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The envisioned application of miRNAs as diagnostic or prognostic biomarkers calls for an in-depth understanding of their distribution and variability in different physiological states. While effects with respect to ethnic origin, age, or gender are known, the inter-individual variability of miRNAs across the four seasons remained largely hidden. We sequentially profiled the complete repertoire of blood-borne miRNAs for 25 physiologically normal individuals in spring, summer, fall, and winter (altogether 95 samples) and validated the results on 292 individuals (919 samples collected with the Mitra home sampling device) by RT-qPCR. Principal variance component analysis suggests that the largest variability observed in miRNA expression is due to individual variability and the individuals’ gender. But the results also highlight a deviation of miRNA activity in samples collected during spring time. Following adjustment for multiple testing, remarkable differences are observed between spring and fall (77 miRNAs). The two most dys-regulated miRNAs were miR-181c-5p and miR-106b-5p (adjusted p-value of 0.007). Other significant miRNAs include miR-140-3p, miR-21-3p, and let-7c-5p. The dys-regulation was validated by RT-qPCR. Systems biology analysis further provides strong evidence for the immunological origin of the signals: dys-regulated miRNAs are enriched in CD56 cells and belong to various signalling and immune-system-related pathways. Our data suggest that besides known confounding factors such as age and sex, also the season in which a test is conducted might have a considerable influence on the expression of blood-borne miRNAs and subsequently might interfere with diagnosis based on such signatures.

The validation of microRNAs (miRNAs) identified by next generation sequencing involves amplification-free and hybridization-based detection of transcripts as criteria for confirming valid miRNAs. Since respective validation is frequently not performed, miRNA repositories likely still contain a substantial fraction of false positive candidates while true miRNAs are not stored in the repositories yet. Especially if downstream analyses are performed with these candidates (e.g. target or pathway prediction), the results may be misleading. In the present study, we evaluated 558 mature miRNAs from miRBase and 1,709 miRNA candidates from next generation sequencing experiments by amplification-free hybridization and investigated their distributions in patients with various disease conditions. Notably, the most significant miRNAs in diseases are often not contained in the miRBase. However, these candidates are evolutionary highly conserved. From the expression patterns, target gene and pathway analyses and evolutionary conservation analyses, we were able to shed light on the complexity of miRNAs in humans. Our data also highlight that a more thorough validation of miRNAs identified by next generation sequencing is required. The results are available in miRCarta ( https://mircarta.cs.uni-saarland.de ).

MiRNAs play a central role in physiological and pathological processes. Both for the biological understanding and for their clinical application, it is essential to understand the interaction of miRNAs and their targets. Target identification largely hinges on in-silico prediction, which requires a complete consideration of miRNA binding sites within the UTRs of target genes. Here, we show that 5-mer sites might also play an essential role for human miRNA-target binding. We implemented and employed an algorithm to all pairs of 2,588 human miRNAs annotated in miRBase and the 3′ UTRs of 16725 genes (>43 million combinations). Our in-silico analysis showed a highly significant enrichment (p = 1.4 × 10(−69)) of 5-mer binding sites in 3′ UTRs across all experimentally validated miRNA-target gene pairs. We next confirmed the central role of 5-mer binding sites by reporter assays and demonstrated that two non-canonical 5-mer sites of miR-34a in the 3′ UTR of T-cell receptor alpha (TCRA) have a significantly stronger influence on its posttranscriptional regulation than the canonical binding sites. These observations indicate an essential role of 5-mer binding sites for the miRNA targeting in human cells.

Small non-coding RNAs, especially microRNAs, are discussed as promising biomarkers for a substantial number of human pathologies. A broad understanding in which solid tissues, cell types or body fluids a microRNA is expressed helps also to understand and to improve the suitability of miRNAs as non- or minimally-invasive disease markers. We recently reported the Human miRNA Tissue Atlas ( http://www.ccb.uni-saarland.de/tissueatlas ) containing 105 miRNA profiles of 31 organs from 2 corpses. We subsequently added miRNA profiles measured by others and us using the same array technology as for the first version of the Human miRNA Tissue Atlas. The latter profiles stem from 163 solid organs including lung, prostate and gastric tissue, from 253 whole blood samples and 66 fractioned blood cell isolates, from body fluids including 72 serum samples, 278 plasma samples, 29 urine samples, and 16 saliva samples and from different collection and storage conditions. While most miRNAs are ubiquitous abundant in solid tissues and whole blood, we also identified miRNAs that are rather specific for tissues. Our web-based repository now hosting 982 full miRNomes all of which are measured by the same microarray technology. The knowledge of these variant abundances of miRNAs in solid tissues, in whole blood and in other body fluids is essential to judge the value of miRNAs as biomarker.

