s2fieldsofstudy: Biology Psychology

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Review Paper Computational Pattern Separation Models of Dentate Gyrus Neural Subpopulation in the Hippocampus

Hippocampus is a part of the brain that has an essential role in memory and learning. It is involved in many cognitive and behavioral phenomena, including the pattern separation process: the ability to distinguish patterns with very high similarity. The present study compared the models of pattern separation in the dentate gyrus of the hippocampus and aimed to investigate the significant cells and factors affecting pattern separation. In this review, we intend to describe the anatomy of the dentate gyrus as a part of the hippocampus, which has an essential role in pattern separation. Other adjacent neural populations are further addressed, too. Models of the dentate gyrus, including neurocomputation and functional, that represent the process of separating patterns in the dentate gyrus are reviewed and analyzed. In this regard, five major models were highlighted and compared from several perspectives. While some models are based on the entorhinal cortex and dentate gyrus regions, others point to the mediation of cornu ammonis (CA3) as well. Models with the lowest cells for pattern separation are addressed first. Finally, inhibition is discussed in the comparison of pattern separation models.

Introduction
ippocampus has a critical role in memory [1] and spatiotemporal cognition [2]. Hippocampus supports cognitive functions such as episodic memory [1][2][3][4], thoughtful responses, recalling past experiences, and representing the temporal order of events [1]. Learning and memory processes in the brain are caused by changes in the neuronal representation of stimuli [5]. Episodic learning triggers specific patterns of neural activities in different places of the brain, leading to longlasting changes in synaptic contact [6]. It is hypothesized that synaptic plasticity, like long-lasting potential (LTP), is the neuronal basis for memory formation [5]. It is suggested that LTP impairment damages subregions in the hippocampus related to learning and memory [6]. Sequence inference in LTP is controlled by the activity of the postsynaptic and presynaptic neurons. This phenomenon is known as metaplasticity [7]. Some studies refer to the hippocampus's role in learning, especially sequential learning [8,9]. This part of the brain is also involved in diseases such as epilepsy and Alzheimer [10][11][12].
The hippocampus includes three main subregions: dentate gyrus (DG), cornu ammonis area 3 (CA3), and CA1 [13]. Each subregion is characterized by available information on biomarker expression and electrophysiological features [14]. The structure of the hippocampus and its different regions' connections are represented in Figure 1.
As is evident in Figure 1, inputs of the hippocampus are received from the entorhinal cortex (EC) [15,16]. DG is the first subregion of the hippocampus that receives incoming information from other parts of the brain [7], mainly from the entorhinal cortex [15,17,18] and the projects resulting in CA3 [15,19,20]. CA3 can further receive homogenous inputs from the dentate gyrus [21].
The primary role of DG is pattern separation which occurs when fired pattern separation out of the network has much less similarity than fired input patterns [22][23][24]. It is believed that pattern completion is related to CA3 [25]. Capability of completing incomplete inputs is named pattern completion [22][23][24]26]. On the other hand, CA1 performs new signal generation by receiving sensory inputs [27].
As mentioned above, diverse and essential phenomena in the learning and memory process are mediated via different hippocampus regions. The substantial role of the hippocampus in many fields of memory, learning, and its involvement in some brain diseases is a declarative reason for more definitive studies of this part of the brain.

Primary cells of the DG and their connections
The dentate gyrus mediates the mnemonic processing of spatially based information [28] and has a principal role in pattern separation [29,30]. DG receives its multiple sensory inputs, including vestibular, olfactory, visual, auditory, and somatosensory, from the perirhinal and lateral entorhinal cortex in conjunction with spatially organized grid cells from the medial entorhinal cortex [28,31].
DG has some essential cells. Several breakdowns of DG cells and their various sections have been represented so far [32]. Figure 2 shows the structure of the primary cells of DG and their connections.
The function of DG in the hippocampus is accomplished via computation, which is called pattern separation [20]. Pattern separation is a process that transforms similar inputs to one without similarity and reports nonoverlapping [22,29].

H Highlights
• Hippocampus has an essential role in memory, especially in episodic and spatial memory.
• The capability of distinguishing related episodes, the main feature of episodic memory, is called pattern separation and is attributed to the dentate gyrus in the hippocampus.
• The essential participant cells in pattern separation are granule cells, mossy cells, interneurons, and hilar perforant path-associated cells.
• Inhibition and neurogenesis have significant effects on pattern separation and its facilitation.

Pattern separation models of DG
For a precise explanation of pattern separation, much research has been performed, and various models have been presented [3,7,33,34]. Several expositions and comparisons of these DG computational models are presented in this section. All models of DG aim to investigate the pattern separation in the hippocampus of the brain. Another essential purpose is to determine cells and sections of the DG that participate in the pattern separation.

Model 1
Four primary cells, i.e., granule cell, mossy cell, hilar perforant path associated (HIPP), and inhibition interneurons, such as basket cells, are components of this model. The Model cycle is represented as entorhinal cortex-dentate gyrus (EC-DG) by the perspective of competing parts. Briefly, the following operations were performed. The inputs to DG from the perforant path exhibit the granule cells ordered in multiple layers. Then inhibition interneurons employ their inhibition on granule cells. The most robust granule cells make other cells silent. This competitive model is called a winnertake-all. Granule cells project their effect on mossy cells, and in turn, mossy cells excite granule cells. HIPP cells receiving their input from the perforant path have an inhibition influence on granule cells. In the implementation phase of the model, 500 granule cells (1/2000 out of one million granule cells in rats) were classified into 25 clusters. There is one interneuron in each cluster that is activated by granule cells in the same cluster, and in turn, it creates inhibition for granule cells.
According to the winner-take-all policy, there is one winner in any cluster, and in total, 25 cells of 500 cells remain active. There are 1200 HIPP cells in the model. Inputs come from 100 afferents and have a ratio of 1:5 in granule cells. Simplicity and a small number of free parameters are benefits of the model. Although this model does not contain the physiological and anatomical complexity, it allows us to test the theory that there are a few essential keys in the DG network for gathering some aspects of empirical data [13].

Model 2
This model [35] includes the CA3 region. The cycle of the model is EC-DG-CA3. In this model, CA3 has a back projection to DG, which is the same as Model 1. Still, a simple automatic associative memory is constructed from CA3, which is capable of providing and saving the pattern, then recalling and allowing it to have a treat with DG. The implementation of the model contains 1000 granule cells in 10 layers. Inhibition of interneuron baskets and axo-axonic cells influence each layer. The current model includes 30 mossy cells, which gives a projection from granule cells as in Model 1 and sends the excitatory effect back. The number of HIPP hilar cells in this model is 12, which gives inputs from the Perforant Path (PP) and creates an inhibitory influence on granule cells. There are 200 inputs from PP in this model.
The critical point is that in Model 1, the output is assumed to be depolarized, but here, DG's output is verified as the firing granule cells. Each granule cell is silent or produces potential action [35].

