3.1 The scheme of gf-targeted PBM treatment for AD mice
The purpose of this study was to explore whether gf-targeted PBM can improve the symptoms and pathology of AD by regulating the intestinal flora. We produced the AD mouse model by Aß1−42 injection and verified the modeling effect by a MWM test in ten mice. The AD mice then received the PBM intervention with different wavelengths for up to two months. After PBM treatment, the cognitive function of AD mice was determined by a MWM test and the changes in Aß amyloid protein, tau protein, microglia cells, and proteomics of hippocampal tissue were detected to determine the therapeutic effect of PBM on AD. In addition, we calculated the intestinal flora diversity after gf-targeted PBM to analyze both the regulatory effect of PBM on the intestinal flora and the abundance of beneficial flora following PBM-treated AD. Finally, we measured cellular immunity in the mesenteric lymph nodes and inflammatory factors in the blood to explore the role of immunity and inflammation as mediators in the treatment of AD by PBM. The design scheme of the complete experiment is shown in Fig. 1.
3.2 Gf-targeted PBM improves the cognition impairment of AD mice
The MWM experiment was divided into two parts: the location navigation test and the space probe test. The former tested the learning ability of AD mice and the latter tested the memory ability of AD mice.
Figure 2A shows that all three PBM groups improved the escape latency period (the time period from the mouse entering the water to finding the platform) in the 6-day navigation experiment. In the later period of the experiment, the performance of mice in the PBM group was better than that of the Ctrl M group, which was close to the normal mice in the Ctrl B group. For the percent of time in the outer annulus, PBM630, PBM730, and PBM850 all decreased the searching time of AD mice in the outer annulus of the pool compared with the untreated AD mice in the Ctrl M group. PBM630 showed the highest effect, as the PBM630-treated AD mice performed the same as the normal mice in the Ctrl B group on the second day. Interestingly, the swimming speed of mice in the three PBM groups was higher than that of the Ctrl M and Ctrl B groups, especially those in the PBM730 group. Untreated AD mice swam as fast as the normal mice in our study, which implies that the method of AD model building by Aß1−42 injection did not impact the athletic ability of the mice. However, there was no significant difference between the three PBM groups in all the measurements, including latency, percent time in the outer annulus, and swimming speed.
We also analyzed the search strategies of the mice. The results are shown in Fig. 2B. Compared with Ctrl B, most of the mice in the Ctrl M group swam on the edge of the pool (dark grey bar) and searched randomly (blue bar), while trend type (red bar) of the mice in the PBM group took up a larger proportion when swimming in the later period of the experiment (after the 3rd day). This was especially evident in the mice from groups PBM630 and PBM850, as they could swim in a straight line towards the platform (green bar). The searching strategy scores of the three PBM groups (PBM630, PBM730, and PBM850) were significantly higher than those of the Ctrl M group (p = 0.004, 0.013, and 0.001, respectively). Moreover, the scores of the PBM850 group recovered close to the Ctrl B group, indicating that the learning ability of AD mice after PBM850 treatment returned to a normal mouse level.
The memory ability of AD mice was measured in a space probe test. Figure 2C-F shows the swimming trajectory, times of traversing the platform, and the percent time in platform quadrants of the mice in the space probe test. The swimming trajectories of the normal mice were near the platform after they were put into the pool, while the AD mice in neither the Ctrl M group nor the PBM groups performed chaotic trajectories. By analyzing these trajectories, we found that regardless of the number of platform traversing times or the percentage of time in a platform quadrant, there was no significant difference between the three PBM groups and the Ctrl M group. This finding suggests that PBM had no effect on the memory ability of AD mice.
