Feeds:
Posts
Comments

Posts Tagged ‘transcripts’


Size Matters

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

MinION Sequencing Untangles RNA Transcripts in a Difficult Gene

By Aaron Krol

http://www.bio-itworld.com/2015/11/3/minion-sequencing-untangles-rna-transcripts-difficult-gene.html

 

RNA isoforms are distinct versions of the same isoforms quotegene. Through a process called alternative splicing, the different subunits, or “exons,” that make up a gene can be reshuffled in new combinations. Many genes have two or more mutually exclusive exons, and which ones are actually expressed as RNA and protein can have big effects on cellular behavior ― in effect, expanding the protein arsenal of the genome.

 

November 3, 2015 | Brenton Graveley received his first MinION shipment in April 2014, at his lab at the University of Connecticut’s Institute of Systems Genomics. His lab was among the first to unwrap one of the candy bar-sized DNA sequencers made by Oxford Nanopore Technologies, and although its accuracy was shaky and its throughput low, right away Graveley and his colleagues could see it was producing real DNA data.

“I’m still amazed to this day that it works at all,” Graveley says. “It’s like Star Trek.”

A lot of buzz around the MinION has focused on its tiny size: early adopters have plotted to take MinIONs into outbreak zones and species-hunting tromps through the rainforest, working with bare-bones labs and laptop computers. But for Graveley, the size of the DNA strands the MinION reads is just as exciting as the size of the sequencer itself. That’s because most other sequencers rely on picking up chemical reactions that become more error-prone over time, meaning DNA can only be read in short fragments. The MinION, which reads genetic material by observing single molecules of DNA as they pass through extremely narrow “nanopores,” keeps producing data for as long as DNA is moving through the pore.

“You get the read length of whatever fragment you put into the MinION,” he says. “We’ve gotten reads that are over 100 kilobases,” hundreds or even thousands of times longer than researchers can expect with most other technologies.

Now, in a paper published in Genome Biology, Graveley and two of his lab members, post-doc Mohan Bolisetty and PhD student Gopinath Rajadinakaran, have shown how these read lengths can help explain the cellular behavior of Dscam1, one of the most difficult-to-study genes known to science. Related to a gene in humans that has been linked to Down syndrome ― the name stands for “Down Syndrome Cell Adhesion Molecule” ―Dscam1 plays a fundamental role in forming the architecture of insect brains. This single gene can produce thousands of subtly different proteins, an ability that makes it both a fascinating subject of research, and almost impossible to understand using standard sequencing technology.

 

Determining exon connectivity in complex mRNAs by nanopore sequencing

Mohan T. Bolisetty12, Gopinath Rajadinakaran1 and Brenton R. Graveley1*
Genome Biology 2015, 16:204       http://dx.doi.org:/10.1186/s13059-015-0777-z                    http://genomebiology.com/2015/16/1/204

Short-read high-throughput RNA sequencing, though powerful, is limited in its ability to directly measure exon connectivity in mRNAs that contain multiple alternative exons located farther apart than the maximum read length. Here, we use the Oxford Nanopore MinION sequencer to identify 7,899 ‘full-length’ isoforms expressed from four Drosophila genes, Dscam1, MRP, Mhc, and Rdl. These results demonstrate that nanopore sequencing can be used to deconvolute individual isoforms and that it has the potential to be a powerful method for comprehensive transcriptome characterization.

High throughput RNA sequencing has revolutionized genomics and our understanding of the transcriptomes of many organisms. Most eukaryotic genes encode pre-mRNAs that are alternatively spliced [1]. In many genes, alternative splicing occurs at multiple places in the transcribed pre-mRNAs that are often located farther apart than the read lengths of most current high throughput sequencing platforms. As a result, several transcript assembly and quantitation software tools have been developed to address this [2], [3]. While these computational approaches do well with many transcripts, they generally have difficulty assembling transcripts of genes that express many isoforms. In fact, we have been unable to successfully assemble transcripts of complex alternatively spliced genes such as Dscam1 or Mhc using any transcript assembly software (data not shown). These software tools also have difficulty quantitating transcripts that have many isoforms, and for genes with distantly located alternatively spliced regions, they can only infer, and not directly measure, which isoforms may have been present in the original RNA sample [4]. For example, consider a gene containing two alternatively spliced exons located 2 kbp away from one another in the mRNA. If each exon is observed to be included at a frequency of 50 % from short read sequence data, it is impossible to determine whether there are two equally abundant isoforms that each contain or lack both exons, or four equally abundant isoforms that contain both, neither, or only one or the other exon.

Pacific Bioscience sequencing can generate read lengths sufficient to sequence full length cDNA isoforms and several groups have recently reported the use of this approach to characterize the transcriptome [5]. However, the large capital expense of this platform can be a prohibitive barrier for some users. Thus, it remains difficult to accurately and directly determine the connectivity of exons within the same transcript. The MinION nanopore sequencer from Oxford Nanopore requires a small initial financial investment, can generate extremely long reads, and has the potential to revolutionize transcriptome characterization, as well as other areas of genomics.

Several eukaryotic genes can encode hundreds to thousands of isoforms. For example, inDrosophila, 47 genes encode over 1,000 isoforms each [6]. Of these, Dscam1 is the most extensively alternatively spliced gene known and contains 115 exons, 95 of which are alternatively spliced and organized into four clusters [7]. The exon 4, 6, 9, and 17 clusters contain 12, 48, 33, and 2 exons, respectively. The exons within each cluster are spliced in a mutually exclusive manner and Dscam1 therefore has the potential to generate 38,016 different mRNA and protein isoforms. The variable exon clusters are also located far from one another in the mRNA and the exons within each cluster are up to 80 % identical to one another at the nucleotide level. Together, these characteristics present numerous challenges to characterize exon connectivity within full-length Dscam1 transcripts for any sequencing platform. Furthermore, though no other gene is as complex as Dscam1, many other genes have similar issues that confound the determination of exon connectivity.

We are interested in developing methods to perform simple and robust long-read sequencing of individual isoforms of Dscam1 and other complex alternatively spliced genes. Here, we use the Oxford Nanopore MinION to sequence ‘full-length’ cDNAs from four Drosophila genes – Rdl, MRP,Mhc, and Dscam1 – and identify a total of 7,899 distinct isoforms expressed by these four genes.

