More Guts Than Brains?' The Role of Gut Microbiota in Idiopathic Intracranial Hypertension

Idiopathic intracranial hypertension syndrome (IIH) is most common among obese women. Weight loss is an important factor in improving papilledema. Over the last decade, growing evidence has identified gut microbiota as a potential factor in the pathophysiology of obesity. Accordingly, we investigated whether the gut microbiome is modified in IIH patients compared with healthy controls, and provide possible new treatment venues.

Original Contribution Section Editors: Clare Fraser, MD Susan Mollan, MD “More Guts Than Brains?”–The Role of Gut Microbiota in Idiopathic Intracranial Hypertension Eran Berkowitz, MD, Yael Kopelman, MD, Dana Kadosh, BSc, Shaqed Carasso, BSc, Beatrice Tiosano, MD, Anat Kesler, MD, Naama Geva-Zatorsky, PhD Background: Idiopathic intracranial hypertension syndrome (IIH) is most common among obese women. Weight loss is an important factor in improving papilledema. Over the last decade, growing evidence has identified gut microbiota as a potential factor in the pathophysiology of obesity. Accordingly, we investigated whether the gut microbiome is modified in IIH patients compared with healthy controls, and provide possible new treatment venues. Methods: Shotgun metagenomic sequencing of the gut microbiome of 25 cases of IIH patients (according to the modified Dandy criteria) and 20 healthy controls. Participants were further stratified according to their body mass index. The total DNA from each sample was extracted using the PureLink Microbiome DNA Purification Kit A29789 (Invitrogen, Thermo Fisher Scientific, US). Library preparation was performed using the Nextera DNA Flex Library Prep Kit. Samples were sequenced on the Illumina Novaseq 6000 device. A list of bacterial species that significantly differed between the IIH patients and healthy controls was produced in addition to species diversity. In addition, patients’ cohort alone was analyzed, (excluding the healthy controls), and the effect of acetazolamide treatment on their gut microbiota was analyzed. Department of Ophthalmology (EB, BT, AK), Hillel Yaffe Medical Center, Hadera, Israel; Institute of Gastroenterology and Hepatology (YK), Hillel Yaffe Medical Center, Hadera, Israel; Department of Cell Biology and Cancer Science (DK, SC, NG-K), Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Technion Integrated Cancer Center (TICC), Haifa, Israel; Sackler Faculty of Medicine (AK), Tel Aviv University, Tel Aviv, Israel; and Canadian Institute for Advanced Research (CIFAR) (NG-Z), Azrieli Global Scholar, MaRS Centre, Toronto, Canada. The Medical Research Fund, Hillel Yaffe Medical Center to The University of Illinois at Chicago for shotgun sequencing. N. GevaZatorsky received “Keren Hanasi,” “Cathedra for Young PIs” and the Technion Integrated Cancer Center and is an Azrieli Global Scholar at CIFAR, and is supported by the Alon Fellowship and Horev of the Taub Foundation. The authors report no conflicts of interest. E. Berkowitz, Y. Kopelman, D. Kadosh, A. Kesler and N. GevaZatorsky are equal contribution. Address correspondence to Anat Kesler, MD, Department of Ophthalmology, Hillel Yaffe Medical Center, Hadera, Israel 38100; E-mail: [e-mail] e70 Results: IIH patients have a lower diversity of bacterial species compared with healthy individuals. These bacteria, that is, Lactobacillus ruminis (L. ruminis) (p,6.95E-08), Atopobium parvulum (p,3.9E-03), Megamonas hypermegale (p,5.61E-03), Ruminococcus gnavus (p,1.29E02), MEL.A1 (p,3.04E-02), and Streptococcus sp. I-G2 (p,3.04E-02), were previously characterized with beneficial health effects. Moreover, we found that Lactobacillus brevis, a beneficial bacterium as well, is more abundant in acetazolamide treated patients (p,7.07E-06). Conclusions: Gut microbiota plays a potential role in IIH etiology and therefore, can provide a promising new treatment approach for this disease. Journal of Neuro-Ophthalmology 2022;42:e70–e77 doi: 10.1097/WNO.0000000000001330 © 2021 by North American Neuro-Ophthalmology Society I diopathic intracranial hypertension (IIH) is a disorder of unknown etiology predominantly affecting obese women during childbearing age. The incidence of IIH is w1–2/ 100,000 in the general population, 3.5/100,000 in women aged 20–44, and further increasing to 19/100,000 in women who weigh 20% over the ideal body weight. The etiology of IIH and the pathophysiological mechanisms of raised intra cranial pressure is obscure. A relationship between obesity and IIH has long been recognized and it has been shown that a 6% weight reduction can improve