Human synthetic lethal inference as potential anti-cancer target gene detection
© Conde-Pueyo et al; licensee BioMed Central Ltd. 2009
Received: 24 February 2009
Accepted: 16 December 2009
Published: 16 December 2009
Two genes are called synthetic lethal (SL) if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods.
In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes) in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases) as well as on existent approved drugs (DrugBank database) supports our selection of cancer-therapy candidates.
Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.
High-throughput analyses have provided a tremendous boost to massive drug screening . However, these improved techniques are still blind to biological or structural knowledge. In this sense, chemogenomics provides a complementary strategy for a rational screening that includes structural information of chemical compounds for gene targets [2, 3]. Computational approaches in this so-called virtual screening allow the matching of compounds to their specific gene-product targets, completing the experimental screening . However, the computational approach is still limited by the huge combinatorics represented by the chemical space of possibilities associated to the compounds and their possible targets. As a consequence, all these experimental and computational approaches require the use of the cumulative biological knowledge. For this purpose, database integration into an ontological organization of the current biological knowledge has been suggested as a way to reduce the combinatorics either in virtual or experimental screenings . The work presented here belongs to this last framework, intended as a tool for identifying potential targets for anti-cancer therapy. Cancer is a heterogeneous disease with numerous causes and typologies . One of the essential traits of cancer progression is the underlying high mutational capacity of tumor cells [7–9], having as a consequence the rapid adaptive capacity of the disease. It has been suggested that these ingredients define cancer progression as a Darwinian micro-evolutionary process . As a consequence, cancer cells which have lost essential genes by a mutation are eliminated from the tumor population. Therefore, it is expected that essential genes are conserved in cancer. Under this perspective, targeting essential genes in anti-cancer therapy could kill malignant cells, but might result to be lethal for healthy cells too. This is the case of the anti-proliferative drugs that also damage high turnover tissues, such as epithelium.
The problems reported from the failure of most single-target drug treatments  suggest that a new perspective is needed. In this context, a different conceptual framework related with synthetic lethality has been suggested for anti-cancer research [12–14]. Two genes are called synthetic lethal (SL) if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. According to screening methodology, two main types of mutations are considered: amorphic and hypormorphic mutations. The former causes a complete loss of gene function, while the latter refers to a mutation leading to a decreased activity in the respective gene function . In genome-wide screenings of genetic interactions, hypomorphs are associated to essential genes such that the decrease of the gene expression does not result in inviable organisms .
Another relevant aspect in drug screening is that one drug is tested only for a specific disease and related pathologies. Given a SL pair of genes as described above, one cancer mutated and the other non-mutated, conceptually it is possible that an already approved and even commercialized drug might block the activity of the non-mutated gene product. Therefore, SL-partner screening has a special interest for gene-target identification but also for drug repositioning, i.e, the discovering of novel uses for old drugs .
Unfortunately, large-scale screenings of SL gene pairs have been performed only in yeast [20–23] and, to a significantly smaller degree, in C. elegans [24–26] and in other model organisms. To overcome this limitation, we propose the use of the phylogenetic inference of SL genes from yeast to human for pharmacological purposes.
Synthetic lethal screens in yeast have been used to identify genes involved in cell polarity, secretion, DNA repair and cell cycle [27, 28]. Due to the high conservation of genome integrity and cell-cycle related genes from yeast to higher organisms and their close relation with cancer disease , massive screenings of yeast SL interactions can provide a valuable information for SL inference applied to novel cancer therapy search. We emphasize though that the aim of this work is not to provide a general inference method of SL network from one organism to another. Instead, it adds to the rationale of drug design by supplying a candidate-list of human gene pairs, potentially SL, that could constitute the basis for future pharmacological testing. Additionally, network thinking has provided an excellent framework for the study of very large genetic systems. In particular, gene-disease [30, 31], gene-target  and synthetic-lethal [23, 33, 34] network representations have contributed to the understanding of these systems as a whole. Following this approach, we integrate the information in a network framework from several databases in order to provide supporting evidence for candidates' reliability.
Obtaining the yeast synthetic lethal network
The collected data on yeast genetic screens including synthetic lethality is available at BioGRID database (version 2.0.38) . From this data, we retain only those genes having a systematic name as it appears in http://www.yeastgenome.org. The resultant compilation is derived from 1233 articles, with half of the total of 12707 SL relations being contained in five main articles [16, 27, 28, 37, 38]. As commented above, the data consists of knock-out and hypomorph experiments, with the latter corresponding to essential genes . The list of essential genes has been downloaded from the Saccharomyces Genome Deletion Project http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html.
