Can You Extract Rna From Beef

• Loading metrics

Open Access

Peer-reviewed

Research Article

Evaluation of RNA quality and functional transcriptome of beefiness longissimus thoracis over time post-mortem

• Stephanie Lam,
• Arun Kommadath,
• Óscar López-Campos,
• Nuria Prieto,
• Jennifer Aalhus,
• Manuel Juárez,
• Michael E. R. Dugan,
• Payam Vahmani

x

• Published: May 25, 2021
• https://doi.org/10.1371/journal.pone.0251868

Figures

Abstruse

Evaluating RNA quality and transcriptomic profile of beef musculus over time postal service-mortem may provide insight into RNA deposition and underlying biological and functional mechanisms that accompany biochemical changes occurring post-mortem during transformation of muscle to meat. RNA was extracted from longissimus thoracis (LT) sampled from British Continental crossbred heifer carcasses (n = vii) stored at 4°C in an shambles drip cooler at five time points post-mortem, i.e., 45 min (0 h), half dozen h, 24 h, 48 h, and 72 h. Following RNA-Sequencing, processed reads were aligned to the ARS-UCD1.2 bovine genome associates. Subsequent differential expression (DE) analysis identified from 51 to 1434 upregulated and 27 to 2256 downregulated DE genes at private time points compared to time 0 h, showing a trend for increasing counts of both upregulated and downregulated genes over time. Gene ontology and biological pathway term enrichment analyses on sets of DE genes revealed several processes and their timelines of activation/deactivation that accompanied or were involved with muscle transformation to meat. Although the quality of RNA in refrigerated LT remained high for several days post-mortem, the expression levels of several known biomarker genes for meat quality began to change from 24 h onwards. Therefore, to ensure accuracy of predictions on meat quality traits based on the expression levels of those biomarker genes in refrigerated beef muscle tissue, it is crucial that those expression measurements exist made on RNA sampled within 24 h post-mortem. The present study also highlighted the need for more than research on the roles of mitochondrial genes and non-coding genes in orchestrating muscle tissue processes later on death, and how pre-mortem immune status might influence post-mortem meat quality.

Citation: Lam S, Kommadath A, López-Campos Ó, Prieto N, Aalhus J, Juárez M, et al. (2021) Evaluation of RNA quality and functional transcriptome of beef longissimus thoracis over fourth dimension postal service-mortem. PLoS 1 16(five): e0251868. https://doi.org/10.1371/periodical.pone.0251868

Editor: Cristina Óvilo, INIA, Kingdom of spain

Received: October 29, 2020; Accustomed: May iv, 2021; Published: May 25, 2021

Copyright: © 2021 Lam et al. This is an open access commodity distributed under the terms of the Artistic Commons Attribution License, which permits unrestricted use, distribution, and reproduction in whatever medium, provided the original author and source are credited.

Data Availability: All relevant information are within the manuscript and its Supporting Information files.

Funding: The financial and material back up for this study was provided by Agronomics and Agri-Nutrient Canada (AAFC). Please note that AAFC had no role in written report blueprint, data collection and assay, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

RNA-Sequencing (RNA-Seq) has many applications, some of which include identification of differentially expressed (DE) genes and functional variants, de novo transcriptome assembly and novel transcript discovery. The use of RNA-Seq for gene expression analysis relies on careful option of experimental weather condition, including sampling fourth dimension, physiological condition of individuals sampled, environmental factors, biological tissue, tissue sampling location, as well equally sample storage conditions. An important requirement in order to guarantee an authentic snapshot of gene expression at the immediate moment of tissue sampling is the availability of loftier quality RNA [1]. Since RNA degrades apace, care should be taken to not just obtain high quality RNA at extraction but also to maintain its quality under appropriate storage conditions until farther assay. Obtaining high quality RNA is especially challenging from post-mortem tissue where RNA extraction may either non be possible immediately after organismal death or where RNA samples are required at extended time points post-mortem. In addition, initial RNA quality and RNA deposition rates are tissue blazon dependent. For example, it has been found in beef carcasses stored at 4°C that RNA sampled from skeletal muscle remained at loftier quality for longer durations mail-mortem than RNA from liver and adipose tissue [2]. A comparing of multiple post-mortem human being tissues too indicated RNA from muscle to be amongst the slowest to degrade [three]. Differences in RNA deposition rates have been attributed in function to the concentration of ribonucleases already present in cells and/or originating from bacteria or other environmental contamination, with ribonuclease-rich organs such equally pancreas and liver exhibiting quicker RNA fragmentation than others [4].