 

Similarity between alternative exons

We were interested in determining the feasibility of using the MinION nanopore sequencer to characterize the connectivity of distantly located exons in the mRNAs expressed from genes with complex splicing patterns. For the purposes of these experiments, we have focused on fourDrosophila genes with increasingly complex patterns of alternative splicing (Fig. 1). Resistant to dieldrin (Rdl) contains two clusters, each containing two mutually exclusive exons and therefore has the potential to generate four different isoforms (Fig. 1a). Multidrug-Resistance like Protein 1(MRP) contains two mutually exclusive exons in cluster 1 and eight mutually exclusive exons in cluster 2, and can generate 16 possible isoforms (Fig. 1b). Myosin heavy chain (Mhc) can potentially generate 180 isoforms due to five clusters of mutually exclusive exons – clusters 1 and 5 contain two exons, clusters 2 and 3 each contain three exons, and cluster 4 contains five exons. Finally, Dscam1 contains 12 exon 4 variants, 48 exon 6 variants, 33 exon 9 variants (Fig. 1d), and two exon 17 variants (not shown) and can potentially express 38,016 isoforms. For this study, however, we have focused only on the exon 3 through exon 10 region of Dscam1, which encompasses the 93 exon 4, 6, and 9 variants, and 19,008 potential isoforms (Fig. 1d).

thumbnail

Fig. 1. Schematic of the exon-intron structures of the genes examined in this study. a The Rdl gene contains two clusters (cluster one and two) which each contain two mutually exclusive exons. b The MRP gene contains contains two and eight mutually exclusive exons in clusters 1 and 2, respectively. Mhc contains two mutually exclusive exons in clusters 1 and 5, three mutually exclusive exons in clusters 2 and 3, and five mutually exclusive exons in cluster 4. The Dscam1 gene contains 12, 48, and 33 mutually exclusive exons in the exon 4, 6, and 9 clusters, respectively. For each gene, the constitutive exons are colored blue, while the variable exons are colored yellow, red, orange, green, or light blue

Because our nanopore sequence analysis pipeline uses LAST to perform alignments [8], we aligned all of the Rdl, MRP, Mhc, and Dscam1 exons within each cluster to one another using LAST to determine the extent of discrimination needed to accurately assign nanopore reads to a specific exon variant. For Rdl, each variable exon was only aligned to itself, and not to the other exon in the same cluster (data not shown). For MRP, the two exons within cluster 1 only align to themselves, and though the eight variable exons in cluster 2 do align to other exons, there is sufficient specificity to accurately assign nanopore reads to individual exons (Fig. 2a). For Mhc, the variable exons in cluster 1 and cluster 5 do not align to other exons, and the variable exons in cluster 2, cluster 3, and cluster 4 again align with sufficient discrimination to identify the precise exon present in the nanopore reads (Fig. 2b). Finally, for Dscam1, the difference in the LAST alignment scores between the best alignment (each exon to itself) and the second, third, and fourth best alignments are sufficient to identify the Dscam1 exon variant (Fig. 2c). This analysis indicates that for each gene in this study, LAST alignment scores are sufficiently distinct to identify the variable exons present in each nanopore read.

thumbnail

Fig. 2. Similarity distance between the variable alternative exons of MRP,Mhc, and Dscam1. a Violin plots of the LAST alignment scores of each variable exon within MRP cluster 1 and MRP cluster 2 to themselves and the second (2nd) best alignments. b Violin plots of the LAST alignment scores of each variable exon within each Mhc cluster to themselves and the second (2nd) best alignments. c Violin plots of the LAST alignment scores of each variable exon within each Dscam1 cluster to themselves (1st), and to the exons with the second (2nd), third (3rd) and fourth (4th) best alignments

Optimizing template switching in Dscam1 cDNA libraries

Template switching can occur frequently when libraries are prepared by PCR and can confound the interpretation of results [9], [10]. For example, CAM-Seq [11] and a similar method we independently developed called Triple-Read sequencing [12] to characterize Dscam1 isoforms, were found to have excessive template switching due to amplification during the library prep protocols. To assess template switching in our current study, we generated a spike-in mixture of in vitro transcribed RNAs representing six unique Dscam1 isoforms – Dscam1 4.2,6.32,9.31 , Dscam14.1,6.46,9.30 , Dscam1 4.3,6.33,9.9 , Dscam1 4.12,6.44,9.32 , Dscam1 4.7,6.8,9.15 , and Dscam1 4.5,6.4,9.4. We used 10 pg of this control spike-in mixture and prepared libraries for MinION sequencing by amplifying the exon 3 through exon 10 region for 20, 25, or 30 cycles of RT-PCR. We then end-repaired and dA-tailed the fragments, ligated adapters, and sequenced the samples on a MinION (7.3) for 12 h each. We obtained 33,736, 8,961, and 7,511 base-called reads from the 20, 25, and 30 cycle libraries, respectively. Consistent with the size of the exon 3 to 10 cDNA fragment being 1,806–1,860 bp in length, depending on the precise combination of exons it contains, most reads we observed were in this size range (Fig. 3a). We used Poretools [13] to convert the raw output files into fasta format and then used LAST to align the reads to a LAST database containing each variable exon. From these alignments, we identified reads that mapped to all three exon clusters, as well as the exon with the best alignment score within each cluster. When examining the alignments to each cluster independently, we found that for these spike-in libraries, all reads mapped uniquely to the exons present in the input isoforms. Therefore, any observed isoforms that were not present in the input pool were a result of template switching during the RT-PCR and library prep protocol and not due to false alignments or sequencing errors.

thumbnail

Fig. 3. Optimized RT-PCR minimizes template-switching for MinION sequencing. a Histogram of read lengths from MinION sequencing ofDscam1 spike-ins from the library generated using 25 cycles of PCR. bBar plot indicating the extent of template switching in Dscam1 spike-ins at different PCR cycles (left). The blue portions indicate the fraction of reads corresponding to input isoforms while the red portions correspond to the fraction of reads corresponding to template-switched isoforms. On the right, plots of the rank order versus number of reads (log10) for the 20, 25, and 30 cycle libraries. The blue dots indicate input isoforms while the red portions correspond to template-switched isoforms

When comparing the combinations of exons within each read to the input isoforms, we observed that 32 % of the reads from the 30 cycle library corresponded to isoforms generated by template switching (Fig. 3b). The template-switched isoforms observed by the greatest number of reads in the 30 cycle library were due to template switching between the two most frequently sequenced input isoforms. In most cases, template switching occurred somewhere within exon 7 or 8 and resulted in a change in exon 9. However, the extent of template switching was reduced to only 1 % in the libraries prepared using 25 cycles, and to 0.2 % in the libraries prepared using 20 cycles of PCR (Fig. 3b). Again, for these two libraries the most frequently sequenced template-switched isoforms involved the input isoforms that were also the most frequently sequenced. These experiments demonstrate that the MinION nanopore sequencer can be used to sequence ‘full length’ Dscam1 cDNAs with sufficient accuracy to identify isoforms and that the cDNA libraries can be prepared in a manner that results in a very small amount of template switching.

