Representing Saccharomycotina, P. These two genomes experienced dramatic intron loss, but U. There was a paucity of introns near both ends of mRNA edge effects , which could be explained by random exon-shuffling as a mechanism for ancestral gene birth, as oppose to random insertion Figure 2A at zero round. As long as RT played a role in intron loss afterwards 70 , the mean RIL would decrease with cumulative exposure to RT-mediated intron loss.
In Figure 2 , the ancestral genome had an average of 7. The most striking result was the preservation of peak length mode although the average exon length increased responsively with intron loss. This explained the similar modes of exon length distribution between C.
Furthermore, the sparse introns near both ends of simulated genes concurred with both fungal Figure 1 and other genomes The L-shaped curve is due to the conservation of introns at random locations in the gene as introns may acquire function during evolution. The linear model, based on the top portion of the simulated data excluding the ancestral data point , underestimated the ancestral IPG —0. This is partly due to the heavy sampling at lower IPG.
With even sampling, the underestimate shall be smaller. Excluding genomes near the bottom end of L-curve, M. RT events may or may not result in intron loss and could not be measured directly RIL is a proxy. On a whole genome level, total length and number of RTF were essentially interchangeable.
Therefore, RTF could be eliminated in fungal genomes at a faster rate. Both of the above were reflected in the much faster decay rate for RTF 2. Accordingly, Ascomycota had lost at least 1, RTF compared to Basidiomycota data points need to be shifted to the right to fit the curve.
This was true within respective Ascomycota and Basidiomycota except for genomes that also lost the bulk of RTF. RT propagation is a growth process exponential , and log transformation converts it to a linear variable. In consistent with simulation, Basidiomycota lost more introns per RTF slope —0. The result also agreed with previous observations: ten adjacent introns were lost in one successful RT event for Basidiomycota 50 , but the highest was four in Ascomycota Our result revealed the link between RT and intron loss 43 , 48 , 50 , 52 , 70 , 82 from an evolutionary perspective.
The natural explanation was that the majority of RTF was removed after they had exerted their effect on intron loss. Less likely, RT-independent mechanisms accounted for the massive intron loss in U.
There was still the possibility that RT in U. The same could be stated for P. Retroposons are dynamic 78 , 83 and go through cycles of boom-and-bust by interacting with the host defense system in fungi 84 , but they are still useful for charting short term evolutionary histories of less than Ma for mammals If the ancestors for Basidiomycota and Ascomycota, respectively, had no active RT, then they had 9.
The lack of RT in the ancestor of Basidiomycota could be deduced from the RIL Figure 1 ; whereas, large scale RT loss from the ancestor of Ascomycota could be deduced from the comparison of simulation and real data.
Nonetheless, if the respective ancestors for Basidiomycota and Ascomycota had one RTF assuming one RT domain for about 1 kb , then they would have 7. Genomes below the regression line had accelerated intron loss owing to either more effective retroelements or selective pressure favoring intron loss. Expansion of genes coding for short peptides in L. Genomes above the regression line were more resistant to intron loss, possibly, through one of the several genome-defense mechanisms such as repeat-induced point mutation RIP 81 or small RNA mechanisms However, EPG of P.
The slight deviation in P. Genome-defense mechanisms may dampen the variation of RT-mediated intron loss. The data from Ascomycota had smaller variations compared to those from Basidiomycota. One explanation is the limited taxonomic distribution of genome-defense mechanisms in Basidiomycota 77 , Diverse Ascomycota species had RIP 75 , 76 , 79 , 80 , 81 , Since the mode of exon length distribution is conserved despite intron loss Figure 2B , extant genomes still contain the information about the predominant exon length in the eukaryotic ancestor.
Not surprisingly, the exon length distributions of fungi and green algae C. The latter genome exhibited features of the ancestor of both plant and animal Both fungi and green algae are lower eukaryotes and are considered to be more close to ancestral life forms than higher eukaryotes.
Ancient exons are usually flanked by short introns in mammalian genomes 89 , but this criterion would not help us find ancient exons in fungi because most fungal introns are short. If intron loss dominates eukaryotic evolution, then genes with the least intron loss must contain the most ancient exons.
This means that genes with the greatest number of introns contain the most ancient exons. However, the number of genes with the greatest number of introns was small and had large variations. The right-hand side of Figure 4A represented the ancestral state of exon length of nt. Seven proteins with 50 or more introns were all ancient Supplemental V; Table S2. Coincidentally, the ancient protein module size is 25 aa 75 nt 91 that is slightly longer than the 60 nt exon based on the theory of exons originating from random ORF During novel gene-birth, stop codons mutated to sense codon, and frame-shifts disappeared by short deletions Stop codon elimination mechanisms would be expected to be deployed in general exon birth process; therefore, the average exon length in the ancestor should be longer than 60 nt.
With the above converging evidence, we concluded that the most prevalent length of ancient exons is 75 nt. However, this value is slightly smaller than the most frequent intron length of 90 nt from human, mouse, and C. Protein length is conserved in all eukaryotes 94 , with median of aa 95 survey and aa in a more recent survey of genomes 96 ; the latter is close to the median aa calculated from the 16 fungal genomes in this study.