Studies on postal service-mortem tissues have helped amend the understanding of biological processes and transcriptional changes that proceed to occur every bit the cells close down later organismal death, with applications such as in forensics to predict time of expiry [three, five]. In livestock species, studies on postal service-mortem muscle transcriptomes have helped improve our understanding of the molecular and biological processes that are concurrent with the biochemical changes that occur mail-mortem as muscle transforms to meat and subsequent crumbling (e.chiliad. pH, proteolysis, and tenderization), and how those processes influence economically of import meat quality characteristics similar meat color and tenderness [2, 6–eight]. Recent studies on muscle transcriptome profiles from cattle and pigs have identified genes influencing meat quality traits such as tenderness and marbling in beef and pork [nine–12]. The potential for drawing valid inferences between factor biomarkers and meat quality traits at the abattoir could, notwithstanding, be improved through a amend understanding of RNA viability post-mortem and expression of specific biomarker genes.

The aims of this written report were to better our understanding of how RNA quality and transcriptomic profiles change in beef muscle over fourth dimension post-mortem under refrigerated abattoir weather condition. The specific objectives were to: 1) determine RNA quality in beef longissimus thoracis (LT) at 5 time points post-mortem (0 to 72 h); 2) concurrently evaluate transcriptomic profiles using RNA-Seq to identify DE genes and stability of known biomarker genes for meat quality traits; and three) identify important biological processes and pathways represented past genes found DE at those fourth dimension points.

Materials and methods

Animals and post-mortem tissue sampling

Experimental conditions were approved by the Agriculture and Agri-Food Canada Lacombe Research and Development Centre (AAFC-Lacombe RDC) Animal Intendance committee (approval #201705) in compliance with the principles and guidelines established by the Canadian Council on Fauna Care (CCAC 2009). This study used 7 (British × Continental crossbred) beef heifers raised at the AAFC-Lacombe RDC farm (Lacombe, AB, Canada) under similar conditions and finished on a barley grain-based diet as described previously [thirteen]. At slaughter, heifers were on boilerplate, 567.9 ± 12.5 d old and weighed 574.ix ± 37.0 kg. Animals were slaughtered at the AAFC-Lacombe RDC federally inspected research slaughterhouse using penetrative captive bolt stunning followed by exsanguination. Core LT samples were collected at 5 time points post-mortem, i.e., 45 min (0 h), 6 h, 24 h, 48 h, and 72 h, from the left carcass sides (stored in the drip cooler at four°C) higher up the grading site, and immediately frozen in liquid nitrogen and stored at -eighty°C until RNA extraction.

RNA extraction, quality assessment and sequencing

Muscle samples were homogenized using a Tissuelyzer (Qiagen, Valencia, CA) in the presence of Trizol reagent. The RNA was extracted using Qiagen RNAeasy mini kit (Qiagen N.Five., Valenca, CA, United States) according to the manufacturer'due south protocol. The RNA sample purity was evaluated past determining A260/230 nm and A260/280 nm ratios using the NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Santa Clara, CA, United states of america, 2007). The RNA sample integrity was assessed using the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, U.s.a.) to obtain the RNA integrity number (RIN) values. A total of 35 RNA samples (7 animals × 5 time points) were sequenced at the Génome Québec Innovation Centre (McGill Academy, Montréal, QC, Canada). Briefly, pooled libraries were prepared and loaded at 225 pM on an Illumina NovaSeq 6000 S4 lane following manufacturer's protocol. The sequencing run was performed for 2x100 cycles in paired-end mode, generating 100 bp length, paired end sequencing reads. Base of operations calling was performed with RTA v3.4.4 program, and the samples were de-multiplexed to generate sample-wise raw sequence files in fastq format using bcl2fastq2 Conversion Software v2.20.

RNA-Seq analysis

The quality of sequence information was assessed with FastQC (https://www.bioinformatics.babraham.ac.great britain/projects/fastqc/) prior to and after performing quality control steps that included quality based read trimming and adapter removal using Trimmomatic v0.39 [14] with the following parameters: ILLUMINACLIP:/adaptors.fa:two:30:10 LEADING:iii TRAILING:3 SLIDINGWINDOW:iv:xv MINLEN:75, where adaptors.fa is a FASTA file containing the oligonucleotide sequences of the Illumina Novaseq adapters used in NEBNext mRNA stranded library preparation kits (New England Biolabs, Ipswich, MA, USA). Reads that passed quality control were mapped to the ARS-UCD1.two bovine genome assembly [fifteen] using STAR v2.7.0f [16] with default parameters and quantMode set up to GeneCounts. Read counts per gene were obtained using featureCounts v1.half dozen.4 [17] in strand specific mode for all genes in the gene annotation file corresponding to the ARS-UCD1.2 bovine genome assembly (from Ensembl [eighteen] release 95) with the following parameters: -due south 0 -p -t exon -g gene_id -a Bos_taurus.ARS-UCD1.two.95.gtf. The raw sequence data and read count matrix from this study have been deposited in NCBI's Gene Expression Omnibus (GEO) database [19] under GEO series accession GSE163766.