Model 3
The structure of this model [26] resembles previous models in the case of a functional role in each region. The current model's cycle is EC-DG-CA3, as in Model 2. In this model, in addition to granule cells and interneurons in DG, CA3 includes interneurons and pyramidal cells.
Since pattern separation needs pairing sparse connections in the DG-CA3 path that provides an index for CA3's population, in this model, relationships among sections are produced so that sparse activity of CA3 cells is resumed via the mossy fiber system of DG-CA3. For implementing this model, the number of cells estimated by the adult rat brain is reduced by a factor of 1000. Two different types of inhibitory neurons were included. Six inhibitory cells in DG received excitatory input. An inhibitory cell in CA3 received input from the local pyramidal cell.
The model contains two inhibition neuron types: six neurons in DG and one inhibition CA3 cell, which give input from local pyramidal cells and operate to repress CA3 network activity. There are two reasons for the inhibition of CA3 in the model. First, it ensures that the low diversity in DG activity does not result in extreme variety in CA3 activity. Second, it acts as a quantity threshold. If an adequate amount of CA3 cells respond to patterns directly, the CA3 inhibition cell signals it as a known one.
In this model, unlike the previous one, which attempts to balance learning against recalling, each represented pattern gets learned or remembered (denoting its novelty) [26].

Model 4
This model represented the exploration of hilar in which the ectopic granule cells affect the pattern separation as in the DG-CA3 cycle. In this model, the influences of CA3 are also mediated and illustrate that the anatomical and physiological structures of DG and CA3 are compatible with pattern separation and pattern completion. In complex DG and CA3 cells, the pyramidal cells in CA3 containing back-projection, play a significant role in the sparse firing of granule cells. As mentioned above, this model is based on models 1 and 2, to which the influence of ectopic hilar is added exclusively [36].
The implementation of this model is based on the model [37], including ten layers, each one with 100 granule cells, one interneuron, three fiber cells, and one HIPP cell. In CA3, each layer includes 30 pyramidal cells and  . The trail starts with the activation of one subset of these inputs and a representation of them to DG and CA3. The trail terminated with the reading of DG and CA3's outputs. Outputs can be supra-threshold or sub-threshold-two output patterns exit. One of them represents the outputs of DG, a vector of zero or one that implicates produced action potentials in granule cells. The other one is the output of CA3, a similarity vector that produces pyramidal cell actions [36].

Model 5
This model was offered for evaluation if dendrites participate in pattern separation and how this participation is. This model [29] has been developed based on Model 1 and has four substantial cells: granule cells, mossy cells, basket cells, and HIPP. The number of granule cells' dendrites affects pattern separation; two approaches are used for assessment: pruning and growth of dendrites.
Firstly, two granule cell models were implemented that differ only in the number of dendrites, but their path length is identical. Then, two granule cell models were performed, which vary in quantity and the length of the dendrite's path. For this implementation, the 2000 granule cells were simulated. This population perch in clusters without any overlapping. Each cluster contains 20 granule cells. There are 80 mossy cells, and 40 HIPP were considered in the model. Network inputs were received from 400 afferents [29].

Comparing pattern separation models
As seen from the comparison of the models of DG, only Model 1 and Model 2 agree with a cycle of EC-DG for pattern separation in the hippocampus. The other models consider that CA3 participated in the pattern separation process.
The comparison of essential features of these models is displayed in Table 1 These models of pattern separation can be implemented in various fields, such as modeling networks for pattern recognition [38]. In research [38], an artificial neural network has been proposed based on Model 1 [13], which is used for handwritten recognition. The proposed network was evaluated with six datasets of digits and characters from five languages. Experiments on all of the used datasets showed promising results. Therefore, modeling the functionality of dentate gyrus pattern separation can be considered a research field in various categories.Neurogenesis Role in the Pattern Separation Hippocampal neurogenesis is a process in which primary neurons are produced in DG and functionally snuggle and integrate into hippocampal circuits in the adult mammal brain [39]. Granules are the cells that undergo neurogenesis in the adult brain [40]. These granule cells are produced in mammal brains during life. This phenomenon is called postal neurogenesis [41][42][43].
Some findings show that adult-born DGs are hyperexcitable between weeks 2 and 6 of their age, meaning that these granule cells are especially adequate for pattern separation [44][45][46]. Granule cells are generated from precursors in the subgranular zone and normally migrate a short distance to the adjacent granule cells layer [37]. Adult-born granule cells (abGC) are categorized in two parts: mature abGCs and immature ones [47,48]. Adult-born GCs migrate via the mature process in several weeks [49], in which immature GCs get to develop the properties of GC [22]. During the activity of these abGCs, mature abGC's populations will be more active than mature GCs [50]. Also, neurogenesis migrated to hilus in the opposite path [41]. Adding a small population of hEGCs (5% of all GSs) with observable empirical features is adequate for discounting pattern separation and pattern completion. Results show that hEGCs effects are essentially due to the back-projection of axons of CA3 pyramidal cells to hilus [41]. Nevertheless, do adult-born neurons have an inhibition feature? huHong Liu et al. (2003) showed that some adult-born neurons have inhibitory effects [51]. Bettina Seri et al. (2005) showed that no inhibitory adult-born cells had been observed in DG. Despite all research in this field, there is a question: Is neurogenesis directly mediated in pattern separation? [52]. Based on the model of Aimone et al., neurogenesis increases the pattern separation for events that occur at a distinct time (pattern separation), but learned patterns have a gradation of similarity (pattern integration). This model is represented based on the biological model of EC-DG [53]. This model predicts that mature DGCs construct a distinct intersection for DG's separation features.

Conclusion
Many studies reported the role of DG as one of the essential sections of the hippocampus. This study has especially focused on pattern separation research. The represented models of pattern separation in the DG of the hippocampus are delineated and compared.
The comparison was performed from several perspectives. While some models were based on EC and DG regions, others believe in the mediation of CA3 as well. Models with the lowest cells for pattern separation were based on granule, mossy, and HIPP cells. In the other models, interneurons of DG, interneurons, and pyramidal cells of CA3 were added for pattern separation. In one model, only HIPP cells are represented for inhibition. Some models also added interneurons for the inhibition of pattern separation. One model performed the inhibition with interneurons of the DG and interneurons of the CA3 without HIPP cells, and one model suggested DG's interneurons, HIPP's DG, and interneurons of CA3 for inhibition. This model suggested the largest number of cells for inhibition.
Sparse connections in mossy fiber facilitate the pattern separation process. Sparse coding is performed in different fired places of mossy cells, which is performed by GCs. On the other hand, inhibition is critical for pattern separation. As can be seen, inhibition generally improves pattern separation. Neurogenesis has a significant effect on DG's pattern separation.

Compliance with ethical guidelines
All study procedures were conducted following the ethical guidelines of the Declaration of Helsinki 2013.

Funding
This research did not receive any grant from funding agencies in the public, commercial, or non-profit sectors. This study was extracted from PhD thesis with code 1154803215556270000162376240

Authors contributions
All authors contributed to conceptualization and supervision, methodology, investigation, original draft writing, review, and editing, funding acquisition, and resources.

