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BY 4.0 license Open Access Published online by De Gruyter August 4, 2022

Meta-analysis of the effects of palmitic acid on microglia activation and neurodegeneration

Heping Zhou and Sulie L. Chang

Abstract

Objectives

Evidence suggests that obesity may represent a risk factor for neurodegenerative pathologies including Alzheimer’s disease (AD). With excessive accumulation of adipose tissue, obesity is associated with chronic low-grade inflammation, increased production of adipokines, elevated levels of free fatty acids (FFAs) including palmitic acid (PA), the most abundant saturated fatty acid (SFA) in circulation. Excessive PA has been shown to induce lipotoxicity in many different types of cells including microglia and neuronal cells. We hypothesized that PA may contribute to the development of obesity-associated neurological conditions.

Methods

This study was designed to examine how increased PA may affect microglia activation and neurodegeneration using QIAGEN Ingenuity Pathway Analysis (IPA). Kramer analysis was used to quantitatively characterize the impact of PA on microglia activation and neurodegeneration.

Results

Simulated increase of PA enhanced the activities of intermediating molecules including CCL5, IL1β, IL1RN, IL6, NF-κB, NOS2, PTGS2, TLR2, TLR4, and TNF. Increased PA level induced microglia activation with a z score of 2.38 (p=0.0173) and neurodegeneration with a z score of 1.55 (p=0.121). Increased PA level also activated neuroinflammation signaling pathway, the top canonical pathway associated with both microglia activation and neurodegeneration.

Conclusions

Our IPA analysis demonstrated that increased PA significantly induced microglia activation and might augment neurodegeneration by altering the activities of key intermediating molecules and canonical pathways. Our findings shed light on how increased PA level may contribute to the development of neurodegenerative pathologies in the course of obesity.

Introduction

Obese subjects have been reported to exhibit abnormalities in the central nervous system, including microglia overactivation, inflammation and reactive gliosis in the hypothalamus [1], [2], [3], atrophy in the hippocampus [4], reduction in the orbitofrontal cortex volume [5], and decreased brain volume [6]. Studies using animal models have also provided evidence for the association of obesity with microglia activation and neurodegeneration. For example, inflammation and reactive gliosis have been detected in the hypothalamus of high fat diet-induced obese rodents [3]. Furthermore, increased activation of microglia in the hippocampus is associated with reduction in dendritic spines and memory impairments in high fat diet-induced obese male mice [7].

With excessive accumulation of adipose tissue, production of adipokines, including hormones, cytokines and peptides that regulate a wide range of functions in adipose tissue, liver, muscle, blood vessels, and brain [8], is increased in obesity [9], [10], [11], [12]. For example, tumor necrosis factor (TNF)-α mRNA expression is higher in the visceral adipose tissue of obese subjects [13]. The circulating level of interleukin (IL)-6 is increased in juvenile and adult overweight/obese subjects [14]. Obesity is also associated with increased expression of TNF-α and IL-6 in the adipose tissue of mice fed with palmitic acid (PA)-supplemented high fat diet as compared to mice fed with low fat control [15].

Obesity has also been associated with increased circulating levels of free fatty acids (FFAs) [9], [10], [11], [12] and elevated FFA uptake in the brain [16]. Lipid composition is reportedly altered in the brain of obese mice [17, 18]. The level of PA, the most abundant saturated fatty acid (SFA) in circulation, is increased in the cerebrospinal fluid (CSF) of overweight and obese patients with cognitive impairment [19]. Intracerebroventricular infusion of PA also increases TNF-α level in the hippocampus and impairs synaptic plasticity and memory in mice [19]. Intraperitoneal injection of PA induces anxiety-like behavior in mice [20]. Excessive PA has been shown to induce lipotoxicity in many different cell types [21, 22], including BV-2 microglia and Neuro-2a neuroblastoma cells [23], [24], [25], [26].