Obtaining the iHSLN
The n-to-one cases merge the SL information from the multiple yeast nodes, and therefore a single human gene inherits the SL interactions from more than one yeast gene. The extreme case is the third one, n-to-m.
Gene function statistical analysis of the iHSLN
Significance of the biological-function representation in iHSLN was evaluated by means of a hypergeometric test using Benjamini & Hochberg false discovery rate with a P-value < 0.05 as a minimal cut-off. All the genes belonging to iHSLN, independent on whether they belong to multi-gene nodes, were considered in the analysis. Only eight genes were not associated to any GOA term. Statistics were performed for biological-function GOA terms using BINGO 2.3 Cytoscape plugging . A detailed report including the list of genes related to specific GOA terms is available at Additional File 1.
Filtering the iHSLN
Biological filtering through the use of databases led to the functional classification of the nodes forming the iHSLN into: (1) cancer-related genes, (2) genes related to the DNA repair mechanism and cell cycle (two relevant functions altered in most cancers) and (3) drug-target genes.
In order to identify cancer-related candidates we use the COSMIC database(COSMIC: http://www.sanger.ac.uk/genetics/CGP/cosmic: 37 th version) that stores the current knowledge on somatic mutations and related details on human cancers. The information was completed by the use of the genes from the Cancer Gene Census http://www.sanger.ac.uk/genetics/CGP/Census that is a project to catalogue those genes for which mutations have been causally implicated in cancer.
Gene Ontology (GO http://www.geneontology.org, April 6 th , 2008) database provides the biological description of gene products for a number of predefined functions. To relate one gene with its GO annotation, we used the gene ontology annotation (GO numbers) from GOA http://www.ebi.ac.uk/GOA/ 8 th ,2008). We also used the available relationships between GO terms  to create different filters for different biological processes, in particular for Cell cycle and DNA damage.
In addition to these databases, we have also used the information extracted from the DrugBank (http://www.drugbank.ca, release 2.0). It provides the available studies from the US Food and Drug Administration that relate tested drugs and their gene targets [32, 41, 42]. In this way, we inquire whether clinical studies of drugs acting on our proposed candidates have been previously described in literature. All the representations of the resultant networks were performed with Cytoscape . Networks are provided as additional files.
Yeast SL network is a graph of 2383 nodes and 12707 links. This network is an extension of the SL network constructed by , who remarked that SL interactions yield a giant component with a non-random topology of small-world characteristics.
According to Ensembl database, phylogenetic inference revealed that 52% of yeast SL nodes (1253 of 2383 SL yeast genes) has at least one putative human ortholog (see Figure 5).
Statistical analysis of iHSLN biological functions.
GO term - Biological function
% of genes in iHSLN
% of genes in GOA
Response to DNA damage stimulus
Proteasomal protein catabolic process
Golgi vesicle transport
Obtaining potential cancer-related SL targets
Cancer-related database approach
A word of caution is required before interpreting Figure 7. Cancer-related information is a compendium obtained from many samples, cancers and types of mutations. Given a SL pair conserved in human, if amorph (or even hypomorph) mutations occur in both genes within a tumor cell then a lethal condition should be expected according to our rationale. Following this reasoning, such a combination of mutants should not be observed within a cell. If such an event results in Figure 7, we would state that this combination is not conserved in humans according to SL definition. Therefore, we interpret cancer-cancer SL interactions to occur in different samples.
Notice that the cancer-related databases include both those genes that are also overexpressed in tumor cells. Even though the expression level information is not always available, this information is determinant for a correct selection of SL candidates. The most likely candidates presented in this work are chosen according to an underexpression scenario. In relation with this issue, among the many types of mutations encountered in the databases we wish to single out a few examples of mutations types. Some cancer-related genes in Figure 7 correspond to cases of nonsense mutations, in other words point mutations causing a premature stop codon. Therefore, we expect that such mutations critically damage the function of corresponding proteins. These genes are ATM, NF1, FBXW7, MSH2, BUB1, ERCC2, BLM and MSH6. The first four genes are also documented in the Cancer Gene Census to suffer deletions in different types of cancers.