Conflict of interest
The authors declared no conflict of interest.


s2fieldsofstudy: Biology Medicine

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Inhibition of high-fat diet–induced obesity via reduction of ER-resident protein Nogo occurs through multiple mechanisms

Obesity is a risk factor for insulin resistance, type 2 diabetes, and cardiovascular diseases. Reticulon-4 (Nogo) is an endoplasmic reticulum–resident protein with unclear functions in obesity. Herein, we investigated the effect of Nogo on obesity and associated metabolic disorders. Human serum samples were collected to explore the relationship between circulating Nogo-B and body mass index value. Nogo-deficient and WT littermate control mice were fed normal chow or high-fat diet (HFD) for 14 weeks, and HFD-induced obese C57BL/6J mice were injected scrambled or Nogo siRNA for 2 weeks. We found that in human and mouse serum, Nogo-B was positively correlated to body mass index/bodyweight and lipid profiles. Reduced Nogo (by genetic deletion or siRNA transfection) protected mice against HFD-induced obesity and related metabolic disorders. We demonstrate that Nogo deficiency reversed HFD-induced whitening of brown adipose tissue, thereby increasing thermogenesis. It also ameliorated lipid accumulation in tissues by activating the adiponectin–adiponectin receptor 1–AMP-activated kinase α signaling axis. Finally, Nogo deficiency potently reduced HFD-induced serum proinflammatory cytokines and infiltration of macrophages into metabolic organs, which is related to enhanced NF-κB p65 degradation via the lysosome pathway. Collectively, our study suggests that reduced levels of Nogo protect mice against HFD-induced obesity by increasing thermogenesis and energy metabolism while inhibiting NF-κB-mediated inflammation. Our results indicate that inhibition of Nogo may be a potential strategy for obesity treatment.

Obesity is a risk factor for insulin resistance, type 2 diabetes, and cardiovascular diseases. Reticulon-4 (Nogo) is an endoplasmic reticulum-resident protein with unclear functions in obesity. Herein, we investigated the effect of Nogo on obesity and associated metabolic disorders. Human serum samples were collected to explore the relationship between circulating Nogo-B and body mass index value. Nogo-deficient and WT littermate control mice were fed normal chow or high-fat diet (HFD) for 14 weeks, and HFD-induced obese C57BL/6J mice were injected scrambled or Nogo siRNA for 2 weeks. We found that in human and mouse serum, Nogo-B was positively correlated to body mass index/bodyweight and lipid profiles. Reduced Nogo (by genetic deletion or siRNA transfection) protected mice against HFD-induced obesity and related metabolic disorders. We demonstrate that Nogo deficiency reversed HFD-induced whitening of brown adipose tissue, thereby increasing thermogenesis. It also ameliorated lipid accumulation in tissues by activating the adiponectinadiponectin receptor 1-AMP-activated kinase α signaling axis. Finally, Nogo deficiency potently reduced HFD-induced serum proinflammatory cytokines and infiltration of macrophages into metabolic organs, which is related to enhanced NF-κB p65 degradation via the lysosome pathway. Collectively, our study suggests that reduced levels of Nogo protect mice against HFD-induced obesity by increasing thermogenesis and energy metabolism while inhibiting NF-κB-mediated inflammation. Our results indicate that inhibition of Nogo may be a potential strategy for obesity treatment.
Obesity has become a public health issue that impairs the quality of life, raises health care costs, and increases the risk of health complications, including insulin resistance, type 2 diabetes, and cardiovascular diseases. The prevalence of obesity was 10.8% in men and 14.9% in women in 2014 and will increase to 18% and 21% by 2025 (1). The rising obesity prevalence calls for effective strategies for prevention and/or treatment. Several approved drugs, such as lorcaserin, phentermine, and liraglutide, are not effective enough while having severe side effects (2). Therefore, exploring effective and safe strategies for obesity treatment is still an urgent need.
Obesity can cause chronic low-grade inflammation in multiple organs, including adipose tissue, liver, skeletal muscle, and pancreatic islets (3)(4)(5). In turn, the inflammation in these tissues can aggravate obesity and obesity-associated complications, leading to vicious cycles. NF-κB is a key transcription factor controlling inflammatory process. NF-κB family consists of RelA/p65, RelB, c-Rel, p50/NF-κB1, and p52/NF-κB2 in mammals. They bind to target DNA as homodimers or heterodimers, of which the p65 and p50 heterodimers is the most common one (6). In physiological state, NF-κB locates in cytoplasm inactively. Once stimulated, it can translocate into nucleus and trigger the transcription of target genes including interlukin-1 (IL-1), IL-6, and interferon-γ (7). NF-κB is deeply involved in the development of obesity and its complications (7). Deficiency of NF-κB p50 protects mice against high-fat diet (HFD)-induced obesity, insulin resistance, and liver steatosis (8). Liver-specific NF-κB p65 deletion ameliorates HFDimpaired insulin sensitivity (9). IL-1β, a target of NF-κB, is highly associated with human body mass index (BMI) and fasting blood glucose levels (10). Inactivation of IL-1β protects mice against HFD-induced glucose tolerance and improves insulin secretion in type 2 diabetes patients (11,12).
Reticulon-4 (Nogo) family consists of several members, including Nogo-A, Nogo-B, and Nogo-C. The expression of Nogo family members is in a tissue-dependent manner. Nogo-A is mainly expressed in central nervous system, whereas Nogo-B is widely expressed in various tissues (13). Nogo-B is the only member detectable in liver and circulation (14). Several biological functions of Nogo have been reported. For instance, it influences ischemic injury and alcoholic liver disease by regulating macrophage infiltration and Kupffer cell polarization in mice (15,16). Recently, we reported that reduced Nogo expression protected mice against highcarbohydrate diet-induced hepatic steatosis and insulin resistance by regulating carbohydrate-responsive elementbinding protein and insulin activity (14). These studies imply that Nogo can play an important role in different types of metabolic disorders. Obesity is contributed by different factors, such as chronic inflammation, imbalance between energy intake, and expenditure. Thus, we investigated if inhibited Nogo expression can reduce HFD-induced obesity by multiple mechanisms.
To determine if tissue Nogo-B expression can be regulated by different dietary conditions, mice were fasted for 24 h and then refed normal chow for 12 h. Figure 1D shows that Nogo-B expression in liver, epididymal white adipose tissue (eWAT), and skeletal muscle was all increased by refeeding. Feeding mice an HFD (35% fat) or a high-cholesterol diet (21% fat plus 0.5% cholesterol) for 1 week also substantially upregulated Nogo-B expression in these tissues (Fig. 1, E and F). Thus, Nogo-B in circulation or tissues with high metabolic activity are increased with obesity or food containing high lipids.
Compared with normal chow, HFD feeding substantially increased bodyweight in both littermate Ctrl and Nogo −/− mice. However, bodyweight gain was much less in Nogo −/− than WT mice, particularly by HFD (Fig. 2, A and B), indicating that Nogo deficiency inhibits HFD-induced obesity. No difference of food intake or excretion of TG and T-CHO in feces between two groups was determined, which confirms that the reduced bodyweight gain in Nogo −/− mice is not Figure 2. Nogo deficiency ameliorates HFD-induced obesity, glucose intolerance, and insulin resistance. Nogo-deficient (Nogo KO, Nogo −/− ) and littermate control mice (Control or Ctrl) (males, 6 weeks old) were fed normal chow (NC) or HFD for 14 weeks. A, during the treatment, mouse bodyweight was determined weekly. B and H, at the end of experiment, mice were photographed; bodyweight and weight of the collected tissues (liver, eWAT, sc-WAT, and iBAT) were determined (n = 8). During the experiment (15 weeks old or the ninth week of experiment), food consumption (C), triglyceride (D), and T-CHO (E) in feces were determined (n = 5). F and G, GTT or ITT was conduced on the mice at the 12th or 13th week of the experiment (n = 8). *p < 0.05, **p < 0.01, ***p < 0.001 versus control NC-fed mice; # p < 0.05, ## p < 0.01, ### p < 0.001 versus control HFD-fed mice. AUC, area under curve; eWAT, epididymal white adipose tissue; GTT, glucose tolerance test; HFD, high-fat diet; iBAT, interscapular brown adipose tissue; ITT, insulin tolerance test; ND, under detectable; Nogo, reticulon-4; sc-WAT, subcutaneous white adipose tissue; T-CHO, total cholesterol. Inhibition of Nogo reduces obesity because of food consumption or absorption (Fig. 2, C-E). Using indirect calorimetry, we found that Nogo deficiency significantly increased body oxygen consumption and energy expenditure, whereas it had little effect on mice physical activity under HFD condition (Fig. S2). Collectively, aforementioned results indicate that Nogo deficiency ameliorates HFD-induced obesity by enhancing energy metabolism. Table S1 shows that Nogo deficiency significantly reduced postprandial hyperglycemia regardless of food types. Correspondingly, the obesity-induced glucose intolerance and insulin resistance were markedly ameliorated in Nogo −/− mice (Fig. 2, F and G), indicating that Nogo deficiency greatly improves HFD-impaired blood glucose homeostasis and insulin sensitivity.