Dscam1 isoforms observed in adult heads

To explore the diversity of Dscam1 isoforms expressed in a biological sample, we prepared aDscam1 library from RNA isolated from D. melanogaster heads prepared from mixed male and female adults using 25 cycles of PCR and sequenced it for 12 h on the MinION nanopore sequencer obtaining a total of 159,948 reads of which 78,097 were template reads, 48,474 were complement reads, and 33,377 were 2D reads (Fig. 4a). We aligned the reads individually to the exon 4, 6, and 9 variants using LAST. A total of 28,971 reads could be uniquely or preferentially aligned to a single variant in all three clusters. For further analysis, we used all 16,419 2D read alignments and 31 1D reads when both template and complement aligned to same variant exons (not all reads with both a template and complement yield a 2D read). The remaining 12,521 aligned reads were 1D reads where there was either only a template or complement read, or when the template and complement reads disagreed with one another and were therefore not used further. We observed 92 of the 93 potential exon 4, 6, or 9 variants – only exon 6.11 was not observed in any read (Fig. 4f). To assess the accuracy of the results we performed RT-PCR using primers in the flanking constitutive exons that contained Illumina sequencing primers to separately amplify the Dscam1exon 4, 6, and 9 clusters from the same RNA used to prepare the MinION libraries, and sequenced the amplicons on an Illumina MiSeq. The frequency of variable exon use in each cluster was extremely consistent between the two methods (R 2  = 0.95, Fig. 5a).

Fig. 4. MinION sequencing of Dscam1 identified 7,874 isoforms. aHistogram of read length distribution for Drosophila head samples. b The total number of Dscam1 isoforms identified from MinION sequencing. cCumulative distribution of Dscam1 isoforms with respect to expression. dViolin plot of the number of isoforms identified using 100 random pools of the indicated number of reads. e Plot of the estimated number of total isoforms present in the library using the capture-recapture method with two random pools of the indicated number of reads. The shaded blue area indicates the 95 % confidence interval. f Deconvoluted expression of Dscam1 exon cluster variants (top) and the isoform connectivity of two highly expressed Dscam1 isoforms (bottom)

thumbnail

Fig. 5. Accuracy of Dscam1 sequencing results. a Comparison of the frequency of variable exon inclusion for the Dscam1 exon 4 (yellow), 6 (red), and 9 (orange) clusters as determined by nanopore sequencing or by amplicon sequencing using an Illumina MiSeq. b Percent identities (left) or LAST alignment scores (right) of full-length template, complement, and two directions (sequencing both template and complements) nanopore read alignments

Over their entire lengths, the 2D reads that map specifically to one exon 4, 6, and 9 variants map with an average 90.37 % identity and an average LAST score of approximately 1,200 (Fig. 5b). The 16,450 full length reads correspond to 7,874 unique isoforms, or 42 % of the 18,612 possible isoforms given the exon 4, 6, and 9 variants observed. We note, however, that while 4,385 isoforms were represented by more than one read, 3,516 of isoforms were represented by only one read indicating that the depth of sequencing has not reached saturation (Fig. 4b and c). This was further confirmed by performing a bootstrapped subsampling analysis (Fig. 4d) and by using the capture-recapture method to attempt to assess the complexity of isoforms present in the library (Fig. 4e), which suggests that over 11,000 isoforms are likely to be present, though even this analysis has not yet reached saturation. The most frequently observed isoforms were Dscam14.1,6.12,9.30 and Dscam1 4.1,6.1,9.30 which were observed with 30 and 25 reads, respectively (Fig. 4e). In conclusion, these results demonstrate the practical application of using the MinION nanopore sequencer to identify thousands of distinct Dscam1 isoforms in a single biological sample.

Nanopore sequencing of ‘full-length’ Rdl, MRP, and Mhc isoforms

To extend this approach to other genes with complex splicing patterns, we focused on Rdl, MRP, and Mhc which have the potential to generate four, 16, and 180 isoforms, respectively. We prepared libraries for each of these genes by RT-PCR using primers in the constitutive exons flanking the most distal alternative exons using 25 cycles of PCR, pooled the three libraries and sequenced them together on the MinION nanopore sequencer for 12 h obtaining a total of 22,962 reads. The input libraries for Rdl, MRP, and Mhc were 567 bp, 1,769-1,772 bp, and 3,824 bp, respectively. The raw reads were aligned independently to LAST indexes of each cluster of variable exons. The alignment results were then used to assign reads to their respective libraries, identify reads that mapped to all variable exon clusters for each gene, and the exon with the best alignment score within each cluster. In total, we obtained 301, 337, and 112 full length reads forRdl (Fig. 6), MRP (Fig. 7), and Mhc (Fig. 8), respectively. For Rdl, both variable exons in each cluster was observed, and accordingly all four possible isoforms were observed, though in each case the first exon was observed at a much higher frequency than the second exon (Fig. 6d). Interestingly, the ratio of isoforms containing the first versus second exon in the second cluster is similar for isoforms containing either the first exon or the second exon in the first cluster indicating that the splicing of these two clusters may be independent. For MRP, both exons in the first cluster were observed and all but one of the exons in the second cluster (exon B) were observed, though the frequency at which the exons in both clusters were used varied dramatically (Fig. 7d). For example, within the first cluster, exon B was observed 333 times while exon A was observed only four times. Similarly, in the second cluster, exon A was observed 157 times whereas exons B, E, F, and G were observed 0 times, thrice, once, and twice, respectively, and exons D, E, and H were observed between 40 and 76 times. As a result, we observed only nine MRP isoforms. For Mhc, we again observed strong biases in the exons observed in each of the five clusters (Fig. 8d). In the first cluster, exon B was observed more frequently than exon A. In the second cluster, 109 of the reads corresponded to exon A, while exons B and C were observed by only two and one read, respectively. In the third cluster, exon A was not observed at all while exons B and C were observed in roughly 80 % and 20 % of reads, respectively. In the fourth cluster, exon A was observed only once, exons B and C were not observed at all, exon E was observed 13 times while exon D was present in all of the remaining reads. Finally, in the fifth cluster, only exon B was observed. As with MRP, these strong biases and near or complete absences of exons in some of the clusters severely reduces the number of possible isoforms that can be observed. In fact, of the 180 potential isoforms encoded by Mhc, we observed only 12 isoforms. Various Mhc isoforms are known to be expressed in striking spatial and temporally restricted patterns [14] and thus it is likely that other Mhc isoforms that we did not observe, could be observed by sequencing other tissue samples.

thumbnail

Fig. 6. MinION sequencing of Rdl identified four isoforms. a Histogram of read lengths. b The number of reads per isoform. c Cumulative distribution of isoforms with respect to expression. d The number of reads per alternative exon (top) and per isoform (below)

thumbnail

Fig. 7. MinION sequencing of MRP identified nine isoforms. a Histogram of read lengths. b The number of reads per isoform. c Cumulative distribution of isoforms with respect to expression. d The number of reads per alternative exon (top) and per isoform (below)

thumbnail

Fig. 8. MinION sequencing of Mhc identified 12 isoforms. a Histogram of read lengths. b The number of reads per isoform. c Cumulative distribution of isoforms with respect to expression. d The number of reads per alternative exon (top) and per isoform (below)