We hypothesize that PA may contribute to the development of obesity-associated neurological conditions. This study was designed to examine the molecular mechanisms by which PA may induce microglia activation and neurodegeneration. The QIAGEN Ingenuity Pathway Analysis (IPA) has a Knowledge Base (QKB) with more than eight million curated findings in biological systems (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis). Molecules associated with microglia activation and neurodegeneration were obtained from QKB and analyzed using the CORE analysis tool in IPA. The shortest paths from PA to microglia activation, neurodegeneration, and top canonical pathways associated with microglia activation and neurodegeneration were examined using the Path Explorer tool, and the effects of excessive PA on these function and pathways were investigated using the Molecular Activity Predictor (MAP) tool in IPA.

Materials and methods

IPA software

The annual license for IPA, a web-based bioinformatics tool, was purchased from QIAGEN Inc. (https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis; Germantown, MD, USA). IPA enabled insightful data analysis building on the comprehensive QKB with more than eight million curated findings in biological systems.

IPA analysis

IPA Core Expression Analysis was conducted to identify the biological canonical pathways associated with molecules in a given dataset. Biological molecules associated with microglia activation and neurodegeneration were retrieved from QKB using the Bioprofiler tool in IPA. Data retrieval and analysis was conducted between January 10, 2022 and March 8, 2022.

The Path Explorer tool was used to examine the shortest paths from PA to different functions and pathways including microglia activation, neurodegeneration, neuroinflammation signaling pathway, amyotrophic lateral sclerosis signaling pathway, and liver X receptor/retinoid X receptor (LXR/RXR) activation pathway. The Molecule Activity Predictor (MAP) tool was used to simulate the increased level of PA and examine its effects on downstream molecules, functions, and canonical pathways.

Kramer analysis

Kramer analysis was performed to determine the contribution of each intermediating molecule on the shortest paths from PA to activation of microglia and neurodegeneration and to calculate the overall z-score to measure the overall impact of these molecules as described by Kramer et al. [27]. Briefly, a local z-score was determined for each of the intermediating molecules using Downstream Effect Analysis algorithm to delineate the quantitative weight for the change in activation of microglia and neurodegeneration in response to an increased level of PA simulated by the MAP tool based on the number and consistency of findings curated in QKB. An overall z-score for all the intermediating molecules was then calculated from the local z-scores of intermediating molecules on the shortest paths of PA’s actions [27], [28], [29].

Results

Analysis flowchart on the effects of PA on microglia activation and neurodegeneration

As shown in Figure 1, the Path Explorer tool was first used to identify the shortest paths from PA to microglia activation and neurodegeneration to examine how PA may contribute to microglia activation and neurodegeneration, and the MAP tool was used to investigate the effects of excessive PA on microglia activation and neurodegeneration. Then Kramer analysis was used to determine the local z-scores for the intermediating molecules on the shortest paths and calculate the overall z-score for collective impact of the intermediating molecules altogether. The shortest paths from PA to microglia were also compared and contrasted with the shortest paths from PA to neurodegeneration. The overlapping intermediating molecules were then identified on the shortest paths between microglia activation and neurodegeneration, and the effects of these overlapping intermediating molecules on microglia activation and neurodegeneration were explored using the MAP tool.

Figure 1: 
Analysis flowchart on the effects of PA on microglia activation and neurodegeneration.

Figure 1:

Analysis flowchart on the effects of PA on microglia activation and neurodegeneration.