Nogo deficiency promotes healthy expansion of adipose tissue
Under the overnutrition, adipose tissue can expand rapidly to maintain nutrient homeostasis. HFD enlarged adipocytes in both WT and Nogo −/− mice with reduced effect on Nogo −/− mice (Fig. 3, A and B), indicating less lipid accumulation in eWAT of Nogo −/− mice. Adiponectin, an important adipokine secreted by adipose tissue, can improve insulin sensitivity and energy metabolism by activating AMP-activated kinase α (AMPKα) (18). Leptin, another major adipokine, is positively correlated to the bodyweight. Nogo −/− mice showed elevated adiponectin mRNA expression associated with increased circulating high-molecular weight (HMW) adiponectin (Figs. 3C and S3A). Nogo deficiency also reduced leptin mRNA level in eWAT under HFD condition (Fig. 3D).
Adipose tissue expansion is not always accompanied by detrimental pathological changes. For instance, that featured by enhanced angiogenesis and reduced fibrosis is regarded as a healthy adipose tissue expansion (19,20). Interestingly, Nogo deficiency activated expression of vascular endothelial growth factor β (Vegf-β), an angiogenesis-related gene, in HFD-fed mouse eWAT (Fig. 3E). Collagen VI is the main form of collagen produced by adipocytes (21). Nogo deficiency reduced HFD-activated collagen type VI α2 expression in eWAT (Fig. 3F). Correspondingly, the results of Sirius red staining further confirm reduced fibrosis in eWAT of Nogo −/− mice (Fig. 3G). Similarly, reduced adipocyte size, leptin, collagen type VI α1, and increased adiponectin and Vegf-β, were determined in the subcutaneous white adipose tissue (sc-WAT) of HFD-fed Nogo −/− mice (Fig. 3, H-N). Therefore, a healthy expansion occurs to Nogo −/− mouse adipose tissues under HFD feeding.
To investigate the regulation of Nogo on aforementioned phenotypes, we first detected Nogo protein expression in adipocytes and stromal vascular fraction (SVF) from eWAT. As shown in Fig. S4A, Nogo protein levels in adipocytes and SVF were comparable and can be induced by HFD. Then, we constructed Nogo-deficient 3T3-L1 cells and found that Nogo deficiency attenuated palmitate-reduced adiponectin mRNA levels, whereas had little effect on Vegf-β mRNA expression in 3T3-L1-derived adipocytes (Fig. S4, B-D). In addition, the induction of transforming growth factor beta on collagen type VI α1 and collagen type VI α2 mRNA expression was ameliorated by Nogo deficiency (Fig. S4, E and F). Taken together, the effects of Nogo deficiency on adiponectin and collagen type VI α1 or α2 expression are partially dependent on the direct deficiency of Nogo in adipocytes.
Nogo deficiency enhances browning of adipose tissue BAT, a metabolically active tissue, supports energy metabolism by converting lipids into heat. The high expression of Nogo-B in iBAT indicates that BAT may be a critical tissue affected by Nogo deficiency (Fig. S1A). HFD increased iBAT weight moderately in WT mice (45%) but potently in Nogo −/− mice (190%) (Fig. 2H). The increase of iBAT weight can be due to the browning of adipocytes and/or enlarged adipocyte size by lipid accumulation. The H&E staining of iBAT sections demonstrates that HFD increased iBAT adipocyte size in both WT (120%) and Nogo −/− mice (80%), indicating lipid accumulation occurred to cells. However, the average adipocyte size in HFD-fed Nogo −/− mice was smaller than that in WT mice (Fig. 4A), suggesting that more brown adipocytes are conserved, or obesity-induced iBAT whitening is reversed in HFD-fed mice by Nogo deficiency. Consistently, mRNA levels of brown adipocyte markers (uncoupling protein-1 [Ucp-1]; T-box transcription factor 1; and cell death-inducing DNA fragmentation factor-alpha-like effector α) were upregulated in HFD-fed Nogo −/− mice (Fig. 4B). HFD-decreased UCP-1 expression was partially restored by Nogo deficiency (Fig. 4, C and D).
BAT stores lipid mainly in TG form and consumes fatty acids as thermogenic substrates (22). Therefore, we evaluated expression of TG hydrolysis-related genes (hormone-sensitive lipase and adipose TG lipase) and fatty acid oxidation-related genes (carnitine palmitoyltransferase 1β; cytochrome C oxidase subunit 5b and 8b; and NADH:ubiquinone oxidoreductase subunit b2) in the tissue and observed that these genes were all notably upregulated in HFD-fed Nogo −/− mice (Fig. 4, E and F).
To further confirm the role of Nogo in adipocyte browning and thermogenesis, we performed the cold adaptation experiment, in which both WT and Nogo −/− mice were housed in 24 h cycles of 12 h at 4 C and another 12 h at room temperature for 1 week. Compared with mice under room temperature (Fig. S1A), Nogo-B protein levels were much lower in iBAT and sc-WAT than eWAT under cold exposure condition ( To determine whether Nogo regulates thermogenesis in a cell autonomous mechanism, we first detected Nogo protein levels in adipocytes and SVF from iBAT. As shown in Fig. S5B, Nogo-B protein expression was much higher in adipocytes than SVF. In vitro, we transfected C3H10T1/2 cell-derived brown adipocytes with Nogo siRNA or Nogo expression vector, followed by isoproterenol treatment (a β-adrenoreceptor agonist that can stimulate thermogenesis). The results in Fig. S5, C and D showed that Nogo knockdown increased UCP-1 expression, whereas Nogo overexpression reduced UCP-1 protein levels. Thus, Nogo regulates thermogenesis in a cell-autonomous manner, at least partly.
Consistent with the results in in vivo study, protein levels of AdipoR1 and p-AMPKα in Nogo-deficient HepG2 (Nogo-Cas9) cells were also higher than Ctrl-Cas9 HepG2 cells, and the activation of AMPKα by AdipoRon (AdipoR1 agonist) was abolished in Nogo-Cas9 cells by AdipoR1 siRNA (Fig. S3, B and C), indicating that Nogo deficiency promotes adiponectininduced p-AMPKα in an AdipoR1-dependent manner. Nogo deficiency also restored HFD-reduced PGC-1α and phosphorylated AKT (p-AKT) protein in mouse liver (Fig. 5G). These data suggest that Nogo deficiency promotes lipid oxidation through adiponectin-AdipoR1-AMPKα pathway, thereby improving obesity-induced metabolic disorders in the liver.
Obesity also elevates blood lipid levels and enhances lipid uptake in adipose tissue and skeletal muscle. The excess ectopic lipid accumulation in skeletal muscle can influence tissue functions (24). Similar to the liver (Fig. 5B), Nogo deficiency also reduced HFD-induced TG accumulation in skeletal muscle (Fig. 5I). PGC-1α is a key regulator of energy metabolism and mitochondrial biogenesis (25). Nuclear respiratory factor 1 is a main target of PGC-1α in this process. Compared with WT mice, Nogo deficiency activated PGC-1α protein expression (Fig. 5J). mRNA levels of nuclear respiratory factor 1 and mitochondrial lipid oxidation-related genes (carnitine palmitoyltransferase 1β; aconitase 2; and cytochrome C oxidase subunit 4 isoform 1) were also activated by Nogo deficiency in HFD-fed mouse skeletal muscle (Fig. 5, K and L). In addition, Nogo deficiency increased p-AMPKα, PPARα, and AdipoR1 expression in the tissue (Figs. 5M and S3, E and F). HFD feeding increased protein kinase C ε, an inhibitor of insulin signaling, which was substantially blocked by Nogo deficiency (Fig. 5M). Therefore, Nogo deficiency also ameliorates HFD-induced lipid accumulation and insulin resistance in mouse skeletal muscle.

