s2fieldsofstudy: Biology Chemistry Medicine

text:
Inhibition of autophagy enhances the antitumour activity of tigecycline in multiple myeloma

Abstract Accumulating evidence shows that tigecycline, a first‐in‐class glycylcycline, has potential antitumour properties. Here, we found that tigecycline dramatically inhibited the proliferation of multiple myeloma (MM) cell lines RPMI‐8226, NCI‐H929 and U266 in a dose and time‐dependent manner. Meanwhile, tigecycline also potently impaired the colony formation of these three cell lines. Mechanism analysis found that tigecycline led to cell cycle arrest at G0/G1 with down‐regulation of p21, CDK2 and cyclin D1, rather than induced apoptosis, in MM cells. Importantly, we found that tigecycline induced autophagy and an autophagy inhibitor bafilomycin A1 further amplified the tigecycline‐induced cytotoxicity, suggesting that autophagy plays a cytoprotective role in tigecycline‐treated MM cells. Mechanisms modulating autophagy found that tigecycline enhanced the phosphorylation of AMPK, but did not decrease the phosphorylation of Akt, to inhibit the phosphorylation of mTOR and its two downstream effectors p70S6K1 and 4E‐BP1. Tigecycline effectively inhibited tumour growth in the xenograft tumour model of RPMI‐8226 cells. Autophagy also occurred in tigecycline‐treated tumour xenograft, and autophagy inhibitor chloroquine and tigecycline had a synergistic effect against MM cells in vivo. Thus, our results suggest that tigecycline may be a promising candidate in the treatment of MM.

| INTRODUCTION
Multiple myeloma (MM) is characterized by the accumulation of malignant plasma cells in the bone marrow and usually accompanied by the secretion of monoclonal immunoglobulins that are detectable in serum or urine. 1 Combined with autologous stem cell transplantation and improvements in supportive care, the employment of novel drugs such as proteasome inhibitors, immunomodulatory agents and monoclonal antibodies has effectively improved response and substantially enhanced overall survival in the past decade. [2][3][4] However, drug resistance resulting in relapse commonly occurs and MM remains an incurable disease. Therefore, novel therapies are urgently needed.
Tigecycline is the first member of a new generation of tetracyclines called glycylcyclines approved by the FDA in 2005, which is a broad spectrum antibiotic used for the treatment of bacterial infections. The mechanism of action is that tigecycline can inhibit bacterial protein synthesis by binding to the 30S ribosomal subunits. 5 Beyond its role as an antimicrobial, accumulating evidence shows that tigecycline has anticancer properties. It can inhibit the growth and metastasis of multiple tumour cells, including acute myeloid leukaemia, 6 gastric cancer, 7 melanoma, 8 neuroblastoma, 9 cervical squamous cell carcinoma 10 and glioma. 11 The anticancer mechanism of tigecycline appears to vary in different tumour types. Besides the inhibition of mitochondrial protein synthesis, other mechanisms including autophagy have been found to be involved in antitumour effects. 7 Autophagy, or cellular self-digestion, is a cellular process by which the cell ensures sufficient metabolites by breaking down its own organelles and cytosolic components when nutrients become limiting. 12 A growing evidence demonstrates that autophagy is involved in development, differentiation and tissue remodelling in various organisms. 13 Autophagy is also implicated in certain human diseases including inflammation, neurodegeneration and cancer. 14 Paradoxically, autophagy can contribute to cell damage but may also serve to protect cells. When autophagy occurs, microtuble-associated protein light chain 3-I (LC3-I) is converted to the membranebound form (LC3-II), which is associated with autophagic vesicles and exhibits classical punctate distribution, as classical protein markers of autophagy. 15 Meanwhile, p62/sequestosome-1 (SQSTM1) is degraded following an increase in autophagic flux for which this protein presently serves as another classical hallmark. 16 Mammalian target of rapamycin (mTOR) as an evolutionarily conserved serine/threonine kinase has two structurally and functionally distinct complexes termed mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), which can tightly regulate autophagy. 17 AMPactivated protein kinase (AMPK) is one of the major stress-sensing enzymes and can actively regulate metabolism and cell proliferation.
Prominently, AMPK is also a critical regulator of autophagy. Phosphorylation of AMPK results in inhibition of mTOR, which activates autophagy. 18 In this study, we have demonstrated that tigecycline significantly inhibits the proliferation and colony formation of MM cell lines RPMI-8226, NCI-H929 and U266 by inducing cell cycle arrest at G0/ G1 phase. Additionally, autophagy also plays a cytoprotective role in tigecycline-induced MM cells, and combination with chloroquine and tigecycline synergistically inhibits the tumour cell growth in a mouse xenograft model of RPMI-8226 cells.

| Cell viability assay
Human MM cell lines RPMI-8226,NCI-H929 and U266 were cultured in RPMI-1640 medium supplemented with 8% fetal bovine serum in a humidified atmosphere containing 5% CO 2 at 37°C. The cell viability was determined using the Cell Counting Kit-8 (CCK-8) assay according to the manufacturer's protocol (Dojindo, Kumamoto, Japan). Briefly, RPMI-8226, NCI-H929 or U266 cells were seeded at a density of 8 × 10 3 /well in 96-well plates and

| Colony formation assay
Multiple myeloma cells were seeded at about 1 × 10 4 cells/well in 6well plates with or without tigecycline (20 μmol/L) in methylcellulose medium (MethoCult ™ H4034, Stemcell Technologies, Vancouver, BC, Canada). After incubation for 7 days in a 5% CO 2 atmosphere incubator at 37°C, the cells were examined using an inverted microscope equipped with a CCD camera. A colony is defined as a cluster of at least 60 cells, and visible colonies were counted. Cells were then washed with PBS twice, and counted using a haemocytometer.

| Electron microscopy assay
After treatment with 20 μmol/L tigecycline for 48 hours, RPMI-8226 cells were fixed in PBS (pH 7.4) containing 2.5% glutaraldehyde at 4°C for more than 2 hours. The cells were postfixed in OsO4 at room temperature for 60 minutes and were subsequently stained with 1% uranyl acetate, dehydrated through graded acetone solutions and embedded. Finally, the autophagosomes were observed under a transmission electron microscope (H-7500, Hitachi, Japan).

| Western blot analysis
After treatment with different concentrations of tigecycline with or without bafilomycin A1 (baf A1), the cells were collected and lysed immediately using RIPA lysis buffer (Beyotime Institute of Biotechnology) supplemented with PMSF and Halt protease and phosphatase inhibitor cocktail (Pierce, Rockford, IL). The protein was boiled for 8 minutes in 1× loading buffer and subjected to Western blot analysis using antibodies against SQSTM1/p62, LC3, p-mTOR, mTOR, p-AMPKa, AMPKa, p-p70S6K, p70S6K, p-4E-BP1, 4E-BP1 or GAPDH as reported previously. 19 The bands were visualized by an enhanced chemiluminescence reagent (Thermo Fisher, Fremont, CA), and the optical densities of the bands were analysed using Image J software (NIH, Bethesda, MD). When tumour volume reached approximately 300 mm 3 , the mice were randomized into four groups. The vehicle group was given saline, and the treatment groups were injected with tigecycline twice daily (75 mg/kg by intraperitoneal injection) or chloroquine daily (50 mg/kg by intraperitoneal injection) (Sigma-Aldrich) or both drugs, respectively. Tumour length and width were measured every 2 days, and the volume was calculated using the formula: volume = length × width 2 × 0.5236. All mice were sacrificed after 14 days. Animal procedures were carried out in accordance with institutional guidelines after Whenzhou Medical University Animal Care and Use Committee approved the study protocol.

| Statistical analysis
The data are presented as mean ± SEM and analysed by one-way ANOVA followed by post hoc Turkey's test to determine the differences between the groups. Differences were considered significant at P < 0.05.

| Tigecycline inhibits the proliferation and colony formation in MM cells
Tigecycline has been reported to possess a potent antitumour activity against multiple solid or haematological malignancies, which arouses our interest to investigate whether tigecycline has a similar antitumour effect on MM. As expected, tigecycline dramatically impaired the cell viabilities of three MM cell lines (RPMI-8226, NCI-H929, and U266) tested in a time-and dose-dependent manner ( Figure 1A). The soft agar clone formation assay is a technique widely used to assess the survival and tumorigenic capabilities of tumour cells. 20 The parallel effects were observed in soft agar assays with the above three cell lines. Obviously, tigecycline at 20 μmol/L considerably inhibited the colony formation, characterized by small colony size, compared with the vehicle cells ( Figure 1B). Both colony number and total cell number were significantly reduced by tigecycline in all three MM cell lines tested ( Figure 1C). These data suggested that tigecycline potently inhibits the proliferation and colony formation in MM cells.

| Tigecycline induces cell cycle arrest at G0/G1 phase in MM cells
As it has been reported that tigecycline impairs the cell viability mainly through inducing cell cycle arrest rather than apoptosis, 11 we first analysed the effect of tigecycline on the cell cycle of MM cells and found that tigecycline treatment led to an increase in G0/G1 phase with diminished S phase (Figure 2A), suggesting that tigecycline is capable of inducing G0/G1 arrest to decelerate the cell cycle, and preventing the cells from entering the S phase and proliferating.
As CDK2 is one of the key kinases controlling G1/S transition and DNA replication and p21 is a critical regulator of CDK2, we measured these two proteins and found that tigecycline markedly decreased the levels of CDK2 and p21 in three MM cell lines RPMI-8226, NCI-H929, and U266 ( Figure 2B). Cyclin D1, a major cyclin driving the G1/S phase transition, 21 was also dramatically decreased by tigecycline in all three MM cell lines tested ( Figure 2B). We next analysed whether tigecycline induces apoptosis of MM cells and found that tigecycline almost did not induce apoptosis in RPMI-8226 cells ( Figure S1). These results strongly indicated that tigecycline impairs the cell viability of MM cells mainly because of cell cycle arrest at G0/G1 phase rather than apoptosis induction.

| Tigecycline induces cytoprotective autophagy in MM cells
Tigecycline has been demonstrated to induce autophagy in gastric cancer cells. 7 To explore whether autophagy is also functionally involved in tigecycline-induced cytotoxicity, we analysed the expression levels of LC3 and SQSTM1/p62 and found that tigecy-

| AMPK/mTOR signalling pathway is involved in tigecycline-induced autophagy in MM cells
The mTOR signalling pathway serves as a central regulator of cell metabolism, growth, proliferation and survival. Recent studies have also revealed that mTOR signalling is tightly related to autophagy. 23 The mTOR kinase downstream targets the eukaryotic initiation factor 4E-BP1 and the p70S6K1. Inhibition of mTOR signalling leads to dephosphorylation of 4E-BP1 and p70S6K1 to induce autophagy. Therefore, we firstly evaluated the phosphorylation of these three proteins and found that tigecycline dramatically inhibited the phosphorylation of mTOR and two downstream effectors 4E-BP1 and p70S6K in three MM cell lines RPMI-8226 ( Figure 4A), NCI-H929 ( Figure 4B) and U266 ( Figure 4C). As the upstream positive regulatory role of Akt in mTOR activation has been reported, 19 we evaluated the Akt phosphorylation and found that tigecycline slightly elevated the Akt phosphorylation in NCI-H929 cells (Figure S2), suggesting that tigecycline-induce mTOR signalling inhibition is not through directly influencing the Akt signalling. We subsequently analysed the phosphorylation of AMPK, another important signalling pathway can regulate the mTOR signalling, 24,25 and found that tigecycline significantly enhanced the phosphorylation of AMPK in all three MM cell lines tested (Figure 4). These findings manifested that tigecycline promoted autophagy in MM cells mainly through regulating the AMPK/mTOR signalling but not Akt signalling.

