MicroRNAs have been long considered synthesized endogenously until very recent discoveries showing that human can absorb dietary microRNAs from animal and plant origins while the mechanism remains unknown. Compelling evidences of microRNAs from rice, milk, and honeysuckle transported to human blood and tissues have created a high volume of interests in the fundamental questions that which and how exogenous microRNAs can be transferred into human circulation and possibly exert functions in humans. Here we present an integrated genomics and computational analysis to study the potential deciding features of transportable microRNAs. Specifically, we analyzed all publicly available microRNAs, a total of 34,612 from 194 species, with 1,102 features derived from the microRNA sequence and structure. Through in-depth bioinformatics analysis, 8 groups of discriminative features have been used to characterize human circulating microRNAs and infer the likelihood that a microRNA will get transferred into human circulation. For example, 345 dietary microRNAs have been predicted as highly transportable candidates where 117 of them have identical sequences with their homologs in human and 73 are known to be associated with exosomes. Through a milk feeding experiment, we have validated 9 cow-milk microRNAs in human plasma using microRNA-sequencing analysis, including the top ranked microRNAs such as bta-miR-487b, miR-181b, and miR-421. The implications in health-related processes have been illustrated in the functional analysis. This work demonstrates the data-driven computational analysis is highly promising to study novel molecular characteristics of transportable microRNAs while bypassing the complex mechanistic details.

Aims As previous reports show an association of chronic hepatitis C (HCV) with hepatocellular carcinoma (HCC) and non‐liver cancers, we examine the association of HCV with liver cancer and non‐liver cancers. Methods Retrospective cross‐sectional study at Kaiser Permanente Southern California (KPSC) evaluating HCV and non‐HCV patients from 1 January 2008 to 12 December 2012. Cancer diagnoses were obtained from the KPSC‐SEER‐affiliated registry. Logistic regression analyses were used for rate ratios and time‐to‐event analyses were performed using Cox proportional hazards models, adjusted for age, gender, race, smoking and cirrhosis. Cancer rate ratios were stratified by tobacco, alcohol abuse, diabetes and body mass index (BMI). Results The initial population and final population of multivariable analysis were N = 5 332 903 and N = 2 080 335 respectively. Cancer burden (all sites) was significantly higher in HCV than in non‐HCV patients and HCV patients had a high rate of liver cancer. When liver cancer was excluded, cancer rates remained significantly increased in HCV. Unadjusted cancer rates were significantly higher in HCV compared to non‐HCV for oesophageal, stomach, colorectal, pancreas, myeloma, non‐Hodgkin's lymphoma, head/neck, lung, renal and prostate cancer. After stratification for alcohol abuse, tobacco, diabetes and BMI, increased cancer rates remained significant for all cancer sites, liver cancer and non‐Hodgkin's lymphoma. Multivariable analyses demonstrated a strong correlation between cirrhosis and cancer. Tobacco use and diabetes were also associated with cancer. In the absence of cirrhosis, HCV, tobacco use and diabetes significantly increased the cancer risk. Mediation analyses showed that cirrhosis was responsible for a large proportion on the effect of HCV on cancer risk. Conclusion This study supports the concept of HCV as a systemic illness and treating HCV regardless of disease severity and prior to progression to cirrhosis.

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/.

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