Differential expression analysis

The DE analysis and associated tests were performed in R (R Version 3.six.0.; R Core Squad, 2020) statistical programming language using mainly the Bioconductor bundle, edgeR (version iii.24.iii) [20, 21] on read counts from the sense strand. Genes with very low expression were filtered out, keeping but those that were expressed at counts per million (CPM) values that corresponded to a read count over x in at to the lowest degree 7 samples (using filterByExpr function from edgeR package). Trimmed hateful of M-values (TMM) normalization [22] was applied to this dataset to account for compositional differences between the libraries. For exploratory assay, a principal component analysis (PCA) was performed and plotted using the plotPCA function of DESeq2 package (version one.26.0) [23]. A power analysis was performed using Bioconductor package RNASeqPower (version one.26.0) [24] to decide the appropriate fold alter (FC) level that can exist reliably detected for this dataset, given the biological co-efficient of variability (BCV) and coverage (read depth) of samples. A negative binomial generalized linear model was used to exam for differential gene expression at different time points post-mortem compared to the initial time, adding a blocking factor to account for repeated measures over time from the same creature.

Functional enrichment analysis of differentially expressed genes

Functional enrichment analyses to identify over-represented factor ontology (Get) and Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathway terms in separate sets of upregulated and downregulated DE genes at different time points were performed using the functional annotation tool from Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.eight database [25, 26]. Ensembl gene IDs were used as the gene identifier and significantly enriched terms (Benjamin-Hochberg corrected p-value < 0.05) were adamant against a background or population set consisting of all genes identified as expressed, following filtering out of low expressed genes as described in the previous department.

Results

RNA quality and RNA-Seq statistics

The RIN values of RNA samples ranged from 7.9 to 8.6, with an average of viii.2 across all sampling time points. The average RIN values across samples for each mail-mortem sampling time indicate were eight.3 (0 h), 8.3 (6 h), eight.3 (24 h), 8.1 (48 h), and 8.1 (72 h), with the average dropping slightly after 24 h post-mortem (Table 1).

The number of reads sequenced (in millions) ranged from 27.89 to 138.24 with an average of 64.05 (s.d. 32.12) million reads, of which 95.79% (southward.d. 0.25) were uniquely mapped to the bovine reference genome (Tabular array i and S1 Fig). The total number of reads postal service quality control that were assigned to the sense strand ranged from 20.57 to 104.28 1000000.

Differentially expressed genes and their characteristics

Read counts were obtained for a total of 27,607 genes annotated in the factor notation file corresponding to the ARS-UCD1.2 bovine genome associates. Of those, 14,630 genes (52.99% of the total) were identified as expressed in the LT samples used in this experiment, following filtering out of low expressed genes. A PCA plot based on the normalised read counts showed samples separated by time point along the kickoff primary component (PC1) with separation condign more distinct towards afterward time points, indicative of substantial and progressively increasing changes in cistron expression with time mail service-mortem (S2 Fig). A ability assay suggested that a fold alter of 1.5 could be reliably detected at a power of 0.9 and fake positive rate of 0.05 with the sample size of 7 per grouping, and accounting for the boilerplate sequencing depth and biological variation within the groups (S3 Fig). At the selected fold change of above i.v and a False Discovery Rate (FDR) adjusted p-value of below 0.05, the number of DE genes identified showed a steady increase with sampling time mail-mortem, ranging from 51 to 1434 for upregulated, and 27 to 2256, for downregulated genes, over 4 time points (Table 2). The number of upregulated genes was slightly higher than the downregulated at 6 h but the trend reversed for later on time points, and both the number and rate of increase for downregulated genes were much higher. All sets of DE genes are listed in S1 Table and illustrated through heat maps (S4 Fig) and volcano plots (S5 Fig).

Looking into the origin of the DE genes, it was observed that a disproportionately high number of mitochondrial genes were downregulated, and none upregulated at early time points. Mitochondrial genes accounted for below 0.18% (n = 26) of the full 14,630 genes identified as expressed; all the same, 44.44% of the downregulated genes at 6h (i.e. 12 of 27) and vii.90% of the downregulated genes at 24 h (i.e., 11 of 139) were of mitochondrial origin.

Next, looking at the distribution of cistron biotypes among the lists of DE genes, a disproportionately high number of not-coding genes, especially small nucleolar RNA (snoRNA) and long not-coding RNA (lncRNA), appear in both the upwards and the downregulated sets of genes at all fourth dimension points. Not-coding genes accounted for below 4% (north = 578) of the total 14,630 genes identified every bit expressed; however, 33.33% (26 of 78), 17.55% (43 of 245), 9.52% (129 of 1355) and half dozen.42% (237 of 3690) of the DE genes at 6, 24, 48 and 72 h, respectively, were non-coding.

