E. coli Nissle 1917 modulates host glucose metabolism without directly acting on glucose

Managing postprandial glycemic response, or the increase in blood sugar following a meal, is a crucial component to maintaining healthy blood sugar in patients with diabetes. To test whether oral probiotics can impact postprandial glycemic response, E. coli Nissle 1917 (EcN) was evaluated in an oral glucose tolerance test. Oral gavage of EcN concurrent with a glucose bolus reduced the post-gavage glycemic response in mice. However, there was no difference in glycemic response when comparing EcN to a mutant deficient in glucose metabolism. This suggests that while EcN can alter glycemic response to a glucose bolus, this effect is not mediated by direct uptake of glucose. Of the possible indirect effects EcN could have, gastric emptying rate was highlighted as a likely cause, but EcN had no effect on gastric emptying rate in mice. This leaves many more possible indirect explanations for the interaction between EcN and host glucose metabolism to be explored in future work.

www.nature.com/scientificreports/ no effect when compared to wild type. We then hypothesized that bacterially-induced changes in gastric emptying rate could explain the change in glycemic response, however we observed no change in gastric emptying in E. coli Nissle supplemented mice versus PBS control. This suggests that while E. coli Nissle appears able to modify the glycemic response, the mechanism is complex. Importantly, we demonstrate that the intuitive explanation of simple bacterial absorption of dietary glucose is likely not responsible for this effect.

Results
We first set out to test the hypothesis that the glucose metabolic capacity of live probiotics can impact glucose uptake rate in the gastrointestinal tract (Fig. 1a). 8-week-old C57-BL/6J mice were gavaged with 10 10 CFU of E. coli Nissle 1917, concurrently with 2 mg/kg glucose, and blood glucose was monitored over 2 h (Fig. 1b). At each time point measured, Nissle-treated mice showed a trend of lower average blood glucose than vehicle control, though this difference was not significant. However, the area under the blood glucose curve (AUC) was significantly lower in Nissle-treated mice compared to vehicle control (Fig. 1c). We then explored whether other probiotic bacteria could elicit a similar response. However, administration of a cocktail of probiotic Lactobacillus strains showed no impact on average blood glucose at any time point measured nor the average AUC ( Supplementary Fig. S1). These data suggest that while probiotic bacteria can impact the glycemic response to a bolus of glucose, this response is species specific, prompting further investigation into what causes E. coli Nissle specifically to have this effect. Escherichia coli strains are well characterized for their high rate of glucose uptake. To test whether glucose uptake was necessary for the improvement of glycemic response, we first generated a glucose-null mutant of E. coli Nissle. E. coli metabolizes glucose via central carbon metabolism, primarily through glycolysis and the pentose phosphate pathway (Fig. 2a). Hexose transporters take up glucose and phsophorylate to glucose-6-phosphate, which is incorporated into glycolysis via phosphoglucoisomerase (pgi), or into the pentose phosphate pathway via glucose-6-phosphate dehydrogenase (G6PDH, encoded by gene zwf) 21 . In E. coli strains lacking both pgi and zwf, glucose-6-phosphate accumulates in the cell, inhibiting hexokinase transporters via feedback inhibition. Using E. coli Nissle 1917 as a base strain, we generated the double knockout EcN ∆pgi∆zwf and confirmed that it was unable to grow in minimal media with glucose as the sole carbon source, while growth on glycerol was unaffected ( Supplementary Fig. S2). In an OGTT, mice supplemented with EcN ∆pgi∆zwf showed no significant change in glycemic profile compared to wild type EcN, and the AUC was unchanged as well (Fig. 2b,c). Thus, while EcN appears to affect the glycemic profile in OGTT, this effect is not dependent on EcN possessing functional glucose metabolism, suggesting that this effect is dependent on some other property of the bacteria.
For E. coli Nissle to impact glycemic response without directly interacting with glucose, it would have to have some effect on host physiology, impacting glycemic response indirectly. A prime candidate for this indirect effect is gastric emptying, as microbiota and their small molecule effectors have been shown to impact gastric emptying rate, and gastric emptying rate has a direct effect on glucose absorption rate 22 . To test whether Nissle impacts gastric emptying rate, we used acetaminophen as a tracer, co-administering a bolus of acetaminophen with either Nissle or PBS as an oral gavage. By monitoring concentration of acetaminophen in the blood, gastric emptying rate can be estimated as the rate of influx of acetaminophen into the bloodstream from the stomach  www.nature.com/scientificreports/ (Fig. 3a). However, we observed no clear effect of Nissle on gastric emptying rate. Inter-mouse variability was high, as was the batch effect between the two trials conducted (Fig. 3b). Aggregated, we observed no difference in average blood concentration of acetaminophen at any time point measured (Fig. 3c). These data suggest that Nissle has no observable impact on gastric emptying rate in mice, and thus gastric emptying cannot explain our initial observation.