the patient’s papilledema (1). The human gastrointestinal tract is populated by a huge number of microorganisms, including bacteria, archaea, and viruses. This community of microorganisms is termed “the gut microbiota.” Environmental and genetic factors have been implicated in obesity including changes in the gut microbiota that may play a role in the development of metabolic disorders (2,3). Although, the exact mechanism linking gut microbiota to obesity is far from being well understood, it has been well established that the gut microbiota can increase energy production from one’s diet, contribute to low-grade inflammation and regulate fatty acid Berkowitz et al: J Neuro-Ophthalmol 2022; 42: e70-e77 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Original Contribution tissue composition (4,5). Mounting evidence has indicated that the gut microbiota influences brain function and behavior through neural, endocrine, and immune pathways (6). Changes in the blood–brain barrier (BBB) permeability, due to the microbiota, have also been depicted as part of the interaction between the gut and the brain (7). Possible mechanisms include BBB modulation by gut-derived neurotransmitters and bacterial metabolites (8). The interesting connection between obesity and IIH, and the established effect of the microbiota on obesity and its link to the brain, led us to hypothesize a new paradigm. The aim of our study was to investigate the importance of the microbiota composition in IIH patients and its possible correlation to the disease. METHODS Inclusion Criteria IIH patients (according to the modified Dandy criteria) (9) arriving for follow-up appointments in the NeuroOphthalmic Clinic, Department of Ophthalmology, Hillel Yaffe Medical Center, Hadera, Israel. Controls were recruited from healthy women, escorting patients to the Gastroenterology Institute, Hillel Yaffe Medical Center. Patients and controls were women aged 18–65. All study participants were recruited between 3/2018 and 6/2019. Exclusion Criteria Patients who were ,18 or .65 years old, did not meet the Modified Dandy Criteria, and suffered from an acute infection or had received recent antibiotic treatment. Standard Protocol Approvals, Registration and Patient Consents The study was approved by the local institutional review board and ethics committee of the Hillel Yaffe Medical Center, Israel. Written informed consent was obtained before enrollment. Patient Classification Patients were classified according to their body mass index (BMI): normal (18.5 , BMI, 25), overweight (25 , BMI , 30), obese Class I (30 , BMI, 35), obese Class II (35 , BMI, 40), and obese Class III (BMI . 40). Sample Collection and Processing Feces samples from each participant were collected in sterile cups and immediately frozen at 280°C. The total DNA from each sample was extracted using the PureLink Microbiome DNA Purification Kit A29789 (Invitrogen, Thermo Fisher Scientific, US), according to the manufacturer’s instructions. Berkowitz et al: J Neuro-Ophthalmol 2022; 42: e70-e77 Shotgun Metagenomic Sequencing Library preparation was performed using the Nextera DNA Flex Library Prep Kit. Samples were sequenced on the Illumina Novaseq 6000 device. Basic processing of the raw data was performed at the Research Informatics Core, University of Illinois-Chicago, USA. The references for taxonomic annotations were matched by a centrifuge via a nucleotide search (https://www.ncbi.nlm.nih.gov/nuccore) (10). Data Analysis Taxonomic data were filtered separately for bacteria and archaea. Only operational taxonomic units with minimal readings of 20 and an 18% prevalence in samples were included in the final analysis of the bacterial population, and a minimal reading of 0 and a 10% prevalence of the archaeal population, respectively. These data were subsequently normalized by the total sum scaling method. Primary data analysis was performed using the MicrobiomeAnalyst (11) followed by a comprehensive analysis using R, a language and environment for statistical computing (v4.0.2, R core team, Vienna, Austria) (12). Results were visualized using the ggplot2 R package (13). Shannon alpha diversity indices were calculated for every sample and grouped by patients or control subjects, and by BMI classifications using the Phyloseq R package (14) and compared by Mann–Whitney U test (for a 2 group comparison) and the Kruskal–Wallis H test for multiple group comparisons. Differences in microbial taxa among the different groups were evaluated by the differential abundancies analysis using the DESeq2 R package. The DESeq algorithm provides a method for differential