Finally, we compared the sets of DE genes found at each time point post-mortem with longissimus dorsi muscle expressed genes reported recently to be either associated with a meat quality alphabetize (MQI; pseudo-phenotype divers through a main component analysis of several meat quality related traits that included marbling, Warner-Bratzler Shear Force (WBSF), cooking loss, juiciness, tenderness, and connective tissue) or DE between multi-breed Angus-Brahman steers of low or loftier MQI [27]. In this previous study, an clan analysis between gene expression in longissimus dorsi muscle from 80 steers with respective MQI revealed 208 genes significantly associated with MQI. Of the 208 cistron names reported in that study, which were based on an older bovine reference genome annotation, 186 could be converted into Ensembl gene IDs from the current annotation. Remarkably, none of those 186 MQI associated gene IDs were institute DE at early on time points mail-mortem (6 and 24 h) in the present study (Fig 1A and 1B). Further, a DE analysis carried out in the previous report betwixt 40 steers with low or high MQI revealed a total of 676, 70 and 198 genes as DE for WBSF, tenderness and marbling, respectively. Combining the 3 sets of DE genes from the previous study resulted in 886 unique gene symbols, of which 812 could be converted to Ensembl cistron IDs from the current annotation. Similar to the ascertainment on MQI associated genes, none of those 812 MQI related DE genes were plant DE at 6 h postal service-mortem in the present report (Fig 1A) and simply 15 were found DE at 24 h (Fig 1B). The number of genes in common among dissimilar gene sets increased over time beyond 24 h (Fig 1C and 1D). Next, we made a like comparison of our combined set of DE genes with a ready of 44 Ensembl cistron IDs (7 of which were listed in the MQI related DE genes and none in MQI associated genes) of genes that encode the poly peptide biomarkers listed in a recent review [28] as associated with meat quality traits in beef. We found that none of those 44 genes were found DE at 6 h and 12 h, 2 were found DE at 48 h and 10 at 72 h post-mortem (Fig 1A–1D). These findings point that many potential gene biomarkers associated with meat quality traits have stable cistron expression for upward to 24 h post-mortem.

Fig ane. Comparison of previously reported potential biomarker genes for meat quality traits with genes found DE over time in beef LT muscle.

Panels A-D represent Venn diagrams comparing genes found DE in beef LT musculus (at 6, 24, 48 and 72 h, respectively) with previously reported meat quality index (MQI) associated genes (MQI_associated_genes) or DE genes related to MQI traits (MQI_de_genes) or genes encoding poly peptide biomarkers associated with meat quality traits in beef (protein_biomarkers).

https://doi.org/ten.1371/periodical.pone.0251868.g001

Biological processes represented in sets of differentially expressed genes

Become biological processes and KEGG pathway terms constitute enriched in sets of DE genes from beef LT indicated an initial downregulation of oxidative phosphorylation at 6 h, and a surprising reversal to upregulation at 72 h (Tabular array 3). The viii genes associated with the downregulation of that pathway at 6 h (S2 Table) were completely different from the 36 genes involved in the upregulation of the same process at 72 h. Surprisingly, the sets of upregulated genes did not reveal whatever significantly enriched processes or pathways at early time-points; however, many allowed related terms were constitute enriched at 48 h, and terms related to ribosome and translation were enriched at 72 h. On the reverse, all iv sets of downregulated genes indicated several biological processes and pathways to exist enriched, progressing from oxidative phosphorylation at half-dozen h to "regulation of lipolysis in adipocytes" and a few signaling pathways at 24 and 48 h, to a drench of multiple signaling pathways by 72 h. A detailed overview of all enriched terms from all functional note categories tested in DAVID likewise as the DE genes annotated to those categories are provided in S2 Table.

Discussion

Sampling tissues from animals immediately after slaughter is challenging due to express accessibility and delays when working alongside routine operations in an abattoir. Delays in sampling can affect the quality of RNA extracted from post-mortem tissues, which in turn can affect the accuracy of gene expression measurements. Yet, the storage of carcasses nether refrigerated conditions in coolers results in lower enzyme activity, thereby slowing down biochemical processes following death [29] and effectively slowing RNA degradation. In improver to sampling fourth dimension after slaughter and storage temperature, variability in RNA degradation and quality is dependent on factors such as species and type of tissue sampled [2, three, 8, 30–33]. For example, a study on RNA quality of pork semimembranosus stored at four°C revealed an average RIN of 3.95 at 48 h [30]. The significantly lower RIN reported in pork compared to the boilerplate RIN of 8.one we obtained for beef LT at 48 h may exist attributed to the microstructural and cellular differences between pork and beef tissue. This may exist due to the variation in muscle fibre blazon composition across species [34] and the known differences in full RNA content by fibre type in skeletal muscle, in which the slow fiber blazon (Type I fibre) contains higher RNA content due to higher myonuclei per mm of myofiber compared to fast fiber types (Type II fibres) [35]. Furthermore, RNA deposition rates may be different across species and tissue [2, 3]; in fact, intact RNA has been recovered from beef skeletal muscle for up to viii d post-mortem [2]. Given our confirmation that loftier quality RNA can be obtained for extended periods in postal service-mortem LT nether refrigeration, it was then of import to follow-upwards and determine the elapsing mail service-mortem that the expression of factor biomarkers for beef quality remained stable. Based on the premise that such biomarkers would be invalid if their expression were to change with time subsequently death, we compared known biomarkers associated with meat quality traits for their presence in our lists of DE genes at unlike time points post-mortem. We constitute that expression of most biomarkers tested remained stable for up to 24 h post-mortem in LT muscle stored at 4°C. This finding has important implications when devising sampling regimes for skeletal muscle RNA from the abattoir. While RNA quality is bodacious for several days mail service-mortem, information technology is prudent to use samples collected within the first 24 h post-mortem when the intent is to predict meat quality based on gene biomarkers associated with meat quality traits.

