive deficits as early as 4-5 months. However, the 5XFAD mice also failed to develop NFTs despite their aggressive phenotypes and pathological changes (refer to section "Limitations of first-generation mouse models").
In efforts to replicate NFT pathology, crossbreeding of mutant Tau-Tg mice with APP-Tg mice enhanced tau pathology in the limbic system and olfactory cortex without affecting Ab pathology (Tg2576 and JNPL3: Lewis et al, 2001;APP23 and JNPL3: Bolmont et al, 2007). Oddo et al (2003) generated a triple Tg model, 3xTg-AD mice, which overexpress APP swe , and Tau P301L transgenes on a PS1 M146V knock-in background. The mice exhibit neuropathology similar to AD patients, including the formation of Ab plaques and NFTs, together with gliosis, synaptic damage, and memory deficits. However, the introduced mutations in the Mapt gene that encode The balance of anabolism and catabolism determines the steady-state quantity of a given protein in a biological system. In FAD, increased anabolism of pathogenic Ab (Ab 42 and Ab 43 ) in cortex results in pathological deposition. In SAD, the causes of Ab accumulation are not fully understood, but an aging-associated decrease in catabolism is a candidate mechanism (Saido & Iwata, 2006;Hellström-Lindahl et al, 2008). The EMBO Journal Alzheimer's disease mouse models Hiroki Sasaguri et al tau protein are not causes of AD but rather of frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17).
In addition, the overexpression of multiple genes causes an increased risk of artificial phenomena making it difficult to interpret the results. APP-Tg mice crossbred with Mapt knockout mice exhibited improvements in memory deficits and survival in APP-Tg mice, which suggests that tau may possibly confer Ab toxicity (J20: Roberson et al, 2007;APP23: Ittner et al, 2010). Several combinations, such as APP-Tg mice crossbred with BACE1 knockout mice (Ohno et al, 2004) or with apolipoprotein E4 (ApoE4) knock-in mice (Fryer et al, 2005), may be more useful for specific applications.
Studies on first-generation mouse models APP-and APP/PS-overexpressing mice exhibit key features of amyloid pathology that have allowed them to be applied in AD research. Although specific details of amyloid pathology such as plaque age of onset, size and regional distribution, and Ab species content vary depending on the line, APP-overexpressing mice recapitulate aspects of cerebral Ab accumulation, including production and deposition of Ab and associated neuroinflammation (microgliosis and astrogliosis). In some cases, downstream pathologic consequences of Ab deposition in overexpressing mice, such as tau hyperphosphorylation, formation of dystrophic neurites, loss of synaptic markers, and the accumulation of BACE1 (Zhao et al, 2007), appear similar to those observed in AD. Other effects of Ab deposition in overexpressing mice may also be relevant to AD. For example, 5XFAD mice exhibit neuron loss and memory deficits that are associated with amyloid pathology (Oakley et al, 2006). Importantly, BACE1 knockout abolishes Ab deposition in 5XFAD mice and at the same time prevents both memory deficits and neuron loss in this and other mouse models (Ohno et al, 2007). Thus, cerebral Ab accumulation is responsible for neuron loss and memory deficits in these lines, rather than transgene overexpression, although effects of b-CTF overexpression cannot be ruled out. APP-overexpressing mice have also been useful in validating and assessing BACE1 and c-secretase inhibition as a therapeutic strategy for AD. BACE1 gene knockout abrogates cerebral Ab accumulation in all APP-and APP/PS-overexpressing mice tested to date (Luo et al, 2001(Luo et al, , 2003Ohno et al, 2004Ohno et al, , 2007Laird et al, 2005;McConlogue et al, 2007;Rabe et al, 2011), validating BACE1 as the major b-secretase enzyme in the brain. Subsequently, overexpressing mice were used to screen small molecule inhibitors of BACE1, some of which could reduce Ab levels in the brain and CSF by 90% or more. Some of which have advanced to clinical trials where they show similar Ab lowering effects in human CSF (May et al, 2015;Neumann et al, 2015;Kennedy et al, 2016;Cebers et al, 2017). Another application of overexpressing mice translated successfully to humans is the preclinical testing of the anti-Ab antibody aducanumab. Plaque-bearing Tg2576 mice that were chronically treated with aducanumab experienced a dose-dependent reduction of cerebral Ab levels by up to~70% compared to vehicle (Sevigny et al, 2016). The mechanism of aducanumab-mediated Ab reduction appeared to involve binding to Ab deposits in both human AD and aged Tg2576 mouse brains stimulating microglial phagocytosis of Ab.
Criticisms of first-generation mouse models have often focused on the failure of c-secretase-based medications. However, mouse work (De Strooper et al, 1999; and many follow up studies in overexpressing models) had predicted almost all the side effects seen in human trials, long before phase III clinical trials were halted. Further work into the potential of tackling c-secretase in a safe manner is based on overexpressing mice (Weggen et al, 2001) and novel insights into more safe approaches continue (De Strooper, 2014), illustrating the utility of the overexpression paradigm for certain types of preclinical studies specifically targeting Ab production and Ab deposits, and possibly also for certain pathophysiologies associated with amyloid plaques such as neuroinflammation.
Limitations of first-generation mouse models APP undergoes sequential limited proteolysis catalyzed by proteases, collectively termed "secretases" (Fig 3A). In first-generation transgenic mouse models, APP overexpression therefore results in the overproduction of various APP fragments in addition to Ab (Fig 3). This makes it technically difficult to distinguish between the functional effects of additional Ab and of other overproduced fragments. It is reasonable to assume that some of the phenotypes of the double and triple transgenic mutant mice might be of uncertain relevance to Alzheimer's disease. Box 1 summarizes the mutant APP or APP/PS-overexpressing mouse models, including prospective ideas that may be experimentally validated to better consolidate the APP overexpression paradigm with human AD clinical pathogenesis (Huang & Mucke, 2012;Saito et al, 2014;Palop & Mucke, 2016).
In the absence of further validation in patient material or additional animal models in most cases, concerns remain with transgenic overexpression. APP overexpression for instance might induce behavioral abnormalities prior to Ab pathology (Hsiao et al, 1996;Mucke et al, 2000), as Ab pathology arises much earlier than overt disease onset in humans. Apart from potential overexpression artifacts, it has been difficult to standardize the phenotypes of the various models because of their construction with different promoters, transgene constructs, and mouse strains (Webster et al, 2014;Foley et al, 2015). In summary, further work is needed to reevaluate published results and to be aware that some phenotypes might be the result of APP-or APP/PS-overexpression and not part of normal disease pathology. For instance, the extremely early lethality of calpastatin (CAST)-deficient APP23 mice, half of which die within 10 weeks (Higuchi et al, 2012;Saito et al, 2014), clearly contradicts the chronic progressive nature of AD. Notably, intrinsic mousehuman differences cannot be ruled out as contributing factors as well (Espuny-Camacho et al, 2017).