An increasing number of studies propose circulating microRNAs (miRNAs) as biomarkers for a large number of human diseases including cancer, cardiovascular diseases, neurologic pathologies and others. To further validate miRNA as biomarkers it is indispensable to understand the variability of circulating miRNAs in healthy individuals. We determined the longitudinal miRNomes of 90 serum samples from the Janus Serum Bank in Norway, which have been stored between 23 and 40 y at -25 °Celsius. We profiled 3 serum samples with microarrays for 30 individuals, each. For each individual the samples were collected with a time interval of approximately 5 y. This design allowed insights into inter-individual variability, age dependent miRNA variability and the impact of storage length and pre-processing. A significant proportion of the miRNome was affected by the age of the blood donor and a not negligible, albeit small, part of the miRNome by the storage time. A substantial part of miRNAs was differentially abundant between individuals, independent of the time when samples were collected. Stepwise filtering of the 529 miRNAs that were detected in the serum samples showed 168 miRNAs with differential abundance depending on the time point analyzed, 56 miRNAs differentially abundant between individuals, and 169 miRNAs with an abundance depending on the sampling procedure. While these groups of miRNAs contain generally interesting and biologically important miRNAs, the remaining 135 miRNAs constitute very promising biomarker candidates as they show an overall low variability between healthy individuals, a likewise overall low variability across a longer life span, and a high independence of the sampling process and the storage length.

MiRNAs are powerful biomarkers for detecting various diseases from tissue and body fluids. The potential of these molecules to monitor patients over time has, however, been less explored. We followed the fate of the plasma miRNome of lung cancer patients starting prior to surgery and ending 18 mo after surgery, with blood taken at three-month intervals. Principal component and clustering analysis showed that the differences of the overall miRNA patterns between the different time points were significantly smaller than between patients. For each patient we found a rather specific fluctuating miRNA pattern. We identified miRNAs that showed a significant correlation between expression level and time distance from surgery. A network analysis revealed 12 correlated miRNAs regulating 48 genes that were deregulated in lung cancer tissue. Our data underline the importance of studies that follow the fate of miRNAs over time, both to further our understanding of the biology of miRNA signatures and to establish these signatures as biomarkers.

Circulating microRNAs in human serum have increasingly been recognized as stable markers for cancer detection. However, there is still a lack of miRNome wide studies over a long period of time with respect to pathogenic processes. We obtained serum samples from the janus serum bank collected prior and after diagnosis of lung cancer. We analyzed the abundance of 904 miRNAs in serum from eight cancer patients at three time points and from six healthy control individuals. Based on the identified miRNA signatures, hierarchical clustering and a self-organizing map identified three major clusters including one cluster containing most of the of the pre-diagnostic samples, a second cluster with mainly post-diagnostic samples, and a third cluster with mainly control samples. Correlation analyses showed that although the profiles were generally stable over several years, most obvious changes of the miRNA pattern seem to occur at a time close to diagnosis. Our findings support the idea that a developing lung cancer might be detectable years prior to diagnosis through a specific miRNA signature and that this signature changes during tumor development.

Genetic impairment of the genes coding for three mammalian trefoil peptides resulted in severe gastrointestinal malfunctions. The trefoil peptides also appear involved in caloric metabolism. Monitoring global miRNA expression of Tff3 deficient mice points to an interplay of Tff3 with a miRNA regulatory network. We identified 21 miRNAs that were deregulated when compared to the wild type strain. In silico evaluation indicated that the majority of the 21 miRNA were connected with the metabolic pathway "glycolysis/gluconeogenesis'' (p=0.032), a signaling pathway including nine target genes Aldh9a1, Aldh2, Pck1, Aldoc, Pgam2, Pck2, Adh4, Adh5, and Fbp1. Association of Tff3 with this metabolic pathway is further supported by the observation that the body mass of adult Tff3 KO mice (five months) showed a clearly reduced weight. Furthermore, the majority of the identified 21 miRNA genes are localized on murine chromosomes 2 and 5 in three clusters (2A1, 2B, 5B3) suggesting a coordinated expression control and function.