How to identify the GPD gene when the sequence varies between organisms?

How to identify the GPD gene when the sequence varies between organisms?

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I'm reading a paper on genetic transformation of a fungi and the plasmid used in the paper uses two forms of the same GPD (glyceraldehyde3-phosphate dehydrogenase) promoter to drive a GFP gene, one from Agaricus bisporus and one from Lentinula edodes (GenBank: GQ457137.1).

However, I noticed that the sequences for the aforementioned GPD promoters do not match the reference sequence in GenBank (NC_007251.2) which itself is derived from another organism.

Why are there different sequences for the same promoter? Furthermore, how would I identify the GPD gene in another organism if I'm unable to compare it to a known sequence?

The organism I wish to transform has had it's complete genome sequenced and my transformation would be much more effective if I could use a native promoter like GPD.

I might be misunderstanding you here, but I take from your links above that you want to match the GPD promoter regions from two (distantly related) fungi Agaricus bisporus and Lentinula edodes, to the GPD promoter of Leishmania major, which belongs to a completely different Kingdom!

Promoter regions tend to be relatively poorly conserved between species compared with protein coding regions, even for closely related species. Given the evolutionary distance between the species you are mentioning, the chance you will find any homology between their promoter regions is probably zero.

Furthermore, how would I identify the GPD gene in another organism if I'm unable to compare it to a known sequence?

What I would do is to take the translated GPD protein sequence, which for Lentinula edodes would be GenBank BAA83550.1. I would then use that to search for protein matches using blastp, specifically subsetting for Leishmania major; and use the result to locate the coding gene in the genome. You can also do this in one single step with tblastn, which looks for matches in a translated nucleotide database (see this tblastn example query).

You can then simply take the 1000 bp or so upstream of the coding region to represent your GPD promoter.

9: Protein Conservation

  • Contributed by Clare M. O&rsquoConnor
  • Associate Professor Emeritus (Biology) at Boston College

At the end of this laboratory, students should be able to:

  • identify amino acids by their 1-letter code.
  • explain the differences between high and low scores on the BLOSUM 62 matrix.
  • use the BLASTP algorithm to compare protein sequences.
  • identify conserved regions in a multiple sequence alignment.

As species evolve, their proteins change. The rate at which an individual protein sequence changes varies widely, reflecting the evolutionary pressures that organisms experience and the physiological role of the protein. Our goal this semester is to determine if the proteins involved in Met and Cys biosynthesis have been functionally conserved between S. pombe andS. cerevisiae, species that are separated by close to a billion years of evolution. In this lab, you will search databases for homologs of S. cerevisiae sequences in several species, including S. pombe. Homologs are similar DNA sequences that are descended from a common gene. When homologs are found in different species, they are referred to as orthologs.

Homologs within the same genome are referred to as paralogs. Paralogs arise by gene duplication, but diversify over time and assume distinct functions. Although a whole genome duplication occurred during the evolution of S. cerevisiae (Kellis et al., 2004), only a few genes in the methionine superpathway have paralogs. Interestingly, MET17 is paralogous to three genes involved in sulfur transfer: STR1 (CYS3), STR2 and STR4, reflecting multiple gene duplications. The presence of these four distinct enzymes confers unusual flexibility to S. cerevisiae in its use of sulfur sources. The SAM1 and SAM2 genes are also paralogs, but their sequences have remained almost identical, providing functional redundancy if one gene is inactivated (Chapter 6).

Our experiments this semester will test whether genes involved in Met and Cys synthesis have been functionally conserved during the evolutionary divergence of S. cerevisiae and S. pombe . A variety of algorithms offer researchers tools for studying the evolution of protein sequences. In this graphic depiction of aligned Sam2p sequences from nine divergent model organisms, the height of the letter reflects the frequency of a particular amino acid at that position.

Protein function is intimately related to its structure. You will recall that the final folded form of a protein is determined by its primary sequence, the sequence of amino acids. Protein functionality changes less rapidly during evolution when the amino acid substitutions are conservative. Conservative substitutions occur when the size and chemistry of a new amino acid side chain is similar to the one it is replacing. In this lab, we will begin with a discussion of amino acid side chains. You will then use the BLASTP algorithm to identify orthologs in several model organisms. You will perform a multiple sequence alignment that will distinguish regions which are more highly conserved than others.

As you work through the exercises, you will note that protein sequences in databases are written in the 1-letter code. Familiarity with the 1-letter code is an essential skill for today&rsquos molecular biologists.


Clustered regularly interspaced short palindromic repeats (CRISPRs) are repetitive structures in Bacteria and Archaea composed of exact repeat sequences 24 to 48 bases long (herein called repeats) separated by unique spacers of similar length (herein called spacers) [1, 2]. The CRISPR sequences appear to be among the most rapidly evolving elements in the genome, to the point that closely related species and strains, sometimes more than 99% identical at the DNA level, differ in their CRISPR composition [3, 4].

Up to 45 gene families, called CRISPR-associated sequences (CASs), appear in conjunction with these repeats and are hypothesized to be responsible for CRISPR propagation and functioning [2, 5, 6]. It has been proposed that CASs can be divided into seven or eight subtypes, according to their operon organization and gene phylogeny [5, 6]. Phylogenetic analysis additionally indicates that CASs have undergone extensive horizontal gene transfer, as very similar CAS genes are found in distantly related organisms [6, 7]. CRISPRs and CASs have been found on mobile genetic elements, such as plasmids, skin mobile elements, and even prophages, suggesting a possible distribution mechanism for the system [7–9].

