More than 80 microbial features differed significantly between the IHH-exposed group and controls. Figure 3.S2a presents a global overview of these changes in gut microbiota per sample . Table S23 provides a list of these differentially represented bacteria that potentially contribute to alterations in gut metabolism due to IHH. Figure 3.2a-f displays trends in relative abundances of bacteria showing the largest differences, which belong to the Mogibacteriaceae , Oscillospira , Lachnospiraceae and Clostridiaceae . Previous studies have consistently associated these taxonomic groups with metabolic and inflammatory disturbances in the host , which suggests that related mechanisms may be at play in driving the consequences of hypoxic and hypercapnic stress.Using the same statistical approach, we found that more than 380 molecular species differed significantly in relative abundance in animals exposed to IHH. Figure 3.S2b provides a global representation of these differentially abundant molecules in samples belonging to treatment and control groups and sorted by treatment duration . To gain insight into the structures of these differentially abundant metabolites, we performed molecular networking using Global Natural Products Social Molecular Networking . The molecular network is constructed using a cosine similarity measure between tandem mass spectral data, then visualized using Cytoscape . Each node in the network, which represents a consensus MS/MS spectrum, was searched against public libraries in GNPS. In total,large plastic pots for plants we annotated about 400 molecular compounds in GNPS including bile acids, fatty acids and phytoestrogens.
Additionally, all key compounds discussed in this work were defined to the highest level of annotation according to the metabolomics standards initiative using commercial standards . Interestingly, the top differentially abundant features detected between IHH and control mice, included molecules known to depend on gut microbes for their production. Below, we discuss some of these metabolites and their implications with respect to consequences of OSA.We observed significant alterations in bile acids between IHH-exposed mice and control groups . Figure 3.2g-n displays these trends in primary and secondary bile acids with increasing IHH exposure duration. These are conjugated to glycine or taurine and released in the biliary tract. Together with other biliary components, these facilitate in emulsification and transportation of dietary fats, cholesterol, and fat-soluble vitamins. About 95% of the BAs are reabsorbed in the terminal ileum and recycled. The remaining 5% reach the colon and are deconjugated, dehydrogenated and dehydroxylated by the intestinal bacteria to form secondary BAs . BAs, including microbially-generated BAs, are potent signaling molecules that interact with the Farnesoid X receptor which modulates BA synthesis by the liver . Perturbations in gut microbial populations disrupt normal signaling properties that regulate BA production, and can profoundly alter the BA composition in the gut. A range of diseases, including cardiometabolic diseases, are characterized by aberrant BA profiles , and prolonged perturbations in the BA pool could also be a factor in mediating the consequences of OSA.The dietary hormones, enterolactone and enterodiol were significantly elevated in the exposed mice compared to controls. Figure 3.2m, n shows the trends in their abundances with increasing duration of IHH-exposure.
These molecules are phytoestrogens i.e. plant-derived hormones that structurally mimic estrogen and are produced by intestinal microbiota on bio-conversion of dietary lignans. Owing to their affinity to estrogen receptors, they perturb many hormone-dependent systems in the body and have been linked to adverse metabolic, reproductive and neurological outcomes . Sex-specific differences in OSA diagnostic symptoms and risk factors suggest hormonal involvement . However, the contribution of microbes in maintaining hormonal homeostasis has not yet been investigated. Therefore, these findings motivate novel avenues of research for biomarkers and therapeutic targets to manage the metabolic consequences of OSA.In addition to changes in bile acids and phytoestrogens, we also detected differentially abundant fatty acid-related chemical families . For example, we noted a significant reduction in molecular features matched to elaidic acid. Elaidic acid is an unsaturated fatty acid that increases plasma cholesteryl ester transfer protein activity that modulates systemic levels of LDL and HDL cholesterol. A decrease in elaidic acid in the IHHexposed group suggests reduction in plasma CETP activity, a mechanism associated with adverse cardiovascular effects . Similarly, phytomonic, jasmonic, hexadecanoic, linoleic acid, and conjugated linoleic acids were also reduced in exposed mice compared to controls. Out of these,phytomonic acid and conjugated linoleic acid are known to be microbially produced , suggesting that changes in the microbiome could be contributing to these changes in metabolome. In summary, we demonstrate that IHH, a hallmark of OSA, changes the microbiota and the chemistry in the gut. We have highlighted the changes of bile acids, phytoestrogens and fatty acids under OSA-related conditions leading to CMDs.