Significant intron loss in the ancestor of Ascomycota was also reported by others 25 , A brief period of dramatic loss of RT from Ascomycota ancestor might have followed the dramatic intron loss because of positive selection for a compact genome 97 , This trend was similar but less dramatic in B.
Four Basidiomycota genomes C. In contrast, conserved genes of C. More introns of non-conserved genes in L. The conserved genes of U. For the U. The broad taxonomic distribution of a gene correlates with conserved protein sequences and gene structure, and is indicative of ancient origin. By comparing EPG at four different conservation levels: conserved in all species, between phyla, within a phylum, and species-specific see Methods for detail , we found three types of genomes, with unchanged, decreasing, or increasing trend of EPG as conservation level went down.
The majority of genomes showed a downtrend, whereas M. Three genomes, N. Although species-specific genes tended to have the smallest EPG, their exon densities were usually higher than those of genes conserved within phylum. Because protein lengths were shorter at lower conservation levels data not shown , we normalized EPG to aa, the median protein length in fungi Figure 5B. The general downtrend of exon density was similar to that of EPG for most Basidiomycota genomes, except for species-specific genes showing an uptrend, compared to phylum level, in four genomes: L.
The downtrend exon density had no parallel in EPG for S. The patterns of exon density and EPG were similar for B. The species-specific genes had more pronounced uptrend in exon density in five Ascomycota genomes: N.
Although species-specific genes have fewer EPG they have higher exon density. This indicated much smaller exons in species-specific genes compared to genes conserved at phylum level. The only exception was S. This speaks to both intron loss and the difference between the ancient and modern gene birth processes.
The knowledge of intron-rich eukaryotic ancestor has significantly changed our understanding of evolution of eukaryotic gene structure. Previous methods of studying intron evolution relied on well-curated and broadly conserved orthologous genes and treated introns as binary character states in the context of protein sequence alignments 25 , 28 , 31 , 37 , 39 , Such methods are powerful 60 but more sophisticated and have been thoroughly evaluated Guided by a simple intron loss model, we have examined intron number evolution by comparing simulated results to observations from 16 fungal genomes and by seeking intra-genomic trends.
The ancestral genome for the simulation contains genes that are assembled by random exon shuffling Figure 2A. The distribution of RIL of this ancestor resembles that of S. This model also assumes random conservation of a small fraction of the introns and a 3'-bias for RT-mediated intron loss. Using this relationship, we arrived at 7. The presumptive ancestor with RIL being 0. This view is corroborated by earlier results where the last fungal common ancestor LFCA lost introns after descending from Opisthokont 25 , 40 that is a descendant of LECA and gained 0.
The RIL method underestimates ancestral EPG given the existence of none 3'-biased intron loss mechanisms: higher recombination rate in the middle of the gene 43 , 48 , random deletion generally accepted but no published evidence , or others Beyond the RIL method, exon length provides another channel for seeking the intron density of the ancestral genome.
Underlying the estimate of 16 EPG in LECA is the conservation of most frequent exon length that is observed both in simulation and divergent genomes green algae and fungi. Several lines of evidence converge on a single value of about 75 nt: I ancient protein module size of 25 aa 20 , 92 , , ; II exons originating from random ORF 92 ; III ancient exon length of fungi Figure 4A ; IV shared most frequent exon length from two primitive life forms Figure 3.
However, the mode of exon length is about 90 nt for three animal genomes This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: All relevant data are within the paper and its Supporting Information files.
Introduction In recent years, RNA-sequencing RNA-seq is emerging as a powerful technology in transcriptome profiling, and this technique allows an in-depth look into the transcriptome [ 1 — 3 ]. Results and Discussions Reads mapping and counting The dataset and analysis protocol are detailed in the Method section, and summarized in Fig 1A.
Download: PPT. Fig 1. Analysis workflow and illustration of different methods for gene quantification. Table 1. Table 2. Fig 2. In general, the results reported by RSEM and featureCounts are very close, and nearly identical for high expression genes.
Fig 3. The effect of intron retention reads on gene quantification. Fig 4. Table 3. Fig 5. Differential analysis of genes and isoforms The counts table for individual transcript was generated by RSEM, and the corresponding counts table for individual gene was derived by adding up all transcript reads of the gene. Fig 6. Table 5. Differential analysis results and read counts for genes ENSG Fig 7. Isoform changes and switches. Fig 8. The inaccuracy of isoform quantification As we have demonstrated in Figs 6 and 7 and 8 , more insights are gained from isoform differential analysis.
Fig 9. The inaccuracy of isoform quantification is influenced by the strengths of the isoforms. Gene quantification and differential analysis A gene counts table and isoform counts table were generated by featureCounts and RSEM, respectively.
Supporting Information. S1 Table. S2 Table. Lists the calculated RPKMs for the 4 samples using different approaches. Author Contributions Conceived and designed the experiments: SZ. References 1. Mapping and quantifying mammalian transcriptomes by RNA-seq.
