Hoen, A. | Search bioRxiv

bioRxiv2025

Optimizing Protocols for MicroRNA Profiling of Infant and Toddler Stool

BackgroundMicroRNAs (miRNAs) are increasingly being investigated as potential biomarkers for child development and disease. Although a growing number of studies are utilizing infant and toddler stool for transcriptomic analyses, no studies have compared protocols for preserving and extracting miRNAs from this specimen type, despite unique challenges, including abundant levels of RNAses and microbial RNA. MethodsTo address this, we first compared three commercially available kits and four preservation methods for their ability to yield high quality RNA from infant and toddler stool (Phase 1). RNA quality was determined by fragment analyzer. ResultsOf the three RNA extraction kits compared, Zymo BIOMICs yielded the highest overall RNA Quality Number (RQN) (median (range) RQN 9.4 (5.7-10.0)). Of the four preservation methods tested, stool collected in RNAlater and Zymo DNA/RNA Shield Fecal Collection Tubes yielded the highest two RQNs (median (range) RQN 9.8 (5.7-10.0) and 9.4 (5.4-10.0), respectively), which did not differ significantly from each other (p = 0.47). Second, using miRNA-seq we directly compared miRNA profiles for RNA extracted using the Zymo BIOMICs kit from paired aliquots of the same stool sample from four infants collected into RNAlater and Zymo DNA/RNA Shield Fecal Collection Tubes (Phase 2). Given that microbial sequences greatly outnumber human miRNAs in stool, reads were first classified as human versus microbial prior to aligning human-classified reads to miRBase v22.1. The percentage of reads classified as human and the percentage of human reads aligning to miRBase did not differ for samples collected in RNAlater versus Zymo Shield (p = 0.12 and p = 0.86, respectively). Furthermore, after multiple testing correction, normalized miRNA counts did not differ significantly between the two preservatives for any of the 42 human miRNAs detected across the eight samples. ConclusionsCollecting infant and toddler stool in either RNAlater or Zymo DNA/RNA Shield Fecal Collection Tubes, when paired with RNA extraction using the Zymo BIOMICs extraction kit, yielded high-quality RNA with similar human miRNA profiles. Moreover, of the 42 miRNAs that were detected, several (i.e., miR-194a-3p, miR-200c-3p, miR-26a-5p) are thought to contribute to overall gut homeostasis. These findings may inform protocols for future studies that aim to profile miRNAs in infant and toddler stool to evaluate their potential utility as biomarkers for childrens health.

developmental biology↗

bioRxiv2019

Information Enhanced Model Selection for High-Dimensional Gaussian Graphical Model with Application to Metabolomic Data

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion (SBIC), in which the prior structure is modeled and incorporated into Bayesian information criterion (BIC). It is shown that the popular extended BIC (EBIC) is a special case of SBIC. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions SBIC is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

bioinformatics↗