| DISCUSSION
The prognosis of patients with MM is still dismal despite improved remissions with novel agents. Tigecycline as a FDA-approved antibiotic can inhibit the synthesis of bacterial proteins and mitochondrial protein translation. Recently, it has been reported that tigecycline alone, or in combination with other therapeutic agents, can effectively kill, even eradicate multiple solid tumours and haematological malignancies. 26,27 In the present study, we demonstrated that tigecycline potently impaired the proliferation in MM cells in a dosedependent manner. Furthermore, the soft agar assay displayed that tigecycline dampened survival and self-renewal of MM cells in vitro.  were injected subcutaneously into the right flank of female NOD/SCID mice (n = 5 for each group). When the tumours reached approximately 300 mm 3 in volume, intraperitoneal injections of tigecycline (Ti; 75 mg/kg, twice a day) or chloroquine (CQ; 50 mg/kg, daily), or saline (Veh), or both Ti and CQ were administered for 14 d, tumour volume and the body weight were measured every other day. C, Size and weight of xenograft tumour were elevated after mice were killed. D, The levels of LC3 and SQSTM1/p62 in tumour tissues were determined by Western blot. E, The levels of p21, cyclin D1 and CDK2 in tumour tissues were determined using Western blot analysis. Results were expressed as mean ± SEM, and images shown were representatives of at least three independent experiments. *P < 0.05, **P < 0.01, vs tigecycline alone group apparatus in plasma cells. 29 To ascertain the role of tigecyclineinduced autophagy in MM cells, we used Baf A1 as an in vitro autophagy inhibitor and chloroquine as an in vivo autophagy inhibitor. These two results revealed that autophagy acted as a cytoprotective role in tigecycline-treated MM cells. Combination of autophagy inhibitor with tigecycline has a stronger antitumour effect on MM cells than tigecycline alone.
The molecular mechanism of autophagy involves several conserved Atg proteins. In the present study, we only simply detected the level of LC3 II and its degradation substrate SQSTM1/p62 to confirm the occurrence of autophagy. We mainly clarified the upstream mechanisms of autophagy induction and found that the activation of AMPK is implicated in tigecycline-induced autophagy.
In contrast to normal cells, MM cells relied heavily on glycolysis and mitochondrial respiration to meet the high demands of energy production and metabolism. [30][31][32] Tigecycline as a mitochondrial protein translation inhibitor leads to energy deprivation, and AMPK is a crucial cellular energy sensor. The activation of AMPK subsequently resulted in the inhibition of mTOR, which can regulate protein synthesis, cell growth and proliferation through its downstream effectors 4E-BP1 and p70S6K1. Targeting mTOR has been proved to be effective for MM treatment. 33 Concurrently, increased activation of Akt may be because of a marked decrease in mTOR activity induced by tigecycline in MM cells.
In conclusion, we first showed that tigecycline inhibited MM cells proliferation and growth both in vitro and in vivo through inducing cell cycle arrest at G0/G1 phase. Second, we found that autophagy did occur and exerted a cytoprotective role in tigecycline-treated MM cells, and autophagy inhibitor could enhance the efficacy of tigecycline. Therefore, it is highlighted that combination of autophagy inhibitor and tigecycline might be a promising therapeutic strategy for MM.


s2fieldsofstudy: Biology Psychology

text:
Early-Life Stress Does Not Aggravate Spatial Memory or the Process of Hippocampal Neurogenesis in Adult and Middle-Aged APP/PS1 Mice

Life-time experiences are thought to influence the risk to develop the neurodegenerative disorder Alzheimer’s disease (AD). In particular, early-life stress (ES) may modulate the onset and progression of AD. There is recent evidence by our group and others that AD-related neuropathological progression and the associated neuroimmune responses are modulated by ES in the classic APPswe/PS1dE9 mouse model for AD. We here extend our previous study on ES mediated modulation of neuropathology and neuroinflammation and address in the same cohort of mice whether ES accelerates and/or aggravates AD-induced cognitive decline and alterations in the process of adult hippocampal neurogenesis (AHN), a form of brain plasticity. Chronic ES was induced by limiting bedding and nesting material during the first postnatal week and is known to induce cognitive deficits by 4 months in wild type (WT) mice. The onset of cognitive decline in APP/PS1 mice generally starts around 6 months of age. We here tested mice at ages 2–4 months to study acceleration and at ages 8–10 months for aggravation of the APP/PS1 phenotype. ES-exposed WT and APP/PS1 mice were able to perform the object recognition (ORT) and location tasks (OLT) at 2 months of age. Interestingly, at 3 months, ES induced impairments in the performance of the OLT in WT, but not in APP/PS1 mice. APP/PS1 mice exhibited alterations in hippocampal cell proliferation and differentiation, but ES exposure did not further change this. At 9 months, APP/PS1 mice exhibited impaired performance in the Morris Water Maze (MWM) task, as well as reductions in markers of the AHN process, which were not further modulated by ES exposure. In addition, we observed a so far unreported hyperactivity in ES-exposed mice at 8 months of age, which hampered assessment of cognitive functions in the ORT and OLT. In conclusion, while ES has been reported to modulate AD neuropathology and neuroinflammation before, it failed to accelerate or aggravate the decline in cognition or the process of AHN in APP/PS1 mice at ages 2–4 and 8–10 months. Future studies are needed to unravel how ES might affect the vulnerability to develop AD.

INTRODUCTION
Alzheimer's disease (AD) is the most prevalent form of dementia in elderly. It is characterized by an age-related accumulation of amyloid β (Aβ) neuropathology and tau-related neurofibrillary tangles in the brain (Querfurth and LaFerla, 2010;Scheltens et al., 2016). While mutations in the amyloid or tau genes induce familial AD in a few percent of the patients, gene-environment interactions likely play an important role in the majority of patients with sporadic AD (Mayeux and Stern, 2012). Indeed, multiple life-style factors have been described to modulate AD age-of-onset and progression (Okonkwo et al., 2014;Külzow et al., 2016). For example, stress experiences in elderly were a potent accelerator of age-related cognitive decline (Aggarwal et al., 2014) and the total amount of life-time distress was in addition associated with an aggravated age-related cognitive decline and AD development (Wilson et al., 2003(Wilson et al., , 2006(Wilson et al., , 2007Johansson et al., 2014;Sindi et al., 2017).
Recent clinical studies have further suggested that exposure to stress or trauma early in life might enhance the vulnerability to develop dementia later in life (Seifan et al., 2015;Wang et al., 2016), although the study of this hypothesis has only recently begun. The early-life period is a sensitive time for brain development, during which the brain is particularly vulnerable to adversities, that can alter developmental trajectories and lead to life-long changes in brain function (Heim and Nemeroff, 2001;Barker, 2004;Barker et al., 2013). In fact, early-life stress (ES) has been shown to induce deficits in adult hippocampal structure and functioning, as well as cognitive impairments in both humans and rodent ES models (Staff et al., 2012;Naninck et al., 2015;Wang et al., 2016;Calem et al., 2017).
Interestingly, accumulating preclinical evidence indicates that ES might also modulate the progression of Aβ pathology. For example, processing of the amyloid precursor protein (APP) was increased in non-transgenic adult rats exposed to maternal separation stress (Solas et al., 2010(Solas et al., , 2013Martisova et al., 2012Martisova et al., , 2013. In addition, ES aggravated Aβ pathology in 4-month-old bi-genic APP and Tau mutant (biAT) mice and in APPswe/PS1dE9 mice at 9-10 months of age, two models that both overexpress AD related genetic variants and present with AD related neuropathology (Lesuis et al., 2016;Hoeijmakers et al., 2017;Hui et al., 2017). These ES-induced alterations in Aβ pathology have also been associated with altered Aβinduced neuroinflammatory signaling and microglial activation in APP/PS1 mice (Hoeijmakers et al., 2017).
There is, furthermore, initial evidence that such alterations in AD-related neuropathology are also associated with a stronger impairment in spatial memory in 9-month-old, maternally stressed APP/PS1 mice, when compared to unstressed APP/PS1 mice (Hui et al., 2017). However, relative to the unstressed transgenic mice, 4-month-old APP/PS1 mice exposed to prenatal stress, or chronic ES-exposed 4-month-old biAT mice, showed reduced cognitive deficits or no differences in cognition, respectively (Sierksma et al., 2013;Lesuis et al., 2016). This indicates that the consequences of ES for cognition might depend on the specific model or ages studied.
One structural substrate implicated in hippocampal plasticity and cognition is adult hippocampal neurogenesis (AHN), which refers of the generation of new neurons in the hippocampus, that can be studied by the expression of various stagespecific cellular markers. Various APP-based mouse models have demonstrated reductions in the numbers of neurogenic cells in their hippocampus (Donovan et al., 2006;Rodríguez et al., 2008;Demars et al., 2010). As neurogenesis could respond to Aβ neuropathology as well as the related neuroinflammation (Biscaro et al., 2012;Varnum et al., 2015;De Lucia et al., 2016), one could hypothesize that an ES-induced aggravation of Aβ and the altered neuroinflammatory profile might also further affect the process of AHN. Together, such ES-induced changes in the hippocampus might contribute to an acceleration or aggravation of cognitive decline.
We therefore here set out to study whether ES-induced changes in AD hallmarks, that we and others have characterized before, are also associated with accelerated and/or aggravated cognitive deficits and impaired neuronal plasticity. To this end, we exposed the well-characterized APPswe/PS1dE9 mouse model to chronic ES from postnatal day (P)2 to P9 to test if ES-exposed AD mice exhibit an earlier onset of cognitive decline (i.e., already at 2-3 months of age), or whether ES exposure might aggravate cognitive deficits at later ages. We further studied if ES might affect the APP/PS1-related alterations in the process of AHN. Finally, we tested if these measures correlate with one another as well as with the previously reported changes in AD-related hallmarks (including Aβ pathology and microglial markers Iba1 and CD68) in this cohort (Hoeijmakers et al., 2017).

Mice and Early-Life Stress Paradigm
Bigenic APPswe/PS1dE9 hemizygous male mice on a C57BL/6J background and their wild type (WT) littermates were used in this study, as described previously (Hoeijmakers et al., 2017). All animals were bred in house and underwent the chronic ES paradigm, consisting of limiting the nesting and bedding material for 1 week (Naninck et al., 2015;Hoeijmakers et al., 2017).
Briefly, the dams and pups were assigned to the ES or control (Ctrl) condition on P2. They were left undisturbed until P9 and moved to standard housing cages until weaning at P21. The standard housing consisted of cage enrichment, ad libitum water, standard chow, 20-22 • C temperature, and 40%-60% humidity. All mice were housed with 2-4 same-sex littermates per cage. Experimental procedures were conducted according to the Dutch national law and European Union directives on animal experiments and were approved by the animal welfare committee of the University of Amsterdam.