Second-generation mouse models
To overcome intrinsic drawbacks of the APP overexpression paradigm, mouse models utilizing an App knock-in strategy were generated to overproduce pathogenic Ab such as Ab 42 without overexpressing APP. Single App knock-in mouse models were generated in which the murine Ab sequence was humanized by changing three amino acids that differ between mice and humans ª 2017 The Authors The EMBO Journal Vol 36 | No 17 | 2017 Hiroki Sasaguri et al Alzheimer's disease mouse models The EMBO Journal (G676R, F681Y, and H684R) and introduced two FAD mutations (KM670/671NL: Swedish and I716F: Beyreuther/Iberian mutations) into the endogenous mouse App gene . The identification of the Beyreuther/Iberian mutation using a phenylalanine scan (Lichtenthaler et al, 1999) opened up the possibility for a knock-in strategy because this particular mutation increased the Ab 42 /Ab 40 ratio by a factor of 30 in vitro. This mutation was subsequently identified as a cause for an aggressive form of FAD in Iberia (Guerreiro et al, 2010).
Mice that carry NL-F mutations, denoted App NL-F , exhibited increased Ab 42 production and a high Ab 42 /Ab 40 ratio without alterations in the expression levels of APP or other fragments. The exception was that App knock-in mice produced more CTF-b and thus s APP b compared with wild-type mice because of the Swedish mutation . Due to the increase in CTF-b and concomitant decrease of CTF-a in the App knock-in mice, the total amount of CTF in App knock-in mice remains the same as in wildtype mice. To examine the effect of increased CTF-b and s APP b in The EMBO Journal Alzheimer's disease mouse models Hiroki Sasaguri et al this case, App NL mice were generated that carried only the Swedish mutation and we confirmed that this amount of CTF-b and s APP b exert no effects on the pathology or cognitive function of the mice Masuda et al, 2016). The high levels of Ab 42 in App NL-F mice led to pathological Ab deposition in the cerebral cortex and hippocampus, which was accompanied by enhanced neuroinflammation, that is, infiltration of astrocytes and microglia that surround plaques from 6 months of age. Of particular note, the amyloid plaques in App NL-F mice mainly consisted of pathogenic Ab 1/3pE-42 (Saido et al, 1995) in a manner similar to the brains of AD patients, whereas the amyloid plaques in APP-Tg mice were predominately composed of Ab 1-40 and were unphysiologically large, compared to those observed in App knock-in mice and AD patients. A notable exception is the 5XFAD mice, which have amyloid deposits with an over twofold greater amount of Ab 1-42 as compared to Ab 1-40 (Oakley et al, 2006). Synaptic alterations in App NL-F mice were also identified by the loss of presynaptic synaptophysin and postsynaptic PSD95 . App NL-F mice developed memory dysfunction at 18 months of age as detected by the Y-maze test. In addition, Masuda et al (2016) analyzed the knock-in mice using IntelliCage and determined that the App NL-F mice exhibited various cognitive dysfunctions, including deficits in spatial memory and flexible learning, enhanced compulsive behavior, and reduced attention performance, depending on the age and pathology of the mice (Masuda et al, 2016). App knock-in mice that harbor a third mutation, an E693G Arctic mutation (App NL-G-F ), were also generated that makes Ab more oligomerization/fibrillization-prone Gessel et al, 2012), and these mice exhibited threefold faster and greater AD pathology and cognitive abnormalities compared with App NL-F mice. Reaume et al (1996) generated App knock-in mice that harbored the Swedish mutation (K670N/M671L) with humanization of the murine Ab sequences (App NLh/NLh ). These mice overproduced human Ab 40 and Ab 42 without overexpressing APP; however, they failed to deposit Ab in the brain at up to 22 months of age. This group subsequently crossbred their App knock-in mice with mutant PSEN1 knock-in mice (Flood et al, 2002;Malthankar-Phatak et al, 2012), and the double knock-in mice successfully exhibited Ab pathology without depending on the overexpression paradigm. The double knock-in mice, App NLh/NLh × PSEN P264L , exhibited less aggressive pathology compared with the double transgenic mice, likely because of the lower expression levels of APP and PS1. These mice are likely more difficult to use because of their double homozygous nature but have, for reasons that are unclear, not been used extensively by the community yet bear reassessment as experimental tools.
Li et al (2014) produced App knock-in mouse models using multiple pathogenic mutations. The mice carried the Swedish (K670N/M671L), Dutch (E693Q), and London (V717I) mutations with the Ab sequence humanized. The Dutch mutation causes intensive cerebral amyloid angiopathy (CAA) in humans, which results in brain hemorrhage and early mortality (Levy et al, 1990;Van Broeckhoven et al, 1990). Thus, this mutation is not a cause of FAD; however, its discovery inspired the first identification of an FAD mutation in the App gene (Goate et al, 1991;Hardy, 2017). These mice alone developed minimal Ab deposits throughout life until the authors crossbred them with PSEN1 M146V knock-in mice. The double knock-in mice exhibited an age-dependent deposition of Ab not only in the parenchyma of the cerebral cortex but also the cerebral vasculature in a manner similar to human CAA pathology. Consistently, the double knock-in mice without the Dutch mutation exhibited virtually no vascular pathology. They likely would not have had to introduce the PSEN knock-in mice if they had used the Beyreuther/Iberian mutation instead of the London mutation in the mouse App gene. Nevertheless, the Dutch mutation-harboring knockin mice can be considered to represent relevant models for CAA.