analysis of count data (15). We produced a list of bacterial species that significantly differed between the IIH patients and healthy controls. These species passed the multiple-test adjustment and were ranked by P-value and fold change between the 2 groups. Selected results were represented in boxplots. Significance was set at P , 0.05 after a false discovery rate (FDR) correction for multiple comparisons using the Benjamini– Hochberg method was performed (16). In addition, we analyzed the patients’ cohort alone, (excluding the healthy controls), and focused on the effect of acetazolamide treatment on their gut microbiota. RESULTS Our study groups included 45 women: 25 IIH patients and 20 healthy controls. Age averages were 35.12 and 48.5, respectively (P , 0.001). According to this BMI classification, our cohort included 3 normal subjects, 13 overweight, 12 obese Class I, 9 obese Class II, and 8 obese Class III. The average BMI in the patient group was 37.07 and in the controls, 41.06 (P = 0.64). Eight patients were currently receiving acetazolamide treatment and 17 were not e71 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Original Contribution receiving any type of treatment. Two samples (one patient and one control) were excluded because of low species richness with a low alpha diversity index and an extremely different bacterial composition. Bacteria and archaea compositions were analyzed from the microbiome sequences. Archaea Analysis Our analysis revealed 3 abundant classes of archaea among all participants: Methanobacteria, Methanomicrobia, and Methanococci, all methanogenic archaea. These 3 archaea classes comprised at least 50% or more (some up to 80%) of the archaea classes in each sample. A few of the samples revealed a high abundance of Methanobacteria archaea, which were mostly affected by a high abundance of the Methanobrevibacter smithii species (Fig. 1). The archaea analysis revealed no difference between the patients and the controls. No differences between different BMI classifications were found. Bacteria Analysis We found that the 10 most abundant phyla in all study participants were: Firmicutes, Actinobacteria, Bacteriodetes, Proteobacteria, Verrucomicrobia, Cyanobacteria, Fusobacteria, Tenericutes, and Spirochaetes (in order of abundance). Among all participants, the average bacterial Firmicutes to Bacteriodetes phyla ratio was 4.24. Alpha Diversity Alpha diversity measures the “richness” of the species within a sample. The high alpha diversity index corresponds to a more diverse population. Our analysis revealed a higher alpha diversity among the controls compared with the IIH patients, with average values of 4.286 and 4.168 and median values of 4.205 and 4.039, respectively. Indices of alpha diversity were compared using the Mann–Whitney rank-sum test with a P-value = 0.2. Differences in Microbiota Between Idiopathic Intracranial Hypertension Patients and Controls On the higher taxonomic levels, certain bacterial taxa were significantly more abundant on average in the controls compared with the patients, that is, the Negativicutes class, the genera Megamonas and Atopobium (Fig. 2A) and the Atopobium parvulum, Megamonas hypermegale, Ruminococcus gnavus, MEL.A1, Streptococcus sp. I-G2, and Lactobacillus ruminis species (Fig. 2B, C). These 6 were at least 2-fold more abundant on average in the controls than in the patients, with a minimal P-adjusted of 0.03 (after FDR FIG. 1. The 10 most abundant classes of archaea across all participants with body mass index classification. Methanogenic archaea are Methanobacteria (purple), Methanomicrobia (blue), and Methanococci (green). Black dashed line indicates the median of the most abundant classes. e72 Berkowitz et al: J Neuro-Ophthalmol 2022; 42: e70-e77 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Original Contribution FIG. 2. A. Boxplots of bacterial taxa counts in control subjects (control, red) and patients (idiopathic intracranial hypertension [IIH], blue). Class Negativicutes, genera Megamonas and Atopobium. Boxes correspond to the interquartile range (IQR)—the distance between the 75th percentile (upper hinge) and the 25th percentile (lower hinge) of the data. Whiskers correspond to the largest (smallest, respectively) observation within a distance of 1.5 times the IQR above the upper hinge (below the lower hinge). Black bold line denotes the median. P-value for each subplot (from left to right): 2.6356E-5, 0.045241, 0.045241. B. Boxplots of bacterial species counts in control subjects (control, red) and patients (IIH, blue). Species are the same as in Figure 4. Boxes correspond