Experimental Design
The mice were tested for cognitive functioning in various behavioral tests. Behavior was investigated in a first cohort of mice (cohort 1) to test if ES impairments were accelerated in APP/PS1 mice and therefore already present at 2 months of age. A second cohort of mice (cohort 2) was tested for accelerated decline in one behavioral task at 3 months, and sacrificed at 4 months for further analyses. A third cohort of mice (cohort 3) was used for behavioral testing at 8 months of age. Finally, cohorts 1 and 3 were used to investigate if ES aggravated cognitive impairments at 9 months of age, and they were sacrificed at 10 months of age for further analyses. All mice were injected with the cell-birth date marker 5-bromo-20deoxyuridine (BrdU) prior to sacrifice (see ''Tissue Collection and Processing'' section).

Behavior
Behavioral testing was performed as previously described (Naninck et al., 2015). The object recognition task (ORT) is a non-spatial, emotionally neutral memory test that makes use of the inherent curiosity and novelty-seeking behavior of mice. The object location task (OLT) is a spatial, emotionally neutral memory test, which like the ORT, depends on novelty-seeking behavior. The elevated plus maze (EPM) is used to address basal exploration and anxiety-like behavior. The Morris Water Maze (MWM) is a spatial memory task in which mice can escape the water bath by locating a hidden platform using spatial cues.
The behavioral tasks were performed during the dark (lightsoff) phase to accommodate to the natural, active period of mice, and they were therefore moved at least 1 month prior to testing, to a housing room with a reversed 12/12 day/night cycle (8 AM lights off). The testing room was lid by three red-light spots (25 W), and mice were transferred daily to the room 1 h prior to testing. Prior to behavioral testing, mice were handled for 2 days in the housing room and for 2 days in the testing room.
The behavioral performance of the mice was recorded and tracked for automated analysis of locomotion and position using Ethovision software (Noldus, Wageningen, Netherlands). ORT and OLT object exploration behavior and EPM arm exploration behavior were scored using Observer software (Noldus, Wageningen, Netherlands), by an investigator who was unaware of the experimental conditions.

ORT
The testing-box for the ORT was a rectangular blue-plastic box (l × w × h: 23.5 × 33.1 × 27 cm) with a sawdust covered bottom, cleaned in between trials with 25% ethanol. The ORT consisted of four consecutive days with on each day a 5-min trial; two habituation trials during which the mice freely explored the testing-box, one training trial when the mice explored two identical objects in the testing-box, and one testing trial 24-h after the training phase. One of the objects was replaced during this testing trial, and the mice were allowed to explore this novel object and the old, familiar object during the 5-min trial.
Locomotion in the box was tested on all days as an indicator of basal exploration behavior. Mice that spent <10 s exploring the objects during the training or testing trial were excluded from the analysis. The exploration time of the objects during the training phase and the testing phase was scored to test if this ratio was equal to 1, indicating no prior preference or bias. The novel object exploration time over familiar object exploration time was calculated after the testing phase. A ratio for novel/familiar exploration time that was >1 indicated a preference for the novel object and discrimination from the familiar object.

OLT
The OLT task was performed in the same box as the ORT, and likewise consisted of 5-min trials on 4 days with sequentially two habituation trials, one training trial and 24-h later the testing trial. During the training trial, the mice were exposed to two new identical objects placed in the middle of the box, of which one was replaced to a new location during the testing trial. If mice recognized this novelty on the testing day, they were expected to explore the object in the novel location more so than the familiar location.
Locomotion in the box was tested on all days as an indicator of basal exploration behavior. Mice that spent <10 s exploring the objects at the different locations during the training or testing trial were excluded from the analysis. Similar to the ORT, the ratio of object exploration time during the training trials should be equal to 1, and a preference for the novel location was indicated by the novel/familiar location exploration time >1.

EPM
The EPM was a plus-shaped maze with a neutral 5 × 5 cm center and 35 × 5 cm long arms, raised 100 cm above the floor. Two opposed arms were enclosed with a 30-cm wall, and the two other arms without any enclosure were open arms (OAs). Every mouse was placed in the center facing one of the OA and allowed to freely explore the maze for 10 min. One animal fell of the maze and was therefore excluded from further analysis. Locomotion during the EPM was tested on all days as an indicator of anxiety and basal exploration behavior. OA exploration time and entries were calculated as a percentage of total arm time or entries.

MWM
We addressed learning behavior during acquisition trials in the MWM and flexibility of this learning behavior during reversal training. We additionally adapted the MWM protocol to make the reversal training more difficult with the use of a smaller platform during reversal training, which requires more precise spatial navigation of the mice (Vorhees and Williams, 2006).
The MWM was a circular pool (110 cm diameter) filled with water (24 ± 1 • C). For acquisition and reversal training, the mice underwent twice daily, 1-min trials with a 30-min inter-trial time by placing them at different, random starting positions in this pool, to exclude egocentric learning strategies. After a trial, the mice were placed in a clean cage in front of an infrared (heating) lamp for ±1 min to prevent hypothermia.
The protocol started with 1 day of cued trials, during which the pool was filled with clear water and a visible 12-cm diameter platform was placed in the center of the pool. When the mice did not locate the platform within the 1-min trial, they were guided to and placed on the platform for 15 s. During the following 6 days of acquisition trials, the pool was surrounded by spatial cues to support allocentric spatial navigation, the water was adjusted to opaque by addition of non-toxic paint and the platform was submerged just below the water-surface in a fixed position within target quadrant 1 (Tq1). For the probe trial on day 8, the platform was removed and the mice were placed in the pool for the full 1-min trial. The protocol then continued with 5 days of reversal trials with a platform that was reduced to half the original size (6-cm diameter) and placed in a new location in the maze within Tq2. A second memory trial followed reversal training on day 14.
The latency to reach the platform (escape latency) was measured manually during acquisition and reversal training. Three mice floated throughout the acquisition and/or reversal trials and were excluded from the analysis. The time spent in Tq1 and Tq2 was recorded during the respective probe trials on day 8 and day 14, as well as locomotion behavior during all trials.

Tissue Collection and Processing
To ensure that brain plasticity measurements reflected basal levels, the mice of cohorts 2 and 3, and several mice from cohort 1 (2 Ctrl WT, 1 ES WT and 2 Ctrl APP/PS1 mice) were sacrificed 4 weeks after behavioral testing, at either 4 months (cohort 2) or 10 months of age (cohort 1 and 3). The mice of cohort 1 were included to balance the group numbers for the analysis of brain tissue at 10 months of age and were randomly selected. The mice were injected intraperitoneally prior to sacrifice with the cell birth date marker 5-bromo-20-deoxyuridine (BrdU, Sigma-Aldrich), dissolved in 0.9% saline containing 0.007 M NaOH. Four-month-old mice received three pulses of 100 mg/kg BrdU with a 2-h interval on two consecutive days. Ten-month-old mice received three pulses of 100 mg/kg BrdU with a 2-h interval on three consecutive days. Two hours after the last BrdU injection, the mice were sacrificed by transcardial perfusion with paraformaldehyde for later immunohistochemical purposes as previously described (Naninck et al., 2015;Hoeijmakers et al., 2017). Tissue was processed to obtain 40 µm coronal sections in six parallel series as described before Naninck et al. (2015) and Hoeijmakers et al. (2017).

Immunolabeling for Adult Hippocampal Neurogenesis Markers
Antibodies BrdU immunofluorescent labeling was obtained with a 1:500 dilution of the rat anti-BrdU antibody (Accurate Chemical and Scientific Corporation OBT0030, Westbury, NY, USA) and doublecortin (DCX) immunohistochemical labeling was obtained with a 1:800 dilution of the goat anti-DCX antibody (SantaCruz Biotechnology, Dallas, TX, USA). Immunolabeling for both antibodies followed previous descriptions (Naninck et al., 2015;Hoeijmakers et al., 2018).

Quantification
An even representation of the hippocampus over the rostralcaudal axis was obtained by selecting 6 coronal, bilateral sections of the hippocampus with 300 µm intersection distance, therewith including three sections rostral of Bregma −2.30 mm and three sections caudal of Bregma −2.30 mm. All quantifications were performed by a researcher blind to the experimental conditions. BrdU+ cells in the sub granular zone (SGZ) of the dentate gyrus (DG) were counted manually on a Leica CTR5500 microscope (40× objective) using the Leica MM AF program (MetaMorph version 1.6.0, Nashville, TN, USA). DCX+ cells were manually counted in the SGZ and granular cell layer (GCL) of the DG on a Zeiss Axiophot light microscope (40× objective) with Microfire camera using StereoInvestigator software (MBF Bioscience, Williston, VT, USA). DCX+ cells were further classified for DCX+ cell maturation based on the morphological appearance; type I, horizontal cells without a process reflected immature, mitotic cells, type II cells with an apical process into the GCL reflected an intermediate stage and type III cells with a dendritic tree reaching to the molecular layer reflected the immature neuronal stage (Hoeijmakers et al., 2018).

Statistics
Statistical analysis was performed using SPSS 20.0 (IBM software) and Graphpad Prism 5 (Graphpad software). Data were considered statistically significant when p < 0.05. All graphical representation of data shows mean + standard error of the mean (SEM).
Data was analyzed with condition (Ctrl-ES) and genotype (WT-APP/PS1) as independent factors in two-way analysis of variance (ANOVA) designs. Mixed statistical models with litter and/or cohort (applicable to MWM data and brain measurements at 10 months) included as random factor were run to assess whether litter or cohort effects influenced the dependent variables. Locomotion behavior over days in the ORT or OLT, and MWM escape latency was assessed using repeated measure ANOVAs, with ''days'' as the repeated factor. Exploration ratios in the ORT and OLT, and probe trial analyses were tested per experimental group using a one-sample T-test to assess the difference from ''1'' (equal ratio) or ''25%'' (chance level performance) respectively. Pearson's correlation was employed for the inter-parameter correlations of brain tissue measurements and/or behavioral outcome.