View Article Google Scholar 2. RNA-seq: a revolutionary tool for transcriptomics. View Article Google Scholar 3. Transcriptome analysis using next-generation sequencing. Curr Opin Biotechnol. View Article Google Scholar 4. Comparison of RNA-seq and microarray in transcriptome profiling of activated T cells. PloS ONE. View Article Google Scholar 5. GTEx Consortium. View Article Google Scholar 6. Human genomics. The human transcriptome across tissues and individuals.
View Article Google Scholar 7. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. View Article Google Scholar 8. Systematic evaluation of spliced alignment programs for RNA-seq data. View Article Google Scholar 9.
Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics. View Article Google Scholar Evaluation of alignment algorithms for discovery and identification of pathogens using RNA-seq. PLoS One. Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads.
MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. Zhao S. Assessment of the impact of using a reference transcriptome in mapping short RNA-seq reads. Zhao S, Zhang B. NCBI reference sequences RefSeq : a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic acids Res.
Ensembl Genome Res. Assessing the impact of human genome annotation choice on RNA-seq expression estimates. RNA-Seq gene expression estimation with read mapping inaccuracy. Li B, Dewey CN. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.
Nat Biotechnol. Estimation of alternative splicing isoform frequencies from RNA-Seq data. Algorithms Mol Biol. HTSeq—A Python framework to work with high-throughput sequencing data. As you also point out, a cross between two chickens with the RrPp genotype would necessarily include offspring with all four phenotypes walnut comb, rose comb, pea comb, and single comb — with a phenotypic ratio. Although the mystery is not solved, we hope this helps!
Hello Gaurav, It seems you are interested in learning more about the biological role of introns and inteins. While it is universally understood that intron-mediated mRNA splicing provides eukaryotic cells a powerful tool for both regulating gene expression and increasing transcript and protein diversity, the biological importance of intein-mediated protein splicing is not well understood.
Although introns were once considered a distinguishing feature between eukaryotic and prokaryotic cells, prokaryotes and archaea have introns as well most often in tRNA and rRNA.
Similarly, inteins have been found throughout all three domains of life eukaryotes, prokaryotes, and archaea. Researchers are also carrying out experiments to better understand the role and evolution of inteins — and to harness their potential in biotechnology and drug development.
As you likely know, an intron is an intervening sequence within an mRNA transcript that is removed in one of three ways — via group I splicing, group II splicing, or via spliceosome-mediated splicing. For example, in the budding yeast S.
The presence of introns in these genes has been shown to regulate both RNA and protein abundance. Interestingly, almost every intron-containing yeast gene possesses only a single intron.
This is in stark contrast to higher eukaryotes, such as humans, whose genes predominantly contain multiple introns. The presence of multiple introns in genes allows for the possibility of alternative splicing. That is, introns can be selectively removed or maintained from a single transcript to produce a variety of different transcripts, each capable of encoding a unique protein.
The biological importance of intein-mediated protein splicing is less clear. Inteins were first discovered in , but they are relatively uncommon compared to the prevalence of introns.
In brief, an intein is an amino acid sequence that excises itself from a protein. The remaining residues, or exteins, come together via a peptide bond to form a truncated protein. As we mentioned earlier, one area of research wherein inteins may be useful is biotechnology where they are currently being studied for applications ranging from drug development to recombinant protein synthesis.
And, of course, scientists may soon find an unexpected function for this remarkable biological process, so stay tuned. Although they are somewhat similar to restriction endonucleases, homing endonucleases have larger typically 12—40 base pairs , asymmetric, and less frequently occurring DNA target sequences.
Likewise, homing endonucleases are more tolerant of base pair changes in their target DNA sequence. Why can't eukaryotic transcription be regulated by attenutation? Hi Gaurav, You would like to know why eukaryotic transcription is not regulated by attenuation. This is a great question! The short answer to your question is based on the major structural difference that distinguishes eukaryotic cells from prokaryotic cells — attenuation does not regulate eukaryotic transcription because eukaryotic cells contain a nuclear envelope, prokaryotic cells do not.
As you have likely learned, attenuation is a form of transcriptional regulation. Since attenuation provides a mechanism for amino acid availability and protein synthesis demands to control the expression of amino acid biosynthesis genes, attenuation is often seen in bacterial operons that regulate amino acid biosynthesis.
We have included a link below for you to explore how attenuation contributes to the famous attenuation-regulated trp operon. While many more genetic changes can be identified with whole exome and whole genome sequencing than with select gene sequencing, the significance of much of this information is unknown.
Because not all genetic changes affect health , it is difficult to know whether identified variants are involved in the condition of interest. Sometimes, an identified variant is associated with a different genetic disorder that has not yet been diagnosed these are called incidental or secondary findings. In addition to being used in the clinic, whole exome and whole genome sequencing are valuable methods for researchers. Continued study of exome and genome sequences can help determine whether new genetic variations are associated with health conditions, which will aid disease diagnosis in the future.
Other chapters in Help Me Understand Genetics. Genetics Home Reference has merged with MedlinePlus. Learn more. The information on this site should not be used as a substitute for professional medical care or advice. Contact a health care provider if you have questions about your health.
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