Dietary MicroRNA Database (DMD): An Archive Database and Analytic Tool for Food-Borne microRNAs

Abstract

With the advent of high throughput technology, a huge amount of microRNA information has been added to the growing body of knowledge for non-coding RNAs. Here we present the Dietary MicroRNA Databases (DMD), the first repository for archiving and analyzing the published and novel microRNAs discovered in dietary resources. Currently there are fifteen types of dietary species, such as apple, grape, cow milk, and cow fat, included in the database originating from 9 plant and 5 animal species. Annotation for each entry, a mature microRNA indexed as DM0000*, covers information of the mature sequences, genome locations, hairpin structures of parental pre-microRNAs, cross-species sequence comparison, disease relevance, and the experimentally validated gene targets. Furthermore, a few functional analyses including target prediction, pathway enrichment and gene network construction have been integrated into the system, which enable users to generate functional insights through viewing the functional pathways and building protein-protein interaction networks associated with each microRNA. Another unique feature of DMD is that it provides a feature generator where a total of 411 descriptive attributes can be calculated for any given microRNAs based on their sequences and structures. DMD would be particularly useful for research groups studying microRNA regulation from a nutrition point of view. The database can be accessed at http://sbbi.unl.edu/dmd/.

Milk MicroRNAs in Health and Disease

“…The majority of milk microRNAs are encapsulated within EVs that ensure their stability against harsh conditions (Benmoussa et al., , ; van Herwijnen et al., ; Wang, ). Milk microRNAs can be specifically loaded into different kinds of EVs (Shu, Chiang, Zempleni, & Cui, ; Villarroya‐Beltri et al., ), leading to specific microRNA profiles between EV subsets (Benmoussa et al., ; Chiang, Shu, Zempleni, & Cui, ; Manca et al., ; Shu et al., ), and this loading process can be influenced by the environment, by dietary habits (Paredes et al., ; Xi et al., ) or food supplementation as observed for dietary ginseng polysaccharide supplementation on porcine milk microRNAs (Sun et al., ).…”

Human absorbable microRNA prediction based on an ensemble manifold ranking model

“…Among 14 most common dietary species, we collected the sequences of 4,910 mature miRNAs and 4,387 corresponding stem-loop precursor miRNAs from the Dietary microRNA Database (DMD) developed by our group [21]. An independent validation set also includes sequence data from two virus species at miRBase [3]: Epstein-Barr virus and Rhesus lymphocrypto-virus.…”

Literature review of baseline information on non‐coding RNA (ncRNA) to support the risk assessment of ncRNA‐based genetically modified plants for food and feed

“…Using public databases of RNA sequencing datasets, several documents report the application of in silico bioinformatics analysis (Table 25) clearly describing the widespread presence of exogenous ncRNAs in biological fluids(from multiple sources, including diet) Wang et al, 2013;Beatty et al, 2014;Yeri et al, 2017;. Five documents report the development of bioinformatics resources to either predict transportable miRNAs, predict functional analysis, or list and compare the exogenous miRNAs found in samples Chiang et al, 2015;Zhang et al, 2016;Zheng et al, 2017). Five documents report the development of bioinformatics resources to either predict transportable miRNAs, predict functional analysis, or list and compare the exogenous miRNAs found in samples Chiang et al, 2015;Zhang et al, 2016;Zheng et al, 2017).…”