Providing a proof of the potential predictability of our iHSLN is not straightforward due to the sparseness of data and the lack of a systematic identification of SL in humans. However, a number of examples can be extracted from literature, supporting the utility of our approach. One of them is the above cited BLM gene. It is a highly connected gene with 24 SL partners in the iHSLN that codifies a helicase homolog to SGS1 yeast gene. Its deficiency causes the Bloom syndrome, an autosomal recessive disorder with high disposition to tumorigenesis process. In a work performed in Drosophyla, BLM-MUS81 double mutation is an experimental verification of a SL interaction observed in our iHSLN . This fact has a special interest since BLM helicase facilitates MUS81 endonuclease activity in human cells . More recently, a SL interaction between the above cited NF1 gene and RAD54B, an homologous of the yeast RAD54, was experimentally determined in cancer cells . Although yeast screenings identified this double mutant as a SL pair, due to database incompleteness, the phylogenetic relation of RAD54 was lost during inference process. As we discuss later, the quality of data is a considerable pitfall for a good performance of this methodology.
Another indirect evidence of a potential verification comes from the lethal effect of ATM gene deletion in defective Fanconi anemia (FA) pathway cells. FA pathway inactivation is strongly associated to tumorogenesis process. Interestingly, an activator of this pathway is ATR, a SL partner of ATM in the iHSLN .
One of the hubs observed in Figure 7 corresponds with CDC73. It has been observed that mutations in this gene (also known as HRPT2) are associated with malignancy in sporadic parathyroid tumors and hereditary hyperparathyroidism-jaw tumor syndrome . Parafibromin is a tumor suppressor protein encoded by HRPT2 that binds to RNA polymerase II as part of a PAF1 transcriptional regulatory complex. Expression inhibition by HRPT2 RNA interference stimulates cell proliferation and increases the levels of the c-myc proto-oncogene product . According to Figure 7, the CDC73 hub establishes a large number of potential SL candidates. We must stress that, due to the long phylogenetic distance between human and yeast, some of them are likely false positives. Nevertheless, biological arguments in favor of their relevance can be sought in order to strengthen their candidacy. This is the case of the SL link between CDC73 and genes related with the RNA polymerase machinery such as RPAP2 , and the transcription elongation factor TCEA3  that capture the common function in RNA processing of these partners. In addition, CDC73 has as SL partner in Figure 7 the JUP (junction plakoglobin or γ-catenin) protein, a component of the catenin complex. JUP is involved in cell adhesion , an apparently very distant function regarding RNA processing. However, γ-catenin strongly activates the proto-oncogene c-myc . According to this evidence, it seems reasonable to consider that a biological pathway relates JUP and CDC73 (depicted as SL partners in our study) with c-myc, but with opposite effects. In this case, our methodology reveals a potential anti-cancer strategy: inhibiting γ-catenin expression would compensate the activation of c-myc by the HTRP2 cancer mutation.
GOA filtering approach
In a second approach, and in agreement the to statistical significance of the functional analysis (notably, all GOA terms presented in Table 1 produced a very significant P-value), we added a new layer of information by using the cancer-related functions extracted from GOA. In so doing, we searched for candidates that might not be included in cancer-related databases, but their functions are essential for cancer progression. These functions have an special interest due to, it is well established that mutations in genes involved in DNA damage repair or in cell cycle checkpoints are associated with tumor progression . In this sense, synthetic lethal relationships between DNA-replication genes (such as certain DNA polymerases) and DNA-repair genes (such as mismatch-repair genes) are well documented in model organisms [29, 56]. Moreover, it seems likely that the efficiency of many anti-cancer drugs that interfere with DNA synthesis is in some cases due to the presence of tumor-associated mutations affecting DNA repair or the response to DNA damage .
Yeast SL gene-pairs.
Single-strand and double-strand-3'-5' exonuclease activity for DNA repair and recombination.
PAF1 complex. Interacts with RNA pol II. Causes hyperparathyroidism-jaw tu- mor syndrome
Interacts with helicase subunits of NER-transcription factor
Sister chromatid cohesion. Resistance to Campothecin anti-tumor agent
Initiation of DNA replication. Oncogenic activity through repression of INK4/ARF locus
Ubiquitin-conjugating enzymes without catalytic activity
Involved in cell cycle checkpoint enforcement. Colorectal cancer
Central roll in postreplicative DNA repair in eukaryotic cells
Removes 5' overhanging flaps in DNA repair and Okazaki fragments. Fast tumor progress
Component of anaphase promoting complex (APC), progression through mitosis and G1 phase
Core component of multiple cullin-RING-based SCF which mediate the ubiquitination of proteins involved in cell cycle progression, signal transduction and transcription.