Nogo deficiency attenuates systemic inflammation
The effect of Nogo deficiency on systemic inflammation was determined by a protein array assay (Fig. 6A). HFD feeding increased levels of various proinflammatory cytokines in WT mice serum. However, Nogo deficiency substantially blocked HFD-induced expression of these molecules (Figs. 6A and S7).
Crown-like structure, a histological hallmark of obesityassociated adipose tissue inflammation, is composed of macrophages surrounding adipocytes. Figure 6B shows that Nogo deficiency reduced the number of crown-like structure in eWAT. HFD-activated infiltration of macrophages into liver, iBAT, and islet was also significantly decreased by Nogo deficiency (Fig. 6, C-E). Moreover, the percentages of antiinflammatory M2 macrophages in eWAT and iBAT were increased in HFD-fed Nogo −/− mice, associated with reduced proinflammatory M1 macrophages (Fig. 6, F-I). These results suggest that Nogo deficiency decreases expression of proinflammatory cytokines, prevents macrophages from being recruited into metabolic organs, and drives polarization of macrophages into M2 phenotype.

Inhibition of Nogo reduces obesity
NF-κB p65 is the main subunit of NF-κB protein. As shown in Figure 7, A and B, lack of Nogo expression not only reduced the basal level of NF-κB p65 but also potentially blocked HFD-activated NF-κB p65 protein expression in mouse liver, WAT, skeletal muscle, iBAT, pancreas, and macrophages in eWAT. Associated with changes in NF-κB p65 protein expression, we determined that Nogo deficiency reduced nuclear NF-κB p65 protein levels in mouse liver (Fig. 7C). Consistent with reduced NF-κB p65 activation, HFD-activated mRNA expression of inflammation cytokines (Il-1β, tumor necrosis factor alpha [Tnf-α], and Il-6) and macrophage chemokine (monocyte chemoattractant protein 1) was decreased in Nogo −/− mice in HFD condition (Figs. 7D and S8A). To further determine the effect of NF-κB p65 on Nogo deficiency-inhibited inflammation cytokine expression and macrophage infiltration, we transfected Ctrl (Ctrl-Cas9) and Nogo-deficient (Nogo-Cas9) HepG2 cells with NF-κB p65 expression vector, followed by treatment with palmitate (mimic high-fat environment). Consistent with the in vivo results, Nogo deficiency blocked palmitate-activated mRNA expression of IL-1β, IL-6, and monocyte chemoattractant protein 1 (Fig. 7E). However, these inhibitory effects of Nogo deficiency were abolished by NF-κB p65 overexpression (Fig. 7E). Taken together, the amelioration of Nogo deficiency on obesity-induced inflammation and macrophage infiltration is dependent on NF-κB p65 inactivation.
IκB can bind to NF-κB and trap it in the cytoplasm. To investigate the underlying mechanisms by which reduced Nogo inactivates NF-κB, we detected IκB and NF-κB p65 protein expression in Ctrl and Nogo-deficient peritoneal macrophages. As shown in Fig. S8C, Nogo deficiency significantly decreased total NF-κB p65 but not IκB levels, indicating that Nogo may regulate NF-κB p65 nuclear translocation by affecting its total protein expression rather than IκB. The results in Figure 8A showed that NF-κB p65 was increased in 293T cells by high-expressing Nogo-B (C2-Nogo-B) and reduced in Nogo-deficient HepG2 cells (Nogo-Cas9), further confirming that Nogo can influence total NF-κB p65 protein level. Interestingly, neither Nogo high expression nor deficiency had effect on NF-κB p65 mRNA expression (Figs. 8B and S8D), suggesting that regulation of NF-κB p65 protein expression by Nogo is completed at the post-transcriptional level. To determine if Nogo can regulate NF-κB p65 protein stability, we treated HepG2 cells with cycloheximide, a potent inhibitor of cellular protein biosynthesis, and found that NF-κB p65 protein degradation was increased in Nogo-deficient HepG2 cells (Fig. 8C), indicating that Nogo deficiency reduces NF-κB p65 protein stability or enhances its degradation, which can be completed by activating proteasome and/or lysosome. To further verify the role of proteasome or lysosome in Nogo deficiency-enhanced NF-κB p65 protein degradation, we treated cells with a proteasome inhibitor (MG132) or a lysosome inhibitor (NH 4 Cl) for different time and determined changes of NF-κB p65 protein. Compared with Ctrl cells, highexpressing Nogo-B did not further increase MG132-increased NF-κB p65 protein levels, indicating Nogo deficiency does not activate proteasome (Fig. 8D). Meanwhile, NH 4 Cl treatment antagonized Nogo deficiency-enhanced NF-κB p65 protein degradation (Fig. 8E), suggesting that Nogo deficiencyactivated lysosome is blocked. Therefore, Nogo deficiencyenhanced NF-κB p65 protein degradation is mainly completed by activating lysosome pathway.