Studies on second-generation mouse models
New studies suggest that a re-examination of previous results obtained using first-generation mouse models with their secondgeneration counterparts is good practice. We previously reported that the activation of calpain, a calcium-activated cysteine protease, is associated with Ab plaque formation in the brains of AD patients and APP23 mice (Higuchi et al, 2012). Genetic ablation of calpastatin (CAST), a calpain-specific inhibitor protein, exacerbated amyloid deposition, neuroinflammation, tau phosphorylation, and somato-dendritic atrophy. Notably, when APP transgenic mice were crossed with CAST knockout mice, there was increased mortality (Higuchi et al, 2012) where half of the mice died in 10 weeks for unknown reason(s). In contrast, the double mutant App NL-F mice Box 1: Limitations of mutant APP-and APP/PS-overexpressing mouse models (i) Transgene insertion may destroy endogenous gene loci (Kuro-o et al, 1997;Verret et al, 2012;Saito et al, 2016). (ii) Absence of non-coding regions of the App gene precludes the analysis of splicing of APP mRNA and transcriptional regulation involving these gene regions (Nicolas et al, 2016). (iii) Non-matched negative controls due to variable transgene copy number and insertion site. (iv) Overexpressed APP interacts unphysiologically with cellular proteins such as kinesin via JIP-1 (Gunawardena & Goldstein, 2001;Chiba et al, 2014;Cassar & Kretzschmar, 2016;Laßek et al, 2016). (vii) Ab species may appear that are different from those found in clinical AD brain (Saido et al, 1995;Saito et al, 2016). (viii) Atypical region specificity of Ab pathology arises. Different Tg mice use different promoters to drive APP transgene expression that may affect Ab in vivo propagation (Jucker & Walker, 2013). Höfling et al (2016) showed differences in expression level and brain regional patterning of exogenous APP among different APP-Tg mouse lines. (ix) Inconsistent drug effects occurred in some cases (Duggan & McCarthy, 2016;Ohno, 2016). Hiroki Sasaguri et al Alzheimer's disease mouse models The EMBO Journal crossbred with CAST knockout mice lived as long as wild-type mice, indicating that the early lethality demonstrated in APP-Tg crossbred with CAST knockout mice was inconsistent with the chronic nature of AD . Furthermore, Ab was suggested to induce calpain-dependent conversion of p35 to p25, a CDK5 activator, which may play an important role in AD pathogenesis (Seo et al, 2014). App NL-F mice crossbred with CAST knockout mice, in which calpain is hyper activated, did not exhibit conversion of p35 to p25 despite the finding that calpastatin deficiency increases Ab amyloidosis in the crossed mice . Thus, the conclusion that Ab accumulation can cause p25 generation in neurons reported in 5XFAD mice may need to be revisited, as it might be caused by a non-specific increase in calcium (Barbero-Camps et al, 2014;Reinhardt et al, 2014) that might be unique to APP/PS1 double transgenic mice (Reinhardt et al, 2014), although further work is required to draw firm conclusions. A third result where differences between first and second-generation mouse models are evident involved the reported down-regulation of Na v 1.1, a sodium channel expressed in PV-positive interneurons in the APP-Tg mouse line J20 and its resultant effect on epilepsy and AD phenotypes (Verret et al, 2012). In contrast, in App NL-F mice, App NL-F mice crossbred with CAST knockout mice, or APP23, down-regulation of Na v 1.1 was not observed  although it is possible that CAST deficiency makes calpain hyper activated Takano et al, 2005). Hypofunction of Na v 1.1 has been also observed in other mouse models such as Tg2576, TgCRND8, and BACE1 transgenic mice (Kim et al, 2007;Corbett et al, 2013;Hamm et al, 2017), and the effect of amyloid on Na v 1.1 expression and its phenotypic consequences in AD mouse models should continue to be reviewed and validated in future studies. Several basic findings using the second-generation mouse models have advanced the basic biology of AD. Hama et al (2015) developed a new sorbitol-based optical clearing method referred to as ScaleS that preserves the cellular structure of the tissue and proteins, including their immunochemical epitopes, enabling a 3D analysis of plaque deposition. App NL-F mice treated with ScaleS allowed quantitative visualization of Ab in an entire hemisphere, mapping of the 3D network of amyloid plaques in association with the vascular structure, and tracking of single plaques via successive light microscopy (LM) and electron microscopy (EM) observations. 3D images of microglial activation during amyloidosis of App NL-F brains demonstrated that microglia association and active inflammation occur at an early stage of plaque formation. Such clearing methods combined with AD mouse models enable analysis of the degree of Ab burden in larger brain volumes compared with conventional immunohistochemistry and are also applicable for the verification of immunotherapy (Sevigny et al, 2016) by visualizing therapeutic anti-Ab antibody binding to regional amyloid in situ.
One proposed mechanism for memory loss in AD is the destabilization of mushroom-shaped postsynaptic spines, which may play an important role in memory storage. In accord, several reports indicate a reduction in mushroom spines in AD brain. In App NL-F mice, Zhang et al (2015) demonstrated that hippocampal mushroom spines are lost and the STIM2 (stromal interaction molecule 2)-nSOC (neuronal store-operated calcium entry) pathway is altered as early as 3 months of age in a time-dependent manner. The authors demonstrate the relationship between extracellular Ab 42 and spine loss concluding that Ab 42 -induced hyperactivation of mGluR5 and the subsequent overload of ER Ca 2+ signaling likely represent the main cause for mushroom spine loss in App knock-in mice. Moreover, an sSOC-positive modulator NSN21778 recovered the reduction of mushroom spines and memory deficits via activation of transient receptor potential canonical 6 in App knock-in mice (Zhang et al, 2016).
The orphan G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptor (GPCR) GPR3 is reported to regulate csecretase activity and Ab generation without affecting Notch receptor proteolysis (Thathiah et al, 2009). Recently, Huang et al (2015) demonstrated that genetic deletion of GPR3 reduced amyloid pathology in the brains of the App knock-in models, as well as the APP-Tg and APP/PS1-Tg models. However, a reduction in the Ab 42 /Ab 40 ratio following a genetic deficiency of GPR3 was detectable only in App NL-F/NL-F mice and not in the transgenic mice. In addition, they demonstrated that both the number and volume of amyloid plaques in App NL-F/NL-F mice crossbred with Gpr3 À/À mice were decreased compared with single App NL-F/NL-F (Gpr3 +/+ ) mice using 3D analysis with another clearing technique, CLARITY. These findings demonstrate that second-generation mouse models can be used to evaluate the effect of new therapeutic targets on Ab pathology. BACE1 activity is up-regulated in AD patients, after Ab deposition, and in traumatic brain injury (Rossner et al, 2006). Kizuka et al (2015) hypothesized that bisecting N-acetylglucosamine (GlcNAc) stabilizes BACE1 protein during oxidative stress, which results in an increase in Ab generation. In a more recent paper, they demonstrated the up-regulation of BACE1 protein and the level of bisecting GlcNAc in App NL-G-F/NL-G-F mouse brains, which was accompanied by an accumulation of oxidative damage (Kizuka et al, 2016).