CRISPRs have been suggested to play roles in replicon partitioning [1], DNA repair [10], regulation [5] and chromosomal rearrangement [11]. It was recently reported that the spacers are often highly similar to fragments of extrachromosomal DNA, such as phage or plasmid DNA [3, 12]. It was suggested that the CRISPR/CAS system participates in an antiviral response, probably by an RNA interference-like mechanism. The proposed mechanism for this CRISPR function involves sampling and maintaining a record of invasive DNA elements, and inhibition of gene functions necessary for invasion [12]. Indeed, it was recently shown that CRISPRs provide acquired resistance against viruses in prokaryotes [13].

Despite in-depth analyses of CASs, the nature of the repeat sequences has not been examined closely. This is presumably because repeats, as short DNA sequences, have less comparative potential than protein-coding genes. Previous studies have noted only that repeats are highly variable, and do not appear to be similar between organisms [2, 7]. However, we show that repeats from diverse organisms can be grouped into clusters based on sequence similarity, and that some clusters have pronounced secondary structures with compensatory base changes. We further show that there is a clear correspondence between CAS subtypes and repeat clusters. Our findings have important implications for CRISPR function and diversity.

Special considerations

Annotation of multiple assemblies

When multiple assemblies of good quality are available for a given organism, annotation of all is done in coordination. To ensure that matching regions across assemblies are annotated the same way, assemblies are aligned to each other before the annotation.

  • Assembly-assembly alignment results are used to rank the transcript and the curated genomic alignments: for a given query sequence, alignments to corresponding regions of two assemblies receive the same rank.
  • Corresponding loci of multiple assemblies are assigned the same GeneID and locus type.

Assembly-assembly alignments are available through the NCBI Genome Remapping Service.


Organisms are periodically re-annotated when new evidence is available (e.g. RNA-Seq) or when a new assembly is released. Special attention is given to tracking of models and genes from one release of the annotation to the next. Previous and current models annotated at overlapping genomic locations are identified and the locus type and GeneID of the previous models are taken into consideration when assigning GeneIDs to the new models. If the assembly was updated between the two rounds of annotation, the assemblies are aligned to each other and the alignments used to match previous and current models in mapped regions.


Bioinformatic workflow for the molecular characterization of GM rice events

Many researchers have difficulties in handling large quantities of bioinformatic data. We developed a user-friendly method for detecting inserted T-DNA junctions using NGS data in place of conventional detection methods. A diagram of the bioinformatic workflow is shown in Fig. 1. In the first step, qualified raw paired-end reads were aligned against a transformation plasmid vector using the Burrows-Wheeler Aligner software with maximal exact matches (BWA-MEM) [22]. As the structure of the transformation plasmid vector is circular, we made a linearized vector reference sequence (pPZP200) where both left and right border sequences contained 150 bp of the opposite end of the plasmid sequence. For selecting those reads spanning junctions, mapped reads were subtracted according to their mapped positions, based on the T-DNA location (from 6392 to 10,291 bp). These collected reads were used as queries for BLASTN analysis to classify false-positive reads against a reference rice genome (O. sativa version 7.0) [23]. As the inserted T-DNA is designed to contain endogenous elements, reads that contained the endogenous promoter sequence RbcS3 were carefully removed based on sequence similarity score (to the native rice sequence) to reduce ambiguous alignment. The remaining reads were aligned against the transgenic vector and visualized using IGV with paired-end reads. From the results, we selected junction reads that partially matched both ends of the T-DNA (i.e., reads that spanned both T-DNA and the rice genome) and extracted FASTA sequences to identify the inserted T-DNA in the junction region of the genome (Fig. 1).

T-DNA location and copy number

Approximately 28 GB of raw sequence data, corresponding to 72× sequencing depth, were obtained from the control parent cultivar “Illmi”. In addition, 30 GB, 21 GB, and 26 GB of raw data were obtained from SNU-Bt9–5, SNU-Bt9–30, and SNU-Bt9–109, respectively, representing approximately 78×, 54×, and 68× genome coverage, respectively (Table 1).

From the consecutive steps applied in our junction detection analysis (as described in the ‘T-DNA insert site analysis’ section of the Methods), 11,539 reads were obtained from the GM rice SNU-Bt9–5, including 2790 paired mapped reads. Additionally, 8371 and 9767 reads were mapped from the GM rice SNU-Bt9–30 and SNU-Bt9–109, respectively, including 1792 and 2336 proper pairs of reads, respectively (Table 2). Unexpectedly, 8125 reads derived from wild-type “Illmi” were mapped to the transgenic vector sequences, including only 648 proper pairs of reads. The remaining unpaired paired-end reads were assumed to be due to a feature of Illumina sequences that can be caused by short sequence length. Also of note is that our T-DNA construct used in this study was designed to contain the rice endogenous promoter gene rbcS3 (Os12g0291100), which takes up 1824 bp of T-DNA and is expressed on rice chromosome 12 [24]. To eliminate deceptive false-positive reads originating from the native genome (i.e., not from T-DNA), each mapped sequence was compared to the rice reference sequence using BLASTN. A total of 915, 1019, 729, and 899 reads corresponding to Illmi rice, SNU-Bt9–5, SNU-Bt9–30 and SNU-Bt9–109, respectively, all aligned to chromosome 12 and were classified as false positives.