Discussion
In this study, we proposed the hypothesis that E. coli Nissle could act as a glucose "sponge" by competing with the host for glucose uptake. Promisingly, we observed that Nissle reduced the glycemic response in mice compared to PBS vehicle control. However, we acknowledged that PBS was not an adequate control to conclude that this effect was caused by direct competition. To test this further, we generated a glucose-null mutant of Nissle (EcN ∆pgi∆zwf), which is unable to grow on or metabolize glucose. However, in a mouse OGTT, EcN ∆pgi∆zwf showed no change in glycemic response compared to wild type, meaning that functional glucose metabolism was not necessary for the glycemic impact we initially observed. Furthermore, this observation meant that in absence of a direct effect, the next best explanation for our initial findings would be an indirect effect: bacterial gavage impacting an aspect of host physiology which alters glycemic response in a separate downstream response. We then tested one likely candidate for this indirect effect via gastric emptying rate, however we found that E. coli Nissle had no effect on gastric emptying. Eliminating this possibility does little to whittle down the long list of potential bioprocesses a probiotic microbe could be interacting with that alter glycemic response. Microbiome signals have been shown to affect the secretion of several endocrine hormones, including GLP-1 and GIP 22 which are both involved in postprandial insulin secretion 23 . Other affected signals include PYY, which has a more complicated role in glucose homeostasis 24 , and amylin, which affects blood glucose via delaying gastric emptying 25 . Microbiome signals have also been shown to interact with the neurological system, via the gut-brain axis 26 . Neurological connections to glucose sensing and homeostasis represent another possible explanation 27 . The microbiome has also been shown to interact with metabolism directly via small molecule secretion. Microbiome derived succinate absorbed from the intestinal lumen can induce intestinal gluconeogenesis, which improves glucose homeostasis and reduces glycemic response 28 . There is even emerging research suggesting that there may be a resident pancreatic microbiome, and oral supplementation of E. coli specifically has been observed entering the pancreatic ducts at high concentrations 29 . Direct pancreatic stimulation could affect glucose homeostasis via a unique mechanism, making this avenue particularly hazy and unknown. We are left without a clear explanation for our initial finding and a wide expanse of possible avenues towards an explanation which lies outside the scope of this study.
The key finding of this work is the observation that probiotic bacteria given just prior to a glucose bolus can impact the body's response to the glucose. Our initial assumption was that this effect was caused by direct competition for glucose, as this was the most likely and most exciting possibility. However, our further experimentation showed that competition could not explain the initial observation, demonstrating that the microbiome and microbe-host interactions are much more complicated than they appear.

Methods
Bacterial strains and growth. To generate material for gavage, bacterial strains were grown in LB at 37 °C overnight, then subinoculated into LB at 1:100 dilution and incubated at 37 °C until OD 600 0.4-0.8. Cultures were then pelleted at 4000 g, 4 °C, and washed once in 1 × PBS. Pellets were resuspended in PBS to a final concentration of ~ 5 × 10 10 CFU/mL and flash frozen in liquid nitrogen. Aliquots were stored at -80 °C. CFU/mL was determined by plating a dilution series of the aliquots onto LB agar plates and counting colonies after 24 h incubation at 37 °C.
To generate the glucose-null mutant, we utilized the lambda red recombineering system encoded on plasmid pREDTKI 30 . Deletion cassettes were designed for ∆pgi and ∆zwf by PCR amplifying FRT-CatR-FRT (chloramphenicol resistance gene) from pKD3 with 60 bp homology to the upstream and downstream regions of each gene. EcN cultures were made electrocompetent by growing cultures to OD 600 0.4-0.6 and washing with 10% glycerol twice and resuspending in 1/100th the original volume. Electrocompetent cultures were transformed with 10 ng plasmid DNA or 100 ng linear DNA at 3 kV 2 mm gap with 1 h recovery in SOC media before plating on LB agar plates. EcN pREDTKI cells were grown in the presence of 0.1% arabinose and electrotransformed with the ∆pgi deletion cassette, then plated on LB Chlor. Activation of the FRT recombinase at 42 °C removed the CatR gene, then the process was repeated for the ∆zwf deletion. Deletions were confirmed by sequencing across the gene and glucose-null growth phenotype confirmed by streaking overnight LB cultures on M9 minimal media with glucose or glycerol as the sole carbon source.

Animal procedures. Protocols for mouse experiments were reviewed and approved by the Joslin Diabetes
Center Animal Care and Use Committee (Protocol #2016-05). The study was carried out in accordance with the ARRIVE guidelines. All experiments were performed according the relevant guidelines and recommendations. All mouse experiments were performed using 8-12 week old male C57/B6J mice ordered from Jackson Labs. Male mice were chosen to eliminate sex variation, as well as to keep in line with most comparable studies. To reduce confounding effects, mice were distributed between groups from different litters and cages. No individuals were removed from downstream data analysis post hoc.
Oral glucose tolerance test. For OGTT, standard best practice guidelines were followed 31  www.nature.com/scientificreports/ of 50% w/v glucose was added to 1 mL bacteria maintained at ~ 10 10 CFU/mL and gavaged at 10µL per gram body weight. This gives final dosages of 2 g/kg BW glucose and 10 9 CFU/g BW bacteria. Gavages were timed exactly 1 min apart, then blood glucose was measured via tail vein bleed and a handheld glucose meter at 15, 30, 45, 60, 90, and 120 min post-gavage. Area under the curve was calculated using the trapezoid rule with 0 mg/dL as baseline. For probiotic blend gavage, a commercial probiotic blend was obtained, consisting of 10 9  Gastric emptying. Gastric emptying rate was measured as previously described 32,33 . Briefly, mice were fasted overnight for 20 h. Gavage mixture was prepared by adding equal volumes 2% w/v acetaminophen and either PBS or bacteria maintained at 10 10 CFU/mL, thawed on ice as above. Baseline blood samples were collected via tail vein bleed, and mice were gavaged 250µL gavage mixture, giving final dosage of 2.5 mg acetaminophen and ~ 10 9 CFU bacteria per animal. Blood samples were collected at 15, 30, and 60 min into heparinized tubes and stored on ice. Blood samples were centrifuged at 4 °C for 20 min at 2000×g. Plasma was collected and stored in separate tubes at − 20 °C. Acetaminophen was quantified in the plasma using an enzymatic colorimetric assay specific to acetaminophen (Cambridge Life Sciences).
Data were analyzed using repeated measures ANOVA with Tukey's post-hoc test in Prism 11.0. Significance threshold set at p = 0.05.
Figures 1c, 2c. Data were analyzed using student's T test in Excel. Specifically, two-sample unequal variance two-tailed T-test was used. Significance threshold set at p = 0.05.