Conclusions

Here we have demonstrated that nanopore sequencing with the Oxford Nanopore MinION can be used to easily determine the connectivity of exons in a single transcript, including Dscam1, the most complicated alternatively spliced gene known in nature. This is an important advance for several reasons. First, because short-read sequence data cannot be used to conclusively determine which exons are present in the same RNA molecule, especially for complex alternatively spliced genes, long-read sequence data are necessary to fully characterize the transcript structure and exon connectivity of eukaryotic transcriptomes. Second, although the Pacific Bioscience platform can perform long-read sequencing, there are several differences between it and the Oxford Nanopore MinION that could cause users to choose one platform over the other. In general, the quality of the sequence generated by the Pacific Bioscience is higher than that currently generated by the Oxford Nanopore MinION. This is largely due to the fact that each molecule is sequenced multiple times on the Pacific Bioscience platform yielding a high quality consensus sequence whereas on the Oxford Nanopore MinION, each molecule is sequenced at most twice (in the template and complement). We have previously used the Pacific Bioscience platform to characterize Dscam1 isoforms and found that it works well, though due to the large amount of cDNA needed to generate the libraries, many cycles of PCR are necessary and we observed an extensive amount of template switching, making it impractical to use for these experiments (BRG, unpublished data). However, over the past year that we have been involved in the MAP, the quality of sequence has steadily increased. As this trend is likely to continue, the difference in sequence quality between these two platforms is almost certain to shrink. Nonetheless, as we demonstrate, the current quality of the data is more than sufficient to allow us to accurately distinguish between highly similar alternatively spliced isoforms of the most complex gene in nature. Third, the ability to accurately characterize alternatively spliced transcripts with the Oxford Nanopore MinION makes this technology accessible to a much broader range of researchers than was previously possible. This is in part due to the fact that, in contrast to all other sequencing platforms, very little capital expense is needed to acquire the sequencer. Moreover, the MinION is truly a portable sequencer that could literally be used in the field (provided one has access to an Internet connection), and due to its size, almost no laboratory space is required for its use.

Although nanopore sequencing has many exciting and potentially disruptive advantages, there are several areas in which improvement is needed. First, although we were able to accurately identify over 7,000 Dscam1 isoforms with an average identity of full-length alignments >90 %, there are several situations in which this level of accuracy will be insufficient to determine transcript structure. For instance, there are many micro-exons in the human genome [15], and these exons would be difficult to identify if they overlapped a portion of a read that contained errors. Additionally, small unannotated exons could be difficult to identify for similar reasons. Second, the current number of usable reads is lower than that which will be required to perform whole transcriptome analysis. One issue that plagues transcriptome studies is that the majority of the sequence generated comes from the most abundant transcripts. Thus, with the current throughput, numerous runs would be needed to generate a sufficient number of reads necessary to sample transcripts expressed at a low level. In fact, this is one reason that we chose in this study, to begin by targeting specific genes rather than attempting to sequence the entire transcriptome. We do note, however, that over the past year of our participation in the MAP, the throughput of the Oxford Nanopore MinION has increased, and it is reasonable to expect additional improvements in throughput that should make it possible to generate a sufficient number of long reads to deeply interrogate even the most complex transcriptome.

In conclusion, we anticipate that nanopore sequencing of whole transcriptomes, rather than targeted genes as we have performed here, will be a rapid and powerful approach for characterizing isoforms, especially with improvements in the throughput and accuracy of the technology, and the simplification and/or elimination of the time-consuming library preparations.

 

The Tangled Transcriptome

Graveley’s lab studies the transcriptome, the mass of RNA molecules in living cells whose job is to translate DNA into proteins. The transcriptome is a sort of snapshot of which parts of the genome are active at a given time and place. Which genes are transcribed into RNA, and in what quantities, changes from organ to organ and even cell to cell, and can vary over an organism’s lifetime or in response to environmental changes.

Of particular interest to Graveley are those RNA molecules than can take different shapes, or “isoforms,” depending on random chance or what the cell needs at a particular time. RNA isoforms are distinct versions of the same isoforms quotegene. Through a process called alternative splicing, the different subunits, or “exons,” that make up a gene can be reshuffled in new combinations. Many genes have two or more mutually exclusive exons, and which ones are actually expressed as RNA and protein can have big effects on cellular behavior ― in effect, expanding the protein arsenal of the genome.

“For the entire field of transcriptomics and gene function, knowing what isoforms are expressed is critical,” says Graveley. “Most genes are complicated, especially in humans, and have alternative splicing that occurs at multiple places.”

That brings us to the challenge of Dscam1, the world record holder for alternative splicing. In fruit flies, a particularly well-studied model organism, Dscam1 is made up of 115 exons, only 20 of which are always transcribed into RNA. The other 95 exist in four “clusters” of mutually exclusive exons, and as a result, over 38,000 possible isoforms of Dscam1 have been predicted.

“This is by far, an order of magnitude, more than any other gene,” Graveley explains. This flexibility makes sense in light of Dscam1’s function. The protein it makes helps to “identify” single neurons in the insect brain, making them distinct enough from their neighbors for these cells to assemble a neural circuit on principles of like avoiding like. In experiments where Dscam1 has been altered to make fewer RNA isoforms, the neural wiring breaks down during development, sometimes severely enough to kill the flies.

Dscam1 also plays a role in the insect immune system, another reason for it to produce a huge variety of isoforms. Each of these molecules might be more or less effective at fighting certain pathogens.

It’s frustratingly hard, however, to figure out exactly which isoforms are in a specific sample. Graveley has been working on Dscam1 in fruit flies for more than a decade, but very basic questions remain unanswered: are some isoforms more common, or more important, than others? Are all the theoretical isoforms expressed? Do the isoforms have different behaviors, or are they just arbitrary ways of tagging neurons?

Size Matters

The trouble is the current state of the art in sequencing technology, which reads just a couple of hundred DNA bases at a time. That works great for identifying which exons are present in the transcriptome, but it’s no good for saying which mix of exons any specific strand of RNA is carrying. Different exons can lie thousands of bases apart on the RNA molecule, and there’s no way to bridge the gap between reads.

Graveley has tried a lot of solutions. He’s used the outdated Sanger sequencing method, which is much slower and more labor-intensive than modern sequencers, but does span longer reads. His lab also worked out a roundabout way of reconstructing RNA transcripts with contemporary Illumina sequencers, through a combination of chemistry and computational approaches.

“It worked,” he says, “but it was complicated by a lot of library preparation artifacts, and you basically had to jury-rig a genome analyzer to do something it was not supposed to do.”

Graveley’s preferred method is to use a sequencer produced by Pacific Biosciences, which, like the MinION, is built on long-read, single-molecule technology. PacBio sequencing is much better established than nanopores, and its results are known to be reliable; it also has the high throughput typical of modern instruments. For researchers working on alternative splicing, it’s clearly the technology to beat.