ES and APP/PS1 Overexpression Do Not Affect Cognitive Functioning at 2 Months
Cognitive functioning was first addressed in 2-month-old mice (belonging to cohort 1) to test for an accelerated onset of cognitive impairments ( Figure 1A). The mice showed equal exploration of the objects during the training Frontiers in Aging Neuroscience | www.frontiersin.org (A) C57BL/6J wild type (WT) mice and APP/PS1 male littermates (cohort 1) were exposed to ES or the control (Ctrl) condition from postnatal day (P)2 to P9 and cognitive performance was assessed at 2 months, using the object recognition task (ORT) and object location task (OLT). At 2 months, (B) the mice equally explored both objects during training and (C) explored the novel object more than the familiar object. (D) Object location exploration during training was equal to a ratio of 1 and (E) all groups explored the novel location more. (F) WT and APP/PS1 mice of cohort 2 were exposed to ES and tested in the OLT at 3 months of age. Mice were injected with 6 injections of 100 mg/kg 5-bromo-20-deoxyuridine (BrdU) over 2 days and sacrificed 2 h after the last injection for the analysis of BrdU+ and Doublecortin (DCX) + cells. (G) At 3 months, all mice explored both objects equally during OLT training. (H) ES WT mice were not able to discriminate between the novel and familiar object location, while other groups explored the novel object location more. (I) Proliferating BrdU+ cells in the hippocampus were reduced in APP/PS1 mice. DCX+ cells in (J) a Ctrl WT mouse and (K) a Ctrl APP/PS1 mouse. (L) DCX+ type 3 immature neurons were increased in APP/PS1 mice. BrdU+ cell numbers correlated with (M) the previously reported cell-associated amyloid levels, (N) microglial Iba1 + cells and (O) microglial CD68 coverage in the dentate gyrus (DG). Scale-bars are 100 µm. Annotations: ∧ sig from 1; # genotype effect. Abbreviations: Ctrl, control; DCX, doublecortin; DG, dentate gyrus; ES, early-life stress; ORT, object recognition task; OLT, object location task; P, postnatal day; SGZ, sub granular zone; WT, wild type.

Spatial Memory Is Impaired by ES at 3 Months of Age in WT Mice, But Not in APP/PS1 Mice With or Without ES Exposure
A second cohort (cohort 2) was tested for cognitive performance in the OLT at the age of 3 months (Figure 1F). Three-month-old mice of both conditions and genotypes equally explored the two objects during the OLT training phase (Ctrl WT t (9) = 0.650, p = 0.532, ES WT t (10) = 0.572, p = 0.580, Ctrl APP/PS1 t (8) = 0.972, p = 0.360, ES APP/PS1 t (7) = 0.160, p = 0.876; Figure 1G). ES exposure impaired discrimination of the novel location in WT mice only (ES WT t (10) = 0.311, p = 0.762), while all other experimental groups (Ctrl WT mice and Ctrl and ES APP/PS1 mice) explored the novel object location more than the familiar object location during the testing day (Ctrl WT t (9) = 8.421, p < 0.001, Ctrl APP/PS1 t (8) = 2.954, p = 0.018, ES APP/PS1 t (7) = 2.517, p = 0.040; Figure 1H).
In addition, the changes in BrdU+ and DCX+ cell numbers did not correlate with the behavioral performance in the OLT at 3 months of age (OLT vs. BrdU: r = 0.009, p = 0957; OLT vs. DCX: r = 0.051, p = 0.763).

Aβ and Microglial Markers Correlate With Markers for the Process of AHN at 4 Months
We had previously analyzed and reported on how ES affected β-amyloid pathology, Iba1 and CD68 in the hippocampus in APP/PS1 mice (Hoeijmakers et al., 2017), using the same 4-month-old mice as used in the current study. We therefore tested to what extent the cell numbers and OLT behavior correlate with these measures. In 4-month-old mice, the number of BrdU+ cells correlated positively with the extent of cell-associated amyloid in the DG of APP/PS1 mice (BrdU vs. cell-associated amyloid: r = 0.593, p = 0.015; Figure 1M). In addition, BrdU+ cell numbers in 4-month-old mice correlated with microglial Iba1 + cell numbers in the DG and CD68 coverage in the DG (BrdU vs. Iba1: r = −0.432, p = 0.008; Figure 1N; BrdU vs. CD68: r = 0.468, p = 0.012; Figure 1O). Interestingly, the analysis of solely the WT mice also revealed a negative correlation of BrdU with Iba1 (WT BrdU vs. Iba1: r = −0.490, p = 0.024).

ES WT and APP/PS1 Groups Exhibit Abnormal Exploratory Behavior During Habituation and Testing of the ORT and OLT
A third cohort of mice of all four experimental groups (cohort 3) was aged until 8 months with the aim to test if ES would have aggravated cognitive deficits in APP/PS1 mice (Figure 2A). However, all ES and APP/PS1 mice showed abnormal exploration behavior during ORT and OLT habituation and testing phases, when compared to Ctrl WT mice at this age, as indicated by the abnormal mobility patterns. This precluded any conclusions about cognitive functioning based on these tasks.

MWM Acquisition and Reversal Training Is Impaired in APP/PS1 Mice and ES Did Not Modulate This in Mice of Both Genotypes
Behavior of the mice from cohorts 1 and 3 was tested in the MWM at 9 months of age ( Figure 3A). Overall, the swimming speed of the mice was not different between the groups during the whole MWM protocol (genotype F (1,51) = 1.602, p = 0.211, condition F (1,51) = 0.945, p = 0.336, interaction F (1,51) = 0.272, p = 0.605). APP/PS1 mice were significantly FIGURE 3 | Spatial memory and adult hippocampal neurogenesis (AHN) are impaired in middle-aged APP/PS1 mice. (A) WT and APP/PS1 mice (cohort 1 and 3) were exposed to ES and tested in the Morris water maze (MWM) at 9 months. A schematic overview illustrates the MWM protocol that consisted of 14 days (D) with first cued trials, an acquisition phase with the platform in target quadrant (Tq) 1 and a reversal phase with platform in Tq2. One month later, BrdU was injected prior to sacrifice. (B) WT mice reduced their escape latency over the days of acquisition training, but APP/PS1 were slower in this. (C) The percentage of time spent in Tq1 was not above chance level (25%) for any of the groups during probe trial 1. slower in locating the platform than WT groups, even though all groups showed a reduced latency to escape during the course of the MWM acquisition (days F (1,51) = 19.846, p < 0.001, genotype F (1,51) = 23.198, p < 0.001, condition F (1,51) = 2.297, p = 0.136, interaction F (1,51) = 0.154, p = 0.696; post hoc for day * genotype: Acq1 p = 0.002, Acq3 p < 0.001, Acq4 p = 0.001, Acq6 p < 0.001; Figure 3B). Although all mice acquired the task, none of the groups spent above 25% (chance level) of the time in Tq1 during the probe trial (Ctrl WT t (13) = 1.031, p = 0.321; ES WT t (19) = 0.723, p = 0.478; Ctrl APP/PS1 t (10) = 1.106, p = 0.295; ES APP/PS1 t (9) = 2.093, p = 0.065; Figure 3C).

DISCUSSION
In this study, we addressed if chronic ES exposure accelerated or aggravated AD-related cognitive decline and alterations in the process of adult neurogenesis in APP/PS1 mice, a classic model for aspects of AD. Cognitive deficits have previously been indicated in ES WT mice at 4 months of age, and we now showed that these deficits were not yet present when ES-exposed mice were studied at 2 months. However, OLT performance was impaired in 3-month-old ES WT, but not ES APP/PS1 mice. At 4 months of age, the APP/PS1 mice further exhibited an increased number of differentiating DCX expressing cells, but a decrease in BrdU+ cell numbers in the SGZ. These indicators of the neurogenic process did not correlate with OLT performance of these mice at 3 months, whereas BrdU+ cell numbers were associated with the APP/PS1-induced neuropathological and microglial changes at 4 months, that we reported before Hoeijmakers et al. (2017). At 9 months of age, we confirm the learning impairments of APP/PS1 mice in the MWM and show that ES did not further modulate cognition of the APP/PS1 mice, nor of WT mice. The process of AHN was reduced in APP/PS1 mice at 10 months of age. Finally, DCX+ cell numbers and cognition in the 10-monthold mice correlated with the previously reported microglial CD68 coverage in the DG (Hoeijmakers et al., 2017). Together, these results show that ES does not accelerate or aggravate the AD-related cognitive decline or the alterations in AHN in APP/PS1 mice.