Reads that partially aligned with both ends of the transgene border region were collected (Fig. 2a and b) based on their mapping position. Then, selected reads were aligned to the entire T-DNA sequence to identify the flanking site. The results represented insert junctions on rice chromosomes (Fig. 2c). Reads spanning junction regions between the host genome and transgene obtained from the SNU-Bt9–5 rice mapped perfectly to rice chromosome 10 from 22,498,218 to 22,498,279 bp with 79-bp deletions. The SNU-Bt9–30 rice event was properly mapped to rice chromosome 11 from 22,473,585 to 22,473,636 bp with 51-bp deletions (Table 3 and Fig. 3). Both transgenic events successfully detected a single copy and a single locus within the rice genome, and both results were identical to those obtained by the Southern blot-based detection method [21].

Molecular characterization of transgenic rice using NGS read alignments. a Illustration of transformation plasmid pPZP200 containing T-DNA used for Agrobacterium-mediated transformation to create SNU-Bt9–5, SNU-Bt9–30, and SNU-Bt9–109. MCS, multiple cloning site. b Detailed example of IGV results. Horizontal lines on the sequence track (top of the panel) indicate the reference sequence (i.e., T-DNA inserted transformation plasmid vector sequence). Featured tracks exhibit a paired orientation (upper panel = read 1, lower panel = read 2). Colored boxes indicate junction region containing reads spanning both the T-DNA border and the genomic flanking sequence. c Sequence alignments of junction-spanning reads (upper = left border flanking sequences, lower = right border flanking sequences). Red and black nucleotides indicate rice chromosome and T-DNA, respectively

Representation of deduced loci of a T- DNA insertion in a rice chromosome

Although integration sites of SNU-Bt9–109 rice were not identified using the method described here (Table 3 and Fig. 3), the integration site near the right border (RB) was found on chromosome 3 from 14,707,459 to 14,707,391 bp. Flanking sequences near the left border (LB) region were not identified. BLASTN analysis (using the NCBI nr database) showed that the junction between the LB region and the host genome showed high similarity to the “Gene trapping Ds/T-DNA vector pDsG8 (e-value: 4e-28)” and the Solanum tuberosum proteinase inhibitor gene (e-value: 6e-28). However, the S. tuberosum gene was regarded as an artifact due to its short query and low specificity.

To validate the above results, we designed primers based on the obtained junction sequence reads (Additional file 1: Table S1). Our PCR results verified that insertion detection of the two transgenic rice events was successfully characterized using NGS. Moreover, the junction sequence of SNU-Bt-109 was also detected by flanking PCR using nearby LB sequences (Additional file 1: Figure S2).

Determining T-DNA rearrangement

To determine the T-DNA sequence, we calculated insert size distributions using reads of mapped pairs against the transgenic plasmid DNA (Additional file 1: Figure S3). By calculating insert size, it is possible to decide whether the inserted DNA has been rearranged. Average insert sizes were 479, 469, and 535 bp for SNU-Bt9–5, SNU-Bt9–30, and SNU-Bt9–109, respectively, which properly matched with the sizes prepared in library construction (Additional file 1: Figure S4). It assumed that there were no internal rearrangements or duplications inside the T-DNA. The results correspond to those of whole T-DNA retrieval by genomic DNA PCR and sequencing analysis in our previous paper [21].

Possible presence of backbone sequences in transgenic plants

Unintended genomic changes may occur during the development of new GM plants. It is possible for plasmid backbone sequences to be integrated into a host’s genome during Agrobacterium-mediated transformation [10]. Therefore, sequence alignments were visualized with IGV to detect possible contamination of plasmid backbones. No reads were mapped to the plasmid backbone structure (Additional file 1: Figure S5 and S6). This finding demonstrates that backbone-derived sequences were not introduced into these transgenic genomes.

Using the canary genome to decipher the evolution of hormone-sensitive gene regulation in seasonal singing birds

Background: While the song of all songbirds is controlled by the same neural circuit, the hormone dependence of singing behavior varies greatly between species. For this reason, songbirds are ideal organisms to study ultimate and proximate mechanisms of hormone-dependent behavior and neuronal plasticity.

Results: We present the high quality assembly and annotation of a female 1.2-Gbp canary genome. Whole genome alignments between the canary and 13 genomes throughout the bird taxa show a much-conserved synteny, whereas at the single-base resolution there are considerable species differences. These differences impact small sequence motifs like transcription factor binding sites such as estrogen response elements and androgen response elements. To relate these species-specific response elements to the hormone-sensitivity of the canary singing behavior, we identify seasonal testosterone-sensitive transcriptomes of major song-related brain regions, HVC and RA, and find the seasonal gene networks related to neuronal differentiation only in the HVC. Testosterone-sensitive up-regulated gene networks of HVC of singing males concerned neuronal differentiation. Among the testosterone-regulated genes of canary HVC, 20% lack estrogen response elements and 4 to 8% lack androgen response elements in orthologous promoters in the zebra finch.

Conclusions: The canary genome sequence and complementary expression analysis reveal intra-regional evolutionary changes in a multi-regional neural circuit controlling seasonal singing behavior and identify gene evolution related to the hormone-sensitivity of this seasonal singing behavior. Such genes that are testosterone- and estrogen-sensitive specifically in the canary and that are involved in rewiring of neurons might be crucial for seasonal re-differentiation of HVC underlying seasonal song patterning.