Recently, the App knock-in mice were used to refute the hypothesis that the new Alzheimer candidate gene PLD3 (Cruchaga et al, 2014) was involved in APP processing. Crossing of Pld3 deficient mice with App knock-in mice demonstrated that there was no modulation of Ab plaque or APP cleavage in these mice (Fazzari et al, 2017) Limitations of second-generation mouse models Like single APP overexpression mice, the knock-in mice do not exhibit tau pathology or neurodegeneration. This finding suggests that Ab pathology may account, at least in part, for the cognitive dysfunction in AD via disturbances in neuronal activities because Zhang et al 2015 identified a reduction of mushroom spines, distinguishing spine structure at excitatory synapses, in the early stage in these mice. However, the Ab-induced memory failure alone might be insufficient to explain all symptoms of AD patients because tauopathy-accompanying irreversible neurodegeneration has previously occurred at disease onset even in FAD-mutation carriers (Bateman et al, 2012). Therefore, App knock-in mice should be considered "models of preclinical AD". A summary of features and limitations in App knock-in mice is shown in Box 2.
The absence of tauopathy and neurodegeneration in these mice, which live less than 3 years, may be simply a matter of AD time course because it requires more than two decades for Ab amyloidosis to induce cortical tauopathy and neurodegeneration in humans (Bateman et al, 2012). To address these questions, further genetic manipulation to study the connection between Ab pathology and The EMBO Journal Alzheimer's disease mouse models Hiroki Sasaguri et al tauopathy/neurodegeneration will be required. There are only three splice variants of tau (Mapt gene product) in adult mouse brain whereas there are six in humans. We therefore have generated human tau knock-in mice, in which all the exons and introns of murine Mapt gene have been humanized . The mice are available to the research community from RIKEN. Other genes should not need to be humanized because overexpression of frontotemporal dementia with parkinsonism (FTDP) mutationcarrying human tau is sufficient to reconstitute tauopathy composed of Neurofibrillary tangles (NFTs) and neurodegeneration (Lewis et al, 2000). Biomarkers for the diagnosis and prognosis of preclinical AD will not only reduce the cost but also shorten the time necessary for drug development. App NL-F and App NL-G-F mice are the only single knockin models that develop Ab pathology and memory deficits. However, the presence of multiple mutations in the App gene, not observed in human patients, could in principle interact with each other in some cases that may not accurately represent clinical AD (Box 2). Thus, App NL-F mice are suitable for analyzing the mechanisms that affect preclinical Ab deposition compared with App NL-G-F that may be more useful for analyzing the mechanisms that alter downstream cascades.
Despite these potential drawbacks, App knock-in mice may be useful as preclinical AD models for a number of purposes including (i) identification of biomarker(s) for preclinical AD, (ii) identification of molecules that evoke tauopathy in an Ab pathologydependent manner, (iii) preclinical studies of preventive medicine(s), (iv) a platform for the generation of improved AD model(s) by crossbreeding with appropriate mutant mice (Table 1), and (v) to study the cellular phase of Alzheimer's disease (De Strooper & Karran, 2016), including the progressive response of vascular, astroglia, oligodendrocyte, and microglial cell populations upon amyloid stress. A distinct advantage is that expression from an endogenous promoter ensures that responses are not directed to cells that artifactually overexpress APP.

Future perspectives on AD mouse modeling
Previous first-generation transgenic mouse models have made substantial contributions to our understanding of AD pathology. Many of these studies carried out with the best available mouse lines at the time have advanced the understanding of AD. The new second-generation mouse lines solve some of the previous limitations and point the way to future third generation models. Until such next generation models become available, studies that investigate the interface of results from first-and second-generation models will continue to reveal discrepancies and may in some cases indicate that findings from previous AD models may in part be a consequence of overexpression artifacts. Sorting out the clinically relevant phenotypes and mechanisms will require years of work with all the models but we urge AD researchers to remain vigilant and not to assume textbook status for any previous findings without extensive validation using the most appropriate mouse lines. We further emphasize that preclinical studies, including immunotherapy, may benefit from a re-examination with new models to identify drug candidates for the preclinical prevention of earlier AD symptoms.
Species differences between rodents and humans in terms of neuroanatomy, genetics, and behaviors are also critical to control (Emes et al, 2003;Molnár & Clowry, 2012;Kaas, 2013;Nithianantharajah & Grant, 2013). Key molecules in AD such as Ab, tau, and ApoE are different between mice and humans in their sequences, pathogenicity or number of isoforms expressed. In addition, immune systems in the brain also differ between mice and humans in certain aspects such as the proportion of microglial phenotypes, or the expression pattern of inflammation-related genes (reviewed by Franco Bocanegra et al, 2017). The development of induced pluripotent stem cells (iPSCs) from AD patients can help to address species differences (Mungenast et al, 2016;Sullivan & Young-Pearse, 2017). Recently, a novel chimeric AD mouse model was developed by transplanting human PSCs into AD mouse brain, showing pathological changes in tau and neurodegeneration in human neurons (Espuny-Camacho et al, 2017). Furthermore, to leverage species differences for near-clinical studies, we are generating non-human primate models of AD (Okano et al, 2016) with more similarity to humans that could reduce species barriers and limit the time and cost of drug development.