The increasing number of DE genes detected in this report with progressive time points compared to but after slaughter is indicative of standing transcriptional action for extended times afterward death. While the increment in number of downregulated genes with time post-mortem may exist intuitively attributed to biological processes shutting downwards in dying cells, concurrent and substantial increases in numbers of upregulated genes with fourth dimension came equally a surprise. This observation is, however, supported past several studies that reported upregulation of thousands of genes, dependent on time post-mortem, tissue type and storage conditions [iii, five, 36]. The sets of upregulated genes were conspicuous past their absenteeism of any enriched biological processes represented in those gene sets during the initial time points. This may suggest that a bulk of the genes upregulated at early mail-mortem were not co-regulated to work in specific pathways or processes. Another possibility is that those genes that would have been part of any processes working in concert may not have been upregulated to the levels required to be detected as significant, given the sample size and statistical ability of the current analysis. At 48 h post-mortem, many immune related pathways and processes were establish upregulated, an observation that has been reported in earlier studies, but at differing fourth dimension points. For example, transcripts related to allowed response increased at one and 12 h in mouse encephalon tissue and transcripts related to immune response in both adaptive and innate immune pathways increased across various time points from 1 h to 24 h in fish liver tissue [v]. Allowed and inflammatory responses may be attributed to recognition past the innate immune system of intracellular molecules exposed by dying cells and subsequent macrophage stimulation [37, 38]. The KEGG pathway, 'Natural killer prison cell mediated cytotoxicity', which was significantly upregulated at 48 h is known to induce decease receptor-mediated apoptosis, leading to caspase activation, mitochondrial dysfunction, and apoptosis [39]. The pathway of apoptosis from initial trigger to devastation of the cell tin take hours to days [40], suggesting the mediators in this pathway could be influencing the expression patterns we observed. Clinical conditions that trigger tissue hypoxia are also known to promote inflammation and many allowed cells are known to conform to low oxygen conditions, such as in high altitude [41]. Post-mortem upregulation of immune and inflammatory responses may thus be triggered by tissue hypoxia associated with cessation of blood flow. By extension, immune status prior to slaughter may take implications during musculus to meat conversion as processes such as inflammation could bear upon several aspects of meat quality. For instance, inflammation could influence oxidative stability of membranes, h2o holding capacity, and maintenance of ion gradients, which could in plough influence calcium dependent protease activeness, and onset and resolution of rigor. The finding of several disease related pathways upregulated at 72 h mail-mortem may too be attributed to the fact that dying and expressionless cells, through apoptotic processes, stimulate inflammatory responses by interim through pathways that likewise underlie the pathogenesis of a number of those diseases [37].

There was no direct support in literature regarding upregulation of enriched terms related to ribosome and translation that nosotros observed at 72 h. A written report on post-mortem human being blood transcriptome [three] reported that the bulk of changes in factor expression occurred betwixt 7 and fourteen h after death, with thousands of genes showing DE every bit in both directions relative to pre-mortem samples, followed by stabilization of the transcriptome betwixt fourteen and 24 h, and eventually deactivation. Therefore, we speculate that, post-mortem induced processes which ordinarily start in the early hours of death may activate later in time for muscle stored in refrigerated conditions. Additionally, the specific characteristics of skeletal muscle that allow for some metabolic processes to go along anaerobically [42] may take contributed to processes continuing afterwards than would be the example in other tissues.

Next, the biological processes and pathways enriched amidst sets of downregulated genes revealed an initial downregulation of oxidative phosphorylation at 6 h, followed past downregulation of "regulation of lipolysis in adipocytes" and signalling pathways at later time points. Oxidative phosphorylation occurs in mitochondria and oxygen is critical for their optimal part. The downregulation of oxidative phosphorylation, which coincided with our observation of a unduly high number of mitochondrial genes appearing equally downregulated at early on fourth dimension points, may be explained past decreases in muscle oxygenation with loss of claret flow and transition of oxy- to deoxymyoglobin in post-mortem musculus. To our surprise, the initially downregulated oxidative phosphorylation procedure was subsequently upregulated at 72 h. This may be explained by several studies [43–46] suggesting mitochondria may be functioning in some chapters mail service-mortem in skeletal musculus, unrelated to coupled respiration, as evidenced by the accumulation of succinate after 24 h post-mortem, and the fact that succinate can increase mitochondria-mediated metmyoglobin reduction.