Unfortunately, it’s also very expensive. So Graveley’s team set out to learn whether the MinION, a low-throughput but extremely cheap alternative, could be an adequate substitute.

For the Genome Biology paper, the team focused on a 1.8-kilobase region of Dscam1 RNA that covers 93 of the gene’s 95 alternatively spliced exons. To get their samples, they crushed fruit fly heads, isolated Dscam1 RNA from the sample using a polymerase, and reverse-transcribed it into cDNA for sequencing. They also sequenced transcripts of three other alternatively spliced genes, Rdl, MRP, and Mhc.

splicing quote

The biggest concern for new applications of the MinION is its shaky accuracy. While most sequencers can achieve comfortably over 99% consensus with reference sequences, Graveley’s group has seen only about 90% identity with the MinION. That’s actually a little better than most MinION users have managed, although the device’s accuracy has been steadily improving. Users have had to pick their projects carefully to account for this: the device is pretty reliable in resequencing studies that map DNA reads to known references, but it’s still a dubious choice for sequencing unknown genetic material from scratch (although it’s been tried).

To accurately pin down the exact isoforms in the transcriptome, the MinION didn’t have to read every RNA molecule perfectly, but it did have to come close enough to decisively tell one exon from another ― and inDscam1, those exons could be as much as 80% identical.

In fact, Graveley and his co-authors found that the MinION was very capable of this. Out of around 33,000 high-quality Dscam1 reads pulled off the sequencer, almost 29,000 were a strong match for one and only one combination of exons. To further check their accuracy, the team also sequenced the same sample on Illumina technology. While the Illumina sequencer could not give whole isoforms, it did show the same proportions of different exons, suggesting that the MinION gave a complete and unbiased picture of the sample.

“Alternative splicing, it turns out, is probably one of the ideal applications for this platform,” Graveley says. “Even with a gene as complicated as this one, we’re able to accurately distinguish the isoforms from one another. Unless you have very, very small exons, or two exons that are almost identical to each other, the accuracy is good enough.”

Make Way for PromethION

The results are good news for researchers studying the transcriptome, but the MinION probably won’t push out other methods for dealing with alternative splicing just yet. Its low throughput means that at best it can cover a very small portion of the transcriptome with each run ― and that means isolating targeted RNA transcripts, a process that can introduce new biases into the data.

“You need a lot of reads to get the whole transcriptome, and what happens is you end up sequencing boring genes like actin and tubulin, the really abundantly expressed things,” Graveley explains. Still, his data from this experiment was good enough to replicate a few earlier findings: for instance, that Dscam1 does appear to make every predicted isoform. In this experiment, his lab observed almost half the possible isoforms, containing 92 of 93 possible exons.

Meanwhile, Oxford Nanopore Technologies is working on a new instrument, the PromethION, which will contain 48 MinION-style flow cells in a battery. Graveley has already signed on to be one of the first recipients, in an access program that is likely to start in the winter.

Judging by studies like this one, the PromethION stands a good chance of becoming the instrument of choice for large-scale RNA sequencing. With Dscam1, Graveley hopes to reach high enough throughput to do functional studies, seeking to learn whether different combinations of isoforms give rise to physical or behavioral differences. He also wants to look at human genes with high levels of alternative splicing, and to test whether the MinION can accurately count total numbers of RNA isoforms.

“The fact that you can use this technology to characterize whole isoforms is very exciting,” Graveley says. “It’s going to help us start characterizing the transcriptome in ways that have been very difficult.”

 

 

 

Read Full Post »


Summary of Transcription, Translation ond Transcription Factors

Author and Curator:  Larry H. Bernstein, MD, FCAP  

 

Proteins are integral to the composition of the cytoskeleton, and also to the extracellular matrix.  Many proteins are actually enzymes, carrying out the transformation of some substrate, a derivative of the food we ingest.  They have a catalytic site, and they function with a cofactor – either a multivalent metal or a nucleotide. Proteins also are critically involved in the regulation of cell metabolism, and they are involved in translation of the DNA code, as they make up transcription factors (TFs). There are 20 essential amino acids that go into protein synthesis that are derived from animal or plant protein.   Protein synthesis is carried out by the transport of mRNA out of the nucleus to the ribosome, where tRNA is paired with a matching amino acid, and the primary sequence of a protein is constructed as a linear string of amino acids.

This is illustrated in the following three pictures:

protein synthesis

protein synthesis

mcell-transcription-translation

mcell-transcription-translation

transcription_translation

transcription_translation

Proteins synthesized at distal locations frequently contain intrinsically disordered segments. These regions are generally rich in assembly-promoting modules and are often regulated by post-translational modifications. Such proteins are tightly regulated but display distinct temporal dynamics upon stimulation with growth factors. Thus, proteins synthesized on-site may rapidly alter proteome composition and act as dynamically regulated scaffolds to promote the formation of reversible cellular assemblies.
RJ Weatheritt, et al. Nature Structural & Molecular Biology 24 Aug, 2014; 21: 833–839 http://dx.do.orgi:/10.1038/nsmb.2876

An overview of the potential advantages conferred by distal-site protein synthesis

An overview of the potential advantages conferred by distal-site protein synthesis

Turquoise and red filled circle represents off-target and correct interaction partners, respectively. Wavy lines represent a disordered region within a distal site synthesis protein. Grey and red line in graphs represents profiles of t…  http://www.nature.com/nsmb/journal/v21/n9/carousel/nsmb.2876-F5.jpg

In the the transcription process an RNA sequence is read.  This is essential for protein synthesis through the ordering of the amino acids in the primary structure. However, there are microRNAs and noncoding RNAs, and there are transcription factors.  The transcription factors bind to chromatin, and the RNAs also have some role in regulating the transcription process. (see picture above)

Transcription factors (TFs) interact dynamically in vivo with chromatin binding sites. Four different techniques are currently used to measure their kinetics in live cells,

  1. fluorescence recovery after photobleaching (FRAP),
  2. fluorescence correlation spectroscopy (FCS),
  3. single molecule tracking (SMT) and
  4. competition ChIP (CC).

A comparison of data from each of these techniques raises an important question:

  • do measured transcription kinetics reflect biologically functional interactions at specific sites (i.e. working TFs) or
  • do they reflect non-specific interactions (i.e. playing TFs)?

There are five key unresolved biological questions related to

  • the functionality of transient and prolonged binding events at both
  • specific promoter response elements as well as non-specific sites.

In support of functionality,

  • there are data suggesting that TF residence times are tightly regulated, and
  • that this regulation modulates transcriptional output at single genes.

In addition to this site-specific regulatory role, TF residence times

  • also determine the fraction of promoter targets occupied within a cell
  • thereby impacting the functional status of cellular gene networks.
  • TF residence times, then, are key parameters that could influence transcription in multiple ways.

