The antimicrobial peptides (AMP) produced by intestinal epithelial cells (IEC) play crucial roles in the regulation of intestinal homeostasis by controlling microbiota. Gut microbiota has been shown to promote IEC expression of RegIIIγ and certain defensins. However, the mechanisms involved are still not completely understood. In this report, we found that IEC expression of RegIIIγ and β-defensins 1, 3, and 4 was lower in G protein-coupled receptor (GPR)43−/−mice compared to that of wild-type (WT) mice. Oral feeding with short chain fatty acids (SCFA) promoted IEC production of RegIIIγ and defensins in mice. Furthermore, SCFA induced RegIIIγ and β-defensins in intestinal epithelial enteroids generated from WT but not GPR43−/−mice. Mechanistically, SCFA activated mTOR and STAT3 in IEC, and knockdown of mTOR and STAT3 impaired SCFA induction of AMP production. Our studies thus demonstrated that microbiota metabolites SCFA promoted IEC RegIIIγ and β-defensins in a GPR43-dependent manner. The data thereby provides a novel pathway by which microbiota regulates IEC expression of AMP and intestinal homeostasis.

IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

Microbial interactions are an underappreciated force in shaping insect microbiome communities. Although pairwise patterns of symbiont interactions have been identified, we have a poor understanding regarding the scale and the nature of co-occurrence and co-exclusion interactions within the microbiome. To characterize these patterns in mosquitoes, we sequenced the bacterial microbiome of Aedes aegypti, Ae. albopictus, and Culex quinquefasciatus caught in the field or reared in the laboratory and used these data to generate interaction networks. For collections, we used traps that attracted host-seeking or ovipositing female mosquitoes to determine how physiological state affects the microbiome under field conditions. Interestingly, we saw few differences in species richness or microbiome community structure in mosquitoes caught in either trap. Co-occurrence and co-exclusion analysis identified 116 pairwise interactions substantially increasing the list of bacterial interactions observed in mosquitoes. Networks generated from the microbiome of Ae. aegypti often included highly interconnected hub bacteria. There were several instances where co-occurring bacteria co-excluded a third taxa, suggesting the existence of tripartite relationships. Several associations were observed in multiple species or in field and laboratory-reared mosquitoes indicating these associations are robust and not influenced by environmental or host factors. To demonstrate that microbial interactions can influence colonization of the host, we administered symbionts to Ae. aegypti larvae that either possessed or lacked their resident microbiota. We found that the presence of resident microbiota can inhibit colonization of particular bacterial taxa. Our results highlight that microbial interactions in mosquitoes are complex and influence microbiome composition.

Background: Wolbachia, a common insect endosymbiotic bacterium that can influence pathogen transmission and manipulate host reproduction, has historically been considered absent from the Anopheles (An.) genera, but has recently been found in An. gambiae s.l. populations in West Africa.  As there are numerous Anopheles species that have the capacity to transmit malaria, we analysed a range of species across five malaria endemic countries to determine Wolbachia prevalence rates, characterise novel Wolbachia strains and determine any correlation between the presence of Plasmodium, Wolbachia and the competing bacterium Asaia. Methods: Anopheles adult mosquitoes were collected from five malaria-endemic countries: Guinea, Democratic Republic of the Congo (DRC), Ghana, Uganda and Madagascar, between 2013 and 2017.  Molecular analysis was undertaken using quantitative PCR, Sanger sequencing, Wolbachia multilocus sequence typing (MLST) and high-throughput amplicon sequencing of the bacterial 16S rRNA gene.  Results: Novel Wolbachia strains were discovered in five species: An. coluzzii, An. gambiae s.s., An. arabiensis, An. moucheti and An. species A, increasing the number of Anopheles species known to be naturally infected. Variable prevalence rates in different locations were observed and novel strains were phylogenetically diverse, clustering with Wolbachia supergroup B strains.  We also provide evidence for resident strain variants within An. species A. Wolbachia is the dominant member of the microbiome in An. moucheti and An. species A but present at lower densities in An. coluzzii.  Interestingly, no evidence of Wolbachia/Asaia co-infections was seen and Asaia infection densities were shown to be variable and location dependent.  Conclusions: The important discovery of novel Wolbachia strains in Anopheles provides greater insight into the prevalence of resident Wolbachia strains in diverse malaria vectors.  Novel Wolbachia strains (particularly high-density strains) are ideal candidate strains for transinfection to create stable infections in other Anopheles mosquito species, which could be used for population replacement or suppression control strategies.