Physiological RNA dynamics in RNA-Seq analysis

The Definition of RNA Age Related to RNA Sequence Changes

Ribonucleic acid (RNA) undergoes dynamic changes in its structure and function under various intracellular and extracellular conditions over time. However, there is a lack of research on the concept of the RNA age to describe its diverse fates. This study proposes a definition of RNA age to address this issue. RNA age was defined as a sequence of numbers wherein the elements in the sequence were the nucleotide ages of the ribonucleotide residues in the RNA. Mean nucleotide age was used to represent RNA age. This definition describes the temporal properties of RNAs that have undergone diverse life histories and reflects the dynamic state of each ribonucleotide residue, which can be expressed mathematically. Notably, events (including base insertions, base deletions, and base substitutions) are likely to cause RNA to become younger or older when using mean nucleotide ages to represent the RNA age. Although information, including the presence of added markers in RNA, chemical modification structure of the RNA, and the excision of introns in the mRNA in cells, may provide a basis for identifying RNA age, little is known about determining the RNA age of extracellular RNA in the wild. Nonetheless, we believe that RNA age has an important relationship with the diverse biological properties of RNA under intracellular and extracellular conditions. Therefore, our proposed definition of RNA age offers new perspectives for studying dynamic changes in RNA function, RNA aging, ancient RNA, environmental RNA, and the ages of other biomolecules.

On taming the effect of transcript level intra-condition count variation during differential expression analysis: A story of dogs, foxes and wolves

The evolution of RNA-seq technologies has yielded datasets of scientific value that are often generated as condition associated biological replicates within expression studies. With expanding data archives opportunity arises to augment replicate numbers when conditions of interest overlap. Despite correction procedures for estimating transcript abundance, a source of ambiguity is transcript level intra-condition count variation; as indicated by disjointed results between analysis tools. We present TVscript, a tool that removes reference-based transcripts associated with intra-condition count variation above specified thresholds and we explore the effects of such variation on differential expression analysis. Initially iterative differential expression analysis involving simulated counts, where levels of intra-condition variation and sets of over represented transcripts are explicitly specified, was performed. Then counts derived from inter- and intra-study data representing brain samples of dogs, wolves and foxes (wolves vs. dogs and aggressive vs. tame foxes) were used. For simulations, the sensitivity in detecting differentially expressed transcripts increased after removing hyper-variable transcripts, although at levels of intra-condition variation above 5% detection became unreliable. For real data, prior to applying TVscript, ≈20% of the transcripts identified as being differentially expressed were associated with high levels of intra-condition variation, an over representation relative to the reference set. As transcripts harbouring such variation were removed pre-analysis, a discordance from 26 to 40% in the lists of differentially expressed transcripts is observed when compared to those obtained using the non-filtered reference. The removal of transcripts possessing intra-condition variation values within (and above) the 97^th and 95^th percentiles, for wolves vs. dogs and aggressive vs. tame foxes, maximized the sensitivity in detecting differentially expressed transcripts as a result of alterations within gene-wise dispersion estimates. Through analysis of our real data the support for seven genes with potential for being involved with selection for tameness is provided. TVscript is available at: https://sourceforge.net/projects/tvscript/.

The transcript integrity index (TII) provides a standard measure of sperm RNA

Standardizing RNA quality is key to interpreting RNA-seq data as a compromised sample can mask the underlying biology. The challenge remains when evaluating RNA quality in samples with high RNA fragmentation. For example, programmed fragmentation and cytoplasmic expulsion, integral to sperm maturation, is a prime example of the complexities of interpreting RNA-seq data, given that fragmentation can be random and\or targeted. To meet this challenge, we developed an algorithm that accurately measures RNA quality in samples with high fragmentation, such as spermatozoa. The integrity of 1,000 previously identified abundant sperm transcripts were independently visualized and evaluated using the Transcript Integrity Index (TII) algorithm to identify intact transcripts. Full-length transcripts from visual and the TII algorithm were evaluated for testis preference in humans using the GTEx tissues database. Samples were then filtered by the Interquartile Range (IQR), identifying those in which the greatest number of transcripts failed to pass the visual or TII thresholds. Transcript lists were overlapped, forming the set of intact transcripts used as TII standards. Each sample was re-evaluated as a function of this TII set of intact transcripts, with poor quality samples identified as those failing in the largest number of transcripts. While ontologically enriched in roles related to spermatogenesis and/or fertilization, samples did not segregate based on birth outcome. The TII algorithm proved an effective means to identify samples of similar quality from sperm, a cell type enriched in biologically fragmented RNAs. The algorithm should facilitate other studies using samples compromised by high levels of RNA fragmentation, such as Formalin-Fixed Paraffin-Embedded samples. Requisite to assessing male health, TII provides a solution to the long-sought-after standard that identifies samples of similar quality.