Quantifying transcription factor kinetics: At work or at play? Mueller F., et al.  http://dx.doi.org:/10.3109/10409238.2013.833891

Dr. Virginie Mattot works in the team “Angiogenesis, endothelium activation and Cancer” directed by Dr. Fabrice Soncin at the Institut de Biologie de Lille in France where she studies the roles played by microRNAs in endothelial cells during physiological and pathological processes such as angiogenesis or endothelium activation. She has been using Target Site Blockers to investigate the role of microRNAs on putative targets.

A few years ago, the team identified

  • an endothelial cell-specific gene which
  • harbors a microRNA in its intronic sequence.

They have since been working on understanding the functions of

  • both this new gene and its intronic microRNA in endothelial cells.

While they were searching for the functions of the intronic microRNA,

  • theye identified an unknown gene as a putative target.

The aim of my project was to investigate if this unknown gene was actually a genuine target and

  • if regulation of this gene by the microRNA was involved in endothelial cell function.

They had already shown the endothelial cell phenotype is associated with the inhibition of the intronic microRNA.
They then used miRCURY LNA™ Target Site Blockers to demonstrate

  • the expression of this unknown gene is actually controlled by this microRNA.
  • the microRNA regulates specific endothelial cell properties through regulation of this unknown gene.

MicroRNA function in endothelial cells – Solving the mystery of an unknown target gene using Target Site Blockers to investigate the role of microRNAs on putative targets

We first verified that this TSB was functional by analyzing

  • the expression of the miRNA target against which the TSB was directed
  • we then showed the TSB induced similar phenotypes as those when we inhibited the microRNA in the same cells.

Target Site Blockers were shown to be efficient tools to demonstrate the specific involvement of

  • putative microRNA targets
  • in the function played by this microRNA.

Some genes are known to have several different alternatively spliced protein variants, but the Scripps Research Institute’s Paul Schimmel and his colleagues have uncovered almost 250 protein splice variants of an essential, evolutionarily conserved family of human genes. The results were published July 17 in Science.

Focusing on the 20-gene family of aminoacyl tRNA synthetases (AARSs),

  • the team captured AARS transcripts from human tissues—some fetal, some adult—and showed that
  • many of these messenger RNAs (mRNAs) were translated into proteins.

Previous studies have identified several splice variants of these enzymes that have novel functions, but uncovering so many more variants was unexpected, Schimmel said. Most of these new protein products

  • lack the catalytic domain but retain other AARS non-catalytic functional domains.

This study fundamentally effects how we view protein-synthesis, according to  Michael Ibba (who was not involved in the work), The Scientist reported. “The unexpected and potentially vast expanded functional networks that emerge from this study have the potential to influence virtually any aspect of cell growth.”

The team—comprehensively captured and sequenced the AARS mRNAs from six human tissue types using high-throughput deep sequencing. They next showed that a proportion of these transcripts, including those missing the catalytic domain, indeed resulted in stable protein products:

  • 48 of these splice variants associated with polysomes.

In vitro translation assays and the expression of more than 100 of these variants in cells confirmed that

  • many of these variants could be made into stable protein products.

The AARS enzymes—of which there’s one for each of the 20 amino acids—bring together an amino acid with its appropriate transfer RNA (tRNA) molecule. This reaction allows a ribosome to add the amino acid to a growing peptide chain during protein translation. AARS enzymes can be found in all living organisms and are thought to be among the first proteins to have originated on Earth.

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly

  • play an important role in determining gene expression outputs, yet
  • the regulatory logic underlying functional transcription factor binding is poorly understood.

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and

  • it is generally accepted that much of the binding does not strongly influence gene expression.

DA Cusanovich et al. PLoS Genet 2014;10(3):e1004226.  http://dx.doi.org:/10.1371/journal.pgen.1004226

We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line

  • to evaluate the context of functional TF binding.

We then identified genes whose expression was affected by the knockdowns

  • by intersecting the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq)
  • within 10 kb of the transcription start sites of expressed genes.

This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were

  • differentially expressed following the knockdown of that factor,
  • suggesting that most interactions between TF and chromatin
  • do not result in measurable changes in gene expression levels
  • of putative target genes.

We found that functional TF binding is enriched

  • in regulatory elements that harbor a large number of TF binding sites,
  • at sites with predicted higher binding affinity, and
  • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone.

Combining a TF knockdown approach with TF binding data can help us to

  • distinguish functional binding from non-functional binding

This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on

  • the regulatory relationship of a single factor with its downstream targets.

The FANTOM consortium knocked down 52 different transcription factors in

  • the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
  • used a subset of these to validate certain regulatory predictions based on binding motif enrichments.

We and others previously studied the regulatory architecture of gene expression in

  • the model system of HapMap lymphoblastoid cell lines (LCLs) using both
  • binding map strategies and QTL mapping strategies.

We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL

  • to refine our understanding of the gene regulatory circuitry of the human genome.

Therefore, We integrated the results of the knockdown experiments with previous data on TF binding to

  • better characterize the regulatory targets of 59 different factors and
  • to learn when a disruption in transcription factor binding
  • is most likely to be associated with variation in the expression level of a nearby gene.

Gene expression levels following the knockdown were compared to

  • expression data collected from six samples that were transfected with negative control siRNA.

Depending on the factor targeted, the knockdowns resulted in

  • between 39 and 3,892 differentially expressed genes at an FDR of 5%
    (Figure 1B; see Table S3 for a summary of the results).

The knockdown efficiency for the 59 factors ranged

  • from 50% to 90% (based on qPCR; Table S1).

The qPCR measurements of the knockdown level were significantly

  • correlated with estimates of the TF expression levels
  • based on the microarray data (P =0.001; Figure 1C).

 

Did the factors tended to have a consistent effect (either up- or down-regulation)

  • on the expression levels of genes they purportedly regulated?

All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).

While there is compelling evidence for our inferences, the current chromatin functional annotations

  • do not fully explain the regulatory effects of the knockdown experiments.

For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),

  • much beyond what is expected by chance alone, but far from accounting for all functional binding.

A slight majority of downstream target genes were expressed at higher levels

  • following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).

The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,

  • the enzymatic component of the Polycomb group complex.

On the other end of the spectrum was JUND, a member of the AP-1 complex, for which

  • 66.7% of differentially expressed targets were down-regulated following the knockdown.

Our results, combined with the previous work from our group and others make for a complicated view

  • of the role of transcription factors in gene regulation as
  • it seems difficult to reconcile the inference from previous work that
  • many transcription factors should primarily act as activators with the results presented here.

One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that

  • the ‘‘repressive’’ effects we observe for known activators may be
  • at sites in which the activator is acting as a weak enhancer of transcription and
  • that reducing the cellular concentration of the factor
  • releases the regulatory region to binding by an alternative, stronger activator.