to the interquartile range (IQR)—the distance between the 75th percentile (upper hinge) and the 25th percentile (lower hinge) of the data. Whiskers correspond to the largest (smallest, respectively) observation within a distance of 1.5 times the IQR above the upper hinge (below the lower hinge). Black bold line denotes the median. C. Bacterial species which are significantly different between control subjects and patients (Padjusted is P-value after false discovery rate correction). Berkowitz et al: J Neuro-Ophthalmol 2022; 42: e70-e77 e73 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Original Contribution FIG. 3. Boxplot of Lactobacillus brevis counts in patients that received acetazolamide treatment or did not receive. Boxes correspond to the interquartile range (IQR)—the distance between the 75th percentile (upper hinge) and the 25th percentile (lower hinge) of the data. Whiskers correspond to the largest (smallest, respectively) observation within a distance of 1.5 times the IQR above the upper hinge (below the lower hinge). Black bold line denotes the median. Untreated - red, treatedblue. P-adjusted = 7.07E-06. correction). The species with the highest log2 fold change was L. ruminis which was 21.7 times more abundant on average in the controls than in the patients, with a Padjusted value of 6.95E-08 (Fig. 2C). Acetazolamide Effect on Gut Microbiome We discovered a higher abundance of Lactobacillus brevis in acetazolamide-treated patients compared with the untreated ones (Fig. 3). To note, we excluded one patient’s sample from this analysis because the patient had consumed iron supplements for iron deficiency anemia, a condition that can affect the gut microbial ecosystem (17). Indeed, this patient’s microbiome differed dramatically when compared with the other acetazolamide-treated patients, therefore, we considered this sample as an outlier for this measurement. The Connection Between Body Mass Index and Microbiota On the species level, the abundance of Streptococcus thermophilus (S. thermophilus) was higher in subjects with a lower BMI (overweight and obese Class I classifications) than in the higher BMI groups (obese Class II and obese Class III classifications). In each of the lower BMI groups, S. thermophilus was found higher among the patients compared with the controls (Fig. 4). To note, the abundance of S. thermophilus was very low in the normal BMI group (2.19 · 102, 4.31 · 102 and 6.05 · 102). DISCUSSION Idiopathic intracranial hypertension syndrome is a visionthreatening disorder predominantly affecting obese women e74 of childbearing age. The main goals of treatment are to preserve visual function and alleviate symptoms, which can usually be achieved with a combination of weight loss, medical therapies, and surgical interventions, depending on the severity of symptoms and vision loss, response to treatment, and the subsequent clinical course. This disorder is becoming more prevalent as the obesity epidemic continues to increase (18). The human gut is populated by a vast number of microorganisms from a variety of taxonomic groups and with diverse characteristics, termed “microbiome.” Obesity is one of the first medical conditions linked to changes in the gut microbiome. Herein, we analyzed the differences between the gut microbial populations of IIH patients and healthy obese controls. In addition to bacteria, the gut microbial ecosystem is inhabited by microorganisms from additional kingdoms such as archaea (19). Methanogenic archaea and specifically M. smithii may promote changes in the host metabolism and subsequently, weight gain in the host. Three classes of methanogens, Methanobacteria, Methanomicrobia, and Methanococci were found extremely abundant in this study cohort, specifically characterized by a high BMI. Similar to archaea, most of the gut bacteria are strict anaerobes. Previous studies have shown that the Firmicutes to Bacteriodetes (19) bacterial phyla ratio increases with BMI and decreases with weight loss (20). In this study, a similar trend was found with an average bacterial Firmicutes to Bacteriodetes phyla ratio of 4.24. The alpha diversity (the species richness within a sample), is inversely correlated with obesity, that is, obese people have a low richness of bacterial population residing in their intestines (21). We found a similar trend in our Berkowitz et al: J Neuro-Ophthalmol 2022; 42: e70-e77 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Original Contribution FIG. 4. Boxplot of Streptococcus thermophilus counts in control subjects (ctrl) and patients (idiopathic intracranial hypertension [IIH]) according to