No Accelerated Cognitive Decline and Neurogenic Alterations in Young Adult (2-4 Months) APP/PS1 Mice Exposed to ES
We show for the first time that ES mice were able to discriminate between novel and familiar objects at 2 months of age and that spatial memory impairments in ES WT mice had developed by 3 months. This observation is in line with, and extends on the previous descriptions of impairments in cognition in this ES model at ages 4-6 months (Rice et al., 2008;Wang et al., 2011Wang et al., , 2013Naninck et al., 2015). Our results further indicate that the ES-induced cognitive deficits in novelty-based learning tasks developed only after 2 months of age. Interestingly, exposure of rats to limited nesting and bedding material from P2 to P9 similarly induced deficits at 10, but not yet at 4 months of age (Brunson et al., 2005). Although these mouse and rat models are not identical, this does imply that chronic ES induced impairments in specific cognitive tasks arise not until later in adulthood.
Although ES WT mice were impaired in the OLT at 3 months of age, ES-exposed APP/PS1 mice were still able to learn the task at this time. It is thus evident that ES does not accelerate impairments in the APP/PS1 mice at this age, as these ES-exposed transgenic mice did not show impairments yet. We suggest that this difference in the phenotype after ES exposure in WT and APP/PS1 mice is potentially mediated by alterations in the neurogenic processes. Although 4-monthold APP/PS1 mice exhibited reduced numbers of proliferating, BrdU+ cells, the number of differentiating, DCX+ cells was enhanced in APP/PS1 mice, irrespective of ES. An increase in among others DCX+ cell numbers has also been reported in 3-month-old APP/PS1 mice, as well as in other AD-related transgenic lines, in particular during the earlier pathological stages when no or little plaque formation has developed yet (Wen et al., 2002;Krezymon et al., 2013;Unger et al., 2016). This phenotype has been proposed to be a potential compensatory mechanism of the brain to combat the degenerative effects of soluble Aβ peptides (Meneghini et al., 2013). However, both the BrdU+ and DCX+ cell numbers at 4 months of age did not correlate with OLT performance at 3 months, and an open question thus remains to what extent alterations in the process of AHN contribute to cognition in the ES-exposed and APP/PS1 mice.
We further addressed whether cognitive performance and markers of the AHN process were associated with previously described changes in hippocampal Aβ levels and neuroinflammation in these mice (Hoeijmakers et al., 2017). Interestingly, BrdU+, but not DCX+, cell numbers correlated positively with cell-associated amyloid levels, and negatively with the microglial markers Iba1 and CD68 in the DG. Clearly, genotype is a strong contributor in these correlation, however, the correlation of microglial Iba1 with BrdU+ cells also remained significant in solely WT mice. This suggests that alterations in the AHN process might also be associated with ES-mediated alterations in neuroinflammatory signaling (Hoeijmakers et al., 2017). Indeed, hippocampal pro-inflammatory signaling per se can impact neuroplasticity, including AHN (Ekdahl et al., 2003;Jakubs et al., 2008;Maggio et al., 2013). However, whether these BrdU+ cell numbers relate to neurogenic and/or glial/astrogenic changes in the hippocampus awaits further investigation.
Cognitive Decline in Middle-Aged, 9-Month-Old APP/PS1 Mice Is Not Aggravated by ES The first cognitive deficits have been reported to arise in APP/PS1 mice after approximately 6 months of age (Jankowsky et al., 2005;Zhang et al., 2012;Edwards et al., 2014;Guo et al., 2015) and we therefore addressed whether ES would have aggravated cognitive performance at 9 months, when the deficits should have been firmly established. In comparison to WT mice, the APP/PS1 mice had indeed a slower learning curve during the acquisition and reversal phases. However, none of the groups fully acquired spatial memory for the platform location, as indicated by the poor probe trial performance of all four groups.
In order to better address more subtle differences between the groups, we used a smaller platform during reversal training that makes learning of the novel location more challenging and requires more precision (Vorhees and Williams, 2006). Now, APP/PS1 mice could indeed no longer locate this novel platform location, whereas Ctrl WT mice showed learning during this reversal phase. However, this strong APP/PS1-induced impairment in reversal training did not allow any possible further impairment in ES-exposed APP/PS1 mice. It is overall evident that APP/PS1 mice were significantly impaired in MWM performance at 9 months and ES did not significantly alter, or worsen this performance any further.
It is interesting that the ES-exposed WT mice were not impaired in MWM acquisition relative to Ctrl WT mice either, as was reported for MWM learning in Ctrl and ES WT mice at 5 months of age (Naninck et al., 2015). A natural decline in the cognitive abilities of Ctrl WT mice might possibly have leveled out the (earlier) difference between Ctrl and ES mice (Lindner, 1997;Koh et al., 2014), resulting in a similar learning capacity around middle-age. Indeed, Ctrl WT mice were not able to locate the platform during the probe trial either.
Although chronic ES exposure in WT mice was reported to impair ORT learning at 8 months (Rice et al., 2008), the 8-month-old ES-exposed and APP/PS1 mice in this study showed hyperactive exploratory behavior in both the ORT and OLT, that was further confirmed by the atypical (distal) OA exploration in the EPM. While others reported normal or reduced exploratory behavior of ES-exposed WT and APP/PS1 mice (Rice et al., 2008;Huang et al., 2016;Olesen et al., 2016), others have described similar (hyperactive) exploratory behaviors (Filali et al., 2011;Rodgers et al., 2012) that, however, hampered the proper assessment and evaluation of cognition with these tasks (Vogel-Ciernia and Wood, 2014;Cohen and Stackman, 2015). To our knowledge, the exact underlying pathways that are responsible for the hyperactive phenotype in APP/PS1 mice remains to be determined, although the phenotype seems to be relate to the APP metabolism and disease progression, as discussed in a recent review (Webster et al., 2014). Next to this, we can only speculate what mediates this phenotype in ES mice; hyperactivity in ES mice could potentially be associated with abnormalities in the striatum, which is among others relevant for locomotor activity (Barbera et al., 2016;Fareri and Tottenham, 2016), however, this requires further investigation.
Prior to our current study, a related report had shown a stronger impairment in MWM performance in APP/PS1 mice that were exposed to daily, 3-h maternal separation from P2-P21 compared to unstressed APP/PS1 mice (Hui et al., 2017). Next to this, a more ''positive'' early-life manipulation, i.e., early-life handling, was found to attenuate some cognitive impairments in 11-month-old APP/PS1 mice (Lesuis et al., 2017), and 4-monthold 3xTgAD mice (Cañete et al., 2015). Interestingly, we reported that 1-week of chronic ES enhanced plaque load in the DG (Hoeijmakers et al., 2017), whereas the APP/PS1 mice exposed to a 3-weeks-long maternal separation paradigm exhibited elevated Aβ plaque load in the total hippocampus as well as the cortex (Hui et al., 2017). Exposure to 3-week long maternal separation thus led to a more severe Aβ phenotype as well as more severe cognitive decline in APP/PS1 mice, relative to 1-week of chronic ES exposure in our current study. Considering that the level of Aβ has been implicate in the progression of cognitive decline in APP/PS1 mice, this might provide a possible explanation for the stronger impact of maternal separation stress on cognition decline (Hui et al., 2017).
Interestingly, MWM acquisition correlated with the previously studied microglial CD68 changes in the DG but not with the DG Aβ plaque load (Hoeijmakers et al., 2017), indicating that, similar to the phenotype at 4 months, the APP/PS1-induced neuroinflammatory changes might be associated with the emergence of the cognitive deficits (Dinel et al., 2011;Czerniawski and Guzowski, 2014;Guo et al., 2015;Fonken et al., 2016). Alternative behavioral protocols can be employed in future studies to address (subtler) ES-induced effects on cognition at different ages.
Next to cognitive deficits, APP/PS1 mice have been described to exhibit reduced AHN around the age of 9-10 months (Taniuchi et al., 2007;Verret et al., 2007;Niidome et al., 2008;Demars et al., 2010;Hu et al., 2010;Hamilton and Holscher, 2012), although there are expectations as well (Taniuchi et al., 2007;Hamilton and Holscher, 2012;Unger et al., 2016). The reduction in basal BrdU+ cell proliferation and DCX+ cell numbers in Ctrl and ES APP/PS1 mice at 9 months is therefore consistent with these previous studies. Furthermore, the reduction in DCX+ cell numbers correlated with the APP/PS1-induced elevation in microglial CD68 coverage in the DG (Hoeijmakers et al., 2017), whereas AHN did not correlated with MWM performance. Such a correlation suggests that the neuroinflammatory activation that follows APP/PS1 overexpression, and thus likely Aβ pathology, might be associated with a reduction of immature neurons as reflected by the changes in DCX+ cell numbers. Anti-inflammatory treatment has indeed been shown to induce pro-neurogenic effects on newborn cell survival in APP/PS1 mice (Biscaro et al., 2012), supporting such a relation between neuroinflammation and AHN.
In addition, exposure to chronic ES did not further aggravate the APP/PS1-induced reductions in AHN. Interestingly, at 6 months, neurogenic cell proliferation and differentiation in the hippocampus were not affected by ES in WT mice either (Naninck et al., 2015). We now extended on these findings at other ages, and further show that even under a pathological condition of APP/PS1 overexpression, these neurogenic stages are not affected by ES exposure. Neurogenic cell survival, an additional measurements for AHN, were decreased in 6-monthold ES WT males (Naninck et al., 2015), while APP/PS1 mice have been reported to exhibit reduced cell survival levels as well (Verret et al., 2007). This process might therefore be more susceptible to ES exposure in APP/PS1 mice and remains to be studied in future experiments. Next to this, other markers for neuroplasticity in ES-exposed APP/PS1 could be addressed to elucidate if (chronic) ES-induced changes in WT mice are indeed differently manifested in the APP/PS1 model (Aisa et al., 2009;Wang et al., 2011;Sierksma et al., 2012).

Limitations and Future Goals
Although we did verify the presence of ES-induced cognitive impairment in young adulthood, we observed no changes after ES in the cognitive measures and parameters of the AHN process in the middle-aged (8-10 months) WT and APP/PS1 mice. As for the behavior, however, it remains to be determined if these results are due to the difficulty of the tasks, and that middle age animals might hence need easier behavioral tasks, in which control WT animals perform better, which would better allow the detection of a possible impairment by ES exposure. As for the brain plasticity aspect, levels of AHN are notably low at this age and it is thus possible that we are at a limit of detection of possible differences between groups. In future studies, addition measures of plasticity might be helpful to elucidate this further.
In addition to this, we now primarily focused on the hippocampus for the study of ES effects, and measurements of AHN in particular. Other hippocampal-based behaviors seem to be more strongly associated with neurogenesis, such as pattern separation (Deng et al., 2010) and might therefore be an additional interesting target for the study of ES effects related to cognition and AHN. In addition, other brain regions than the hippocampus are involved in the studied behaviors too. For instance, impairments in MWM reversal learning in APP/PS1 mice is thought to be primarily mediated by pathologyrelated prefrontal cortex impairments (Vorhees and Williams, 2006). Notably, we showed before that ES leads to regionspecific differences, with e.g., significant effects on pathology and microglia in the hippocampus, but not the entorhinal cortex (Hoeijmakers et al., 2017). It will be interesting to address in the future whether other brain regions, besides the hippocampus, contribute to the ES cognitive phenotypes in APP/PS1 mice.

CONCLUSION
To summarize, ES failed to accelerate or aggravate the decline in cognition and AHN in APP/PS1 mice. The severe cognitive deficits and already low neurogenic numbers in the middle-aged APP/PS1 mice might have prevented a further reduction by ES at this late age. Given the altered neuropathological Aβ levels and neuroinflammation in ES-exposed APP/PS1 mice, this raises the question whether ES exposure affects other forms of hippocampal neuroplasticity in APP/PS1 mice and aggravates the decline at other ages. Such knowledge can help to identify vulnerability for later-life cognitive decline in populations exposed to stress early in their lives, and may help to develop specified therapeutic interventions.

AUTHOR CONTRIBUTIONS
LH and AK designed the experiments. LH, AA, FV and JK performed the experimental work and data analysis. LH, PJL and AK wrote the article.

ACKNOWLEDGMENTS
LH, PJL and AK were supported by funding from Internationale Stichting Alzheimer Onderzoek (ISAO; award no. 12536) and Alzheimer Nederland.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
Comprehensive immunoproteogenomic analyses of malignant pleural mesothelioma

We generated a comprehensive atlas of the immunologic cellular networks within human malignant pleural mesothelioma (MPM) using mass cytometry. Data-driven analyses of these high-resolution single-cell data identified 2 distinct immunologic subtypes of MPM with vastly different cellular composition, activation states, and immunologic function; mass spectrometry demonstrated differential abundance of MHC-I and -II neopeptides directly identified between these subtypes. The clinical relevance of this immunologic subtyping was investigated with a discriminatory molecular signature derived through comparison of the proteomes and transcriptomes of these 2 immunologic MPM subtypes. This molecular signature, representative of a favorable intratumoral cell network, was independently associated with improved survival in MPM and predicted response to immune checkpoint inhibitors in patients with MPM and melanoma. These data additionally suggest a potentially novel mechanism of response to checkpoint blockade: requirement for high measured abundance of neopeptides in the presence of high expression of MHC proteins specific for these neopeptides.