Serology: Overview

Other Body Fluids

DNA profiling has been performed successfully on a wide range of body fluids and tissues for which there are no common tests. Examples include skin (including dandruff), perspiration, nasal mucus, pus, breast milk, and ear wax. For the most part, the biological origin in these cases is inferred from the appearance of the material or its location on the item tested, for example, perspiration from hat bands, nasal mucus on tissues, and so on. There is little call for specific tests to determine the cellular identity of these materials each, however, has a characteristic biochemistry that could be exploited to develop an identification test should it be necessitated.

Results and discussion

We chose the 5 ′ -UTR of the well-studied S. cerevisiae CYC1 promoter [15, 16]. We fused pCYC1min (starting at position −143) to a yeast-enhanced green fluorescent protein (yEGFP) [17] and the CYC1 terminator. Compared to the complete CYC1 promoter, pCYC1min contains two of the three TATA boxes and no upstream activating sequences. pCYC1min is a moderately weak promoter and, for this reason, appears to be an ideal candidate for detecting both positive and negative effects of point mutations in the leader sequence on the expression of the downstream reporter protein. The CYC1 promoter 5 ′ -UTR is 71 nucleotides long.

In the following analysis, we refer to the portion of CYC1 5 ′ -UTR at position −1 to −8 as the extended Kozak sequence and that at −9 to −15 as the upstream region. In the extended Kozak sequence adenine is strongly conserved in five positions, whereas in the upstream region no nucleotide is strongly conserved. However, adenine is the most frequent at almost every site (see Background).

The extended Kozak sequence

The original CYC1 sequence from positions −15 to −1 is CACACTAAATTAATA (hereafter referred to as k 0). According to Dvir et al. [9], the presence of an adenine at positions −1, −3, and −4, together with the absence of guanine at position −2, should make this leader sequence almost optimal for high expression. However, thymine at position −2 and cytosine at position −13 have a frequency lower than 20 % and 10 %, respectively, among highly expressed S. cerevisiae genes [8]. We built our first synthetic CYC1 leader sequence (k 1) by placing an adenine at each position from −1 to −15.

The fluorescence level associated with k 1 was 6.5 % higher than that measured with k 0. However, no statistically significant difference arose from the data gathered on these two leader sequences (p-value =0.13). We kept k 1 (the optimized leader sequence) as a template for our next synthetic constructs and built 57 more synthetic 5 ′ -UTRs by mutating single or multiple nucleotides in k 1.

The first group of synthetic leader sequences was made by a single point mutation from position −1 to position −8 (see Table 1). Hence, we modified the extended Kozak sequence only, whereas the upstream region was kept in an optimized configuration for high gene expression with adenines at positions −9 to −15.

The highest fluorescence was recorded for k 16 (where a guanine substituted the adenine at position −5) and the lowest by k 9 (where a thymine replaced the adenine at position −3). Moreover, the fluorescence level of k 16 was statistically significantly different from that of k 0 and k 1. An enhancement in fluorescence due to a guanine at position −5 was a surprising result because guanine is the least frequent nucleotide in yeast S. cerevisiae leader sequences. Moreover, no guanine was ever detected at this position among highly expressed genes [8] or provoked any fluorescence enhancement in the work by Dvir et al. [9].

Despite the absence of a statistically significant difference from k 1, the only constructs other than k 16 that resulted in an increase of >5 % on the fluorescence level of k 1 were k 3, k 10, and k 24. In particular, in k 3, a thymine replaced an adenine at position −1, and in k 10 the adenine at position −3 was mutated into a guanine. As reported above, adenine at positions −1 and −3 should guarantee high gene expression. Nevertheless, on such an adenine background, less frequent nucleotides at positions −1 or −3 seem to be required to further enhance gene expression. In contrast, a thymine instead of an adenine at position −3 (k 9) was the only mutation that induced a >5 % reduction in k 1 fluorescence level. This result is consistent with the observation in [9] that a thymine at position −3 is abundant in poorly expressed genes (Fig. 1 a).

Effect of point mutations in the extended Kozak sequence on fluorescence expression. Fluorescence levels are plotted relative to k 1 (a) and k 0 (b). Control corresponds to a yeast strain without the yEGFP gene. The nucleotide that replaced an adenine in k 1 and the position at which the mutation took place are given below the name of each synthetic leader sequence. Asterisks, p-value <0.05 vs. k 1 (a) or k 0 (b)

With respect to k 0, all 25 new synthetic leader sequences contained between six and eight mutations. Apart from k 9, all synthetic 5 ′ -UTRs showed a fluorescence level higher than that of k 0, five of which were significantly higher. These included positions −1, −4, and −5. As already noted in the comparison with k 1, an adenine just upstream of the START codon seemed to be of no particular advantage for gene expression. Here, a cytosine and a thymine (k 2 and k 3, respectively) performed much better than an adenine. However, with respect to k 0, there were seven more point mutations upstream. At position −4 a thymine (k 12) resulted in the highest fluorescence increment, whereas at position −5, both a cytosine (k 14) and a guanine (k 16) enhanced fluorescence to >10 % above that of k 0. Since k 0 has a thymine at positions −2, −5, and −6, each of the five synthetic 5 ′ -UTRs that showed statistically significant differences from k 0 were affected by a point mutation at two or more adjacent sites. Three more synthetic leader sequences (k 10,k 17, and k 24) caused a >10 % increase in fluorescence compared to k 0, though these differences were not significant (p-value >0.05). k 10 and k 17 also had double point mutations at adjacent sites (Fig. 1 b).