Shortest molecular paths from PA to activation of microglia

As the immune sentinels of the central nervous system, microglia play important roles in immune defense and neuronal function in the brain [30]. To examine the relationship between PA and microglia activation, “palmitic acid” and “Activation of microglia” were added to a new pathway, then the Path Explorer tool was used to identify potential shortest paths from PA to activation of microglia. As shown in Figures 2, 36 shortest paths were identified from PA to activation of microglia. Among the 36 intermediating molecules on the shortest paths from PA to microglia activation, three intermediating molecules, interferon (IFN)β1, orosomucoid (Orm)1, and nuclear receptor subfamily 4 group A member 1 (NR4A1), were activated by simulated increase in PA and inhibited microglia activation; one intermediating molecule, autophagy related 7 (ATG7), was inhibited by simulated increase in PA and inhibited microglia activation; and 23 intermediating molecules were activated by simulated increase in PA and induced microglia activation. Among these 23 intermediating molecules, nine were extracellular molecules including fibronectin (FN)1, lipocalin 2 (LCN2), and seven cytokines/chemokines such as chemokine (C-C motif) chemokine ligand 2 (CCL2), CCL5, C-X-C motif chemokine ligand 10 (CXCL10), IL1β, IL1RN, IL6, and TNF; six were plasma membrane molecules including toll-like receptor (TLR)2, TLR4, CD36, F2R like trypsin receptor 1 (F2RL1), prostaglandin E receptor 3 (PTGER3), and TP53; seven were cytoplasmic molecules, such as mitogen-activated protein kinase (MAPK)14, nitric oxide synthase (NOS)2, pyruvate dehydrogenase kinase (PDK)4, caspase 1 (CASP1), cytochrome b-245 beta chain (CYBB), heme oxygenase 1 (HMOX1), and prostaglandin endoperoxide synthase (PTGS)2; and one was a transcription factor, CCAAT enhancer binding protein beta (CEBPβ). The effects of 9 intermediating molecules, NOS3, p38 MAPK, adiponectin (ADIPOQ), apolipoprotein (APOE), colony stimulating factor 2 (CSF2), IL10, transforming growth factor β1 (TGFβ1), and nuclear factor-κB (NF-κB), on microglia activation could not be identified because the relationship between these molecules and activation of microglia currently available in QKB did not allow for the calculation of a z-score. These data suggest that PA may induce microglia activation by increasing the production of cytokines and chemokines via membrane receptors including TLR2, TLR4 and CD36 and activation of transcription factors such as CEBPβ.

Figure 2: 
The shortest molecular paths from PA to activation of microglia were established using the path explorer tool in IPA, and the MAP tool was used to examine the effects of increased level of PA on the activities of molecules on the shortest paths.

Figure 2:

The shortest molecular paths from PA to activation of microglia were established using the path explorer tool in IPA, and the MAP tool was used to examine the effects of increased level of PA on the activities of molecules on the shortest paths.

Quantitative characterization of the effects of PA on activation of microglia

Kramer analysis was then performed to quantitate the contribution of each intermediating molecule on the shortest paths from PA to activation of microglia using Downstream Effect Analysis algorithm described previously by Kramer et al. [27]. The z-scores for intermediating molecules that contributed to activation of microglia were designated positive; the z-scores for intermediating molecules that contributed to the inhibition of microglia activation were designated negative; and the z-scores for intermediating molecules with effects unidentified were not calculated because the relationship between these molecules and microglia activation currently available in QKB did not allow for the calculation of a z-score. As shown in Figure 3, the local z-scores of 23 intermediating molecules that were activated by increased level of PA and in turn activated microglia were calculated and assigned positive. The local z-scores of 4 intermediating molecules that inhibited microglia activation were calculated and assigned negative. The overall z-score calculated for these 27 intermediating molecules altogether was 2.38 (p=0.0173). These data suggest that increased PA level could lead to significant overall activation of microglia via modulation of these intermediating molecules on the shortest paths.

Figure 3: 
Quantitative illustration of the change in microglia activation in response to increased PA. The contribution of 27 intermediating molecules on the shortest paths from increased PA to microglia activation with known directional expression or phosphorylation data was measured by the local z-score for each molecule.

Figure 3:

Quantitative illustration of the change in microglia activation in response to increased PA. The contribution of 27 intermediating molecules on the shortest paths from increased PA to microglia activation with known directional expression or phosphorylation data was measured by the local z-score for each molecule.

