Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking

Intraindividual variability of semen quality, proteome, and sncRNA profiles in a healthy cohort of young adults

BACKGROUND

Within-subject variability of semen parameters and molecular components of ejaculates in young men remains poorly understood.

OBJECTIVES

To investigate intraindividual variability (IIV) of semen parameters and molecular markers in repeated ejaculates from young men.

MATERIALS AND METHODS

Semen parameters were assessed in samples collected 6-8 days apart from 164 18-19-year old participants of the Russian Children's Study, a prospective cohort. Subsets of paired samples were used for label-free quantitation and targeted mass-spectrometry of proteins in seminal plasma (SP) and seminal extracellular vesicles (EVs), and for small non-coding RNA (sncRNA) profiling in EVs and spermatozoa using RNA-seq. The mean difference between two ejaculates, within-subject variation, intraclass correlation, and concordance correlation were used to assess IIV for all parameters. Low variability with high reproducibility and high reliability was considered if CVw < 15% and ICC > 0.90, respectively.

RESULTS

Analytical variability was low for all investigated parameters in technical replicates. IIV was assessed for basic semen parameters and proteins in SPs and EVs: 319 and 777 proteins, respectively, using untargeted analysis; 9 and 10 proteins using targeted quantification. We also described the IIV for sncRNA, including microRNA, piwi-interacting RNA, tRNA, and tRNA-derived small RNA (tsRNA) in EVs (409 sncRNA and 78 tsRNA) and in spermatozoa (265 sncRNA and 15 tsRNA). We identified 22 and 27 non-overlapping proteins in SP and EVs, respectively, and 46 and 9 sncRNA, including 5 and 0 tsRNA in seminal EVs and spermatozoa, respectively, with low variability. The fatty acid synthase (FAS) had the lowest IIV in both media in targeted protein quantification.

DISCUSSION

We identified a number of proteins and sncRNA with low variability among 111 proteins, 176 sncRNA, and 12 tsRNA which were previously suggested as biomarkers of male fertility and reproductive outcomes: lactotransferrin, cysteine-rich secretory protein 3, alpha-1-antichymotrypsin, epididymal sperm-binding protein 1, glutathione S-transferase Mu 3, alpha-1-acid glycoprotein 2, serum amyloid P-component, aminopeptidase N, neprilysin, FAS, and miR-10b-3p, miR-122-5p, miR-205-5p, miR-222-3p, miR-34c-5p, miR-509-3-5p, miR-888-5p, miR-892a, miR-363-3p, miR-941, miR-146a-5p, miR-744-5p.

CONCLUSION

These molecules have low IIV and may be promising candidate biomarkers of male fertility and reproductive health.

Benchmarking of five NGS mapping tools for the reference alignment of bacterial outer membrane vesicles-associated small RNAs

Advances in small RNAs (sRNAs)-related studies have posed a challenge for NGS-related bioinformatics, especially regarding the correct mapping of sRNAs. Depending on the algorithms and scoring matrices on which they are based, aligners are influenced by the characteristics of the dataset and the reference genome. These influences have been studied mainly in eukaryotes and to some extent in prokaryotes. However, in bacteria, the selection of aligners depending on sRNA-seq data associated with outer membrane vesicles (OMVs) and the features of the corresponding bacterial reference genome has not yet been investigated. We selected five aligners: BBmap, Bowtie2, BWA, Minimap2 and Segemehl, known for their generally good performance, to test them in mapping OMV-associated sRNAs from Aliivibrio fischeri to the bacterial reference genome. Significant differences in the performance of the five aligners were observed, resulting in differential recognition of OMV-associated sRNA biotypes in A. fischeri. Our results suggest that aligner(s) should not be arbitrarily selected for this task, which is often done, as this can be detrimental to the biological interpretation of NGS analysis results. Since each aligner has specific advantages and disadvantages, these need to be considered depending on the characteristics of the input OMV sRNAs dataset and the corresponding bacterial reference genome to improve the detection of existing, biologically important OMV sRNAs. Until we learn more about these dependencies, we recommend using at least two, preferably three, aligners that have good metrics for the given dataset/bacterial reference genome. The overlapping results should be considered trustworthy, yet their differences should not be dismissed lightly, but treated carefully in order not to overlook any biologically important OMV sRNA. This can be achieved by applying the intersect-then-combine approach. For the mapping of OMV-associated sRNAs of A. fischeri to the reference genome organized into two circular chromosomes and one circular plasmid, containing copies of sequences with rRNA- and tRNA-related features and no copies of sequences with protein-encoding features, if the aligners are used with their default parameters, we advise avoiding Segemehl, and recommend using the intersect-then-combine approach with BBmap, BWA and Minimap2 to improve the potential for discovery of biologically important OMV-associated sRNAs.

Advances in methods for tRNA sequencing and quantification

In the past decade tRNA sequencing (tRNA-seq) has attracted considerable attention as an important tool for the development of novel approaches to quantify highly modified tRNA species and to propel tRNA research aimed at understanding the cellular physiology and disease and development of tRNA-based therapeutics. Many methods are available to quantify tRNA abundance while accounting for modifications and tRNA charging/acylation. Advances in both library preparation methods and bioinformatic workflows have enabled developments in next-generation sequencing (NGS) workflows. Other approaches forgo NGS applications in favor of hybridization-based approaches. In this review we provide a brief comparative overview of various tRNA quantification approaches, focusing on the advantages and disadvantages of these methods, which together facilitate reliable tRNA quantification.