Introduction
Malignant pleural mesothelioma (MPM) is a highly aggressive malignancy of the pleura that is fatal in most cases and has defied standard approaches to treatment. Palliative therapy is associated with median survival of 7 months (1), while cytotoxic systemic therapy combined with targeted therapy (bevacizumab) can extend survival to 18 months (2). In selected patients with early-stage disease, surgery-based multimodal approaches incorporating cytotoxic chemotherapy and/or radiotherapy may improve median survival to 26 months (3). In early-phase clinical trials, immune checkpoint inhibitors targeting programmed cell death 1 (PD-1) have recently shown encouraging clinical activity with good tolerability in patients with advanced MPM who progressed after first-line chemotherapy (4)(5)(6). Similar to the results of checkpoint blockade in other human tumors (7,8), however, less than 50% of patients with MPM will benefit from PD-1 inhibition.
The variance in clinical responses to checkpoint inhibition emphasizes the complexities of productive antitumor immunity. Whereas we and others have shown that individual immune cell types influence the clinical behavior of MPM (9,10), these cells have been studied in relative isolation, outside of their complex cellular communities. The high-dimensional, single-cell platform time-of-flight mass cytometry (CyTOF) has substantially augmented the ability of single-cell cytometry to investigate the complex cellular networks of human disease states (11)(12)(13). By utilizing probes conjugated to stable heavy metal isotopes, CyTOF avoids the signal overlap inherent to the fluorophore-conjugated antibodies of conventional flow cytometry and enables simultaneous quantification of over 40 parameters at single-cell resolution with minimal signal overlap. Further, data-driven analysis techniques have facilitated straightforward analyses and graphical repre-We generated a comprehensive atlas of the immunologic cellular networks within human malignant pleural mesothelioma (MPM) using mass cytometry. Data-driven analyses of these high-resolution single-cell data identified 2 distinct immunologic subtypes of MPM with vastly different cellular composition, activation states, and immunologic function; mass spectrometry demonstrated differential abundance of MHC-I and -II neopeptides directly identified between these subtypes. The clinical relevance of this immunologic subtyping was investigated with a discriminatory molecular signature derived through comparison of the proteomes and transcriptomes of these 2 immunologic MPM subtypes. This molecular signature, representative of a favorable intratumoral cell network, was independently associated with improved survival in MPM and predicted response to immune checkpoint inhibitors in patients with MPM and melanoma. These data additionally suggest a potentially novel mechanism of response to checkpoint blockade: requirement for high measured abundance of neopeptides in the presence of high expression of MHC proteins specific for these neopeptides.
sentation of these complex data (14,15). Thus, it is now possible to discern the identity and behavior of numerous cell types from a single experiment and to contextualize these individual metrics into a broader immunologic state. These features have substantially augmented the ability of single-cell cytometry to investigate the complex cellular systems and processes that influence tumor biology (11)(12)(13) and uniquely position CyTOF to identify the cellular networks that are responsible for successful immunotherapy. We hypothesized that dissection of the intratumoral cellular networks within MPM would define distinct immunologic subtypes of this tumor and would generate a framework for investigation of the immunogenomic mechanisms responsible for cancer-related patient outcomes and response to PD-1 inhibition.

Results
Mass cytometry identifies 2 distinct immune microenvironments in MPM. Tumor tissues from 12 consecutive treatment-naive MPM patients undergoing surgical resection were prepared for CyTOF, mass spectrometry (MS), and mRNA transcriptome profiling. The study scheme and patient characteristics are illustrated in Figure 1 and Supplemental Table 1 (supplemental material available online with this article; https://doi.org/10.1172/ jci.insight.98575DS1). The tumor immune microenvironment (TiME) of MPM was characterized using CyTOF with a 35-antibody panel (Supplemental Table 2) and a single-cell analysis by fixed force-and landmark-directed (SCAFFOLD) map (16) was generated from 742 cellular subpopulations (nodes) based on 15 cellular phenotypes (Figure 2A, Supplemental Figures 1-4). Unsupervised clustering of these cellular subpopulations (17) defined 2 vastly disparate immunologic subtypes of MPM tumors, referred to as TiME-I and TiME-II subtypes (n = 6 in each subset) ( Figure 2B). We next generated SCAFFOLD maps for TiME-I and -II MPM to compare differences in the frequency of major cellular subpopulations between these subtypes ( Figure 2C), and a heatmap was generated to further distinguish these subtypes by differential activation states of their immune cell populations ( Figure 2D). The TiME-I subtype contained significantly greater numbers of partially exhausted CD8 + T cells (PD-1 + CTLA-4 + CD8 + T cells), which have been shown to respond to checkpoint blockade through the release of IFN-γ (18,19). Consistent with these reports, we found that PD-1 + CTLA-4 + CD8 + T cells in MPM had the ability to produce higher levels of IFN-γ and demonstrated increased phosphorylation of transcription factors including ERK, p38, and STAT4, when compared with non-PD-1 + CTLA-4 + CD8 + T cells (Supplemental Figures 5 and 6). TiME-I tumors also contained a greater frequency of HLA-DR + cancer cells, which positively correlate with response to checkpoint blockade in melanoma patients (20) and which we have found to demonstrate increased expression of cytokines (IL-10, IL-6, and TNF-α) and phosphorylated transcription factors (HIF-1A, cPARP, and STAT3) in MPM (Supplemental Figure 5). We also identified plasmacytoid DC (pDC) in the TiME-I subtype expressing high levels of CD40 and CD86 (Figure 2, C and D). In contrast, TiME-II tumors contained more Tregs expressing high ICOS and CTLA-4 markers, as well as CXCR4 + CD38 -(naive) CD8 + T cells (21). Also increased in TiME-II tumors were neutrophils, conventional DCs (cDC), cancer-associated fibroblasts (CAF), and tumor-associated macrophages (TAM) with high PD-L1, which were associated with greater IL-10 production and phosphorylation of Akt and NF-κβ ( Figure 2D and Supplemental Figure 5).
Neoantigen abundance and MHC protein expression underlie TiME. Neoantigens have recently been shown to influence the clinical behavior and response to immunotherapy in a number of human malignancies (22)(23)(24)(25)(26). Determination of neoantigen burden, in this context, has relied exclusively on exome and transcriptome analyses and in silico prediction of the presence of neoantigens. MS has recently been applied to human tumors for direct identification of neoantigens (27), and we utilized MS to compare measured neoantigen loads between TiME-I and -II immunologic subsets of MPM. Whereas it may be ideal to perform whole exome sequencing (WES) to quantify tumor mutation burden on an individual patient basis, this methodology may not be feasible or expedient at the scale of clinical practice. Therefore, potentially novel approaches to measure tumor mutation burden have been investigated by others through targeted next-generation sequencing (28,29) and by utilizing the Catalogue Of Somatic Mutations In Cancer (COSMIC; http://cancer.sanger. ac.uk/cosmic) mutation database (30)(31)(32). The correlation of tumor mutation burden determined by targeted sequencing with tumor mutation burden determined by WES provided rationale for our development of a mesothelioma-specific mutation database to apply clinically in the prediction of response to PD-1 blockade. From next-generation sequencing data from a total of 640 tumors in the Brigham Women's Hospital (BWH) (33) and Memorial Sloan Kettering Cancer Center (MSKCC) (34) cohorts and the COSMIC database (http://cancer.sanger.ac.uk/cosmic) (35), we generated a reference database containing 2,299 missense mutation sites in 1,885 genes where a detected mutation alters the amino acid sequence (Supplemental Figure  7 and Supplemental Table 3) in order to create a list of potential mutated peptides. MS was then performed on 11 of the 12 MPM tumors, which underwent CyTOF (described above), and blood samples from these patients were utilized for molecular HLA typing. A total of 140 mutated peptides were detected among these 11 tumors by MS, to which we applied standard prediction algorithms of the Immune Epitope Database (IEDB; http://www.iedb.org/) (36) to identify potential MHC class I (37,38) and class II neoantigens (39,40). We found that the median number of potential neoantigens for MHC-I (19 neoantigens[range 9-27]) and MHC-II (16 neoantigens [range 9-23]) was not different between TiME-I and TiME-II tumors (each P > 0.05). AUC analyses of peak neoantigen intensities were performed to quantify each of the detected neoantigens, a metric that we termed neoantigen abundance (Supplemental Tables 4-7). As an example, the RBP3 V282M (GESDFFFTVPMS) neopeptide that is predicted to have high affinity to HLA-B*18:01 in patient MPM.003 (TiME-I) demonstrated high abundance in this tumor ( Figure 3A). By calculating the average abundance of all neoantigens (average of all AUC values of neoantigens in each patient [sum AUCs divided by the number of neoantigens]), we were able to quantify a representative amount of measured neoantigens in each patient and study this metric as a predictive biomarker for response to checkpoint blockade. We then compared neoantigen abundance between TiME-I and -II MPM subtypes and found that the average neoantigen abundance for both MHC-I and MHC-II was greater in TiME-I tumors ( Figure 3B). As neoantigen presentation to T cells requires MHC-I or -II proteins that may themselves have variable levels of expression, we utilized MS to additionally quantify the expression of total MHC-I (HLA-A, -B, and -C) and MHC-II proteins (HLA-DRB1, -DRB3, -DRB4, -DRB5, -DPA1, -DPB1, -DQA1, and -DQB1) in each tumor. These data demonstrated that TiME-I tumors had elevated levels of both MHC-I and -II proteins compared with TiME-II tumors ( Figure 3C) and that the predominantly expressed MHC-I proteins were HLA-A and HLA-B and the predominant MHC-II protein was HLA-DRB1.
Because each neoantigen has specificity for distinct MHC proteins, we examined the relationship of each neoantigen's abundance and the level of expression of its specific corresponding MHC protein. Compared with TiME-II tumors, TiME-I tumors contained more high-abundance neoantigens with concordant high expression of their specific MHC-I proteins and/or MHC-II proteins ( Figure 3D, Supplemental Figures  8 and 9). For example, in a patient with a TiME-I tumor (MPM.003; HLA-A*01:01, HLA-A*30:02, and HLA-B*18:01), the BAP1 N645K neoantigen (LKCVEAEIAKY) (derived from the most common gene mutation in MPM; ref. 33) was present in high abundance, and its corresponding MHC-I proteins were also present at high levels. In contrast, in a patient with a TiME-II tumor (MPM.004; HLA-A*26:01, HLA-DRB1*08:01, and HLA-DRB1*13:01), the NF2 E166V neoantigens (QVELLPKRVINLY and RGFLAQVELLPKRVI) with high predicted affinity to HLA-A and HLA-DRB1, respectively, were demonstrated at low abundance, along with low expression of its corresponding HLA proteins ( Figure 3D).
The TiME signature is a robust prognosticator in MPM. Although a single-cell assay could be used as a platform to test the correlation of an immunologic signature with clinical outcomes (41), we reasoned that a molecular representation of TiME subsets in MPM may currently be a more practical and applicable approach. To achieve this goal, we performed mRNA microarrays and MS profiling on the same tumors that underwent CyTOF. Among 2,944 mRNAs with differential expression (P < 0.05) between the TiME-I and TiME-II subtypes, we selected 137 genes whose expression was also statistically different between the TiME subtypes at the protein level, which was defined as our molecular TiME signature ( Figure 4A, Supplemental Figure 10, and Supplemental Table 8). We then evaluated the relationship of the TiME signature with prognosis in MPM by utilizing mRNA sequencing data of 211 MPM patients from the BWH cohort (33) and 69 MPM patients from The Cancer Genome Atlas (TCGA) portal (https://gdc-portal.nci.nih.gov/) (TCGA cohort), as well as mRNA microarray data of 50 patients from MSKCC cohort (34) (Supplemental Table 1). Significant association of the TiME signature with overall survival (OS) was demonstrated in each of these 3 independent MPM cohorts ( Figure 4B), and multivariable analysis in a merged cohort (n = 330) accounting for age, asbestos exposure, histology (epithelial vs. nonepithelial), and pathologic stage revealed the TiME signature to be a robust independent prognostic factor in MPM (P = 0.017, hazard ratio = 1.74, 95% CI, 1.32-2.30) (Supplemental Table 9). Given the association of TiME-I and TiME-II subsets with favorable and unfavorable OS, respectively, the TiME-I subset is hereafter referred to as good-TiME and the TiME-II subset as bad-TiME. Pooled data from these 12 patients was used to generate a SCAFFOLD reference map of MPM's intratumoral immune system. This approach provides a data-driven representation of cellular networks, while also denoting the location of landmark immune cell populations defined using prior knowledge of the immune system. For example, landmark nodes are visualized as black nodes and represent 15 manually defined major cellular phenotypes. The same cells are subjected to unsupervised clustering to provide an objective view of cell composition and organization, and 742 cellular subpopulations were identified and represented by the colored nodes. In these maps, node size represents the relative number of cells in that grouping, and line length indicates similarity between cells. In other words, 2 groups of cells are connected by a short line if the proteins they express are relatively similar, and a longer line if they are relatively disparate. (B) Two distinct subsets of MPM patients were identified by unsupervised clustering of pooled CyTOF data from 12 MPM tumors: 6 tumors of the TiME-I subset and 6 tumors of the TiME-II subset. (C) The SCAFFOLD maps of TiME-I and TiME-II subsets. SCAFFOLD maps were generated from pooled data from the 6 patients in each of the TiME-I and -II immunologic subsets of MPM, and cellular subpopulations were statistically compared between each subset. The internodal differences in the same phenotypes were analyzed with 2-tailed paired t test according to the corresponding nodes. (D) Differential activation states of the immune cell populations between TiME-I and TiME-II MPM tumors. Immune stimulatory or inhibitory markers were significantly altered between 2 TiME subsets. Z ratios were calculated by taking the difference between the averages of the observed marker Z scores and dividing by the SD of all the differences for that particular comparison. A Z ratio of ±1.96 was inferred as significant (P < 0.05). CAF, cancer-associated fibroblast; cDC, conventional DCs; CyTOF, time-of-flight mass cytometry; MPM, malignant pleural mesothelioma; pDC, plasmacytoid DCs; SCAFFOLD, single-cell analysis by fixed force-and landmark-directed; TiME, tumor immune microenvironment; and Treg, CD4 + Tregs.
Good-TiME is associated with favorable responses to immune checkpoint blockade. Considering the vastly different intratumoral immune environments of good-TiME and bad-TiME tumors and their differential expression of targets for checkpoint inhibitors (PD-1, PD-L1, and CTLA-4) between these immunologic subsets of MPM (Supplemental Figure 11), we hypothesized that such a signature could also predict response to checkpoint inhibitor therapy. We first examined publicly available mRNA expression data from a study in which immunocompetent mice were inoculated with bilateral s.c. murine AB1-HA mesothelioma tumors and treated with an anti-CTLA-4 antibody (42). All original microarray data were deposited in the NCBI's Gene Expression Omnibus database (GEO GSE63557). Application of the TiME signature to this model identified 10 of 11 mice whose tumors responded to anti-CTLA-4 treatment (good-TiME) and 9 of 9 mice whose tumors did not respond (bad-TiME) ( Figure 5, A and B). To evaluate TiME signature in human tumors, we analyzed mRNA sequencing data obtained from pretreatment tumor biopsies (GEO GSE78220) in metastatic melanoma patients treated with anti-PD-1 antibodies (n = 27) (43). Patients whose tumors demonstrated a good-TiME signature had statistically improved responses to PD-1 inhibition ( Figure 5C). Finally, to evaluate utility of the TiME signature in MPM patients, we applied our TiME signature to the pretreatment biopsies (prior to immunotherapy) of 10 consecutive MPM patients with advanced and unresectable MPM whom we treated with anti-PD-1 therapy after they had progressed after treatment with a platinum-based agent and pemetrexed, in accordance with current National Comprehensive Cancer Network guidelines (44). Among 5 patients whose tumors demonstrated a good-TiME signature, 3 had a complete response by modified response evaluation criteria in solid tumors (mRECIST), 1 had partial response, and 1 had stable disease. The dramatic effects of the PD-1 inhibitor nivolumab in 1 patient with biphasic MPM (good-TiME) at 8 months after treatment is shown in Figure 5D, and chest CTs of the 2 additional complete responders are shown in Supplemental Figure 12. Among 5 patients whose tumors demonstrated a bad-TiME signature, 4 had progressive disease and 1 had stable disease. Further, the percentage or intensity of the PD-L1 clinical IHC test did not correlate with response to anti-PD-1 therapy (Figure 5D), and the TiME signature outperformed previously reported predictive immune signatures (45,46) of response to PD-1 blockade (Supplemental Figure 13). Notably, mutational load, neoantigen burden, copy number alteration, and diversity of T cell clonality did not correlate with patients likely to respond to PD-1 blockade (i.e., good-TiME tumors) (Supplemental Figure 14). Thus, immunogenomic elements previously reported to correlate with response to checkpoint blockade were not present in MPM tumors that were likely to respond to PD-1 inhibition; however, these tumors could be identified by high abundance of neoantigens and, in particular, high abundance of neoantigens concomitant with high expression of their corresponding, specific MHC proteins.