AD research can benefit from modern views in the fields of immunology and cancer progression. Cancer stem cells arise in human bodies every day, but fail to develop into cancer in most cases because of cancer immunity (Yarchoan et al, 2017; Yeo & Box 2: Limitations of single App knock-in mouse model and potential solutions (i) All the lines carry Swedish mutations (NL), which may exhibit different sensitivity to b-secretase inhibitors from the wild-type lines (KM). This is however easy to fix by converting NL to KM by gene editing, which cannot be applied to the APP-overexpressing mice. (ii) The Swedish mutation causes an increase of CTF-b and concomitant reduction of CTF-a. Consequently, total CTF levels (CTF-b + CTF-a) remain unchanged. If CTF-b possesses particular biological or pathological functions, this may cause artifacts. Still, the amount of CTF-b in single App knock-in mice is much smaller than that in APP-overexpressing mice. (iii) The knock-in mice possess two or three independent FAD mutations in the App gene. There is no evidence for an interaction between the Swedish mutation and Beyreuther/Iberian mutations, but the Arctic mutation increases CTFs (CTF-b + CTF-a) by 50% by unknown mechanisms (T Saito & TC Saido, unpublished;Cheng et al, 2004) and results in an unnatural Ab conformation. It is important to perform delipidation pretreatment for the analysis of CTFs (Sato et al, 2003;Saito et al, 2014). (iv) The negative control mice, App NL , accumulate no Ab throughout life, but appear to induce minor microgliosis specifically in hippocampus and compulsive behavior (Masuda et al, 2016). (v) The knock-in mice are used in a homozygous status, in order to achieve early pathology and to remove murine Ab. It is advised to backcross them with the original line, B6/J, occasionally, to protect the mice from accumulating de novo mutations that could cause recessive defects. (vi) APP may behave different from human APP because the App gene except for part of intron 15-17 is a murine sequence. For example, KPI domain-containing APP variants are not expressed in mouse brain unlike in human brain . Therefore, App knock-in mice may not be suitable for addressing the properties of KPI domain-containing APP variants in endothelial cells, since these variants are expressed mostly in endothelial cells (Kitazume et al, 2010).  Fig 4). We can draw an analogy between cancer immunity and AD protective mechanism(s) in the brain, which can maintain cognition essentially unaffected for more than two decades after the initial deposition of Ab. If we could identify the molecules responsible for this protection, we should be able to facilitate studies of the major AD pathologies in mice by knocking out or modifying the corresponding genes. In the progressive systemic dysfunction that characterizes the cellular phase of AD, many types of cells are involved with complex feedback and feedforward responses at different disease stages. The emerging insight that AD is a multidimensional and multicellular process will require more integrated and complex forms of analysis (Tarasoff-Conway et al, 2015;De Strooper & Karran, 2016). Knock-in animals are most suitable to study these processes with target genes that are expressed in the correct cell types, with appropriate timing and amounts under the control of endogenous promoters. In addition, a number of common variants associated with LOAD have been identified in genes that participate in Ab clearance or neuroinflammation in genome-wide association studies (GWAS; Rosenberg et al, 2016;Naj et al, 2017). Effects of these variants can be precisely assessed by additional gene manipulations in App knock-in mice. Both crossbreeding App knock-in mice with other existing mutants and gene editing, which utilizes novel techniques, such as transcription activator-like effector nuclease (TALEN) and clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 will reveal new mechanisms of Ab pathology (Lee et al, 2016).
In conclusion, major steps to improve mouse models for AD are underway. While first-generation models will remain important and relevant for AD research, the second-generation mice solve some of the limitations of previous models for both basic and preclinical studies of AD. Standardization and sharing of disease models is essential for the objective interpretation of data, and the second-generation mice can serve as one of the important baseline models for mechanisms of Ab pathology obtained in different laboratories or under different conditions. However, every mouse model has limitations, and further side-by-side comparisons between App knock-in mice, APP-overexpressing mice, and other models and observations from human AD patients are required to move toward effective treatments. AD researchers can and should develop an ethic of sharing and comparing data and tools, carefully selecting the most suitable models for their purposes, and analyzing the data with an eye toward maximizing replication. To conquer AD as soon as possible, such collaboration ethics on a global scale will maximize the speed of drug development. Figure 4. Analogy between cancer immunity and protective mechanism in brain. Analogy between cancer and AD. Because of cancer immunity mechanisms, it generally takes cancer stem cells more than a decade to develop pathological cancer. Human brains may possess similar protective mechanisms for AD, which can explain why it takes Ab amyloidosis decades to induce neurodegeneration, whose identification may improve animal models and protective medications. The EMBO Journal Alzheimer's disease mouse models Hiroki Sasaguri et al


s2fieldsofstudy: Biology Medicine

text:
SFRP4 gene expression is increased in aggressive prostate cancer

Increased knowledge of the molecular differences between indolent and aggressive prostate cancer is needed for improved risk stratification and treatment selection. Secreted frizzled-related protein 4 (SFRP4) is a modulator of the cancer-associated Wnt pathway, and previously suggested as a potential marker for prostate cancer aggressiveness. In this study, we investigated and validated the association between SFRP4 gene expression and aggressiveness in nine independent cohorts (n = 2157). By differential expression and combined meta-analysis of all cohorts, we detected significantly higher SFRP4 expression in cancer compared with normal samples, and in high (3–5) compared with low (1–2) Grade Group samples. SFRP4 expression was a significant predictor of biochemical recurrence in six of seven cohorts and in the overall analysis, and was a significant predictor of metastatic event in one cohort. In our study cohort, where metabolic information was available, SFRP4 expression correlated significantly with the concentrations of citrate and spermine, two previously suggested biomarkers for aggressive prostate cancer. SFRP4 immunohistochemistry in an independent cohort (n = 33) was not associated with aggressiveness. To conclude, high SFRP4 gene expression is associated with high Grade Group and recurrent prostate cancer after surgery. Future studies investigating the mechanistic and clinical usefulness of SFRP4 in prostate cancer are warranted.

SFRP4 expression in cancer. In our study cohort, there was significantly higher SFRP4 expression in cancer samples compared with normal samples (t-test p < 0.001, Fig. 1a). This was also true for four of the five independent validation cohorts which included expression data from both cancer and normal samples (Fig. 1a). Meta-analysis of all the cohorts gave a significant combined Cohen's d of 0.85 (p < 0.001, Fig. 1c). This is considered a large effect-size and indicates a considerable difference in mean SRFP4 expression between normal and cancer tissue. Together, this clearly shows significant upregulation of SFRP4 in prostate cancer compared with normal prostate tissue.
SFRP4 expression in cancer with high Grade Group. In our study cohort, there was significantly higher SFRP4 expression in high Grade Groups (3)(4)(5) compared with low Grade Groups (1-2) cancer samples (t-test p < 0.001, Fig. 1b), and this was confirmed in six of the seven validation cohorts (Fig. 1b). Meta-analysis of all the analysed cohorts further strengthened this finding, giving a significant combined Cohen's d of 0.57 (p < 0.001, Fig. 1d). The Mortensen et al. cohort was excluded from differential expression analysis between high and low Grade Groups due to the low number of high Grade Group samples (n = 4).
SFRP4 expression and pathological T-stage and preoperative PSA value. SFRP4 expression was significantly higher in samples from patients with a high pathological T-stage (≥T3a) compared with low T-stage samples (≤T2c) in six out of the seven cohorts that included information on T-stage (Supplementary Table S1). SFRP4 expression was not correlated with preoperative PSA in any of the cohorts (Supplementary Table S1).