Nogo siRNA ameliorates obesity in mice
To determine if inhibited Nogo can reduce obesity and associated metabolic disorders, we initially induced obesity in C57BL/6J mice by 25 weeks of HFD feeding and then injected the obese mice Ctrl siRNA (si-scramble) or Nogo siRNA (si-Nogo) five times (once every 3 days). Compared with Ctrl siRNA, Nogo siRNA constantly reduced mouse bodyweight but increased body temperature (Fig. 9, A and B). Both liver weight and TG levels were reduced by Nogo siRNA (Fig. 9, C and D). Nogo siRNA also improved HFD-induced hypertriglyceridemia (Fig. 9D) without effect on serum cholesterol levels (Table S2) or muscle TG levels (Fig. 9D). In addition, mice-injected Nogo siRNA displayed increased iBAT weight (Fig. 9C). Associated with reduced lipid accumulation and increased thermogenesis in si-Nogo-injected mice, expression of lipid oxidation-related proteins, such as p-AKT, p-AMPKα, and PGC-1α, in liver and skeletal muscle, was substantially upregulated (Fig. 9, E and F). Nogo siRNA injection also downregulated NF-κB p65 protein levels (Fig. 9G) without affecting its mRNA levels in tissues (Fig. S9A). Nuclear translocation of NF-κB p65 was attenuated in si-Nogo-injected mouse liver (Fig. 9H), which was associated with decreased mRNA expression of inflammatory cytokines (Il-1β, Tnf-α, and Il-6) and M1 macrophage marker (inducible NO synthase) in liver, skeletal muscle, and WAT, and increased expression of M2 macrophage marker (arginase 1) (Fig. 9I). Therefore, these results indicate that inhibited Nogo can ameliorate established obesity by regulating energy metabolism and inflammation.
Obesity is associated with imbalance between energy intake and expenditure. Reducing food intake or nutrient absorption and increasing energy consumption can restrain obesity development. For example, most of the approved drugs for obesity treatment can increase satiety or reduce food absorption (2). In this study, we determined that neither food (normal chow or HFD) intake nor secreted TG and T-CHO in feces was altered by Nogo deficiency (Fig. 2, C-E). While, increased oxygen consumption and energy expenditure were observed in HFD-fed Nogo −/− mice (Fig. S2, A and C). Taken together, inhibition of HFD-induced obesity by Nogo deficiency should be related to enhanced energy metabolism.
Activation of AMPKα increases lipid oxidation and insulin sensitivity, whereas inhibits lipogenesis, thereby reducing obesity development (27). Nogo deficiency activated AMPKα, promoted expression of lipid oxidation-related genes, and attenuated TG deposition in liver and skeletal muscle (Fig. 5). Circulating adiponectin secreted from adipose tissue exists as multimeric complex, such as a lower molecular weight hexamer and/or HMW larger multimeric structures (12-36 multimers). Increased serum HMW adiponectin is primarily associated with enhanced insulin sensitivity, reduced abdominal fat, and high basal lipid oxidation in humans (28). Promotion of adiponectin multimerization protects mice against diet-induced obesity and insulin resistance (29). Our results showed that Nogo deficiency increased circulating HMW adiponectin levels and AdipoR1 protein expression, associated with reduced lipid content in tissues and circulating TG levels, particularly under HFD feeding (Figs. 5 and S3A, E, and F; Table S1), indicating Nogo deficiency activates AMPKα through adiponectin-AdipoR1 axis, which was further confirmed by the in vitro study (Fig. S3, B and C).
Fibroblast growth factor 21 (FGF-21) plays an important role in regulating metabolic homeostasis. Inhibition of Nogo expression reduced high-carbohydrate diet-induced fatty liver, insulin resistance, and other metabolic disorders, which is partially attributed to Nogo deficiency-activated liver FGF-21 expression and circulating FGF-21 (14). In this study, although Nogo deficiency can enhance HFD-increased circulating FGF-21 levels (Fig. S3G), it attenuated HFD-induced liver FGF-21 expression (Fig. S3H). The different regulation of FGF-21 expression by Nogo deficiency between HFD and highcarbohydrate diet feedings might be related to inactivated carbohydrate response element binding protein and de novo lipogenesis under high-carbohydrate diet feeding (14).
The thermogenic activity of BAT and beige cells is also related to obesity development (30). Compared with other cell types, both BAT and beige cells dissipate chemical energy as heat, thereby consuming more lipids to generate the same amount of ATP. Reduced BAT in obese population suggests that promoting the browning of adipose tissue can be a  . Nogo siRNA ameliorates HFD-induced bodyweight gain and associated metabolic disorders in mice. After 25 weeks of HFD feeding, the obese mice (C57BL/6J, male, 31 weeks old) were divided into two groups (five mice per group) randomly and received tail vein injection of Nogo siRNA (Nogo KD) or scrambled siRNA (control) once every 3 days for five times. A, during the treatment, mouse bodyweight was determined at the indicated time points, and the average bodyweight change was calculated. At the end of experiment (3 days after the last siRNA injection), mouse body temperature was measured (B). After sacrifice, mouse tissues were collected for the following assays. Mouse liver, iBAT, and WAT were weighed and calculated as percent of tissue weight in bodyweight (C); TG levels in liver, serum, and skeletal muscle were determined by the assay kit or the automatic biochemical analyzer (D). Protein expression of Nogo-B, p-AMPKα, AMPKα, p-AKT, AKT2, and PGC-1α in liver or skeletal muscle (E and F), NF-κB p65 in liver, skeletal muscle, and WAT whole extract (G), liver nuclear or cytosolic extract (H) was determined by Western blot with quantitative analysis of band density; expression of Il-1β, Tnf-α, Il-6, Arg-1, and iNOS mRNA in liver, skeletal muscle, and WAT (I) was determined by qRT-PCR. *p < 0.05, **p < 0.01, ***p < 0.001 versus control mice, n = 5. AMPKα, AMP-activated kinase α; Arg-1, arginase 1; HFD, high-fat diet; iBAT, interscapular brown adipose tissue; IL, interleukin; iNOS, inducible NO synthase; Nogo, reticulon-4; p-AMPKα, phosphorylated AMPKα; PGC-1α, peroxisome proliferator-activated receptor gamma coactivator 1α; qRT, quantitative RT; TG, triglyceride; Tnf, tumor necrosis factor; WAT, white adipose tissue. therapeutic strategy for treatment of obesity and metabolic diseases (31,32). Interestingly, Nogo deficiency significantly increased iBAT weight (Fig. 2H) and activated expression of UCP-1 and lipid oxidation-related genes (Fig. 4, B-F) after a long-term HFD feeding. Nogo deficiency also increased iBAT weight, multilocular beige cells, and body temperature when mice faced cold exposure (Fig. 4, H-J). Similarly, injection of Nogo siRNA to obese mice reduced bodyweight gain and tissue lipid content, associated with increased iBAT weight and body temperature, and activated tissue AMPKα (Fig. 9, A-F). These data strongly suggest that promoting adipose tissue browning and thermogenesis are the important anti-obese mechanisms of reduced Nogo.
The metaflammation is a detrimental factor for obesity. Inflammation cytokines, such as IL-1β and TNF-α, generated by infiltrating macrophages, can damage insulin signaling and AMPKα-mediated energy expenditure (33,34). M2 type macrophages can promote browning of adipose tissue (35). Nogo deficiency substantially reduced HFD-induced inflammatory cytokines in serum, particularly those regulated by NF-κB (Figs. 6A and S7). Along with reduced infiltration of monocytes/macrophages into eWAT, liver, iBAT, and islet ( Fig. 6, B-E), Nogo deficiency also increased M2 macrophages, whereas reduced M1 macrophages in HFD-fed mouse adipose tissues (Fig. 6, F-I). Consistently, expression of molecules responsible for macrophage phenotype in mouse tissues was regulated by Nogo siRNA (Fig. 9I). These results indicate that reduced Nogo can inactivate NF-κB, the main regulator for inflammation and obesity development (8,9). Indeed, NF-κB expression and nuclear translocation were clearly attenuated by reduced Nogo (Figs. 7, A-C, 9, G and H, and S8C), and the inactivation of NF-κB might be completed by enhanced NF-κB degradation through activation of lysosome pathway (Fig. 8, C-E). Therefore, amelioration of HFD-activated inflammation by reduced Nogo is an important mechanism for its anti-obese properties.
Endoplasmic reticulum (ER) stress is also involved in the development of obesity and related diseases. Previous study has shown that overexpression of Nogo can induce ER stress (36). In this article, our results showed that Nogo deficiency ameliorated HFD-induced ER stress-related gene expression (Fig. S11). Adiponectin multimerization is reported to be impaired by ER stress (37). ER stress can also hinder thermogenesis-related gene expression. We supposed that increased HMW adiponectin and enhanced thermogenesis in Nogo −/− HFD-fed mice may be attributed to decreased ER stress. Previous studies have demonstrated that ER stress is associated with biogenesis and mature of lysosome (38,39). Increased ER stress can impair autophagosome-lysosome fusion, thereby reducing lysosomal degradation of macromolecules, such as protein (38,40). Thus, we speculated that decreased ER stress may also contribute to enhanced NF-κB p65 lysosome degradation in Nogo-deficient mice as well as the beneficial effects of Nogo deficiency on obesity and related disorders.
Nogo-B receptor (NGBR) is initially identified as a putative receptor for Nogo-B in endothelial cells (41). In this study, we determined that Nogo deficiency had little effect on NGBR protein expression under different conditions (Fig. S12). In addition, our previous article has demonstrated that neither NGBR overexpression nor NGBR inhibition affected Nogo-B expression (42). These results suggest that Nogo-B and NGBR may exert their functions independently.
Taken together, our study demonstrates that reduced Nogo in tissues and circulation can ameliorate HFD-induced obesity and associated metabolic diseases. These functions may, at least in part, be attributed to enhanced lipid oxidation through AMPKα activation, increased thermogenesis by activating UCP-1, and ameliorated inflammatory cytokine expression, macrophage infiltration, and macrophage phenotype switch by inactivating NF-κB pathway. Collectively, our findings provide novel insights into the physiological roles of reduced Nogo in regulating metabolism and inflammation in obesity, indicating that Nogo inhibition can be a therapeutic option for treatment of obesity and its related diseases.