Shortest molecular paths from PA to neurodegeneration

We next examined the relationship between PA and neurodegeneration. We added “palmitic acid” and “Neurodegeneration” to a new pathway, then used the Path Explorer tool in IPA to identify potential shortest paths from PA to neurodegeneration. As shown in Figures 4, 67 shortest paths were identified from PA to neurodegeneration. Among the 67 intermediating molecules on the shortest paths from PA to neurodegeneration, 13 intermediating molecules, AKT1, AKT, autophagy-related gene (ATG)7, BCL2, BCL2L1, beclin (BECN)1, cyclic AMP response element binding protein (Creb), cathepsin V (CTSV), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), insulin-like growth factor 1 (IGF1), PPARG coactivator 1 alpha (PPARGC1A), sirtuin 1 (SIRT1), and signal transducer and activator of transcription (STAT)3, were inhibited by increased level of PA but activated neurodegeneration; 18 intermediating molecules, ATG5, CASP3, CCL2, CTSB, eukaryotic translation initiation factor 2 alpha kinase 3 (EIF2AK3), IL1RN, NFE2 like BZIP transcription factor 2 (NFE2L2), NFKB1, NOS2, nuclear receptor subfamily 4 group A member 2 (NR4A2), NR4A3, NPAS4, peroxisome proliferator activated receptor (PPAR)D, serpin family E member 1 (SERPINE1), Sequestosome 1 (SQSTM1), transferrin receptor (TFRC), TGFB1, and X-box binding protein (XBP)1, were activated by increased level of PA but inhibited neurodegeneration; 20 intermediating molecules, JUN, TP53, tribbles pseudokinase 3 (TRIB3), NF-κB, GSK3B, BAX, CYBB, sphingosine kinase 1 (SPHK1), NOX4, PTGS2, glycogen synthase kinase (GSK)3, CCL5, IL6, TLR2, cannabinoid receptor 1 (CNR1), TLR4, eukaryotic translation initiation factor 2 alpha kinase 3 (EIF2AK3), CASP1, CASP 3/7, and c-Jun N-terminal kinases (JNK), were activated by increased level of PA and induced neurodegeneration. The effects of 16 intermediating molecules, β-secretase 1 (BACE1), mitochondrial permeability transition (MPT) pore, fatty acid binding protein 3 (FABP3), 3-hydroxyisobutyryl-CoA hydrolase (HIBCH), ubiquitin C-terminal hydrolase L1 (UCHL1), X-linked inhibitor of apoptosis (XIAP), apoptosis inducing factor mitochondria associated 1 (AIFM1), ADIPOQ, APOE, CXCL3, IL1, IL1B, TNF, epidermal growth factor receptor (EGFR), microtubule associated protein Tau (MAPT), and MYD88, on neurodegeneration could not be identified. These data suggest that PA may induce neurodegeneration by increasing the activities of cytokines and chemokines via plasma membrane receptors such as TLR2 and TLR4.

Figure 4: 
The shortest molecular paths from PA to neurodegeneration were established using the path explorer tool in IPA. The MAP tool was then used to simulate the effects of increased PA on neurodegeneration via these intermediating molecules.

Figure 4:

The shortest molecular paths from PA to neurodegeneration were established using the path explorer tool in IPA. The MAP tool was then used to simulate the effects of increased PA on neurodegeneration via these intermediating molecules.