Integrative study of Arabidopsis thaliana metabolomic and transcriptomic data
with the interactiveMarVis-Graph software

M Landesfeind, A Kaever, K Feussner, C Thurow, C Gatz, I Feussner and P Meinicke
PeerJ 2:e239;   http://dx.doi.org /10.7717/peerj.239

High-throughput technologies notoriously generate large datasets often including data from different omics platforms. Each dataset contains data for several thousand experimental markers, e.g., mass-to-charge ratios in mass spectrometry or spots in DNA microarray analysis. An experimental marker is associated with an intensity profile which may include several measurements according to different experimental conditions (Dettmer, Aronov & Hammock, 2007).

The combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics.We present here theMarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and

  • sub-networks, according to connected high-scoring reactions, are identified.

Finally, MarVis-Graph scores the detected sub-networks,

  • evaluates them by means of a random permutation test and
  • presents them as a ranked list.

Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results.

The key advantage ofMarVis-Graph is the analysis of reactions detached from their pathways so that

  • it is possible to identify new pathways or
  • to connect known pathways by previously unrelated reactions.

TheMarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.

Significant differences or clusters may be explained by associated annotations, e.g., in terms of metabolic pathways or biological functions. During recent years, numerous specialized tools have been developed to aid biological researchers in automating all these steps (e.g., Medina et al., 2010; Kaever et al., 2009; Waegele et al., 2012). Comprehensive studies can be performed by combining technologies from different omics fields. The combination of transcriptomic and proteomic data sets revealed a strong
correlation between both kinds of data (Nie et al., 2007) and supported the detection of complex interactions, e.g., in RNA silencing (Haq et al., 2010). Moreover, correlations
were detected between RNA expression levels and metabolite abundances (Gibon et al., 2006). Therefore, tools that integrate, analyze and visualize experimental markers from different platforms are needed. To cope with the complexity of genome-wide studies, pathway models are utilized extensively as a simple abstraction of the underlying complex mechanisms. Set Enrichment Analysis (Subramanian et al., 2005) and Over-Representation Analysis (Huang, Sherman & Lempicki, 2009) have become state-of-the-art tools for analyzing large-scale datasets: both methods evaluate predefined sets of entities, e.g., the accumulation of differentially expressed genes in a pathway.

While manually curated pathways are convenient and easy to interpret, experimental studies have shown that all metabolic and signaling pathways are heavily interconnected (Kunkel & Brooks, 2002; Laule et al., 2003). Data from biomolecular databases support these studies: the metabolic network of Arabidopsis thaliana in the KEGG database (Kanehisa et al., 2012; Kanehisa & Goto, 2000) contains 1606 reactions from which 1464 are connected in a single sub-network (>91%), i.e., they
share a metabolite as product or substrate. In the AraCyc 10.0 database (Mueller, Zhang & Rhee, 2003; Rhee et al., 2006), more than 89% of the reactions are counted in a single sub-network. In both databases, most other reactions are completely disconnected. Additionally, Set Enrichment Analyses can not identify links between the predefined sets easily. This becomes even more important when analyzing smaller pathways as provided by the MetaCyc (Caspi et al., 2008; Caspi et al., 2012) database. Moreover, methods that utilize pathways as predefined sets ignore reactions and related biomolecular entities (e.g., metabolites, genes) which are not associated with a single pathway. For example, this affects 4000 reactions in MetaCyc and 2500 in KEGG, respectively (Altman et al., 2013). Therefore, it is desirable to develop additional methods

  • that do not require predefined sets but may detect enriched sub-networks in the full metabolic network.

While several tools support the statistical analysis of experimental markers from one or more omics technologies and then utilize variants of Set Enrichment Analysis (Xia et al., 2012; Chen et al., 2013; Howe et al., 2011),

  • no tool is able to explicitly search for connected reactions that include
  • most of the metabolites, genes, and enyzmes with experimental evidence.

However, the automatic identification of sub-networks has been proven useful in other contexts, e.g., in the analysis of protein–protein-interaction networks (Alcaraz et al., 2012; Baumbach et al., 2012; Maeyer et al., 2013).

MarVis-Graph imports experimental markers from different high-throughput experiments and

  • analyses them in the context of reaction-chains in full metabolic networks.

Then, MarVis-Graph scores the reactions in the metabolic network

  • according to the number of associated experimental markers and
  • identifies sub-networks consisting of subsequent, high-scoring reactions.

The resulting sub-networks are

  • ranked according to a scoring method and visualized interactively.

Hereby, sub-networks consisting of reactions from different pathways may be identified to be important

  • whereas the single pathways may not be found to be significantly enriched.

MarVis-Graph may also connect reactions without an assigned pathway

  • to reactions within a particular pathway.

TheMarVis-Graph tool was applied in a case-study investigating the wound response in Arabidopsis thaliana to analyze combined metabolomic and transcriptomic high-throughput data.

Figure 1 Schema of the metabolic network representation in MarVis-Graph. Metabolite markers are shown in gray, metabolites in red, reactions in blue, enzymes in green, genes in yellow, transcript markers in pink, and pathways in turquoise color. The edges are shown in black with labels that comply with the biological meaning. The orange arrows depict the flow of score for the initial scoring (described in section “Initial Scoring”). (not shown)

In MarVis-Graph, metabolite markers obtained from mass-spectrometry experiments additionally contain the experimental mass. The experimental mass has to be
calculated based on the mass-to-charge ratio (m/z-value) and specific isotope- or adduct-corrections (Draper et al., 2009) by means of specialized tools, e.g.,MarVis-Filter
(Kaever et al., 2012).

For each transcript marker the corresponding annotation has to be given. In DNA microarray experiments, each spot (transcript marker) is specific for a gene and can
therefore be used for annotation. For other technologies an annotation has to be provided by external tools.

In MarVis-Graph, each reaction is scored initially based on the associated experimental data (see “Initial scoring”). This initial scoring is refined (see “Refining the scoring”) and afterwards reactions with a score below a user-defined threshold are removed. The network is

  • decomposed into subsequent high-scoring reactions that constitute the sub-networks.

The weight of each experimental marker (see “Experimental markers”) is equally distributed over all metabolites and genes associated with the metabolite marker or
transcript marker, respectively. For all vertices, this is repeated as illustrated in Fig. 1 until the weights are accumulated by the reactions.

The initial reaction scores are used as input scoring for the random walk algorithm. The algorithm is performed as described by Glaab et al. (2012) with a user-defined
restart-probability r (default value 0.8). After convergence of the algorithm, reactions with a score lower than the user-defined threshold t (default value t = 1−r) are removed from the reaction network. During the removal process,

  • the network is decomposed into pairwise disconnected sub-networks containing only high-scoring reactions.

In the following, a resulting sub-network is denoted by a prime: G′ = (V′,L′) with V′ = M′ ∪C′ ∪R′ ∪E′ ∪G′ ∪T′ ∪P′.

