Circulating miRNA repertoire as a biomarker of metabolic and reproductive states in rainbow trout

Abstract

Background Circulating miRNAs (c-miRNAs) are found in most, if not all, biological fluids and are becoming well-established non-invasive biomarkers of many human pathologies. However, their features in non-pathological contexts and whether their expression profiles reflect normal life history events have received little attention, especially in non-mammalian species. The aim of the present study was to investigate the potential of c-miRNAs to serve as biomarkers of reproductive and metabolic states in fish. Results The blood plasma was sampled throughout the reproductive cycle of female rainbow trout subjected to two different feeding regimes that triggered contrasting metabolic states. In addition, ovarian fluid was sampled at ovulation, and all samples were subjected to small RNA-seq analysis, leading to the establishment of a comprehensive miRNA repertoire (i.e., miRNAome) and enabling subsequent comparative analyses to a panel of RNA-seq libraries from a wide variety of tissues and organs. We showed that biological fluid miRNAomes are complex and encompass a high proportion of the overall rainbow trout miRNAome. While sharing a high proportion of common miRNAs, the blood plasma and ovarian fluid miRNAomes exhibited strong fluid-specific signatures. We further revealed that the blood plasma miRNAome significantly changed depending on metabolic and reproductive states. We subsequently identified three evolutionarily conserved muscle-specific miRNAs or myomiRs (miR-1-1/2-3p, miR-133a-1/2-3p, and miR-206-3p) that accumulated in the blood plasma in response to high feeding rates, making these myomiRs strong candidate biomarkers of active myogenesis. We also identified miR-202-5p as a candidate biomarker for reproductive success that could be used to predict ovulation and/or egg quality. Conclusions Together, these promising results reveal the high potential of c-miRNAs, including evolutionarily conserved myomiRs, as physiologically relevant biomarker candidates and pave the way for the use of c-miRNAs for non-invasive phenotyping in various fish species.

Physical Enrichment Triggers Brain Plasticity and Influences Blood Plasma Circulating miRNA in Rainbow Trout (Oncorhynchus mykiss)

“…Among the 354 known annotated trout c-miRNAs [ 31 ], 203 were detected on average above a threshold of 10 reads per million reads (RPM). A significant upregulation of four c-miRNAs was observed in trout reared with EE: miR-203a-3p levels were increased with a log two-fold change of 1.62 ( p ˂ 0.01), miR-205-1a-5p with a log two-fold change of 1.24 ( p ˂ 0.01), miR-218a-5p with a log two-fold change of 1.37 ( p ˂ 0.01), and miR-200b/c-3p with a log two-fold change of 1.38 ( p ˂ 0.02) ( Figure 6 A).…”

“…Prost! was ran using the trout c-miRNA annotation, as previously established [ 31 ], for all the circulating fluids sequencing samples.…”

“…( B ) Abundance of c-miRNAs differentially expressed in blood plasma in a panel of 14 tissues. Values are from small sequencing libraries of precedent studies [ 31 , 42 ].…”

“…This confers a great potential to c-miRNAs as prognostic biomarkers. Recently, the high potential of c-miRNAs was highlighted as a physiologically relevant biomarker candidate for the phenotyping of reproductive and metabolic states in trout [ 31 ]. However, no research in fish has currently evaluated the association of c-miRNAs with cognitive and behavioral responses, despite the potential interest of these c-miRNAs as biomarker candidates of cognition-related behavioral plasticity, as revealed by analyses of numerous genes’ expressions.…”

FishmiRNA: An Evolutionarily Supported MicroRNA Annotation and Expression Database for Ray-Finned Fishes

“…In 2015, MirGeneDB was created and covers a breadth of metazoan species, but this database provides miRNA annotation for only one fish species, zebrafish ( Fromm et al 2020 ). miRNAs, however, are of great interest in fish research with studies ranging from genomic evolution ( Xiong et al 2019 ; Desvignes et al 2021 ), development ( Giraldez et al 2006 ; Kasper et al 2017 ; Gay et al 2018 ), medical models ( Hsu et al 2017 ), aquaculture ( Herkenhoff et al 2018 ; Blödorn et al 2021 ; Cardona et al 2021 ), pathology ( Andreassen and Høyheim 2017 ; Wang et al 2018 ), toxicology ( Goodale et al 2019 ; Ahkin Chin Tai and Freeman 2020 ), to adaptation, and speciation ( Franchini et al 2019 ; Kelley et al 2021 ). In most fish miRNA studies, the lack of consistent annotation resources leads to using, by default, combinations of annotations existing in other, often distantly related species, or to de novo prediction of miRNAs using different algorithms in different research groups, leading to inconsistent results among species.…”