their BMI classification. Boxes correspond to the interquartile range (IQR), the distance between the 75th percentile (upper hinge) and the 25th percentile (lower hinge) of the data. Whiskers correspond to the largest (smallest, respectively) observation within a distance of 1.5 times the IQR above the upper hinge (below the lower hinge). Black bold line denotes the median. Red color corresponds to control subjects and green color to patients. P-value = 0.01476. The P-value is shown after false discovery rate correction. results (except for obese Class III) where IIH patients had a lower diversity index compared with the controls. Idiopathic Intracranial Hypertension Patients Versus Controls Our analysis revealed that there are certain bacteria that are more abundant among the healthy controls compared with the IIH patients, that is, the Negativicutes class, the genera Megamonas and Atopobium and the L. ruminis,, A. parvulum, M. hypermegale, R. gnavus, MEL.A1 and Streptococcus sp. I-G2 species. We investigated their possible effects on an individual’s health status. L. ruminis is a lactic acid bacterium belonging to the genus Lactobacillus, the family Lactobacillaceae, the order Lactobacillales, the Bacilli class and the phylum Firmicutes. L. ruminis was shown to beneficially effect the host gut. This bacterium has the ability to inhibit pathogens, synthesize bacteriocins, and most importantly, maintain the epithelial barrier functions. Furthermore, in vitro studies have demonstrated that whole cells or fractions of L. ruminis generate immunomodulatory effects on the host, such as inhibition of NF-kB activation and interleukin-8 production, stimulating responses via the toll-like receptor (TLR)-2 or flagellin-TLR5 signaling pathBerkowitz et al: J Neuro-Ophthalmol 2022; 42: e70-e77 ways, andstimulatory effects on the secretion of tumor necrosis factor and antiviral effects. Accordingly, researchers have recommended L. ruminis as a potential probiotic candidate because of its immunomodulatory effects (22,23). Takaishi et al found that Atopobium beneficially effects the intestinal mucosa, specifically, inflammatory bowel diseases. Its prevalence is lower in ulcerative colitis and Crohn disease patients compared with healthy controls (24). Furthermore, a specific species of the Atopobium genus, Atopobium minutum, has been shown to induce apoptosis in a human colonic carcinoma cell line (25). M. hypermegale, another beneficial species was found higher in the healthy obese control patients compared to Behçet disease patients (26). Acetazolamide’s Effect on Gut Microbiome Focusing on IIH patients treated with acetazolamide, we found that L. brevis was more abundant in patients who ingested this medication compared with those who did not. This species harbors probiotic properties, such as supporting the intestinal epithelial barrier (27). It is known that acetazolamide, the first line of drugs in treating IIH acts as a diuretic, influences bowel e75 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Original Contribution microenvironment by intracellular and extracellular space fluid alterations, and alters cellular permeability. In addition, acetazolamide interferes with the protective effect of prostaglandin E2, causing the inhibition of carbonic anhydrase activity and mucus secretion (28) in the gut, thus, exacerbating the acidic conditions. Lactobacilli possess attributes allowing them to overcome adverse environmental conditions generated through high acidity, that is, different proteases that counteract nitric oxide and IgA exposure in the gut (29). This may present an advantage over other bacteria to thrive in this harsh acidic environment mediated by acetazolamide. The Association Between Body Mass Index and Microbiota In each of the lower BMI groups, S. thermophilus was found higher among the patients compared with the controls. S. thermophilus was shown to possess many beneficial health effects in humans, such as improving gut inflammation, possessing antimutagenic and antitumorigenic effects, lactose digestion in lactose intolerant individuals, stimulation of the immune system, and more (30,31). Intriguingly, a recent study demonstrated a significant reduction in body weight and BMI following the ingestion of probiotic bacteria such as S. thermophilus, Lactobacillus acidophilus, Bifidobacterium infantis, Bifidobacterium breve, and Enterococcus faecalis (32). Study Limitations Although the study sample is relatively small, one should take into account that this is a prospective study reporting on a rare disease. Nevertheless, this study yielded significant and