The scores of the identified sub-networks can be assessed using a random permutation test, evaluating the marker annotations under the null hypothesis of being connected
randomly. Here, the assignments

  • from metabolite markers to metabolites and from transcript markers to genes are randomized.

For each association between a metabolite marker and a metabolite,

  • this connection is replaced by a connection between a randomly chosen metabolite marker and a randomly chosen metabolite.

The random metabolite marker is chosen from the pool of formerly connected metabolite markers. Each connected transcript marker

  • is associated with a randomly chosen gene.

Choosing from the list of already connected experimental markers ensures that

  • the sum of weights from the original and the permuted network are equal.

This method differs from the commonly utilized XSwap permutation (Hanhij¨arvi, Garriga & Puolam¨aki, 2009) that is based on swapping endpoints of two random edges. The main difference of our permutation method is that it results in a network with different topological structure, i.e., different degree of the metabolite and gene nodes.

Finally, the sub-networks are detected and scored with the same parameters applied for the original network. Based on the scores of the networks identified in the random
permutations, the family-wise-error-rate (FWER) and false-discovery-rate (FDR) are calculated for each originally identified sub-network.

MarVis-Graph was applied in a case study investigating the A. thaliana wound response. Data from a metabolite fingerprinting (Meinicke et al., 2008) and a DNA microarray
experiment (Yan et al., 2007) were imported into a metabolic network specific for A. thaliana created from the AraCyc 10.0 database (Lamesch et al., 2011). The metabolome
and transcriptome have been measured before wounding as control and at specific time points after wounding in wild-type and in the allene oxide synthase (AOS) knock-out
mutant dde-2-2 (Park et al., 2002) of A. thaliana Columbia (see Table 1). The AOS mutant was chosen, because AOS catalyzes the first specific step in the biosynthesis of the hormone jasmonic acid, which is the key regulator in wound response of plants (Wasternack & Hause, 2013).

Both datasets have been preprocessed with theMarVis-Filter tool (Kaever et al., 2012) utilizing the Kruskal–Wallis p-value calculation on the intensity profiles. Based on the ranking of ascending p-values,

  • the first 25% of the metabolite markers and 10% of the transcript markers have been selected for further investigation (Data S2).

The filtered metabolite and transcript markers were imported into the metabolic network. For metabolite markers, metabolites were associated

  • if the metabolite marker’s detected mass differs from the metabolites monoisotopic mass by a maximum of 0.005u.

Transcript markers were linked to the genes whose ID equaled the ID given in the CATMA database (Sclep et al., 2007) for that transcript marker.

Table 2 Vertices in the A. thaliana specific metabolic network after import of experimental markers. Number of objects in the metabolic network
in absolute counts and relative abundances. For experimental markers, the with annotation column gives the number of metabolite markers and
transcript markers that were annotated with a metabolite or gene, respectively. The direct evidence column contains the number of metabolites
and genes, that are associated with a metabolite marker or transcript marker. For enzymes, this is the number of enzymes encoded by a gene with
direct evidence. The number of vertices with an association to a reaction is given in the with reaction column. In the last column, this is given for
associations to metabolic pathways. (not shown)

MarVis-Graph detected a total of 133 sub-networks. The sub-networks were ranked according to size Ss, diameter Sd, and sum-of-weights Ssow
scores (Table S4). Interestingly, the different rankings show a high correlation with all pairwise correlations higher than 0.75 (Pearson correlation
coefficient) and 0.6 (Spearman rank correlation).

Allene-oxide cyclase sub-network
In all rankings, the sub-network allene-oxide cyclase (named after the reaction with the highest score in this sub-network) appeared as top candidate.

This sub-network is constituted of reactions from different pathways related to fatty acids. Figure 2 shows a visualization of the sub-network.
Jasmonic acid biosynthesis. The main part of the sub-network is formed by reactions from the “jasmonic acid biosynthesis” (PlantMetabolic Network, 2013)
resulting in jasmonic acid (jasmonate). The presence of this pathway is very well established because of its central role in mediating the plants wound response
(Reymond & Farmer, 1998; Creelman, Tierney & Mullet, 1992). Additionally, metabolites and transcripts from this pathway were expected to show prominent
expression profiles because AOS, a key enzyme in this pathway, is knocked-out in themutant plant. Jasmonic acid derivatives and hormones.

Jasmonic acid derivatives and hormones. Jasmonate is a precursor for a broad variety of plant hormones (Wasternack & Hause, 2013), e.g., the derivative (-)-
jasmonic acid methyl ester (also Methyl Jasmonic Acid; MeJA) is a volatile, airborne signal mediating wound response between plants (Farmer&Ryan, 1990).
Reactions from the jasmonoyl-amino acid conjugates biosynthesis I (PMN, 2013a) pathway connect jasmonate to different amino acids, including L-valine,
L-leucine, and L-isoleucine. Via these amino acids, this sub-network is connected to the indole-3-acetylamino acid biosynthesis (PMN, 2013b) (IAA biosynthesis).
Again, this pathway produces a well known plant hormone: Auxine (Woodward & Bartel, 2005). Even though, jasmonate and auxin are both plant hormones, their
connection in this subnetwork is of minor relevance because amino acid conjugates are often utilized as active or storage forms of signaling molecules.While
jasmonoyl-amino acid conjugates represent the active signaling form of jasmonates, IAA amino acid conjugates are the storage form of this hormone (Staswick et al.,
2005).

polyhydroxy fatty acids synthesis

polyhydroxy fatty acids synthesis

 

Figure 2 Schema of the allene-oxide cyclase sub-network. Metabolites are shown in red, reactions in blue, and enzymes in green color. Metabolites and reactions without direct experimental evidence are marked by a dashed outline and a brighter color while enzymes without experimental evidence are hidden. The metabolic pathways described in section “Resulting sub-networks” are highlighted with different colors. The orange and green parts indicate the reaction chains required to build jasmonate and its amino acid conjugates. The coloring of pathways was done manually after export from MarVis-Graph.

The ω-3-fatty acid desaturase should catalyze a reaction from linoleate to α-linolenate. Metabolite markers that match the mass of crepenynic acid do also match α-linolenate
because both molecules have the same sum-formula and monoisotopic mass. As mentioned above, MarVis-Graph compiled the metabolic network for this study
from the AraCyc database version 10.0. On June 4th, a curator changed the database to remove theΔ12-fatty acid dehydrogenase prior to the release of AraCyc version 11.0.

The presented new software tool MarVis-Graph supports the investigation and visualization of omics data from different fields of study. The introduced algorithm for
identification of sub-networks is able to identify reaction-chains across different pathways and includes reactions that are not associated with a single pathway. The application of MarVis-Graph in the case study on A. thaliana wound response resulted in a convenient graphical representation of high-throughput data which allows the analysis of the complex dynamics in a metabolic network.

 

Read Full Post »