reverse causation
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Systems Biology of Early Atopy: Role of Human Milk (SunBEAm-Milk)
Surprisingly little is known about the effect of breastfeeding (BF) on infant immune system development besides an effect on the gut microbiome, but its impact on metabolites and Tregs could support protection against food allergy (FA). BF is currently recommended to prevent the development of allergic diseases, especially asthma/recurrent wheezing and AD in early childhood, but firm conclusions could not be drawn regarding FA due to high heterogeneity and low quality of studies. Reverse causation, recall bias and the poor accuracy of outcome assessment are significant limitations. Most are inadequately powered to specific FA; however, a recent study showed that exclusively BF infants had lower odds of egg, sesame, and peanut allergies. Importantly, immunomodulatory composition of HM varies between mothers, which has not been taken into consideration. For over two decades we have been developing methods to assess immunomodulatory factors in the complex matrix of HM and their association with infant FA. We have shown that high levels of HM total and specific IgA are associated with protection against cow’s milk allergy, but it is unclear whether HM IgA is responsible for or is a biomarker of the vertical transfer of protection. Infant fecal and systemic IgA levels during breastfeeding and after weaning are also elevated in infants at low risk for atopic disease raising the question of whether HM factors such as cytokines can promote IgA production in infants. Consistent with this, we showed that HM cytokines, such as APRIL, induce IgA production in naïve infant B cells, and infants receiving HM with higher levels of APRIL had lower incidence of allergic disease. Finally, lower levels of several HM fatty acids including short-chain fatty acids and DHA were associated with FA. While some these factors were are associated with maternal atopic disease, several of them are not and suggest a role for diet instead. The System Biology of Early Atopy (SunBEAm) population-based cohort of 2500 mother-infant pairs is >50% recruited and provides an unprecedented opportunity to assess association of HM feeding and immune factors in HM with development of infant immune system and FA/AD. The Common Sample comprises a subset of 100 dyads with FA, 100 with FA+AD, 100 with AD, 100 with no FA or AD and more extensively profiled biological data. Utilizing all 2-month HM samples available in the Common Sample, we will assess levels of immune factors in HM and their association with maternal/infant characteristics (Aim 1). Utilizing data from the whole cohort, we will assess the association between HM vs formula feeding on well-defined FA/AD further adjusted based on high vs low levels of HM immune components in the Common Sample (Aim 2b). Finally, we will examine the immune cell and epithelial effects of HM on infant immune markers and intestinal organoids (Aim 3). Key findings will be validated in an independent birth cohort. The ultimate goal is to uncover protective properties of BF and HM in FA and subsequent design of policies and prevention strategies to address the increasing rates of FA.
Network inference via process motifs for lagged correlation in linear stochastic processes
A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider network interference from simulations of linear stochastic processes, and we show that our proposed PEMs can infer networks accurately and efficiently. Specifically, for slightly autocorrelated time-series data, our approach achieves accuracies higher than or similar to Granger causality, transfer entropy, and convergent crossmapping -- but with much shorter computation time than possible with any of these methods. Our fast and accurate PEMs are easy-to-implement methods for network inference with a clear theoretical underpinning. They provide promising alternatives to current paradigms for the inference of linear models from time-series data, including Granger causality, vector-autoregression, and sparse inverse covariance estimation.
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