Quantitative characterization of the influence of PA on neurodegeneration

Kramer analysis was performed to determine the contribution of intermediating molecules on the shortest paths from PA to neurodegeneration using the Downstream Effect Analysis algorithm previously described by Kramer et al. [27]. As shown in Figure 5, the z-scores of 13 intermediating molecules that were inhibited by increased level of PA and contributed to neurodegeneration were calculated and designated positive indicating activation of neurodegeneration; the z-scores of 20 intermediating molecules that were activated by the increased level of PA and contributed to neurodegeneration were also calculated and designated positive; the z-scores of 18 intermediating molecules that were activated by increased level of PA but contributed to the inhibition of neurodegeneration were calculated and designated negative indicating inhibition of neurodegeneration; and the z-scores for 16 intermediating molecules with unidentified effects were not calculated because the relationship between these molecules and neurodegeneration currently available in QKB did not allow for the calculation of z-scores. The calculated overall z-score for the 51 intermediating molecules with known effects on neurodegeneration was 1.55 (p=0.121). These data suggested stimulation of PA may lead to neurodegeneration even though the overall z score was not statistically significant.

Figure 5: 
Quantitative illustration of the change in neurodegeneration in response to increased PA. The contribution of 51 intermediating molecules with known directional expression or phosphorylation data in response to increased PA was measured by the local z-score of each molecule.

Figure 5:

Quantitative illustration of the change in neurodegeneration in response to increased PA. The contribution of 51 intermediating molecules with known directional expression or phosphorylation data in response to increased PA was measured by the local z-score of each molecule.

Comparison of the intermediating molecules on the shortest paths from PA to microglia activation with those from PA to neurodegeneration

We then compared the intermediating molecules on the shortest paths from PA to microglia activation with those from PA to neurodegeneration. As shown in Figure 6A, among the 36 intermediating molecules on the shortest paths from PA to microglia activation, 17 intermediating molecules were unique to the paths from PA to microglia activation. Among the 67 intermediating molecules on the shortest paths from PA to neurodegeneration, 48 intermediating molecules were unique to the paths from PA to neurodegeneration. 19 intermediating molecules were on the shortest paths from PA to microglia activation and from PA to neurodegeneration. For these 19 intermediating molecules, increased PA level enhanced the activities of ADIPOQ, CASP1, CCL5, CYBB, IL1β, IL6, IL1RN, NF-κB, NOS2, PTGS2, TGFβ1, TLR2, TLR4, TNF, and TP53 while decreasing the activity of ATG7 with its effects on APOE, IL1, and MAPT undefined.

Figure 6: 
Analysis of intermediating molecules on the shortest paths from PA to activation of microglia and those from PA to neurodegeneration.
(A) 19 intermediating molecules were on the shortest paths from PA to microglia activation and neurodegeneration with 17 intermediating molecules unique to the shortest paths from PA to microglia activation and 48 intermediating molecules unique to the shortest paths from PA to neurodegeneration. (B) The shortest molecular paths between microglia activation and neurodegeneration were established using the path explorer tool, and the effects of increased PA on 16 intermediating molecules on the shortest paths from PA to microglia activation and from PA to neurodegeneration with known activities were simulated using the MAP tool.

Figure 6:

Analysis of intermediating molecules on the shortest paths from PA to activation of microglia and those from PA to neurodegeneration.

(A) 19 intermediating molecules were on the shortest paths from PA to microglia activation and neurodegeneration with 17 intermediating molecules unique to the shortest paths from PA to microglia activation and 48 intermediating molecules unique to the shortest paths from PA to neurodegeneration. (B) The shortest molecular paths between microglia activation and neurodegeneration were established using the path explorer tool, and the effects of increased PA on 16 intermediating molecules on the shortest paths from PA to microglia activation and from PA to neurodegeneration with known activities were simulated using the MAP tool.

We then examined how microglia activation and neurodegeneration may interact with each other using the Path Explorer tool in IPA. As shown in Figures 6B, 60 shortest paths were identified between microglia activation and neurodegeneration. The 19 intermediating molecules on the shortest paths from PA to microglia activation and from PA to neurodegeneration were identified on these paths. The effects of PA on IL1, ApoE, and MAPT were undefined and therefore not simulated using the MAP tool. The effects of increased PA level on these molecules, activation of ADIPOQ, CASP1, CCL5, CYBB, IL1β, IL1RN, IL6, NF-κB, NOS2, PTGS2, TGFβ1, TLR2, TLR4, TNF, and TP53 and inhibition of ATG7, were simulated using the MAP tool, which led to increased microglia activation and neurodegeneration.

Analysis flowchart on the effects of PA on canonical pathways associated with microglia activation and neurodegeneration

To examine the effects of PA on the canonical pathways associated with microglia activation and neurodegeneration, the molecules associated with microglia activation and neurodegeneration were first identified in Bioprofiler (Figure 7). CORE analysis was then conducted to identify the canonical pathways associated with microglia activation and neurodegeneration. The shortest paths from PA to top canonical pathways associated with microglia activation and neurodegeneration were then identified using the Path Explorer tool, and the effects of excessive PA on the top canonical pathways were simulated using the MAP tool (Figure 7).

Figure 7: 
Analysis flowchart on the effects of PA on canonical pathways associated with microglia activation and neurodegeneration.

Figure 7:

Analysis flowchart on the effects of PA on canonical pathways associated with microglia activation and neurodegeneration.

Effects of PA on top canonical pathways associated with microglia activation

In order to further examine the effects of PA on microglia activation, 163 microglia activation-associated molecules were identified in Bioprofiler of IPA. CORE analysis was then performed on these molecules to identify the canonical pathways associated with microglia activation. As shown in Figure 8A, the top ten canonical pathways included neuroinflammation signaling pathway with the lowest p-value (p=1.05E-37), liver X receptor/retinoid X receptor (LXR/RXR) activation (p=5.99E-22), role of macrophages, fibroblasts and endothelial cells in rheumatoid arthritis (p=7.23E-21), agranulocyte adhesion and diapedesis (p=1.35E-18), role of cytokines in mediating communication between immune cells (p=4.70E-18), osteoarthritis pathway (p=9.42E-18), role of pattern recognition receptors in recognition of bacteria and viruses (p=4.33E-17), IL-17 Signaling (p=4.63E-17), granulocyte adhesion and diapedesis (p=5.61E-17), and atherosclerosis signaling (p=5.95E-17).

Figure 8: 
Effects of PA on the top canonical pathways associated with microglia activation.
(A) Top ten canonical pathways identified for molecules associated with microglia activation. (B) Effects of excessive PA on neuroinflammatory signaling pathway. (C) Effects of excessive PA on LXR/RXR activation.

Figure 8:

Effects of PA on the top canonical pathways associated with microglia activation.

(A) Top ten canonical pathways identified for molecules associated with microglia activation. (B) Effects of excessive PA on neuroinflammatory signaling pathway. (C) Effects of excessive PA on LXR/RXR activation.

In order to examine how PA affected the top two canonical pathways, neuroinflammatory signaling pathway and LXR/RXR activation, PA and neuroinflammatory signaling pathway or LXR/RXR activation were added to a new pathway. The shortest paths from PA to each of the top canonical pathways were established using the Path Explorer tool, and the effects of increased PA were then simulated using the MAP tool. As shown in Figure 8B, simulated increase of PA activated neuroinflammation signaling pathway by increasing the activities of inflammatory cytokines and chemokines via membrane receptors such as TLR2 and TLR4 and transcription factors such as NF-κB, JUN and FOS. Simulated increase of PA increased the activities of TNF, IL1β, TLR4, NF-κB and PTGS2, and inhibited LXR/RXR activation (Figure 8C).

Effects of PA on top canonical pathways associated with neurodegeneration

In order to further examine the effects of PA on neurodegeneration, CORE analysis was performed on 152 neurodegeneration-associated molecules from Bioprofiler to identify the canonical pathways associated with neurodegeneration. As shown in Figure 9A, the top ten canonical pathways included amyotrophic lateral sclerosis signaling (p=6.59E-15), neuroinflammation signaling pathway (p=5.32E-13), coordinated lysosomal expression and regulation (CLEAR) signaling pathway (p=1.33E-11), Huntington’s disease signaling (p=1.40E-09), neurovascular coupling signaling pathway (p=9.86E-8), endocannabinoid neuronal synapse pathway (p=2.53E-07), amyloid processing (p=5.4E-07), cAMP response element-binding protein (CREB) signaling in neurons (p=5.94E-07), reelin signaling in neurons (p=1.73E-06), and glutamate receptor signaling (p=2.53E-06).

Figure 9: 
Effects of PA on the top canonical pathways associated with neurodegeneration.
(A) Top ten canonical pathways identified for molecules associated with neurodegeneration. (B) Effects of excessive PA on amyotrophic lateral sclerosis signaling pathway.

Figure 9:

Effects of PA on the top canonical pathways associated with neurodegeneration.

(A) Top ten canonical pathways identified for molecules associated with neurodegeneration. (B) Effects of excessive PA on amyotrophic lateral sclerosis signaling pathway.

We next examined the shortest paths from PA to amyotrophic lateral sclerosis signaling pathway using the Path Explorer tool. Then the effects of increased PA were simulated using the MAP tool. As shown in Figure 9B, increased PA level activated the amyotrophic lateral sclerosis signaling pathway by increasing the activities of CASP1, 3, 7, 9 and TP53 and inhibiting the activities of IGF1 and BCL2L1.

Discussion

In this study, we first examined the effects of increased PA on microglia activation and neurodegeneration. With 17 intermediating molecules unique to the shortest paths from PA to microglia activation and 48 intermediating molecules unique to the shortest paths from PA to neurodegeneration, 19 intermediating molecules were overlapped on the shortest paths from PA to microglia activation and neurodegeneration, including ATG7, CASP1, CCL5, CYBB, IL1β, IL1RN, IL6, NF-κB, NOS2, PTGS2, TLR2, TLR4, TNF, and TP53.

Neurodegeneration is a pathological condition that affects the structure and function of neurons. Various neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease, and amyotrophic lateral sclerosis, affect different subsets of neurons in different areas of the brain, and exhibit specific subsets of clinical and pathological manifestations [31]. While the etiology of neurodegeneration is largely unknown, it is recognized that neurodegeneration is a complex process with a multifactorial nature. Neurodegeneration has been associated with a number of risk factors, such as age [32], microbial infections [33], gut dysbiosis [34], genetic factors [33], hypertension [35], obesity [36], and diabetes [37]. Many different biological processes are reportedly involved in neurodegeneration, such as cellular senescence [38], apoptosis [39], oxidative stress [40], and neuroinflammation [41], which could be triggered via various biological pathways. In our study, simulated increase of PA level led to neurodegeneration with an overall z-score of 1.55 and a p value of 0.121, suggesting that excessive PA may contribute to neurodegeneration even though the effects may not be statistically significant. This may be due to mixed effects of excessive PA on the intermediating molecules involved in autophagy, cell death, oxidative stress, and transcription regulation. Our finding also reflected the complexed nature of neurodegeneration.

Microglia activation has been associated with deficits in hippocampus-dependent learning and memory, decreased synaptic density, and dysregulation of genes involved in regulation of synaptic plasticity as well as hippocampal impairment, which potentially contribute to cognitive deficits in obesity [30, 42, 43]. PA has been reported to not only induce lipotoxicity in microglia but also activate microglia via TLR4/NF-κB signaling [23, 44]. In our study, simulated increase of PA significantly enhanced microglia activation with an overall z score of 2.38 (p=0.0173). Our studies found that increased PA enhanced the activities of inflammatory molecules including transcription factors such as NF-κB and CEBPβ, cytokines such as IL1β, IL6, and TNF, and chemokines such as CCL2 and CCL5, and increased the activities of TLR2, TLR4 and CD36, leading to microglia activation. Consistently, knockdown of TLR4 markedly attenuates PA-induced NF-κB activation and IL-8 expression in human aortic vascular smooth muscle cells and significantly reduces PA-induced increase of NF-κB activation and release of IL-1β and MCP-1 in THP-1 cells [45, 46]. Furthermore, our study also found that increased PA enhanced the activities of the neuroinflammation signaling pathway, the top canonical pathway associated with PA [47], microglia activation, and neurodegeneration, which suggests that neuroinflammation is a key mechanism by which PA affects microglia activation and neurodegeneration. Our study found that PA may also affect the activities of molecules involved in inflammation and apoptosis, leading to inhibition of LXR/RXR activation and activation of the amyotrophic lateral sclerosis signaling pathway.

Several limitations of this study may potentially affect the findings. First, as more progress is made on the understanding of PA’s biological effects, microglia activation and neurodegeneration, more molecules and relationships will be identified, which will improve the comprehensiveness of relationships in QKB database. The new studies added to the QKB may reflect new information on the relationship between two nodes to allow for calculation of z-scores for more molecules involved. Secondly, IPA analysis gives each relationship an equal weight without consideration of its significance [47, 48]. Finally, considering the importance of elevated levels of FFAs in obesogenic pathologies, this study analyzed the role of PA in microglia activation and neurodegeneration. It is recognized that obesity is a complexed condition. Obesity may be associated with hyperglycemia, hyperinsulinemia, insulin resistance, reactive oxidative stress, endoplasmic reticulum (ER) stress, metabolic disturbances, and other pathologies in multiple tissues and organs, which may also contribute to microglia activation and neurodegeneration. Furthermore, obesity also reportedly affects the metabolism of aromatic amino acids in gut microbiota [5], which may in turn affect neuroinflammation [49] and neurodegeneration [50].

While the genetic and environmental causes of neurodegeneration in AD remain to be defined, there is evidence associating obesity with AD development [36]. Nasaruddin et al. have reported that subjects with moderate AD pathologies exhibit higher levels of such FAs as arachidonic acid, stearic acid, oleic acid and PA [51]. PA has been reported to exhibit neurotoxicity [24], [25], [26], and induce hyperphosphorylation of tau protein in primary rat cortical neurons [52], which is a key characteristic of AD patients [53]. Our IPA analysis demonstrated that PA significantly induced microglia activation and might augment neurodegeneration by altering the activities of key inflammatory mediators including CCL5, IL1β, IL1RN, IL6, NF-κB, NOS2, PTGS2, TLR2, TLR4, and TNF. Our study also found that excessive PA may promote neurodegeneration via multiple pathways, impacting the activities of apoptosis-related proteins such as BCL2L1, BAX and CASP3, enhancing the activities of inflammatory mediators, such as CCL5, IL1β, IL6, IL1RN, NF-κB, NOS2, PTGS2, TGFβ1, TLR2, TLR4, and TNF, affecting the activities of autophagy-related molecules, such as ATG7 and BECN1, and altering the activities of metabolic proteins, such as GAPDH, ADIPOQ, and APOE. Our findings may facilitate further investigation into how neurodegenerative pathologies develop in the course of obesity.


Corresponding author: Heping Zhou, PhD, Department of Biological Sciences, Seton Hall University, 400 South Orange, NJ, USA, Phone: 973 275 2889, E-mail:

Funding source: National institute of health

Award Identifier / Grant number: DA046258

Acknowledgments

The authors thank Wenfei Huang for the assistance in Kramer analysis.

  1. Research funding: This study was supported, in part, by research funds from Department of Biological Sciences and College of Arts and Sciences at Seton Hall University. This study was also partially sponsored by the NIH DA046258 to Sulie L. Chang.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest

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Received: 2022-06-06
Accepted: 2022-07-05
Published Online: 2022-08-04

© 2022 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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