Discussion
Within the tumor microenvironment, a unique and multifaceted immune system encompasses elements such as innate and adaptive immune cells, stromal cells, cytokines and chemokines, and targets of checkpoint blockade that dictate clinical outcome and responses to immunotherapy (47,48). As an emerging high-dimensional single-cell analysis platform, CyTOF is uniquely suited for investigating the complex intratumoral immune system by capturing the phenotypes and behavior of integrated cellular systems (13). We comprehensively characterized the intratumoral immune system of human MPM with CyTOF and applied unsupervised clustering of these data to identify a distinct immunogenic TiME signature that was associated with favorable OS and with response to checkpoint blockade. Tumors with a good-TiME signature were enriched for partially exhausted CD8 + T cells that have enhanced capacity to release IFN-γ, activated pDC, HLA-DR + cancer cells, and decreased numbers of IL-10-and IL-17-releasing Tregs and PD-L1 + TAM. Proteomic analyses of these tumors with MS facilitated development of a simple gene signature that could be applied to all MPM tumors to determine their TiME profile.
Checkpoint inhibitors are changing the landscape of treatment for patients with solid tumors (49); however, less than half of all patients show favorable response (50)(51)(52)(53)(54), underscoring the unmet need for a clinical test that could predict response to these drugs. Whereas immunohistochemical expression of PD-L1 on tumors has been shown in some studies to correlate with response to checkpoint blockade (50-54), this is not true for all tumor types, and PD-L1 expression does not correlate with clinical response in a number of studies (50)(51)(52)(53)(54). In our experience in patients with advanced and unresectable MPM who were treated with anti-PD-1, the biomarker most predictive of response to anti-PD-1 therapy was the good-TiME signature. In contrast, PD-L1 immunohistochemical staining and previously reported immune signatures (45,46,55) did not correlate with therapeutic response. Application of this signature to a cohort of patients with advanced melanoma also demonstrated prediction of response to PD-1 inhibitors, suggesting a potential generalizability of this TiME signature to other solid tumors.
To eliminate cancer cells in the presence of immune checkpoint inhibitors, T cells must recognize antigens displayed by MHCs on tumor cells. Several clinical trials have suggested that the frequency of somatic mutations within a tumor type and, by extension, the potential for expression of tumor-specific neoantigens are correlated with sensitivity to immune checkpoint inhibitors. For example, in patients with melanoma, PD-1-expressing neoantigen-specific T cells have been identified in the peripheral blood and correlate with activity of PD-1 inhibitors (56). Similarly, in non-small cell lung cancer (NSCLC), anti-PD-1 therapy can induce neoantigen-specific T cell responses, and neoantigens have been associated with durable clinical benefits in patients with NSCLC and melanoma treated with checkpoint blockade (44). However, in many instances, tumor mutation burden and prediction of neoantigen burden do not correlate with response to checkpoint blockade (21,25,45,46,57). Whereas neoantigen burden has generally been defined based on epitope prediction algorithms using genomic and transcriptomic data (47), detection of neopeptides via MS is an innovative and evolving approach (27). We utilized MS to detect and quantify the abundance of potential neoantigens in MPM tumors and to quantify protein expression of their corresponding MHC-I and MHC-II HLA proteins. Our data demonstrated that high abundance of a neoantigen, in the presence of high expression of its corresponding MHC protein, underlies the strongest clinical responses to checkpoint blockade and highlights the importance of both neoantigen abundance and neoantigen presentation in clinical responses to these agents.
A limitation of our study is the small number of cases and exclusive number of surgical specimens used to generate the immunologic profile that was applied to both cohorts with earlier disease (surgical cohorts) and those with more advanced disease (treated with checkpoint blockade). However, the high fidelity of mass cytometry and simultaneous evaluation of more than 700 cellular subpopulations may compensate, to some extent, for our modest cohort sizes. Delineation of 2 distinct TiME profiles by comprehensive single-cell analyses demonstrated clinical utility in stratifying OS and in selection of patients for receipt of immunotherapy. Such a newly identified signature could potentially be applied to pretreatment tumor biopsies as a gene expression test or via a single-cell cytometry platform (41). Although prospective validation will be required before clinical adaptation, this signature may be useful for identifying patients who should undergo immune checkpoint blockade (good-TiME) and patients who should undergo alternative systemic therapy (bad-TiME).
In summary, a comprehensive, data-driven investigation of the intratumoral cellular immune system in MPM identified a distinct immunogenic tumor microenvironment that could be represented with a simple molecular signature prognostic for survival and prediction of response to checkpoint blockade. This work further proposes a potentially novel mechanism of response to checkpoint blockade in MPM that may be applicable to other human tumors: the requirement of a high abundance of neoantigens in the presence of high expression of MHC proteins specific for these neoantigens. Although prospective validation will be required before clinical adaptation, this signature may be useful for identifying patients who should undergo immune checkpoint blockade (good-TiME) and patients who should undergo alternative systemic therapy (bad-TiME).