Multiple mutations to guanine

The analysis of our first 25 synthetic 5 ′ -UTR sequences gave the surprising result that a single point mutation to guanine—which is essentially absent from the extended Kozak sequence of highly expressed S. cerevisiae genes—can enhance the fluorescence level of k 1, a leader sequence optimized for gene expression. Moreover, five of our synthetic 5 ′ -UTRs unambiguously (>9 %) increased the fluorescence level associated with pCYC1min.

According to our data, a single mutation to guanine can enhance gene expression. However, two previous papers [18, 19] reported that multiple guanines placed in front of a START codon would considerably reduce protein synthesis. Therefore, we assessed how multiple point mutations to guanine affected the translation efficiency of pCYC1min, to determine if they could be used to modulate gene expression.

According to [8], among highly expressed S. cerevisiae genes, guanine is the least frequent nucleotide between positions −1 and −15, with the exception of position −7, in which the least frequent nucleotide is cytosine. We constructed a synthetic 5 ′ -UTR that reflects this sequence (k 26 Table 2). This shut down gene expression, as shown by the corresponding fluorescence level not being significantly different (p-value =0.21) from our negative control (an S. cerevisiae strain that did not contain the yEGFP gene).

We tested whether multiple mutations to guanine (cytosine at position −7) would affect gene expression in a different way when they covered either the whole extended Kozak sequence (k 27) or the upstream region (k 28). Since mutations were made with respect to k 1, all the non-mutated sites contained an adenine. Surprisingly, we found that the two configurations were equivalent for gene expression (p-value >0.40) and reduced k 1 fluorescence level by about half.

Starting from k 27, we replaced the guanine at positions −1 (k 29), −2 (k 30), and −3 (k 31) with an adenine to determine whether a single adenine at the three positions just upstream of the START codon would enhance fluorescence expression when the other sites of the extended Kozak sequence were occupied either by a guanine or a cytosine. At position −1 an adenine showed no improvement on the fluorescence of k 27. Interestingly, at positions −2 and −3, an adenine caused a drop in gene expression to approximately 7 % of the k 1 fluorescence level. These results demonstrate that an adenine per se cannot improve gene expression even when it occupies position −3 or −1. More generally, we can conclude that the effect on gene expression of a single point mutation in the leader sequence is strongly context-dependent.

Finally, to understand better how important the upstream region is for gene expression, we progressively reduced the number of guanines from seven (k 28) to one (k 38). Starting from position −9, we replaced a guanine with an adenine at each step and saw that the fluorescence level increased almost linearly with the number of adenines (Fig. 2 and Additional file 1). The last sequence in which the fluorescence level was statistically significantly different from that of k 1 was k 36, in which guanines were present at positions −13 to −15. A guanine alone at position −15 or accompanied by another at position −14 did not result in a significant difference in fluorescence level from that of k 1. Therefore, even in the presence of an extended Kozak sequence optimized for high gene expression, multiple mutations in the upstream region have evident repercussions on protein synthesis and can be used as a means of tuning protein abundance. An explanation for this result is presented in the Computational Analysis section, below. Interestingly, four guanines intermixed with adenines (k 33) in the upstream region reduced k 1 fluorescence to a smaller extent than four guanines in a row (k 32), providing further confirmation that the effect on gene expression of point mutations inside the 5 ′ -UTR is highly dependent on the nucleotidic context (Fig. 2 see Additional file 1 for a comparison with k 0 fluorescence).

Multiple point mutations to guanine. The ratio between the fluorescence level of the synthetic 5 ′ -UTRs from k 26 to k 38 and that of k 1 are reported. The number of adenines or guanines in the upstream region is given below the leader sequence name (from k 27 to k 38). The subscripts −1, −2, and −3 indicate that an adenine is present in the extended Kozak sequence only at the corresponding position. Subscript i represents intermixed (see main text). Asterisks, p-value <0.05 vs. k 1

The upstream region

The previous analysis confirmed that the effect on gene expression due to both single and multiple mutations within the 5 ′ -UTR is strongly context-dependent. Moreover, our data clearly showed that changes not only in the Kozak sequence but also inside the upstream region markedly affect gene expression. We therefore performed point mutations on k 1 between positions −9 and −15 (Table 3) to assess whether a single nucleotide different from adenine can change the translation rate when placed into the upstream region.

All point mutations (except the one in k 38) resulted in a fluorescence level higher than that associated with k 1. Notably, in eight cases, the increase in fluorescence was statistically significant (>10 % higher than k 1 fluorescence). These eight mutations included four contiguous positions, from −11 to −14. None of these were taken into account in the reference work by Dvir et al. [9].

At position −11, a guanine instead of an adenine (k 47) enhanced fluorescence expression by >15 %, whereas cytosine and thymine had no significant effects. Every mutation at position −12 increased the fluorescence of k 1. The greatest change (>15 %) was due to a guanine (k 50). Mutations at position −13 also strongly enhanced k 1 fluorescence level. Two point mutations—cytosine (k 51) and guanine (k 53)—resulted in statistically significant differences in fluorescence from k 1, whereas a thymine (k 52) augmented k 1 fluorescence by about 14 % but this did not reach statistical significance. It should be noted that among all our 58 synthetic 5 ′ -UTRs, k 51 had the highest fluorescence level—almost 17 % higher than that of k 1.