SFRP4 and patient follow-up. In our study cohort, the continuous value of SFRP4 expression was a significant predictor of biochemical recurrence (PSA ≥ 0.2 ng/mL) after radical prostatectomy by univariate Cox proportional hazards analysis (p = 0.007, Fig. 2). This was further confirmed in five of the six validation cohorts with biochemical recurrence as endpoints (Fig. 2). Meta-analysis of the six cohorts with microarray based gene expression gave a significant combined SFRP4 standardised hazard ratio (HR) of 1.70 for prediction of biochemical recurrence (p < 0.001, Fig. 2). Continuous SFRP4 expression was not a predictor of prostate cancer-specific death in the watchful waiting Sboner et al. cohort (HR 1.0, p = 0.96, Fig. 2). Logistic regression analysis showed SFRP4 expression to be a predictor of metastases after radical prostatectomy in the Erho et al. cohort (odds ratio 2.34, p < 0.001, Fig. 2). SFRP4 expression and metabolism. In our study cohort, the SFRP4 expression level was negatively correlated with concentrations of citrate (Pearson's r = −0.53, p < 0.001) and the polyamine spermine (Pearson's r = −0.49, p < 0.001) (Fig. 3). These were the strongest correlations to citrate and spermine of all the genes in our previously published NCWP-EMT gene expression signature 12 (Supplementary Table S2). SFRP4 immunohistochemistry. In our IHC cohort, seven of the 40 samples were excluded from further analysis due to insufficient or lack of tumour cells in the immunohistochemically stained sections. We did not detect membranous SFRP4 staining of prostate cancer cells in any samples. However, cytoplasmic SFRP4 staining of different intensities was identified and categorised into four different scores (Fig. 4). Proportion of positive cancer cells were also scored and multiplied with staining intensity to create a staining index (Supplementary  Table S3). Full immunohistochemistry scoring of each sample along with clinical, histopathological and metabolic data is shown in Supplementary Table S4. There was no relationship between Grade Groups and SFRP4 staining index (Fisher's exact p = 1.0). This was also the case when looking at staining intensity and proportion separately (Fisher's exact p = 0.80 and p = 0.82, respectively). Furthermore, no significant associations between SFRP4 staining and biochemical recurrence (Log-rank: staining index p = 0.87, intensity p = 0.82, proportion p = 0.95), nor any significant correlation between SFRP4 staining index and citrate and spermine concentrations (Pearson's r = 0.13 p = 0.47 and Pearson's r = 0.18 p = 0.32, respectively) were detected. SCIENTIFIC REPORTS | 7: 14276 | DOI:10.1038/s41598-017-14622-3

Discussion
In this study, we performed analyses of SFRP4 gene expression and validated the results in eight independent prostate cancer cohorts. We showed SFRP4 expression to be increased in prostate cancer, and further increased in high Grade Group (3-5) compared with low Grade Group (1-2) cancers. Additionally, SFRP4 gene expression was found to be a predictor of worse outcome in prostatectomy-treated prostate cancer patients. Furthermore, the SFRP4 expression level was negatively correlated with the concentrations of citrate and spermine in the samples. Together, these results underpin SFRP4 as a biomarker candidate of prostate cancer aggressiveness.
We showed SFRP4 gene expression to be increased in prostate cancer compared with normal tissue in five of six cohorts, and in the combined meta-analysis of all cohorts. This is in agreement with Luo et al. 8 and Wissmann et al. 9 , who investigated matched tumour and normal tissue samples from 16 and 56 prostate cancer patients, respectively. Contradictory, García-Tobilla et al. 20 did not find significantly different expression levels of SFRP4 between normal and prostate cancer tissue, however, this study suffered from small sample size (normal n = 4, cancer n = 11). In a previous paper, we also showed increased SFRP4 expression in prostate cancer when balancing for stroma content in the samples 12 . Interestingly, two studies have shown increased SFRP4 expression in prostate cancer tissue compared with benign prostate hyperplasia 20,21 , but this approach was not possible to pursue in  We showed increased expression of SFRP4 in high Grade Group compared with low Grade Group cancer samples, as well as an association between SFRP4 expression and risk of biochemical recurrence and metastasis after radical prostatectomy. SFRP4 gene expression has previously been linked to more aggressive prostate cancer; Luo et al. 8 showed increased expression of SFRP4 in tissue samples from prostate cancer patients with pathological stage T3a-b compared with pathological stage T2b. Mortensen et al. 10 discovered SFRP4 to be a part of two aggressive gene expression clusters, as well as being an independent predictor of recurrence after prostatectomy in an independent cohort. Our previously published NCWP-EMT gene expression signature, which included SFRP4 as one of 15 genes, was associated with biochemical recurrence and metastasis after prostatectomy 12 . Furthermore, Oncotype DX ® Prostate Cancer, a commercially available gene expression signature, includes SFRP4 as one of its 17 genes, and has been associated with clinical recurrence of prostate cancer after prostatectomy 11 . Our analyses of multiple independent cohorts in the current study, further support high SFRP4 expression to be associated with more aggressive prostate cancer. To conclude, several studies 8,10-12 , including the current study, support SFRP4 gene expression to be upregulated in aggressive compared with less aggressive prostate cancer.
SFRP4 is classified as an inhibitor of Wnt signalling, a pathway implicated in carcinogenesis 3 . Consequently, SFRP4 is expected to be a tumour suppressor, and to be downregulated in aggressive cancer. As reviewed by Pohl et al., DNA hypermethylation of the SFRP4 promotor and reduced SFRP4 gene expression is observed in many  types of cancers, including endometrial, ovarian, bladder, and oesophageal cancer 7 . Although SFRP4 expression in prostate cancer tissue seems to deviate from this, a few prostate cell line studies have supported tumour suppressor properties of SFRP4 also in prostate cancer; Horvath et al. 13,14 showed that PC3 and LNCaP cell lines modified to overproduce SFRP4 proteins had reduced cellular proliferation compared to controls. Furthermore, García-Tobilla et al. 20 showed reduced gene expression of SFRP4 in prostate cancer cell lines (LNCaP, PC3, DU145 and 22Rv1) compared with control cells (PREC). However, they did not detect DNA hypermethylation at the SFRP4 promotors that could explain this downregulation in any of the cell lines 20 . Absence of SFRP4 hypermethylation was also shown in both prostate cancer cell lines and in tumour tissue in a study by Perry et al. 21 . In contrast to the study by García-Tobilla et al. 20 , and in coherence with human prostate cancer tissue studies, Perry et al. 21 also detected upregulation of SFRP4 in all prostate cancer cell lines (LNCaP, PC3, DU145 and 22Rv1) compared with controls (PWR-1 and RWPE1). Interestingly, in the two latter mentioned studies 20,21 , DNA hypermethylation of SFRP2, SFRP3, and SFRP5 was detected in both cell lines and human prostate cancer tissues 20,21 . This is in agreement with findings in colorectal cancer, where Suzuki et al. 22 suggested that SFRP4 may not be an important inhibitor of the Wnt signalling pathway due to lower frequency of DNA hypermethylation and weaker Wnt signalling inhibition compared with other SFRP family members. This may be translatable to prostate cancer, and could explain why SFRP4 is not downregulated in cancer. However, studies of how SFRP4 regulates the Wnt signalling pathway and other pathways in prostate cancer are necessary before a conclusion can be drawn.