The present results reveal a previously unrecognized mechanistic link between OSA and gut microbes. It suggests that targeting gut microbiota and their metabolites may serve as a potential therapeutic approach for the treatment of cardiometabolic consequences of OSA patients.Intermittent hypoxia and hypercapnia was maintained in a computer-controlled atmosphere chamber system as previously described . IHH exposure was introduced to the mice in short periods of synchronized reduction of O2 and elevation of CO2 separated by alternating periods of normoxia and normocapnia with 1-2 min ramp intervals for 10 hours per day during the light cycle, for 6 weeks. This treatment protocol mimics the severe clinical condition observed in obstructive sleep apnea patients. Mice on the same HFD but in room air were used as controls. As the experimental setup requires IHH-exposed mice in a controlled atmosphere chamber and controls in room air, we ensured that the effect of treatment is not confounded by the effect of distinct housing conditions. To do so, we used two cages per treatment group, and we compared the relative effect size of treatment and cages with redundancy analysis , which estimates the independent effect size of each covariate on microbiome composition variation based on unweighted UniFrac Distance . The RDA results showed that treatment had a higher effect size than the cages, more specifically, that treatment contributed to 11.6% of the microbiome community variation, while cages had an independent effect size of around 9.8%; with respect to the metabolome, treatment contributed to 6.2% of the variation, while cages only about 0.7%.Prior to LC-MS/MS analysis, fecal samples were prepared using the following extraction procedures. For extraction, 500 μL of 50/50 methanol/H2O was added to all fecal samples and vortexed. Fecal pellets in extraction solvent were placed in an ultrasonic bath and sonicated for 30 minutes to break apart the pellet, then allowed to incubate for an additional 30 minutes. Extracted samples were the centrifuged to separate insoluble material and 450 uL of each liquid extract was subsequently transferred to a 96-deep-well plate and dried completely using centrifugal evaporation . The dried extracts were resuspended in 150 µL of methanol/H2O including 1 µM amitriptyline as an autosampler injection standard. After resuspension, the samples were transferred into 96-well plates and analyzed on a Vanquish ultra-high performance liquid chromatography system coupled to a Q-Exactive orbital ion trap . For the chromatographic separation, a C18 core-shell column with a flowrate of 0.5 mL/min , Solvent B: Acetonitrile + 0.1 % FA was used. After injection,plant pots with drainage the samples were eluted during a linear gradient from 0-0.5 min, 5 % B, 0.5-4 min 5-50 % B, 4-5 min 50-99 % B, followed by a 2 min washout phase at 99% B and a 2 min re-equilibration phase at 5 % B. For online MS/MS measurements, the flow was directed to heated ESI source . The electrospray ionization parameters were set to 35 L/min sheath gas flow, 10 L/min auxiliary gas flow, 2 L/min sweep gas flow and 400 °C auxiliary gas temperature. The spray voltage was set to 3.5 kV and the inlet capillary was set to 250 °C. 50 V S-lens radio frequency level was applied. Product ion spectra were recorded in data dependent acquisition mode. Both MS1 survey scans and up to 5 MS/MS scans of the most abundant ions per duty cycle were measured with a resolution of 17,500 with 1 micro-scan in positive mode.