Finally, two different point mutations at position −14 led to an increase in fluorescence: a cytosine (k 54) and a thymine (k 55) (Fig. 3 see Additional file 1 for a comparison with k 0).

Effect of point mutations in the upstream region on fluorescence relative to k 1. The nucleotide that replaced an adenine in k 1 and the position at which the mutation took place are given below the name of each synthetic leader sequence. Asterisks, p-value <0.05 vs. k 1

Together, the results of this last analysis of the upstream region underline another surprising result: single point mutations upstream of the Kozak sequence, in particular at positions −12 and −13, were those that most enhanced gene expression from a context rich in adenines.

Computational analysis

We carried out simulations with RNAfold to investigate possible correlations between computed mRNA secondary structures, together with their corresponding minimum free energies (MFEs), and measured fluorescence levels. Our analysis provides an explanation for the drop in fluorescence due to multiple mutations from adenine to guanine (and cytosine) in the −15…−1 region. In contrast, no plausible justification for the effects of single point mutations on translational efficiency emerged from simulations with RNAfold.

As an input for RNAfold, we used mRNA sequences starting at the transcription start site of pCYC1min [16] and ending at the poly-A site of the CYC1 terminator [20]. Each sequence was 937 nucleotides long. From preliminary simulations, we observed that a poly-A chain with a variable length of 150–200 nucleotides had no significant effect on mRNA folding. All mRNA secondary structures were calculated at 30 °C (the temperature at which we grew S. cerevisiae cells for the FACS experiments).

k 0 and k 1 have the same MFE: −241.21 kcal/mol. This is the highest—and the most common—within the collection of 59 sequences analyzed in this work (see Additional file 1). The mRNA secondary structure corresponding to this MFE is characterized by the presence of a giant hairpin between positions −40 and +10. The hairpin loop goes from position −31 to position +1 and contains the whole 5 ′ -UTR portion we have targeted here. The hairpin stem is made of nine base-pairs, of which only one gave a “mismatch” because of an adenine at position −38 and +8 (see Fig. 4 a).

mRNA secondary structures. a A giant hairpin is present in the mRNA secondary structure corresponding to the MFE of both k 0 and k 1. The hairpin loop contains the −15…−1 region. The portion of the 5 ′ -UTR in our analysis is free from any pairing interactions in its wild-type configuration (k 0) and in that theoretically optimized for high protein expression (k 1). The loop of the giant hairpin is reduced in k 4 owing to the base-pairing interaction between the guanine at position −1 and the cytosine at position −31. In every mRNA structure presented, a green arrow indicates position +1, and a red arrow indicates position −15. b The disruption of the giant hairpin induces a decrease in the MFE of the mRNA secondary structure. k 26 and k 31 are associated with the lowest MFEs computed in our analysis. The two sequences contain multiple guanines in the extended Kozak sequence involved in pairing interactions with the CDS. A similar pattern is also present in k 30. Here, however, a second mini-loop around the START codon provokes an increase in MFE. The MFE of k 26 is substantially lower than those of k 30 and k 31 because of the presence of another stem due to pairing interactions between the upstream region and the CYC1 terminator. Nevertheless, the fluorescence levels of k 30 and k 31 are only approximately 1.2-fold higher than that of k 26

Multiple mutations to guanines either in the upstream region or the extended Kozak sequence originate base-pairing interactions between, at least, a portion of the −15…−1 region and the CDS (yEGFP) or the CYC1 terminator. As a consequence, the giant hairpin is destroyed and replaced by one or two stems that lower the MFE of the mRNA secondary structure (Table 2). Most of the MFE values smaller than −241.21 kcal/mol were associated with fluorescence levels lower than that of k 1 (Fig. 5). This result is in agreement with the notion, supported also by [8, 9], that stable mRNA secondary structures in the 5 ′ -UTR reduce protein expression. However, the fluorescence levels we measured did not increase proportionally to increments in the MFE. Moreover, in two cases (k 32 and k 36) RNAfold predicted a giant hairpin in the mRNA structure, whereas the fluorescence levels from our experiments were significantly lower than that of k 1 (Fig. 5 and Additional file 1).

Low MFE values are associated with reduced fluorescence expression. Red bars, difference between MFEs of the corresponding 5 ′ -UTR and k 1 (ΔMFE). Blue bars, 10-fold magnified ratio between the fluorescence level of the indicated 5 ′ -UTR and that of k 1. Apart from k 1, sequences are sorted by increasing ΔMFE. All sequences except k 4 contain multiple point mutations with respect to k 1. Asterisks above blue bars, p-value <0.05 vs. k 1

k 26 was designed by choosing the least frequent nucleotides between positions −15 and −1 among a set of highly expressed S. cerevisiae genes. The corresponding MFE (−261.39 kcal/mol) was the lowest within the ensemble of transcription units considered in this work. No giant hairpin was present in the MFE mRNA secondary structure as the −15…−1 region was sequestered into two different stems. The guanines between positions −1 and −6 were part of a long stem and paired with a hexamer at the beginning of the yEGFP sequence (positions +33 to +38). In contrast, positions −9 to −15 paired with a region of the CYC1 terminator, at positions +750 to +758 (Fig. 4 b).