consistent results. We believe that this preliminary study can provide a basis for future studies with larger cohorts. Because the average age of the IIH patients differed from the controls by 13 years, we analyzed the data for beta diversity (by the Bray–Curtis dissimilarity), grouping by age, and found that age could not explain the differences between the groups. Furthermore, previous studies have demonstrated a decrease in the microbiome diversity with age, where beneficial genera are lost and genera related with inflammation and cancer are increased (33). Our results demonstrated the opposite: in the IIH group, which was younger on average, the level of beneficial bacteria was reduced compared with the healthy, older control group. This finding highlights and emphasizes the differences in the gut microbiome composition between these 2 groups. L. ruminis, M. hypermegale, and MEL.A1 seem to have a bimodal population. We analyzed the abundance of these bacteria using several pairs of variables: age, BMI, and reproductive age compared with the disease status (i.e., healthy vs IIH patients). None of these comparisons explained the bi-modal distribution of their abundances. In conclusion, we found that IIH patients have a lower diversity of bacterial species compared with healthy cone76 trols, and typically contain lower amounts of bacterial species characterized with beneficial health effects on the host. In addition, we found a possible effect of acetazolamide treatment on the microbiota of IIH patients, enriching a well-known beneficial bacterial species. The recognition that gut bacteria plays a role in the treatment of many diseases, including obesity, raises the possibility that these groups of beneficial bacteria may be used to improve IIH patients’ health state and reduce their BMI. An increase in beneficial bacteria can interfere with harmful microorganisms, and bacteria related to diseases by direct competition on gut colonization, or indirectly, by affecting the host immune system. This study revealed a novel association between the gut microbiota composition and IIH disease possibly through the microbiota–gut–brain axis. Our understanding of this disease’s etiology may be further enhanced and pave the way for future research investigating new treatment modalities based on the gut microbiota. STATEMENT OF AUTHORSHIP E. Berkowitz was responsible for the data curation, investigation, writing the original draft, data interpretation, the literature review. Y. Kopelman was responsible for the literature review, acquisition of data, and writing the original draft. D. Kadosh was responsible for literature review, data curation and interpretation, formal analysis, investigation, software, validation, visualization, and writing the original draft. S. Carasso was responsible for the investigation, methodology, project administration, and software. B. Tiosano was responsible for the funding acquisition, resources, supervision, writing, reviewing and editing the manuscript. A. Kesler was responsible for the conceptualization, data curation, funding acquisition, investigation, methodology, supervision, writing the original draft, reviewing and editing. N. Geva-Zatorsky was responsible for the conceptualization, data curation, investigation, methodology, supervision, writing the original draft, reviewing and editing the manuscript. ACKNOWLEDGMENTS The authors thank Hagai Kariti for his assistance in creating the graphs, Naama Eshal and Tal Gefen for assisting in the design of the study, Ilana Shnaiderman for assisting in the sample collection and Mrs. Phyllis Curchack Kornspan for her editorial services. REFERENCES 1. Kesler A, Kliper E, Shenkerman G, Stern N. Idiopathic intracranial hypertension is associated with lower body adiposity. Ophthalmology. 2010;117:169–174. 2. Rosenbaum M, Knight R, Leibel RL. The gut microbiota in human energy homeostasis and obesity. Trends Endocrinol Metab. 2015;26:493–501. 3. Cox AJ, West NP, Cripps AW. Obesity, inflammation, and the gut microbiota. Lancet Diabetes Endocrinol. 2015;3:207–215. 4. Cani PD, Osto M, Geurts L, Everard A. Involvement of gut microbiota in the development of low-grade inflammation and type 2 diabetes associated with obesity. Gut Microbes. 2012;3:279–288. 5. Kallus SJ, Brandt LJ. The intestinal microbiota and obesity. J Clin Gastroenterol. 2012;6:6–24. Berkowitz et al: J Neuro-Ophthalmol 2022; 42: e70-e77 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Original Contribution 6. Cryan JF, Dinan TG. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci. 2012;13:701–712. 