The disproportionately loftier number of non-coding genes appearing DE at all fourth dimension points post-mortem may be of involvement, non only to sympathise the roles they play in regulating processes after expiry but also in providing clues on how their disruption in life could involve disease states and departure from well-being. Long not-coding RNA are known to regulate gene expression at the epigenetic, transcriptional and post-transcriptional level, while small nucleolar RNA play essential roles in the nucleolytic processing of rRNAs and as guide RNAs in the post-transcriptional synthesis. Previous studies [vii] indicate that epigenetic regulatory genes proceed to modify chromatin construction in organismal death and thus change the accessibility of transcription factors to the promoter or enhancer regions. Our findings implicate the need for more research into the roles of mitochondrial genes and not-coding RNA in orchestrating processes afterward decease, and by extension, to regulate processes during life.

Conclusion

This study contributes to our current understanding of the muscle transcriptome and associated biological processes up to 72 h post-mortem, which coincides with the conversion of musculus to meat. Measurements of RNA quality and stability indicate alternative sampling regimes can exist implemented on-line in the slaughter-house up to 72 h postal service-mortem. However, transcriptional activity changes over fourth dimension, affecting different genes to different extents, and this must be considered when studying transcriptional activity and interpreting gene biomarkers related to desirable meat quality traits. Interestingly, at to the lowest degree for those biomarkers tested in this report, information technology may be valid to link their expression to meat quality traits for up to 24 h mail service-mortem. This written report also highlights the importance of further research into the roles of mitochondrial and non-coding genes in the regulation or disruption of transcription in post-mortem muscle too as in life, and how pre-slaughter immune status might influence post-mortem meat quality. Future directions of research to extend upon the findings in the electric current study would be to consider more tissue types, pre-mortem immune status and time points post-mortem to study how gene expression and associated biological processes progress in unlike tissues stored under refrigeration in slaughterhouse conditions.

Supporting data

S5 Fig. Volcano plots of DE genes in beef LT over time.

The genes are plotted and coloured based on false discovery rate (FDR) and fold change (FC): red if FDR<0.05, orange if absolute FC>one.v, and greenish if both. The x virtually pregnant DE genes (sorted by FDR) are labelled in cases where the gene symbols are known.

https://doi.org/ten.1371/periodical.pone.0251868.s005

(PDF)

Acknowledgments

The staff at the AAFC-Lacombe RDC Beef Unit are acknowledged for the provided animal care, animal management, and sample collection. The slaughter and processing of the cattle by the AAFC-Lacombe RDC butchery staff is likewise gratefully acknowledged.