Component of the SCF.
form a complex required by DNA polymerase delta and epsilon.
DNA polymerase subunit.
form a complex recruited by DNA polymerase
DNA polymerase subunit
An illustrative case by means of the GOA filtering is again CDC73, also observed in the previous section. In this case, GOA filtering offers evidence of its relation with the DNA damage related gene MMS19 (see Table 2) and helicase component. Interestingly, helicases have been proposed as targets for anti-cancer therapy  as they are closely related with the required genetic instability for tumor progression.
Drug association to SL human target genes
DrugBank database information was used here to establish possible relations between existing drugs and inferred SL human genes. We have found that 130 nodes of our iHSLN contain genes associated to one or several drugs in the DrugBank. More specifically, 17% of them are anti-cancer drugs. See Additional file 3 for pattern of interactions for this set of nodes. As we shall see in the next section, the combination of cancer-related and drug-target information into the iHSLN produces a set of genes that are known drug targets and have cancer-related SL partners, i.e. the suitable candidates according to our methodology illustrated in Figure 1.
Finding SL partners of cancer-related genes associated to drugs
Figure 9 also singles out (large circles and triangles) three SL pairs that are fundamental to existent anti-cancer treatments consisting in targeting SL partners of the cancer-related genes. In the first example, ERCC2 (also known as XPD) is a cancer-related gene . Studies of its homologous gene in Drosophila show that an excess of XPD produces a titration of CAK complex and reduces CDK7 activity, leading to a cell-cycle arrest . Moreover, CDK7 is a protein of the CAK complex required for cell-cycle progression . It has already been suggested as a potential target for anti-cancer therapies. Pharmacological implications are discussed in .
In the second example, PPP6C is a phosphatase involved in cell-cycle progression in human cells through the control of cyclin D1 . Its SL partner, PSMB2, is a proteasome subunit related with protein degradation processes. Bortezomib, a therapeutic proteasome inhibitor is a target of PSMB2 (according to DrugBank information). It is pharmacologically approved for treating relapsed multiple myeloma and mantle cell lymphoma. In addition, the use of Bortezomib in hepathocarcinoma cells reduces the transcriptional levels of cyclin D1, among other effects, leading to cell-cycle arrest. It is postulated that cyclin D1 can act as an oncogene [62, 63]. In this case, we observe that PSMB2 and PPP6C affect the levels of this cell-cycle related gene.
The third example, BUB1* (here the * symbol implies that more than one phylogenetically-related proteins is contained in this node) is a prototype member of a family of genes, some of which encode proteins that bind to the kinetochore and all of which are required for a normal mitotic delay in response to spindle disruption. Mutations in this gene have been associated with aneuploidy and several forms of cancer . Our results reveal that this gene is associated to TUBB* in the SL network. TUBB* represents a number of genes encoding tubulin proteins. Tubulins are targets of the anti-cancer drug Paclitaxel and the vinca alkaloids Vincristine and Vinblastine that also affect the mitotic-spindle assembly process .
A selection of cancer-unrelated SL partners of cancer-related genes and their associated drugs.