7. Zhu S, Jiang Y, Xu K, Cui M, Ye W, Zhao G, Jin L, Chen X. The progress of gut microbiome research related to brain disorders. J Neuroinflammation. 2020;17:25. 8. Braniste V, Al-Asmakh M, Kowal C, Anuar F, Abbaspour A, Toth M, Korecka A, Bakocevic N, Guan NL, Kundu P, Gulyas B, Halldin C, Hultenby K, Nilsson H, Hebert H, Volpe BT, Diamond B, Pettersson S. The gut microbiota influences blood-brain barrier permeability in mice. Sci Transl Med. 2014;6:263ra158. 9. Friedman DI, Liu GT, Digre KB. Revised diagnostic criteria for the pseudotumor cerebri syndrome in adults and children. Neurology. 2013;81:1159–1165. 10. Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 2016;26:1721–1729. 11. Dhariwal A, Chong J, Habib S, King IL, Agellon LB, Xia J. Microbiome analyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 2017;45:W180–W188. 12. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2020. Available at: https://www.r-project.org/. Accessed August 2, 2020. 13. Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer Science & Business Media, 2000. 14. McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217. 15. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21. 16. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300. 17. Yilmaz B, Li H. Gut microbiota and iron: the crucial actors in health and disease. Pharmaceuticals. 2018;11:98. 18. Madriz Peralta G, Cestari DM. An update of idiopathic intracranial hypertension. Curr Opin Ophthalmol. 2018;29:495–502. 19. Sekirov I, Russell SL, Caetano M, Antunes L, Finlay BB. Gut microbiota in health and disease. Physiol Rev. 2010;90:859–904. 20. Gomes AC, Hoffmann C, Mota JF. The human gut microbiota: metabolism and perspective in obesity. Gut Microbes. 2018;9:308–325. 21. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit P, Eghaolm M, Henrissat B, Heath AC, Knight R, Gordon JI. A core gut Berkowitz et al: J Neuro-Ophthalmol 2022; 42: e70-e77 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. microbiome in obese and lean twins. Nature. 2009;457:480– 484. Yu X, Åvall-Jääskeläinen S, Koort J, Lindholm A, Rintahaka J, von Ossowski I, Palva A, Hynonen U. A comparative characterization of different host-sourced Lactobacillus ruminis strains and their adhesive, inhibitory, and immunomodulating functions. Front Microbiol. 2017;8:657. Wang S, Yang B, Ross RP, Stanton C, Zhao J, Zhang H, Chen W. Comparative genomics analysis of Lactobacillus ruminis from different niches. Genes. 2020;11:70. Takaishi H, Matsuki T, Nakazawa A, Takada T, Kado S, Ashahar T, Kamada N, Sakuraba A, Yajima T, Higuchi H, Inoue N, Ogata H, Iwao Y, Nomoto K, Tanaka R, Hibi T. Imbalance in intestinal microflora constitution could be involved in the pathogenesis of inflammatory bowel disease. Int J Med Microbiol. 2008;298:463–472. Altonsy MO, Andrews SC, Tuohy KM. Differential induction of apoptosis in human colonic carcinoma cells (Caco-2) by Atopobium, and commensal, probiotic and enteropathogenic bacteria: mediation by the mitochondrial pathway. Int J Food Microbiol. 2010;137:190–203. Shimizu J, Kubota T, Takada E, Takai K, Fujiwara N, Arimitsu N, Ueda Y, Wakisaka S, Suzuki T, Suzuki N. Relative abundance of Megamonas hypermegale and Butyrivibrio species decreased in the intestine and its possible association with the T cell aberration by metabolite alteration in patients with Behcet’s disease (210 characters). Clin Rheumatol. 2019;38:1437–1445. Ramos CL, Thorsen L, Schwan RF, Jespersen L. Strain-specific probiotics properties of Lactobacillus fermentum, Lactobacillus plantarum and Lactobacillus brevis isolates from Brazilian food products. Food Microbiol. 2013;36:22–29. Cho CH, Chen SM, Chen SW, Chow CK, Lai KH, Pfeiffer CJ. Pathogenesis of gastric ulceration produced by acetazolamide in rates. Digestion. 1984;29:5–11. Jens W. Ecological role of Lactobacilli in the gastrointestinal tract: implications for fundamental and biomedical research. Appl Environ Mcirobiol. 2008;74:4985–4996. Vitetta L, Llewellyn H, Oldfield D. Gut dysbiosis and the intestinal microbiome: streptococcus thermophilus a key probiotic for reducing uremia. Microorganisms. 2019;7:228. Iyer R, Tomar SK, Uma Maheswari T, Singh R. Streptococcus thermophilus strains: multifunctional lactic acid bacteria. Int Dairy J. 2010;20:133–141. Mazloom K, Siddiqi I, Covasa M. Probiotics: how effective are they in the fight against obesity? Nutrients. 2019;11:258. Nagpal R, Mainali R, Ahmadi S, Wang S, Singh R, Kavanagh K, Kitzman DW, Kushugulova A, Marotta F, Yadav H. Gut microbiome and aging: physiological and mechanistic insights. Nutr Healthy Aging. 2018;4:267–285. e77 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited.