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Gastric mucus is a complex hydrogel that protects the epithelial surface of the stomach from gastric acid and prevents invasion of pathogens such as Helicobacter pylori. The viscoelastic properties of the mucus layer are considered essential for barrier function. However, since native human gastric mucus is not readily accessible, the majority of previous studies were performed with animal mucus or with purified gastric mucin that lacks functionally important mucus components. Moreover, commonly used purification protocols may damage mucin structure and impact gel-forming capacity. Therefore, the objective of this study was to engineer sterile human gastric mucus in vitro to study interactions between mucus and H. pylori bacteria. We hypothesize that our bioengineered mucus layer is both structurally and functionally relevant and applicable to in vivo gastric mucus. Based on previous studies with organoid-derived monolayers, we developed and optimized a protocol to produce mucus in 2D air-liquid-interface (ALI) cultures derived from human gastric organoids. The structure of the mucus was analyzed using cryo-scanning electron microscopy, and viscoelastic properties were quantified using particle tracking microrheology. Live imaging and particle tracking also were used to quantify the motility of H. pylori in the bioengineered mucus. 2D monolayers of human gastric organoids cultured at an ALI on transwell inserts consistently produced milliliter quantities of sterile human gastric mucus. This bioengineered mucus could be easily harvested and did not require purification or processing. Mucus production by the ALI cultures was maintained for over one month, demonstrating the robustness of our culture system. Electron microscopy confirmed that the bioengineered mucus had a characteristic honeycomb structure with pore sizes of 10.8 ± 3.4 microns, consistent with previous studies on porcine and canine gastric mucus. Particle tracking microrheology confirmed the viscous behavior of the collected mucus. Compared to liquid media, the bioengineered mucus also caused a significant decrease in H. pylori swimming speed, with 42.8 ± 0.2 microns/second in brucella broth and 10.9 ± 0.1 microns/second in mucus (P ≤ 0.001). We here leveraged the ability of gastric organoids cultured as monolayers at an ALI to secrete sterile mucus to produce native human gastric mucus for in vitro studies. Using this system, we showed that the mucus inhibited bacterial motility, limiting bacterial invasion into the mucus layer. Our observations support previous hypotheses on gastric mucus function and demonstrate the potential of organoid-derived mucus for mechanistic studies of gastric mucus layer physiology. NIH R01GM131408-01, NIH UL1 TR002319 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Bats are natural reservoirs for several zoonotic viruses, potentially due to an enhanced capacity to control viral infection. However, the mechanisms of antiviral responses in bats are poorly defined. Here we established a Jamaican fruit bat (JFB) intestinal organoid model of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. JFB organoids were susceptible to SARS-CoV-2 infection, with increased viral RNA and subgenomic RNA detected in cell lysates and supernatants. Gene expression of type I interferons and inflammatory cytokines was induced in response to SARS-CoV-2 but not in response to TLR agonists. Interestingly, SARS-CoV-2 did not lead to cytopathic effects in JFB organoids but caused enhanced organoid growth. Proteomic analyses revealed an increase in inflammatory signaling, cell turnover, cell repair, and SARS-CoV-2 infection pathways. Collectively, our findings suggest that primary JFB intestinal epithelial cells can mount a successful antiviral interferon response and that SARS-CoV-2 infection in JFB cells induces protective regenerative pathways.

Helicobacter pylori infection is commonly treated with a combination of antibiotics and proton pump inhibitors. However, since H. pylori is becoming increasingly resistant to standard antibiotic regimens, novel treatment strategies are needed. Previous studies have demonstrated that black and red berries may have antibacterial properties. Therefore, we analyzed the antibacterial effects of black and red raspberries and blackberries on H. pylori. Freeze-dried powders and organic extracts from black and red raspberries and blackberries were prepared, and high-performance liquid chromatography was used to measure the concentrations of anthocyanins, which are considered the major active ingredients. To monitor antibiotic effects of the berry preparations on H. pylori, a high-throughput metabolic growth assay based on the Biolog system was developed and validated with the antibiotic metronidazole. Biocompatibility was analyzed using human gastric organoids. All berry preparations tested had significant bactericidal effects in vitro, with MIC90 values ranging from 0.49 to 4.17%. Antimicrobial activity was higher for extracts than powders and appeared to be independent of the anthocyanin concentration. Importantly, human gastric epithelial cell viability was not negatively impacted by black raspberry extract applied at the concentration required for complete bacterial growth inhibition. Our data suggest that black and red raspberry and blackberry extracts may have potential applications in the treatment and prevention of H. pylori infection but differ widely in their MICs. Moreover, we demonstrate that the Biolog metabolic assay is suitable for high-throughput antimicrobial susceptibility screening of H. pylori.

Helicobacter pylori is an important bacterial pathogen that causes chronic infection of the human stomach, leading to gastritis, peptic ulcer disease and gastric cancer. Treatment with appropriate antibiotics can eliminate H. pylori infection and reduce the risk for severe disease outcomes. However, since H. pylori is becoming increasingly resistant to standard antibiotic regimens, novel treatment strategies are needed. Previous studies have demonstrated that black and red berries may have antibacterial properties. Therefore, we analyzed organic extracts and powders from black and red raspberries and blackberries and determined their antibacterial effects on multiple H. pylori strains. We used high-performance liquid chromatography to measure berry anthocyanins, which are considered the major active ingredients. To monitor antibiotic effects of the berry preparations on H. pylori, we developed a high-throughput metabolic growth assay based on the OmniLog™ system. All berry preparations tested had significant bactericidal effects in vitro, with MIC90 values ranging from 0.49 to 4.17%. We next used human gastric epithelial organoids to evaluate biocompatibility of the berry preparations and showed that black raspberry extract, which had the strongest antimicrobial activity, was non-toxic at the concentration required for complete bacterial growth inhibition. To determine whether dietary black raspberry application could eliminate H. pylori infection in vivo, mice were infected with H. pylori and then were placed on a diet containing 10% black raspberry powder. However, this treatment did not significantly impact bacterial infection rates or gastric pathology. In summary, our data indicate that black and red raspberry and blackberry products have potential applications in the treatment and prevention of H. pylori infection, because of their antibacterial effects and good biocompatibility. However, delivery and formulation of berry compounds needs to be optimized to achieve significant antibacterial effects in vivo.

The biomedical literature provides an extensive source of information in the form of unstructured text. One of the most important types of information hidden in biomedical literature is the relationships between human proteins and their phenotypes, which, due to the exponential growth of publications, can remain hidden. This provides a range of opportunities for the development of computational methods to extract the biomedical relationships from the unstructured text. In our previous work, we developed a supervised machine learning approach, called PPPred, for classifying the validity of a given sentence-level human protein-phenotype co-mention. In this work, we propose DeepPPPred, an ensemble classifier composed of PPPred and three deep neural network models: RNN, CNN, and BERT. Using an expanded gold-standard co-mention dataset, we demonstrate that the proposed ensemble method significantly outperforms its constituent components and provides a new state-of-the-art performance on classifying the co-mentions of human proteins and phenotype terms.