Plasmid construction
Human NF-κB p65 expression vector was generated using complementary DNA (cDNA) from HepG2 cells, followed by PCR with forward and reverse primers of 5 0 -CGGggtac-cATGGACGAACTGTTCCCCCTCATCTT-3 0 and 5 0 -CCGct cgagT TAGGAGCTGATCTGACTCAGCAGG-3 0 . Letters in lowercase represent the restriction sites of KpnI and XhoI, respectively. The PCR product was digested with KpnI and XhoI and then ligated into a pcDNA3.1(+) plasmid. Human Nogo-B expression vector was constructed as described and named as C2-Nogo-B (14).

Cell culture
HepG2 (human hepatic cell line) or 293T (human embryonic kidney cell line) cells were purchased from American Type Culture Collection and cultured in Dulbecco's modified Eagle's medium containing 10% fetal bovine serum and 50 μg/ ml penicillin/streptomycin with or without 2 mmol/l glutamine, respectively.
HepG2 cells lacking Nogo-B expression were established using CRISPR-Cas9 technology as described (13) and named as Nogo-Cas9 cells, whereas the corresponding Ctrl cells were named as Ctrl-Cas9 cells.

Collection of human serum samples for biochemical assay
All experiments with human blood samples were approved by the Clinical Ethics Committee of the First Affiliated Hospital of Anhui Medical University and adhered strictly to the Declaration of Helsinki Principle 2008. Based on the classification on obesity in China (underweight, BMI < 18.5 kg/m 2 , n = 9; normal, 18.5 ≤ BMI < 24 kg/m 2 , n = 12; overweight, 24 ≤ BMI < 28 kg/m 2 , n = 14; obese, BMI ≥ 28 kg/m 2 , n = 15), we recruited 50 volunteers at the BMI range of 16.9 to 34.1 kg/m 2 . All the members were informed and signed the consent at the beginning of recruitment. After collection, blood samples were used to isolate serum by technicians who were blinded to the samples, followed by determination of T-CHO, LDL, and TG levels using an automatic biochemical analyzer (model #3100; Hitachi High-Technologies Corporation). Serum Nogo-B levels were detected using a human Nogo-B Elisa kit.