In the current study, we detected an association between SFRP4 expression and development of metastases after prostatectomy in the Erho et al. cohort. Bones are the most frequent site for haematogenous metastases in prostate cancer 23 , and, interestingly, SFRP4 has been suggested to have an important role in bone homeostasis 24,25 . However, to our knowledge, the function of SFRP4 in bone metastases has not been investigated. A hypothesis to explain the association between SFRP4 gene expression and high Grade Groups, as well as recurrence and metastasis after prostatectomy, could therefore be that SFRP4 increases the cancer cell's ability to metastasise to bone. Future studies investigating the role of SFRP4 in prostate cancer bone metastases would consequently be of interest.
For patient follow-up in this study, we used the surrogate endpoints of biochemical recurrence and metastases, in all except one cohort, Sboner et al., in which prostate cancer-specific death was used. Such surrogate endpoints are commonly used in prostate cancer studies, due to a natural long survival time of patients. Although recurrence after prostatectomy in general represents more aggressive disease, we also recognise that the use of surrogate endpoints is a limitation to the clinical value of the results, as only a minority of patients with biochemical recurrence will experience systemic progression or prostate cancer-specific death 26 . In the Sboner et al. cohort, we did not see any association between SFRP4 gene expression and cancer-specific death. This cohort did, however, differ substantially from the other analysed cohorts, where all patients included had incidental prostate cancer discovered by trans-urethral resection of the prostate (TURP), and were classified as stage T1a-T1b, NX, and M0 disease. The samples used for gene expression were from the TURP procedure. Although most prostate cancers arise from the peripheral zone, resection performed by TURP represents the transition zone, and is likely to detect a higher rate of transition zone prostate cancers. Substantial differences in gene expression between tumours of different zonal origin has previously been observed 27 . This may limit future clinical use of SFRP4 expression for risk stratification in patients with transitional zone prostate cancers, and potentially also in patients with very early stage prostate cancer, and this should be further investigated.
Changes in metabolism is regarded as one of the hallmarks of cancer 15 . In prostate cancer, the concentrations of the metabolites citrate and spermine are shown to be reduced in cancer compared with normal tissue 28,29 , and further reduced in high Gleason score prostate cancer 16 . A recent study has also shown citrate and spermine to be predictors of prostate cancer biochemical recurrence in three independent cohorts 17 . The high negative correlation between SFRP4 expression and spermine and citrate in our study cohort further supports SFRP4 expression to be associated with aggressive cancer. One of the normal functions of prostate cells is production of citrate and spermine for the prostatic fluid, and reduced concentration of these metabolites may signify loss of normal prostate function. However, whether these metabolic mechanisms are directly related to SFRP4 expression was not investigated in the current study.
We did not find any association between immunohistochemical staining of SFRP4 and histopathological, metabolic, or follow-up data in our IHC cohort in this study. Our cohort only included tissue samples from 33 patients, as it was originally part of a demanding integrated analysis of metabolomics, histopathology, and patient follow-up 12,30 . This small sample size limits the interpretation of our immunohistochemistry results. There are only four previous studies including immunohistochemistry of SFRP4 in prostate cancer, and there are no standardised protocols for staining or scoring. Three of these studies were based on the same SFRP4 stained cohort of tissue microarray (TMA) samples from 229 radical prostatectomy patients 13,14,31 , where membranous SFRP4 staining was found to be associated with good prognosis 13 . In the current study, we did not observe any membranous staining of SFRP4. The lack of membranous staining is in accordance with a previous study by Mortensen et al. 10 , which included TMA sections from 517 radical prostatectomy patients. Our IHC cohort was stained by the same commercially available antibody and in the same dilution as used in the Mortensen et al. study 10 , which may explain the similar staining pattern. The use of different antibodies compared with the Horvath et al. study 13 may be a possible cause of the observed disparity in membranous staining. In addition, the relatively weak staining of SFRP4 in the current study (Fig. 4) may have been insufficient to demonstrate membrane staining. In contrast to the TMA sections used in both the Mortensen et al. 10 and Horvath et al. 13 studies, our IHC cohort consisted of sections from needle biopsy samples. These biopsy sections were larger than the standard TMA sections, and we observed staining intensity heterogeneity within each sample. Consequently, we experienced some challenges when determining the immunohistochemistry intensity score of the samples. As mentioned, there are limitations of the immunohistochemistry evaluation of SFRP4 in the current study, especially with regard to the small sample size, and as a consequence, no certain conclusion can be made based on our results. Nevertheless, we have demonstrated a few issues that are important to address before immunohistochemistry of SFRP4 can have a role in prostate cancer risk stratification, including the lack of standardised staining and evaluation protocols, and the uncertain impact of staining heterogeneity.
In the current study, we did not directly investigate possible clinical applications of SFRP4 expression, and this should be examined in future studies. Absolute quantification of SFRP4 mRNA by real time PCR in biopsies may have a role for risk stratification and treatment selection for prostate cancer patients, including selection of patients for active surveillance and adjuvant treatment. Another interesting possibility for further studies, is investigation of the SFRP4 gene and protein expression levels in less invasive liquid biopsies such as serum, urine, and prostatic and seminal fluid.
In this study, we have validated the presence of increased SFRP4 gene expression in prostate cancer tissue, and we detected and validated higher SFRP4 gene expression in high Grade Group compared with low Grade Group cancer. We further showed that the SFRP4 gene expression level was as a predictor of recurrence and metastasis after prostatectomy. Finally, we showed a negative correlation between SFRP4 gene expression and the indolent metabolic markers, citrate and spermine. To conclude, SFRP4 expression is associated with more aggressive disease, and is a biomarker candidate for risk stratification of prostate cancer patients. SFRP4 may therefore potentially be useful in the selection of candidates for active surveillance as well as for patients in need of adjuvant or more aggressive treatment, and SFRP4 deserves further attention in prostate cancer studies.