References

1. 1. Schroeder A, Mueller O, Stocker South, Salowsky R, Leiber K, Gassmann M, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006;vii(1):iii. pmid:16448564
   • View Article
   • PubMed/NCBI
   • Google Scholar
2. ii. Bahar B, Monahan FJ, Moloney AP, Schmidt O, MacHugh DE, Sweeney T. Long-term stability of RNA in mail service-mortem bovine skeletal muscle, liver and subcutaneous adipose tissues. BMC molecular biology. 2007;8(1):108–. pmid:18047648
   • View Article
   • PubMed/NCBI
   • Google Scholar
3. 3. Ferreira PG, Muñoz-Aguirre M, Reverter F, Sá Godinho CP, Sousa A, Amadoz A, et al. The effects of decease and post-mortem cold ischemia on human tissue transcriptomes. Nature communications. 2018;nine(one):490–15. pmid:29440659
   • View Article
   • PubMed/NCBI
   • Google Scholar
4. iv. Bauer Yard. RNA in forensic scientific discipline. Forensic Sci Int Genet. 2007;1(ane):69–74. pmid:19083730
   • View Article
   • PubMed/NCBI
   • Google Scholar
5. 5. Pozhitkov AE, Neme R, Domazet-Loso T, Leroux BG, Soni South, Tautz D, et al. Tracing the dynamics of cistron transcripts later organismal expiry. Open up Biol. 2017;seven(1). pmid:28123054
   • View Article
   • PubMed/NCBI
   • Google Scholar
6. six. Neath KE, Del Barrio AN, Lapitan RM, Herrera JRV, Cruz LC, Fujihara T, et al. Difference in tenderness and pH decline between water buffalo meat and beef during postmortem aging. Meat Science. 2007;75(3):499–505. pmid:22063807
   • View Article
   • PubMed/NCBI
   • Google Scholar
7. 7. Zhang Y, Zhang J, Gong H, Cui 50, Zhang W, Ma J, et al. Genetic correlation of fatty acid limerick with growth, carcass, fat deposition and meat quality traits based on GWAS information in six hog populations.(Report). Meat Scientific discipline. 2019;150:47. pmid:30584983
   • View Commodity
   • PubMed/NCBI
   • Google Scholar
8. eight. Yu Q, Tian Ten, Sun C, Shao L, Li X, Dai R. Comparative transcriptomics to reveal musculus-specific molecular differences in the early postmortem of Chinese Jinjiang yellow cattle. Food chemistry. 2019;301:125262–. pmid:31377625
   • View Article
   • PubMed/NCBI
   • Google Scholar
9. 9. Muniz MMM, Fonseca LFS, Magalhães AFB, Dos Santos Silva DB, Canovas A, Lam Due south, et al. Utilize of gene expression profile to place potentially relevant transcripts to myofibrillar fragmentation index trait. Functional & integrative genomics. 2020. pmid:32285226
   • View Article
   • PubMed/NCBI
   • Google Scholar
10. ten. Chen D, Li W, Du Yard, Wu M, Cao B. Sequencing and characterization of divergent marbling levels in the beef cattle (Longissimus dorsi muscle) transcriptome.(p. 165f-165zy). Asian—Australasian Journal of Fauna Sciences. 2015;28(2).
    • View Article
    • Google Scholar
11. 11. Te Pas MFW, Keuning E, Hulsegge B, Hoving-Bolink AH, Evans G, Mulder HA. Longissimus muscle transcriptome profiles related to carcass and meat quality traits in fresh meat Pietrain carcasses. Journal of creature scientific discipline. 2010;88(12):4044. pmid:20833764
    • View Article
    • PubMed/NCBI
    • Google Scholar
12. 12. Ropka-Molik K, Bereta A, Żukowski G, Tyra M, Piórkowska K, Żak Chiliad, et al. Screening for candidate genes related with histological microstructure, meat quality and carcass characteristic in pig based on RNA-seq data. Asian-Australasian journal of brute sciences. 2018;31(10):1565–74. pmid:29531190
    • View Article
    • PubMed/NCBI
    • Google Scholar
13. 13. Vahmani P, Rolland DC, Block HC, Dugan MER. Scarlet blood cells are superior to plasma for predicting subcutaneous trans fatty acrid composition in beefiness heifers. Canadian journal of animal science. 2020;100(3):570–6.
    • View Article
    • Google Scholar
14. 14. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–xx. pmid:24695404
    • View Article
    • PubMed/NCBI
    • Google Scholar
15. fifteen. Rosen BD, Bickhart DM, Schnabel RD, Koren South, Elsik CG, Tseng E, et al. De novo assembly of the cattle reference genome with unmarried-molecule sequencing. GigaScience. 2020;9(3). pmid:32191811
    • View Article
    • PubMed/NCBI
    • Google Scholar
16. sixteen. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(one):15–21. pmid:23104886
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
17. 17. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(vii):923–xxx. pmid:24227677
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
18. eighteen. Yates Ad, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48(D1):D682–D8. pmid:31691826
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
19. nineteen. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013;41(Database issue):D991–v. pmid:23193258
    • View Article
    • PubMed/NCBI
    • Google Scholar
20. 20. Robinson Dr., McCarthy DJ, Smyth GK. edgeR: a Bioconductor packet for differential expression assay of digital gene expression data. Computer applications in the biosciences. 2009;26(1):139–40. pmid:19910308
    • View Article
    • PubMed/NCBI
    • Google Scholar
21. 21. McCarthy DJ, Chen Y, Smyth GK. Differential expression assay of multifactor RNA-Seq experiments with respect to biological variation. Nucleic acids research. 2012;40(ten):4288–97. pmid:22287627
    • View Article
    • PubMed/NCBI
    • Google Scholar
22. 22. Robinson Medico, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. GenomeBiologycom. 2010;eleven(3):R25–R. pmid:20196867
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