Methods
Patient cohort. A prospectively maintained single institution database was retrospectively reviewed. We enrolled 12 consecutive MPM patients who underwent macroscopic complete resection without preoperative treatment from August 2015 to December 2015 and had enough size of tumor to perform mass cytometry requiring at least 1 cm 3 of tumor, MS requiring snap-frozen tissue, and mRNA microarray requiring snap-frozen tissue. We also tested our TiME signature to the preimmunotherapy tumor biopsies of 10 consecutive MPM patients with advanced and unresectable MPM, whom we treated with anti-PD-1 therapy after they had progressed after treatment with a platinum-based agent and pemetrexed between 2014 and 2016.
Spanning-tree progression analysis of density-normalized events (SPADE) algorithm. TiME was characterized using CyTOF with a 35-antibody panel (Supplemental Table 2) and SCAFFOLD maps (16) were generated. SPADE is a visualization tool that organizes heterogeneous populations of single-cell data into a 2-dimensional (2-D) tree representation based on similarities across user-selected markers (56,57). The nodes of the tree represent clusters of cells that are similar in protein marker expression. SPADE uses the size and color of each node to denote the number of cells and median marker expression, respectively, thereby enabling users to quickly review a high-dimensional parameter space with a 2-D tree display. The branching structure of the tree, or the edge, can be used to infer cellular hierarchies when the tree is built using lineage-related surface markers. We used SPADE to perform density-dependent downsampling for each individual sample separately. We next applied the clustering step to the subset of the downsampled data comprising the overlapping core surface markers measured across downsampled cells in the 12 MPM samples and normal lung and pleura. The number of clusters was set to 500 because the increased number of markers could capture more cell types and branch points. Eventually, 742 nodes were generated from SPADE.
SPADE-based SCAFFOLD map generation as mixture of human-guided knowledge and automated clustering. Total live nucleated cells were used for all analyses. We defined 15 cellular phenotypes (Supplemental Figure 2): CD4 + T cells (CD45 + CD3 + CD4 + ), CD4 + Tregs (CD45 + CD3 + CD4 + CD25 + FOXP3 + CD127 -), CD8 + T cells (CD45 + CD3 + CD8 + ), partially exhausted CD8 + T cells (CD45 + CD3 + CD8 + PD-1 + CTLA-4 + ), monocytes (CD45 + CD3 -HLA-DR + CD14 + CD11c + ), TAM (CD45 + CD3 -HLA-DR + CD68 + CD11b + CD11c -CD123 -), pDCs (CD45 + CD3 -HLA-DR + CD11c -CD123 + CD68 -), cDC (CD45 + CD3 -HLA-DR + CD14 -CD11c + ), neutrophils (CD45 + CD3 -HLA-DR -CD15 + CD56 -), NK cells (CD45 + CD3 -HLA-DR -CD56 + CD15 -), cancer cells (CD45 -Pan-cytokeratin + [CD45 -Pan-CK + ]), cancer stem cells (CD45 -Pan-CK -CD200 -Vimentin -D24 + CD326 + [CD45 -Pan-CK -CD200 -Vim -CD24 + EpCAM + ]), CAFs (CD45 -Pan-CK -Vim + CD200 -), mesothelial cells (CD45 -Pan-CK -CD200 + Vim -), and stromal cells (CD45 -Pan-CK -CD200 -Vim -CD24 -CD326 -). Among the 742 nodes generated from SPADE, a representative node in each phenotype was marked. To facilitate exploration by domain experts of deeper hierarchical inferences by annotating nodes on the tree as known cell types and thereby attributing directionality to specific branches or parts of a branch, cell populations and proteins were rearranged according to unsupervised average linkage hierarchical clustering of all nodes (Supplemental Figure 4). On the basis of the SPADE tree, newly generated hierarchical inferences were combined with the representative node. A cluster of similar pattern of protein expression was regarded as the same phenotype containing the representative node. After the arrangement of all nodes to phenotypes, their connection was displayed with SCAFFOLD maps. SCAFFOLD maps were generated as previously reported (16). Briefly, a graph was constructed by connecting the nodes representing the manually gated landmark populations and then connecting to them the nodes representing the cell clusters, as well as connecting the clusters to one another. Each node was associated with a vector containing the median marker values of the cells in the cluster (unsupervised nodes) or gated populations (landmark nodes). Edge weights were defined as the cosine similarity between these vectors after comparing the results from the implementation of several distance metrics. Each circle represented a node, a population of cells with the same pattern of expression, and the size of the node represented the percentage of cells in each patient. The clusters for all the tissues were combined in a single graph, with edge weights defined as the cosine similarity between the vectors of median marker values of each cluster. All the pairwise distances were calculated. The graph was then laid out using the ForceAtlas2 algorithm in Gephi 0.9.1 (https://gephi.org). In this representation of the cellular immune system of human MPM, each node was a population of cells with a similar pattern of protein expression, and the size of the node correlates with the average number of cells in the corresponding cell population. To overlay the additional samples on the SCAFFOLD map, the position and identity of the landmark nodes was fixed and the clusters of each sample were connected to the landmark nodes as described above.
To investigate the immunogenomic determinants of TiME, we investigated tumor mutational load, neoantigen burden, copy number alteration, diversity of T cell clonality, and neoantigen abundance. The experimental and analytical methods for integrated analyses of CyTOF, MS, and mRNA transcriptome data are available in the Supplemental Methods.
Direct identification and quantification of mutated peptides using MS. Next-generation sequencing data from the BWH cohort (33), the MSKCC cohort (34), and the COSMIC database (http://cancer.sanger. ac.uk/cosmic) (35) were used to generate a reference database containing 2,299 missense mutation sites in 1,885 proteins from 640 MPM tumors where a detected mutation alters the amino acid sequence (Supplemental Figure 7 and Supplemental Table 3). A peptide database containing a 29-amino acid length sequence, which includes point mutation site at the middle of the sequence, was generated. Obtained MS/MS spectra are searched against a customized human RefSeq database containing a mutated protein sequence obtained by DNA sequencing in the Proteome Discoverer 2.1 interface (Thermo Fisher Scientific) with Mascot algorithm (Mascot 2.4, Matrix Science). The peptides identified from the Mascot result file are validated with 5% FDR. An in-house intensity-based absolute quantification (iBAQ) algorithm and integrated peak alignment corrector (iPAC) are used to calculate protein abundance with optimal AUC estimates for the detected peptide peaks. In this way, neoantigen abundance -the intensity (amount) of each neoantigen -is quantified.
The MS raw files from proteome profiling was recalculated against mutated peptide database using Proteome Discoverer 1.4 (PD1.4) with Mascot search engine (2.4.1) with <5% FDR. The AUC from the peptide-containing mutated point was extracted from PD1.4 and used for relative quantification (Supplemental Table 4).
HLA molecular typing. Study samples were HLA genotyped by Luminex-based sequence-specific oligonucleotide typing assays (Supplemental Table 5). Genomic DNA was extracted from cells using the Qiagen EZ-1 machine. Intermediate resolution of all HLA loci (HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1) were performed using the LABType SSO HD kits (One Lambda). Briefly, the DNA was amplified using primers that were specific for each of the 11 HLA loci. The PCR product was biotinylated and denatured to make single-stranded DNA. This DNA was then hybridized to a series of probes specific for nucleotide sequences that were used to define the HLA genes. Each probe was bound to a uniquely fluorescent coded Luminex bead. The mixture was then washed to remove any excess, unbound PCR product. The bound PCR product was labeled with streptavidin conjugated with a PE fluorescent tag (SAPE). The DNA-bead complexes were run on a Luminex 100 platform and analyzed using HLA-FUSION v3.5.6 software.
Prediction of binding affinity of mutated neopeptides to MHC molecules. We then applied standard prediction algorithms of the IEDB (http://www.iedb.org/) (36) for identification of HLA class I (37, 38) and class II predicted (39,40) neopeptides on all missense mutations and measured the AUC in MS, representing the amount of measured peptide ligands, termed neoantigen abundance. According to HLA molecular typing from blood DNA in each patient, the binding affinities of peptides to MHC-I (HLA-A, HLA-B, and HLA-C) and MHC-II (HLA-DRB1/B3/B4/B5, HLA-DP, and HLA-DQ) were estimated.
To choose a MHC-I binding prediction method, the prediction method list box allowed us to choose from a number of MHC-I binding prediction methods: artificial neural network (ANN), stabilized matrix method (SMM), SMM with a peptide/MHC binding energy covariance matrix (SMMPMBEC), scoring matrices derived from combinatorial peptide libraries (Comblib_Sidney2008), Consensus, NetMHCpan, NetMHCcons, PickPocket, and NetMHCstabpan (37,38). The list of peptides was filtered to include 8to 14-mer peptides that bind to HLA-A, HLA-B, and HLA-C allotypes. IEDB Recommended was the default prediction method selection. Based on availability of predictors and previously observed predictive performance, this selection tried to use the best possible method for a given MHC molecule. Currently, for peptide/MHC-I binding prediction for a given MHC molecule, IEDB Recommended used the Consensus method consisting of ANN, SMM, and Comblib if any corresponding predictor was available for the molecule. Otherwise, NetMHCpan was used. This choice was motivated by the expected predictive performance of the methods in decreasing order: Consensus > ANN > SMM > NetMHCpan > Comblib.
To choose a MHC-II binding prediction method, the prediction method list box allowed us to choose between 7 currently implemented MHC class II binding prediction methods: IEDB Recommended, Consensus method, Comblib, NN-align (netMHCII-2.2), SMM-align (netMHCII-1.1), Sturniolo, and NetMHCIIpan (39,40). The list of peptides was filtered to include 15-mer peptides that bind to HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DPA1, HLA-DPB1, HLA-HQA1, and HLA-DQB1 allotypes. The default selection IEDB Recommended was provided. Based on availability of predictors and previously observed predictive performance, this selection tried to use the best possible method for a given MHC molecule.
To consider HLA binding strength relative to its nonmutant (WT) counterpart, peptides with IEDB percentile rank ≤2% affinity to mutant peptide sequence and rank >2% affinity to WT peptide sequence were regarded as potential binders to MHC-I. Among them, peptides with IEDB percentile rank ≤1% affinity to mutant peptide sequence and rank >1% affinity to WT peptide sequence were regarded as those with high affinity to MHC-I, and the others were regarded as those with intermediate affinity to MHC-I (Supplemental Table 6). Peptides with IEDB percentile rank ≤10% affinity to mutant peptide sequence and rank >10% affinity to WT peptide sequence were regarded as potential binders to MHC-II. Among them, peptides with IEDB percentile rank ≤5% affinity to mutant peptide sequence and rank >5% affinity to WT peptide sequence were regarded as those with high affinity to MHC-II, and the others were regarded as those with intermediate affinity to MHC-II (Supplemental Table 7).
Statistics. Student's t tests, paired t tests, χ 2 tests, and Fisher exact tests were used to compare the data. Survival curves were generated with the Kaplan-Meier's method, and intergroup comparisons were performed with the log-rank test. OS was defined as the time from surgery to death. Data were censored when a patient was alive without recurrence at last follow-up. Univariable Cox regression analysis was used to determine what factors were associated with OS. We used multivariable Cox proportional hazards regression analysis to evaluate variables with P values less than 0.2 in univariable analysis (Supplemental Table  9). Statistical significance was accepted for P < 0.05, and all tests were 2-tailed. Biometric Research Branch (BRB) Array Tools were used for statistical analysis of the gene-expression data, and receiver operating characteristics (ROC) and all other statistical analyses were performed with SPSS 24.0 (SPSS Inc.) and R language and software environment (http://www.r-project.org).
Study approval. This study was performed in accordance with an IRB protocol at Baylor College of Medicine (H-36302). All human samples were collected with informed patient consent.

Author contributions
HSL and BMB conceptualized and planned the study. HSL, HJJ, BMB, and DJS contributed to collection of surgical samples and associated clinical information. JZ administered immunotherapy to MPM patients. VVLDR conducted radiology assessment. TMW conducted pathology assessment. HSL, HJJ, JMC, SYJ, and BMB coordinated the data generation and data analysis. HSL and HJJ helped to generate mRNA data from TCGA database. HSL helped to generate mRNA sequencing data from BWH database. HSL and HJJ generated the gene expression data. JMC and SYJ generated the MS data. TT, PTJ, and RHK generated HLA molecular typing. HSL, HJJ, and JSL processed, analyzed, and participated in discussions related to the genomics data with statistics. HSL, HJJ, JZ, JMC, VR, TW, PTJ, JSL, SYJ, FK, DJS, and BMB participated in discussions and provided critical scientific input, analysis suggestions, and logistical support toward the project. HSL and BMB wrote the manuscript.