A fluorescence level just above that of k 26 was registered for k 30 and k 31. Both differed from k 26 for the upstream region (made of seven adenines) and the presence of an adenine in the extended Kozak region (at positions −2 and −3, respectively). Similarly to k 26, the first five nucleotides of the extended Kozak region of k 30 and the first six of k 31 were sequestered into a stem with the CDS. However, differently from k 26, the upstream regions of k 30 and k 31 were entirely free from any pairing interactions (see Fig. 4 b). Their MFEs (−244.28 and −247.26 kcal/mol, respectively) were also significantly higher than that of k 26. These three sequences suggest that a condition for markedly lowering protein expression is to enclose the nucleotides at positions −1 to −5 in an mRNA secondary structure. Moreover, not all of these nucleotides have to participate in base-pairing interactions. Indeed, a guanine at position −1 (k 30) or −2 (k 26 and k 31) is “free” and responsible for the presence of a mini-loop in the mRNA structure.

However, this hypothesis is contradicted by k 29. The MFE of this sequence (−245.97 kcal/mol) is comparable to that of k 30 and k 31, and the corresponding mRNA secondary structure is very similar to that of k 31 (Fig. 6 a). Nevertheless, the fluorescence level associated with k 29 was more than 6-fold higher than that of k 31 and amounted to 45% of that of k 1.

mRNA secondary structures. a k 27 differs from k 29 only by a guanine instead of an adenine at position −1. However, their mRNA secondary structures are dissimilar. In k 27, the extended Kozak sequence is involved in base-pairing interactions with the CYC1 terminator, whereas in k 29 the extended Kozak sequence is locked into a stem with the CDS. The MFE associated with k 27 is lower than that of k 29, but there is no difference between the fluorescence levels of the two sequences (p-value =0.20). b Multiple guanines in the upstream region give rise to mRNA structures characterized by base-pairing interactions between the 5 ′ -UTR and the CYC1 terminator. k 28 and k 34 have six guanines in a stem with the CYC1 terminator, whereas k 35 has only 5 guanines in an analogous structure. This causes an increase in MFE and consequently a higher fluorescence

k 27 shared with k 29k 31 an upstream region made only of adenines. However, unlike in these three sequences, the extended Kozak sequence of k 27 did not contain any adenine. The MFE of k 27 (−247.04 kcal/mol) was comparable to that of k 29k 31, but its corresponding mRNA secondary structure had a different configuration. Indeed, all nucleotides of the extended Kozak sequence (with the exception of the cytosine at position −7) were involved in base-pairing interaction not with the CDS but with the CYC1 terminator (positions +755 to +762 Fig. 6 a). The fluorescence level of k 27 was slightly higher than that of k 29, i.e. almost 7-fold greater than that of k 31.

The five sequences considered so far (k 26, k 27, k 29k 31) have in common an extended Kozak region rich in guanine that was sequestered into a stem in the MFE mRNA secondary structure. In four cases, the extended Kozak sequence paired (partially) with the CDS, and in one case (k 27) with the CYC1 terminator. The MFE of k 26 was the lowest, as its upstream region was also sequestered into a stem. The other four sequences showed very similar MFE values but rather different fluorescence levels.

The other group of sequences affected by multiple mutations with respect to k 1 had only adenines in the extended Kozak sequence and a variable number of guanines in the upstream region.

k 28, k 34, and k 35 had, respectively, 7, 6, and 5 guanines in a row from position −15 downstream. Although the MFE of k 35 was clearly higher than that of k 28 and k 34 (Table 2), the three sequences gave rise to similar mRNA structures where at least five guanines of the upstream region (plus the first adenine downstream) were locked into a stem due to base-pairing interactions with the CYC1 terminator (see Fig. 6 b).

Interestingly, both the MFE and fluorescence level of k 28 were comparable to those of k 27 and k 29. Hence, even if the Kozak sequence was free of pairing interactions, the sequestering of the upstream region into a stem was enough to guarantee a clear drop in protein expression. This is further confirmation of the role played by the nucleotides upstream of the Kozak sequence in tuning protein expression.

A different MFE mRNA secondary structure was obtained for k 33 (four guanines, intermixed with adenines), in which half of the extended Kozak sequence and almost the whole upstream region were involved in base-pairing interactions with the CDS, giving rise to a long stem. However, compared to k 35, where only five nucleotides of the upstream region were locked into a stem with the CYC1 terminator, k 33 showed a higher MFE as well as a higher fluorescence level (Fig. 5 and Additional file 1).

Finally, for k 32, k 36, and k 37 (with four, three, and two guanines in the upstream region, respectively) RNAfold returned the same MFE as for k 1. The corresponding mRNA secondary structures were all characterized by the presence of the the giant hairpin (see Additional file 1). Compared to our experimental data, this result was plausible only for k 37 but in apparent disagreement with the measurements for k 32 and k 36, whose fluorescence levels were significantly lower than that of k 1 (Fig. 5). In particular, the fluorescence of k 32 only corresponded to about 69% of that of k 1. Therefore, it can be argued that in vivo k 32 and k 1 share the same MFE and mRNA secondary structure, as suggested by the in silico simulations.

In contrast to the multiple point mutations, of the single point mutations on k 1, only k 4 caused a modification in the structure of the giant hairpin and a consequent decrease in the MFE. k 4 carries a guanine at position −1 that pairs with the cytosine at position −31 such that the length of the loop is reduced from 32 to 29 nucleotides and the MFE is lowered to −241.42 kcal/mol (Fig. 4 a). According to our data, this minimal change has no effect on fluorescence expression. All the other point mutations that induced a fluorescence level significantly higher than that of k 1 (namely, k 16, k 47k 51, and k 53k 55) were characterized by the same MFE and corresponding mRNA secondary structure as k 1, according to the RNAfold simulations.