23. 23. Love MI, Huber W, Anders Southward. Moderated interpretation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. pmid:25516281
    • View Article
    • PubMed/NCBI
    • Google Scholar
24. 24. Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher J-P. Calculating Sample Size Estimates for RNA Sequencing Information. Periodical of computational biology. 2013;xx(12):970–eight. pmid:23961961
    • View Article
    • PubMed/NCBI
    • Google Scholar
25. 25. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large cistron lists. Nucleic acids research. 2009;37(ane):1–13. pmid:19033363
    • View Article
    • PubMed/NCBI
    • Google Scholar
26. 26. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols. 2008;4(one):44–57.
    • View Article
    • Google Scholar
27. 27. Leal-Gutierrez JD, Elzo MA, Carr C, Mateescu RG. RNA-seq assay identifies cytoskeletal structural genes and pathways for meat quality in beefiness. PLoS One. 2020;fifteen(11):e0240895. pmid:33175867
    • View Article
    • PubMed/NCBI
    • Google Scholar
28. 28. Huang C, Hou C, Ijaz M, Yan T, Li Ten, Li Y, et al. Proteomics discovery of poly peptide biomarkers linked to meat quality traits in post-mortem muscles: Current trends and future prospects: A review. Trends in Food Science & Technology. 2020;105:416–32.
    • View Commodity
    • Google Scholar
29. 29. Pastsart U, De Boever M, Claeys E, De Smet S. Outcome of muscle and post-mortem rate of pH and temperature autumn on antioxidant enzyme activities in beef. Meat Science. 2013;93(3):681–6. pmid:23273481
    • View Article
    • PubMed/NCBI
    • Google Scholar
30. 30. Fontanesi L, Colombo M, Beretti F, Russo V. Evaluation of post mortem stability of porcine skeletal muscle RNA. Meat Science. 2008;80(4):1345–51. pmid:22063878
    • View Article
    • PubMed/NCBI
    • Google Scholar
31. 31. Damsteegt EL, McHugh N, Lokman PM. Storage by lyophilization–Resulting RNA quality is tissue dependent. Analytical Biochemistry. 2016;511:92–half dozen. pmid:27515991
    • View Article
    • PubMed/NCBI
    • Google Scholar
32. 32. McGovern F, Boland T, Ryan M, Sweeney T. Cess of RNA Stability in Postmortem Tissue from New-Born Lambs. Creature Biotechnology. 2018;29(4):269–75. pmid:29172984
    • View Article
    • PubMed/NCBI
    • Google Scholar
33. 33. Vennemann K, Koppelkamm A. mRNA profiling in forensic genetics I: Possibilities and limitations. Forensic Science International. 2010;203(ane):71–5. pmid:20724085
    • View Article
    • PubMed/NCBI
    • Google Scholar
34. 34. Lawrie RA, Ledward D. Lawrie'southward meat science: Woodhead Publishing; 2014.
35. 35. Habets PE, Franco D, Ruijter JM, Sargeant AJ, Pereira JASA, Moorman AFJJoH, et al. RNA content differs in wearisome and fast musculus fibers: implications for interpretation of changes in muscle gene expression. 1999;47(viii):995–1004.
    • View Commodity
    • Google Scholar
36. 36. Zhu Y, Wang L, Yin Y, Yang E. Systematic assay of gene expression patterns associated with postmortem interval in man tissues. Sci Rep. 2017;vii(1):5435. pmid:28710439
    • View Article
    • PubMed/NCBI
    • Google Scholar
37. 37. Stone KL, Lai JJ, Kono H. Innate and adaptive immune responses to cell decease. Immunol Rev. 2011;243(1):191–205. pmid:21884177
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
38. 38. Nery RA, Kahlow BS, Skare TL, Tabushi FI, do Amaral e Castro A. Uric Acid and Tissue Repair. Arq Bras Cir Dig. 2015;28(four):290–2. pmid:26734804
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
39. 39. Prager I, Watzl C. Mechanisms of natural killer cell‐mediated cellular cytotoxicity. Periodical of leukocyte biology. 2019;105(half-dozen):1319–29. pmid:31107565
    • View Article
    • PubMed/NCBI
    • Google Scholar
40. forty. Green DR. Apoptotic Pathways: Ten Minutes to Dead. Cell. 2005;121(5):671–4. pmid:15935754
    • View Article
    • PubMed/NCBI
    • Google Scholar
41. 41. Eltzschig HK, Carmeliet P. Hypoxia and inflammation. Due north Engl J Med. 2011;364(seven):656–65. pmid:21323543
    • View Article
    • PubMed/NCBI
    • Google Scholar
42. 42. Matarneh SK, England EM, Scheffler TL, Gerrard DE. Affiliate 5—The Conversion of Muscle to Meat. In: Toldra F, editor. Lawrie´south Meat Science (Eighth Edition): Woodhead Publishing; 2017. p. 159–85.
43. 43. England EM, Matarneh SK, Mitacek RM, Abraham A, Ramanathan R, Wicks JC, et al. Presence of oxygen and mitochondria in skeletal muscle early postmortem. Meat Sci. 2018;139:97–106. pmid:29413683
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
44. 44. Ramanathan R, Mancini RA. Effects of pyruvate on bovine heart mitochondria-mediated metmyoglobin reduction. Meat Sci. 2010;86(3):738–41. pmid:20659785
    • View Article
    • PubMed/NCBI
    • Google Scholar
45. 45. Tang J, Faustman C, Hoagland TA, Mancini RA, Seyfert K, Chase MC. Postmortem oxygen consumption by mitochondria and its effects on myoglobin class and stability. J Agric Nutrient Chem. 2005;53(four):1223–xxx. pmid:15713045
    • View Article
    • PubMed/NCBI
    • Google Scholar
46. 46. Abraham A, Dillwith JW, Mafi GG, VanOverbeke DL, Ramanathan R. Metabolite Profile Differences between Beef Longissimus and Psoas Muscles during Display. Meat and muscle biological science. 2017;1(1):18–27.
    • View Article
    • Google Scholar

gordonberaing1955.blogspot.com

Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251868

Share this post