Modelling the effect of birth and feeding modes on the development of human gut microbiota

The human gut microbiota is transmitted from mother to infant through vaginal birth and breastfeeding. Bifidobacterium, a genus that dominates the infants’ gut, is adapted to breast milk in its ability to metabolize human milk oligosaccharides; it is regarded as a mutualist owing to its involvement in the development of the immune system. The composition of microbiota, including the abundance of Bifidobacteria, is highly variable between individuals and some microbial profiles are associated with diseases. However, whether and how birth and feeding practices contribute to such variation remains unclear. To understand how early events affect the establishment of microbiota, we develop a mathematical model of two types of Bifidobacteria and a generic compartment of commensal competitors. We show how early events affect competition between mutualists and commensals and microbe-host-immune interactions to cause long-term alterations in gut microbial profiles. Bifidobacteria associated with breast milk can trigger immune responses with lasting effects on the microbial community structure. Our model shows that, in response to a change in birth environment, competition alone can produce two distinct microbial profiles post-weaning. Adding immune regulation to our competition model allows for variations in microbial profiles in response to different feeding practices. This analysis highlights the importance of microbe–microbe and microbe–host interactions in shaping the gut populations following different birth and feeding modes.     

History of safe exposure and bioinformatic assessment of phosphomannose-isomerase (PMI) for allergenic risk

Newly expressed proteins in genetically engineered crops are evaluated for potential cross reactivity to known allergens as part of their safety assessment. This assessment uses a weight-of-evidence approach. Two key components of this allergenicity assessment include any history of safe human exposure to the protein and/or the source organism from which it was originally derived, and bioinformatic analysis identifying amino acid sequence relatedness to known allergens. Phosphomannose-isomerase (PMI) has been expressed in commercialized genetically engineered (GE) crops as a selectable marker since 2010 with no known reports of allergy, which supports a history of safe exposure, and GE events expressing the PMI protein have been approved globally based on expert safety analysis. Bioinformatic analyses identified an eight-amino-acid contiguous match between PMI and a frog parvalbumin allergen (CAC83047.1). While short amino acid matches have been shown to be a poor predictor of allergen cross reactivity, most regulatory bodies require such matches be assessed in support of the allergenicity risk assessment. Here, this match is shown to be of negligible risk of conferring cross reactivity with known allergens.

     

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.