A model explaining mRNA level fluctuations based on activity demands and RNA age

Cellular RNA levels typically fluctuate and are influenced by different transcription rates and RNA degradation rates. However, the understanding of the fundamental relationships between RNA abundance, environmental stimuli, RNA activities, and RNA age distributions is incomplete. Furthermore, the rates of RNA degradation and transcription are difficult to measure in transcriptomic experiments in living organisms, especially in studies involving humans. A model based on activity demands and RNA age was developed to explore the mechanisms of RNA level fluctuations. Using single-cell time-series gene expression experimental data, we assessed the transcription rates, RNA degradation rates, RNA life spans, RNA demand, accumulated transcription levels, and accumulated RNA degradation levels. This model could also predict RNA levels under simulation backgrounds, such as stimuli that induce regular oscillations in RNA abundance, stable RNA levels over time that result from long-term shortage of total RNA activity or from uncontrollable transcription, and relationships between RNA/protein levels and metabolic rates. This information contributes to existing knowledge.

Detected cellular RNA levels usually fluctuate. The understanding of the fundamental relationships between RNA level fluctuations, the rates of RNA degradation and transcription, environmental stimuli, RNA activities, and RNA age distributions is incomplete. In the present research, we developed a model based on the demands of RNA (related to intrinsic and/or extrinsic information), RNA age (determines the survival time and biological activity of an RNA), transcription, and RNA degradation to explain the mechanism underlying intracellular RNA level fluctuations. We also explored applicability of the model for analysing dynamic processes between interacting biomolecules, such as the relationship between RNA and protein level fluctuations. Using single-cell time-series gene expression experimental data, we assessed some biological parameters, such as transcription rates, RNA degradation rates, and RNA life spans. This model could also predict RNA levels under simulation backgrounds, such as stimuli that induce regular oscillations in RNA abundance, stable RNA levels over time that result from long-term shortage of total RNA activity or from uncontrollable transcription, and relationships between RNA/protein levels and metabolic rates. This information contributes to existing knowledge and provides a new perspective for future studies.

The enzymatic hydrolysates from deer sinew promote MC3T3-E1 cell proliferation and extracellular matrix synthesis by regulating multiple functional genes

Background

Deer Sinew serves as a medicinal food, and has been used for treating skeletal diseases, especially bone diseases in a long history. Thus, it could become an alternative option for the prevention and therapeutic remedy of bone-related diseases. In our previous study, we established an optimal extraction process of the enzymatic hydrolysates from Chinese Sika deer sinews (DSEH), and we demonstrated that DSEH significantly promoted the proliferation of MC3T3-E1 cells (an osteoblast-like cell line) with a certain dose-effect relationship. However, the precise molecular mechanism of deer sinew in regulating bone strength is still largely unknown. The aim of this study was to explore the underlying molecular mechanism of DSEH on MC3T3-E1 cells proliferation and extracellular matrix synthesis.

Methods

Preparation and quality control were performed as previously described. The effect of DSEH at different administrated concentrations on cell proliferation was measured using both CCK-8 and MTT assays, and the capacity of DSEH on extracellular matrix synthesis was detected by Alizarin red staining and quantification. The gene expression pattern change of MC3T3-E1 cells under the treatment of DSEH was investigated by RNA-seq analysis accompanied with validation methods.

Results

We demonstrated that DSEH promoted MC3T3-E1 cell proliferation and extracellular matrix synthesis by regulating multiple functional genes. DSEH significantly increased the expression levels of genes that promoted cell proliferation such as Gstp1, Timp1, Serpine1, Cyr61, Crlf1, Thbs1, Ctgf, P4ha2, Sod3 and Nqo1. However, DSEH significantly decreased the expression levels of genes that inhibited cell proliferation such as Mt1, Cdc20, Gas1, Nrp2, Cmtm3, Dlk2, Sema3a, Rbm25 and Hspb6. Furthermore, DSEH mildly increased the expression levels of osteoblast gene markers.

Conclusions

Our findings suggest that DSEH facilitate MC3T3-E1 cell proliferation and extracellular matrix synthesis to consolidate bone formation and stability, but prevent MC3T3-E1 cells from oxidative stress-induced damage, apoptosis and further differentiation. These findings deepened the current understanding of DSEH on regulating bone development, and provided theoretical support for the discovery of optional prevention and treatment for bone-related diseases.