Methods
Ethics statement. The study was approved by and carried out in accordance to the regulations of the Patients and samples. The samples in our study and IHC cohort were donated from patients diagnosed with localised or locally advanced prostate cancer and treated with radical prostatectomy between 2007 to 2010 at St. Olav's Hospital, Trondheim University Hospital. None of the patients had received any prostate cancer treatment prior to surgery. Samples in the study cohort were harvested from fresh-frozen prostatectomy specimens with a highly standardised method as previously described by Bertilsson et al. 18 . The samples in the IHC cohort were collected as needle biopsies within approximately two minutes after the prostatectomy, and were snap frozen. Follow-up. At least five-year follow-up data were collected for the patients in our study and IHC cohort.
Biochemical recurrence was defined as serum PSA levels of at least 0.2 ng/mL in two independent measurements, or, in case of missing PSA values/time-point, onset of salvage therapy.
Histopathology. For histopathological evaluation, a cryosection from each fresh frozen tissue sample in our study cohort and two formalin-fixed paraffin-embedded sections of each sample in our IHC cohort were used. All sections were evaluated by an experienced pathologist as previously described 12 . The reproducibility of the histopathological evaluation has previously been assessed in our study cohort, by an independent pathologist blinded for previous evaluation, where high interrater agreement (κ = 0.66) was reported 12 . Post-operative Gleason score was obtained from the whole-mount prostate sections according to the clinical criteria for prostate cancer. The reported Gleason scores were directly converted to Grade Groups according to the new grading system for prostate cancer 32 . For statistical analyses, the samples and patients were divided into two groups: low Grade Group (1-2) and high Grade Group (3)(4)(5).
Metabolomics. The samples in the study cohort and IHC cohort were analysed by proton high-resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS MRS) using a Bruker Avance DRX600 Spectrometer (Bruker Biopsin, Germany). LCModel was applied for absolute quantification of 23 metabolites from the spectra. More details on the HR-MAS MRS acquisition and metabolite quantification have been described by Giskeødegård et al. for the study cohort 16  Immunohistochemistry. In the IHC cohort, immunohistochemistry was performed using 4 μm thick, formalin fixed, paraffin embedded tissue sections. Rabbit polyclonal antibody against SFRP4 (Protein Tech catalogue: 15328-1-AP) was used in a 1:200 dilution at pH 9. The sections were counterstained with haematoxylin. Every section was evaluated for SFRP4 staining location (membranous or cytoplasmic). The most common staining intensity of each sample was scored from 0 to 3 (Fig. 4) based on the staining intensities described by Mortensen et al. 10 . Additionally, the proportion of positive cancer cells was scored from 0 to 3, and was multiplied by the intensity score to obtain a staining index (0-9). For statistical analyses, the staining index was categorised into three groups (0, 1-3, and 4-9). Further details on the scoring procedure are given in Supplementary Table S3. Two independent readings were performed by one pathologist experienced in immunohistochemistry and one physician. When scoring differed, consensus was reached by re-evaluation of the sections together.  10 . In addition, a RNA sequencing (RNA Seq) cohort of prostate adenocarcinomas, TCGA PRAD, was downloaded from TCGA 41,42 . Cancer samples for all cohorts were from radical prostatectomy specimens, except Sboner et al. which was from a watchful waiting patient cohort of incidental prostate cancer discovered by transurethral resection of the prostate. Normal samples in Mortensen et al. were from surgical prostate specimens from patients with bladder cancer, four of the normal prostate samples in Wang et al. were autopsy samples from normal subjects, the rest and the other cohorts were adjacent normal prostate tissue from prostatectomy specimens. According to histopathology, the samples were divided into the same two groups as our cohorts: low Grade Group (1-2) and high Grade Group  Table 1, and an overview table of the cohorts is included as Supplementary Table S5. Statistical analysis. When more than one probe for SFRP4 existed in a cohort, the probe with the highest variance was chosen for statistical analyses. For all analyses, SFRP4 gene expression data were log 2 transformed if not previously performed. For the gene expression cohorts, independent sample t-tests (two-tailed) were used for comparisons between two groups. Q-Q plots were used to check the normality assumption; small deviations were accepted due to the robustness of the test. Equal variance assumption was tested by Levene's test, and corrected for when applicable. Fieller's method was used to obtain pooled confidence interval for the log 2 fold changes. To obtain Cohen's d, a standardised effect size for meta-analyses, the difference between two means (cancer and normal, and high and low Grade Group) were divided by their pooled standard deviation. Meta-analyses by random-effect model were performed using the metafor package 43 in R 44 .
In the two cohorts with multiple samples per patients (our study cohort and the Wang et al. cohort), one sample per patient was randomly selected for survival analyses. Univariate Cox proportional hazard regression analyses were performed on the continuous SFRP4 expression. The proportional hazard assumption was tested using the survival package 45 in R 44 . Standardised hazard ratios were obtained by multiplying the natural logarithm of the hazard ratio (beta) by its standard deviation 46 . Cohorts with microarray based gene expression data and biochemical recurrence as endpoint were included in a random-effect model meta-analysis, which was performed in R 44 using the metafor package 43 . Due to unavailable time-point of the events in the Erho et al. cohort, logistical regression was used for the follow-up analyses of this cohort.
A two-tailed t-test was performed on SFRP4 expression between high pathological T-stage (≥T3a) and low T-stage (≤T2c) for the seven cohorts that included information about T-stage. The two cohorts excluded from this analysis were the Erho et al. cohort where T-stage information was only available for the whole cohort and not for the individual samples, and the Sboner et al. cohort which only included clinical T-stage (all T1c). Pearson correlation was performed to calculate the correlation coefficient (Pearson's r) between preoperative PSA and SFRP4 expression for the same cohorts.
Pearson correlation coefficients (two-tailed) were used to test the correlations between gene expression and log 2 transformed concentrations of the metabolites citrate and spermine in our study and IHC cohort. Fisher exact tests (two-tailed) were used to examine the relationship between immunohistochemistry staining and histopathological Grade Groups, and log-rank statistics were used to investigate the relationship between SFRP4 staining and time to biochemical recurrence.
For all statistical tests the significant level was set at p = 0.05. When mentioned, analyses were performed in R 44 , all other analyses were performed in SPSS 47 .