Animals and in vivo studies
All animal experiments were granted by the Ethics Committee (no. HFUT-NKU20190215001C) and conformed to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health (publications no.: 8023; revised 1978). C57BL/6J and Nogo-deficient (Nogo KO or Nogo −/− , C57BL/6J background) mice originally obtained from Nanjing Biomedical Research Institute of Nanjing University. Nogo −/− mice used in this study were generated using CRISPR-Cas9 system as described (13). The mice were housed in SPF units of the Animal Center at Nankai University (with a 12 h light cycle from 8 AM to 8 PM, 23 ± 1 C, 60 to 70% humidity) with free access to normal chow and water. Up to five mice were kept per ventilated cage with corn cob bedding material. Mice were allowed to acclimatize to the housing environment for at least 7 days before experiments.
For the fasting experiment, male C57BL/6J mice (7 week old) were deprived of normal chow for 24 h. For refeeding experiment, mice were fasted for 24 h and then refed normal chow for 12 h. For a short-term HFD (35% fat containing 60% kcal; catalog no.: D12492; Research Diets) or a high-cholesterol diet (21% fat plus 0.5% cholesterol) feeding, male C57BL/6J mice (7 week old) were divided into three groups and fed normal chow, HFD, or high-cholesterol diet for 1 week.
For the long-term HFD feeding experiment, male Nogo −/− mice (6 week old) and their age-matched littermate Ctrl (WT, 6 week old) mice were divided into two groups randomly. Mice in Ctrl groups were fed normal chow, and in HFD groups were fed HFD for 14 weeks. During the experiment, mouse bodyweight was detected weekly. At the ninth week of experiment, the food intake was determined, and mouse feces was collected. At the 12th week of experiment, the glucose tolerance test was performed. The mice were fasted for 12 h before i.p. injection of D-glucose (2 g/kg bodyweight). Tail vein blood glucose levels were determined before or at the time point of 20, 30, 60, 90, or 120 min after glucose injection using a glucometer. At the 13th week of experiment, the insulin tolerance test was conducted. The mice were fasted for 6 h before i.p. injection of insulin (1 U/kg bodyweight), and tail vein blood glucose levels were determined before or at the time point of 15, 30, 60, 90, or 120 min after insulin injection.
For the cold adaptation experiment, male Nogo −/− mice (10 week old) and WT mice (10 week old) were housed in 24 h cycles of 12 h at 4 C and 12 h at room temperature. During the experiment, mice were free to access drinking water and normal chow. The experiment was lasted for 1 week. Mouse body temperature was detected using an electronic thermometer.
For Nogo knockdown experiment, male C57BL/6J mice (6 week old) were fed HFD for 25 weeks, then divided into two groups randomly, and received tail vein injection of Nogo siRNA or scrambled siRNA (sequences were listed in Table S3; 40 μg per mouse) once every 3 days for five times. According to the manufacturer's instructions, Nogo siRNA or scrambled siRNA was dissolved in 0.9% saline solution. Entransterin Vivo Transfection Reagent was premixed with 1.8% saline solution at 1:1 (v:v). The siRNA solution and premixed transfection reagent solution were mixed for 15 min at room temperature and then used for mice injection. During the experiment, mouse bodyweight was determined.
At the end of experiment, all mice were anesthetized and euthanized in a CO 2 chamber, followed by collection of blood and tissue samples. Serum was prepared from blood and used to determine alkaline phosphatase, aspartate transaminase, alanine aminotransferase, T-CHO, high-density lipoprotein cholesterol, LDL, and TG using an automatic biochemical analyzer. Serum FFA, Nogo-B, and FGF-21 levels were determined using the corresponding assay kits.

Fluorescence-activated cell sorting
SVFs from the eWAT and iBAT were prepared as described with minor modifications (43). Briefly, the fresh tissue was minced into small pieces and then digested in a dissociation buffer (10 mg/ml bovine serum albumin, 2 mmol/l glucose, and 4 mg/ml collagenase type II in PBS) under slow continuous rotation (200 rpm) for 30 min at 37 C. After digestion, the mixture was filtered through a 100 μm filter and then centrifuged for 8 min at 500 g. SVF cells were resuspended with red blood cell lysis buffer for 1 min and then centrifuged for 8 min at 500 g after addition of PBS. SVF cells were then incubated with FITC anti-mouse F4/80, APC anti-mouse CD80, and PE antimouse CD206 antibodies for 15 min at 4 C in dark, followed by addition of PBS to wash away antibodies. After centrifuged for 8 min at 500 g, the PBS-resuspended cells were subjected to flow cytometry analysis using a FACS Calibur (fluorescence-activated cell sorting; Calibur BD Bioscience). Data were analyzed with the FlowJo software (version 10, BD Bioscience).

Determination of protein expression by Western blot
Cells or mouse tissues were lysed or homogenized in the lysis buffer to extract total proteins. Nuclear proteins were isolated from fresh mouse liver samples using the nuclear extraction kit (Solarbio). The cellular lysates or tissue homogenates were centrifuged and transferred into new tubes. The protein sample was separated by SDS-PAGE and transferred to a nitrocellulose membrane (PALL). The membrane was blocked with 5% fat-free milk in PBS for 1 h at room temperature, followed by incubation with primary antibody in PBS containing 1% bovine serum albumin overnight at 4 C. Membrane was washed for 3 × 10 min with PBS containing 0.05% Tween-20, then incubated with appropriate HRP-conjugated secondary antibody in PBS containing 1% fat-free milk for 2 h at room temperature. After washed for 3 × 10 min with PBS containing 0.05% Tween-20, the membrane was incubated in the mixture of Western blotting chemiluminescence reagents 1 and 2 (Millipore) followed by scanning with Clinx ChemiCapture (CLINX).

Quantitative real-time PCR
Total RNA was isolated from cells or tissue samples using TRIzol reagent (Invitrogen). cDNA was synthesized with a reverse transcription kit (New England Biolabs). Gene expression was determined by quantitative real-time-PCR using SYBR Green PCR reagents (Bio-Rad) and primers with sequences listed in Table S4. The mRNA levels were normalized by Gapdh mRNA in the corresponding samples (44).

Determination of tissue TG, T-CHO, and FFA levels
To determine TG content, 30 mg tissue or 80 mg feces was homogenized in 1 ml PBS. About 900 μl of homogenate was used to extract total lipids for TG determination with the TG assay kit, and the rest of the homogenate was used to determine protein content for normalization of TG levels. TG level was expressed as microgram/milligram protein. T-CHO and FFA levels were measured with cholesterol or FFA assay kit according to the corresponding instruction, respectively.

H&E, Sirius red, Oil red O, immunohistochemical, and immunofluorescent staining
After collection, fresh tissues were fixed in 4% paraformaldehyde at 4 C overnight, dehydrated using graded ethanol or 30% sucrose solutions, and embedded in paraffin or optimum cutting temperature compound, respectively. Then the embedded tissues were cut into sections of 5 μm for further staining. Before staining, the 5 μm paraffin sections were deparaffinized in xylene and hydrated using graded ethanol solutions. To conduct histologic analysis, H&E or Sirius red staining was conducted on the tissue sections. The paraffin sections were also used to conduct immunohistochemical or immunofluorescent staining to determine MOMA2, UCP-1, CD68, and Nogo protein expression (14). To determine lipid content in the liver, the 5 μm frozen liver sections were incubated in PBS solution for 15 min and then conducted Oil red O staining (14). All images were captured with a Leica microscope. The mean density of images was quantified by the segmentation color-threshold analysis using Photoshop CS6 and normalized by the value of Ctrl mice. The adipocyte size and Sirius red staining were quantified using ImageJ software (National Institutes of Health).

Statistical analysis
Data were presented as mean ± SD, and all experiments were repeated at least three times independently. At first, all the data were tested for the normal distribution analysis with Graphpad Prism software (version 8; GraphPad Software, Inc). The data followed normal distribution were then analyzed by one-way or two-way ANOVA or unpaired t test (two-tailed). Correlation coefficient was calculated using Pearson correlation test, and two-tailed p value was calculated. Significant values were considered if p < 0.05 (n ≥ 3).

Data availability
All data presented are contained within the main article and supporting information.
Supporting information-This article contains supporting information.