The maximum ion injection time was set to 100 ms. MS/MS precursor selection windows were set to m/z 3 with m/z 0.5 offset. Normalized collision energy was stepwise increased from 20 to 30 to 40 % with z = 2 as default charge state. MS/MS experiments were automatically triggered at the apex of a peak within 2 to 15 s from their first occurrence. Dynamic exclusion was set to 5 s.DNA extraction and 16S rRNA amplicon sequencing was done using EMP standard protocols . In brief, DNA was extracted using the MO BIO PowerSoil DNA extraction kit . Amplicon PCR was performed on the V4 region of the 16S rRNA gene using the primer pair 515f–806r with Golay error-correcting barcodes on the reverse primer. Amplicons were barcoded and pooled in equal concentrations for sequencing. The amplicon pool was purified with the MoBio UltraClean PCR Clean-up kit and sequenced on the Illumina HiSeq 2500 sequencing platform. Sequence data were demultiplexed and minimally quality filtered using the QIIME 1.9.1 script split_libraries_fastq.py with Phred quality threshold of 3 and default parameters to generate per-study FASTA sequence files.A.T. wrote the initial manuscript, managed, analyzed and interpreted the data. A.V.M. contributed to the manuscript, analyzed and interpreted the data. J.X. contributed to the manuscript and the study design. O.P. contributed to the study design and collected samples for analysis. M.J.M and G.H. performed mass-spectrometry and amplicon sequencing, respectively. L.J. analyzed and interpreted the data. G.A. curated the metadata. D.M. contributed to the manuscript and interpreted the data. D.Z. contributed to the study design. R.K., P.C.D., G.G.H. conceived and designed the study, interpreted the data and contributed to the manuscript. The co-authors listed above supervised or provided support for the research and have given permission for the inclusion of the work in this dissertation.Studying perturbations in the gut ecosystem using animal models of disease continues to provide valuable insights into the role of the microbiome in various physiological and pathological conditions. However, understanding whether these changes are consistent across animal models of different genetic backgrounds, and hence potentially translatable to human populations remains a major unmet challenge in the field. Nonetheless, in relatively limited cases have the same interventions been studied in two animal models in the same laboratory. Moreover, such studies typically examine only one data layer and one-time point. Here, we show the power of utilizing time series microbiome and metabolome data to relate two different mouse models of atherosclerosis: ApoE-/- and Ldlr-/- that are exposed to intermittent hypoxia and hypercapnia longitudinally to model chronic obstructive sleep apnea. Using Random Forest classifiers trained on each data layer, we show excellent accuracy values in predicting IHHexposure within ApoE-/- and Ldlr-/- knockout models, and in cross-applying predictive features found in one animal model to the other. Some of the key microbes and metabolites that predicted IHHexposure across animal models included bacterial species from the family Clostridiaceae, muricholic acid and vaccenic acid , providing a refined set of biomarkers reproducibly associated with this intervention. The results highlight that time series, multi-omics data can be used to relate different animal models of disease to one another using supervised machine learning techniques, and can provide a pathway towards identifying robust microbiome and metabolome features that underpin translation from animal models to understanding human disease.Reproducibility of microbiome research is a major topic of contemporary interest. Although it is often possible to distinguish individuals with specific diseases within a study, the differences are often inconsistent across cohorts, often due to systematic variation in analytical conditions. Here we study the same intervention in two different mouse models of cardiovascular disease by profiling the microbiome and metabolome in stool specimens over time. We demonstrate that shared microbial and metabolic changes are involved in both models with the intervention. We then introduce a pipeline for finding similar results in other studies. This work will help find common features identified across different model systems, which are most likely to apply in humans. Obstructive sleep apnea is a common sleep disorder marked by obstructed breathing due to episodic upper airway collapse. Chronic OSA is associated with adverse cardio-metabolic outcomes such as atherosclerosis ; however, potential causal pathways remain elusive. We previously modeled human OSA and its cardiovascular consequences in Ldlr knockout mice by exposing individuals to intermittent hypoxia and hypercapnia , a hallmark of OSA .