3.3 Improvement of pathology in brains of AD mice
Pathological changes in AD, such as Aß amyloid plaques, phosphorylated tau proteins (p-tau(s396)), and microglial cell activation are shown in Fig. 3. Aß amyloid plaques were clustered in the upper edge of the hippocampus in Ctrl M mice. The PBM630 and PBM730 eliminated most of the clusters of Aß amyloid plaques which should be present in the hippocampus of mice. However, PBM850 with a long irradiation time only weakly affected Aß amyloid plaques (the upper right box in Fig. 3 showing the Aß1−42 image of PBM850). In accordance with the results of Aß amyloid plaques, a mass of microglial cells (stained by Iba1) clustered in the hippocampus of Ctrl M and PBM850 mice, whereas only a few microglial cells were clustered in the PBM630 and PBM730 groups. A number of phosphorylated tau proteins were also present in the hippocampus of Ctrl M and PBM850 mice. Encouragingly, p-tau protein was virtually absent in the hippocampus of mice in the PBM630 and PBM730 groups. These results imply that irradiation with PBM630 and PBM730 contributes to the elimination of Aß amyloid plaques and inhibits the neuroinflammation and tau phosphorylation caused by Aß amyloid plaques.
3.4 Response of mesenteric blood flow and the immune system after PBM treatment
We observed the mesenteric blood flow of mice using a laser speckle technique. The results are showed in Fig. 4A–C. We found that the blood flow of AD mice increased significantly compared with normal mice (p = 0.017). However, the PBM intervention did not correct this abnormal increase. We also observed that PBM increased the blood vessel diameter, which seems to have little substantial effect on AD development.
Mesenteric lymph nodes are an important part of the body's immune system. To determine whether gf-PBM regulates AD by stimulating the intestinal mucosal immune system, we performed immunostaining for CD45 (marker for leukocytes) and CD11b (marker for phagocytes) in the mesenteric lymph nodes. As shown in Fig. 4F, we observed no significant difference between the groups when the positive cells were counted by ImageJ. In addition, we detected proinflammatory cytokines IL-6 and INF-γ (Fig. 4D), which reflect humoral immunity. Both IL-6 and INF-γ were inhibited in AD mice. PBM630 and PBM730 significantly increased the levels of INF-γ in AD mice and exceeded the levels in normal mice (8.37–fold, P = 0.0004, and 3.97–fold, P = 0.018, respectively).
3.5 Proteomic changes in the hippocampus after gf-targeted PBM
The original mass spectrometry data from the hippocampus of five groups of mice were filtered and searched by false discovery rate (FDR) < 1%. A total of 3,872 proteins were identified, matching 17,296 peptides and 289,377 spectra. A quantitative analysis was carried out on samples according to the peak strength of tagged ions, and the number of DEPs obtained (p < 0.05) is shown in Fig. 5A and 5B. A fold-change of DEPs greater than 1.2 is upregulated (red) and a fold-change of less than 0.8 is downregulated (green). Compared with Ctrl M group, the number of DEPs in the hippocampal tissues of groups PBM630 and PBM730 were 1,209 and 1,329, respectively, while the number of DEPs in PBM850 was 634. We found that the DEPs between groups PBM630 and PBM730 groups was low, while the DEPs between PBM850 and PBM630 or PBM730 was high. This indicates that the mechanisms of action associated with the PBM630 and PBM730 treatments may be similar, while the PBM850 treatment may occur via other mechanisms. The heat map in Fig. 5B shows that the expression of DEPs (compared with Ctrl M) in the PBM groups is opposite to Ctrl M. This indicates that PBM treatment corrects the abnormal changes in hippocampal proteins induced by AD.
The DEPs (compared with Ctrl M) of the three PBM groups were analyzed by GO classifications, including cellular components, molecular functions, and biological processes, to obtain functional annotation information from each protein. The first five proteins with the lowest p values are shown in Fig. 5C. In the PBM630 and PBM730 groups, DEPs were similar in terms of cellular components, molecular functions, and biological processes involved. For example, cellular components were mostly parts of proteins that composed cells and cytoplasm, molecular functions were mainly related to binding, and the biological processes involved were reorganization of cellular components and regulation of biological processes. Group PBM850 had both shared and unique DEPs compared with groups PBM630 and PBM730. For example, some DEPs in group PBM850 are components of neuronal projection. For molecular functions, some DEPs are related to G protein-coupled receptors, and for biological processes, the DEPs are more involved in the negative regulation of metabolic processes.
We also used the KEGG database to analyze the DEPs of the three PBM groups in Pathway. In all pathways with a p value of less than 0.05, the ratio of DEPs in background proteins of a pathway were sorted, as shown in Fig. 6. In the pathway of AD, the proportion of DEPs in the three PBM groups was high, with values of 58.7% (PBM630), 50.8% (PBM730), and 31.7% (PBM850). Mitochondrial respiratory chain complex enzymes were most affected by PBM (the lower right corner in Fig. 6) and Cx1 was downregulated, while CxII and CxV were upregulated under the three PBM treatments. CytC, a key member of the apoptosis pathway, was also downregulated in the PBM groups. In addition, Tau, RTN3/4, and SNCA, which all take part in AD development, were downregulated by the PBM interventions. Furthermore, some DEPs were involved in oxidative phosphorylation, phagocytosis, metabolism of some biological macromolecules, and most importantly, the secretion of many hormones, including insulin, thyroxine, and glucagon. The pathways associated with the DEPs of the PBM850 group were less than that of PBM630 and PBM730.
We also conducted a comparative analysis of the DEPs in the three PBM groups and found that there were 1,007 identical DEPs in PBM630 and PBM730, and 509 identical DEPs in PBM630, PBM730, and PBM850. We performed GO and KEGG pathway analyses on these 509 common DEPs to elucidate the possible association between the mechanism of AD treatment with gf-targeted PBM. The results are shown in Fig. 7. Many of the common DEPs are related to components in the cell and on the cell membrane, specifically, the binding of G protein complexes and receptors on the cell membrane and the binding of the cytoskeleton, RNA, and proteins. These DEPs are mainly involved in the negative regulation of cellular processes such as metabolism, especially the metabolism of nitrogen compounds, and the regulation of transport and localization. For the KEGG pathway analysis, in addition to the DEPs associated with AD, the DEPs are mainly involved in oxidative phosphorylation, calcium signaling pathways, inflammation, phagocytosis, the secretion of various hormones (such as thyroid hormones, parathyroid hormones, aldosterone, renin, cortisol, and insulin), and the formation of synaptic structures. These findings imply that inflammation, repair, and reconstruction are all related to the improvement of AD with PBM.
3.6 Changes of intestinal flora diversity after gf-targeted PBM treatment
We compared the changes in intestinal flora diversity of each group after PBM treatment using 16S rRNA gene amplicon sequencing. Figure 8A shows the principal components analysis (PCA) of gut microbiome composition at the operational taxonomic unit (OTU) level for the mice. The gut flora of the five groups clustered separately, and the clusters of the three PBM groups were located between the clusters of the AD mice and the normal mice. ANOVA and Kruskal–Wallis tests were used to analyze the species with significant differences between groups. As shown in Fig. 8B, there were differences at all taxonomic levels, especially at the genus level. A boxplot analysis was conducted to determine the relative abundances of four of the top ten species present at different levels among the study groups. As shown as Fig. 8C, the abundances of Helicobacter, Oscillibacter, Ruminiclostridium-5, and uncultured Bacteroidales increased, while Rikenella, Desulfovibrio, Ruminococcus-2, and Butyricicoccus decreased in AD mice compared with the normal mice. PBM treatment corrected this imbalance of bacteria to some extent. In particular, all PBM treatments (PBM630, PBM730 and PBM850) reversed the increase of Helicobacter and uncultured Bacteroidales and the decrease of Rikenella in AD mice. Some of the findings were wavelength-specific. For example, PBM630 decreased the abundance of Oscillibacter, PBM730 increased the abundance of Desulfovibrio and Ruminococcus-2, and PBM850 corrected the imbalance of Butyricicoccus and Ruminiclostridium-5 in AD mice. All PBMs decreased the abundance of Ruminococcus-1. However, the abundance of Ruminococcus-1 was not significantly different between the AD and normal mice. We also performed a KEGG function prediction based on the 16S sequencing data, conducted a statistical analysis among groups according to the Kruskal–Wallis algorithm, and homogenized the results to form a heat map (Fig. 8D). We found that the functional composition of the intestinal flora in AD mice was different from that of normal mice, especially for pyrimidine metabolism, lipopolysaccharide synthesis, and bacterial toxins. PBM630 and PBM850 performed to correct the functional abnormality, bringing the functional composition closer to that of the normal flora.