The next steps: making new DNA

One of the original DNA strands is used as a template for the synthesis of new DNA. The primers anneal to the template strand, and the DNA polymerase enzyme makes a new strand of DNA by creating a complementary sequence of nucleotides drawn from the reaction mixture.

The new DNA strand is made by complementary base pairing with the original DNA template. Because all four ordinary DNA nucleotides are present in large amounts, the chain elongation continues normally – until by chance a dideoxynucleotide (terminator) is added in the place of a normal DNA nucleotide.

The dideoxynucleotides are just like ordinary DNA nucleotides except that one hydroxyl (OH) group has been chemically changed to a hydrogen (H). With normal DNA nucleotides, one nucleotide can be attached to another and so on, forming a chain. The chemical change in a dideoxynucleotide, however, means that no additional nucleotides can be added, hence the name ‘terminator nucleotides’.

The synthesis of new DNA is terminated when one of the dideoxynucleotides is added to the strand. Because there are many more ordinary nucleotides than dideoxynucleotides, some chains will be several hundred nucleotides long before a dideoxynucleotide is added. The end result is a whole lot of new DNA fragments, of varying length, all ending with a dideoxynucleotide.

How to identify the GPD gene when the sequence varies between organisms? - Biology

Proteomics is the study of the entire set of proteins produced by a cell type in order to understand its structure and function.

Learning Objectives

Explain how the field of genomics led to the development of proteomics

Key Takeaways

Key Points

  • Proteomics investigates how proteins affect and are affected by cell processes or the external environment.
  • Within an individual organism, the genome is constant, but the proteome varies and is dynamic.
  • Every cell in an individual organism has the same set of genes, but the set of proteins produced in different tissues differ from one another and are dependent on gene expression.

Key Terms

  • proteomics: the branch of molecular biology that studies the set of proteins expressed by the genome of an organism
  • proteome: the complete set of proteins encoded by a particular genome
  • genomics: the study of the complete genome of an organism

Proteomics is a relatively-recent field the term was coined in 1994 while the science itself had its origins in electrophoresis techniques of the 1970’s and 1980’s. The study of proteins, however, has been a scientific focus for a much longer time. Studying proteins generates insight into how they affect cell processes. Conversely, this study also investigates how proteins themselves are affected by cell processes or the external environment. Proteins provide intricate control of cellular machinery they are, in many cases, components of that same machinery. They serve a variety of functions within the cell there are thousands of distinct proteins and peptides in almost every organism. The goal of proteomics is to analyze the varying proteomes of an organism at different times in order to highlight differences between them. Put more simply, proteomics analyzes the structure and function of biological systems. For example, the protein content of a cancerous cell is often different from that of a healthy cell. Certain proteins in the cancerous cell may not be present in the healthy cell, making these unique proteins good targets for anti-cancer drugs. The realization of this goal is difficult both purification and identification of proteins in any organism can be hindered by a multitude of biological and environmental factors.

The study of the function of proteomes is called proteomics. A proteome is the entire set of proteins produced by a cell type. Genomics led to proteomics (via transcriptomics) as a logical step. Proteomes can be studied using the knowledge of genomes because genes code for mRNAs and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins. Proteomics complements genomics and is useful when scientists want to test their hypotheses that were based on genes. Even though all cells of a multicellular organism have the same set of genes, the set of proteins produced in different tissues is different and dependent on gene expression. Thus, the genome is constant, but the proteome varies and is dynamic within an organism. In addition, RNAs can be alternately spliced (cut and pasted to create novel combinations and novel proteins) and many proteins are modified after translation by processes such as proteolytic cleavage, phosphorylation, glycosylation, and ubiquitination. There are also protein-protein interactions, which complicate the study of proteomes. Although the genome provides a blueprint, the final architecture depends on several factors that can change the progression of events that generate the proteome.

Large-scale proteomics machinery: This machine is preparing to do a proteomic pattern analysis to identify specific cancers so that an accurate cancer prognosis can be made.

Few steps to find amino acid sequence

STEP 1 – Know which DNA strand is given. There are two strands: Coding strand or non-coding strand.

One can either read the coding strand from 3’ to 5’ or read the template strand from 5’ to 3’ when making the corresponding m-RNA strand.

STEP 2 – Write the corresponding m-RNA strand.

Using Coding strand: (A= U, T= A, G=C, C=G) Read from left to right

Using template strand: (T=U)Read from left to right

We can see that we achieve the same sequence irrespective of the strand used.

STEP 3 – Convert m-RNA as a sequence of codons. ALWAYS start from the codon AUG and NEVER count the same nucleotide twice!

STEP 4 – Use the below table to find the relevant amino acid sequence.

Also remember,
a. Start codon AUG stands for Methionine.
b. If you come across a stop codon UAA, UGA, UAG you should stop sequencing.


  1. Brenton

    What phrase ...

  2. Lundy

    is the special case.

  3. Migore

    I think you are not right. I'm sure. We will discuss. Write in PM, we will talk.

  4. Tityus

    You are not right. Email me at PM, we will discuss.

  5. Sigwald

    I believe you were wrong. Write to me in PM, speak.

  6. Tojakora

    This is your opinion

  7. Faujind

    I have removed it a question

  8. Horus

    It happens ...

  9. JoJozshura

    this is the particular case.

Write a message