Beta-Mercaptoethanol (Glutathione S-Transferase inhibitor); Cysteinesulfonic Acid(Peroxiredoxin inhibitor); Double Oxidized Cysteine (Peptide Deformylase Pdf1 inhibitor)
Aminophosphonic Acid-Guanylate Ester (G25K GTP-Binding Protein inhibitor)
4-(2,4-Dimethyl-Thiazol-5-Yl)-Pyrimidin-2-Ylamine (Cell Division Protein Kinase 2 inhibitor)
D-tartaric acid (D-tartaric acid)
7-Methyl-Gpppa (Eukaryotic Translation Initiation Factor 4E inhibitor); 7n- Methyl-8-Hydroguanosine-5'-Diphosphate (Vp39 inhibitor)
Geran-8-Yl Geran(Rab GDP Disossociation Inhibitor Alpha inhibitor)
4- [(10s,14s,18s)-18-(2-Amino-2-Oxoethyl)-14-(1-Naphthylmethyl)-8,17,20-Trioxo- 7,16,19-Triazaspiro[5.14]Icos-11-En-10-Yl]Benzylphosphonic Acid (Growth Factor Receptor-Bound Protein 2 inhibitor)
9-Butyl-8-(3,4,5-Trimethoxybenzyl)-9h-Purin-6-Amine (Heat Shock Protein Hsp 90-Beta inhibitor)
antihemophilic factor (Coagulation factor VIII precursor)
Dihydrofolic Acid (Dihydrofolate Reductase inhibitor)
9,10-Deepithio-9,10-Didehydroacanthifolicin (Serine/Threonine Protein Phos phatase Pp1-Gam inhibitor)
Vitamin E (Dietary supplement)
Cyclosporine (Investigational Immunomodulatory Agents; Immunosuppressive Agents; Antifungal Agents; Dermatologic Agents; Enzyme Inhibitors; An tirheumatic Agents For treatment of transplant rejection, rheumatoid arthritis, severe psoriasis)
3-Sulfinoalanine (3-Hydroxy-3-Methylglutaryl-Coa Synthase inhibitor)
Palmitic Acid (Enzyme Inhibitors)
Botulinum Toxin Type A (Anti-Wrinkle Agents; Antidystonic Agents; Neuromuscular Blocking Agents)
S-Oxy Cysteine (Prolyl Oligopeptidase inhibitor)
Naftifine (Anti-Inflammatory Agents, Non-Steroidal; Antifungal Agents)
The second example is the SL link between CDC42 and the previously described PPP6C. CDC42 is a gene participating in the rearrangement of actin cytoskeleton, membrane trafficking and cell-cycle progression, and it appears to be involved in cardiovascular diseases, diabetes and neuronal degenerative diseases . This is a target gene of two other compounds (aminophosphonic acid-guanylate ester and guanosine 5'diphosphate) with no relation to cancer treatment in the DrugBank. However, it has been described that this gene is also involved in tumorigenesis and tumor progression, and the aberrant expression of CDC42 has been associated to colorectal tumors [69, 70]. It is worth mentioning that it has been observed that CDC42 controls the cell growth of anaplastic large cell lymphoma through its activation. Pharmacologic inhibition of CDC42 activity by secramine results in a cell-cycle arrest and apoptosis of these cells [71, 72]. We suggest thus that CDC42 inhibition by secramine constitutes a potential anti-cancer treatment, but unfortunately neither CDC42 nor secramine appeared in their respective databases used in this study. This example emphasizes once more that database information is a useful starting point for selecting new candidates. Interestingly, yeast ortholog of CDC42 is an essential gene and thus its mutant in the screening experiments is a hypomorph. Thus it suggests that partial inhibition of CDC42 could be enough to cause a lethal condition in those tumor cells where an amorphic mutation for PPP6C exists. In view of these comments, novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.
By the present study we propose a methodology for providing liable candidates for future experimental validation as drug targets for anti-cancer therapy. The methodology is based on the existence of synthetic-lethal relation between pairs of genes: two genes are synthetically lethal if their simultaneous mutation leads to inviable organism, while their separate mutation has no substantial effect on the organism's fitness. As conceptualized by previous works [17, 18], we used here the extensive experimental data on yeast in order to extend the knowledge to the human genome, and more precisely to anti-cancer therapy.
The rationale behind such approach is that, assuming that there are specific mutations for cancer cells, the identification and artificial mutation (drug action) of their SL partners would result in the death of cancerous cells alone. The mutations would affect also healthy cells, but would not drastically injure them. The combination of different biological databases provides potential filters for reducing the number of false positives. In this work a gene is considered cancer-related if it belongs to COSMIC or Cancer Gene Census databases. This implies certain limitations to our study since it depends on the accuracy and completeness of these databases. In this context, the better annotation of the used database, the more reliable the results. One example of that is the case of CDC42-PPP6C SL pair and the use of secramine drug resulting from a literature search but not from the current DrugBank version.
We have commented that cancer-cancer SL interactions could be interpreted as false positives as we do not expect them to occur in the same cell. We consider that the proof of this statement is an interesting working hypothesis to be tested in future research. Such a future research would aim at providing a quantitative estimation about the likelihood of observing a double mutation in tumor cells as it results from the data analysis. A supporting evidence of this hypothesis would be that SL cancer-related gene pairs are less likely to be observed in the double-mutation dataset. In spite of being related to the current work, we consider such a study to be outside the aim of the current work directed at introducing the potential of this methodology.
In addition, false positives can result also from the long evolutionary distance and different architectures between yeast and human genomes. At this point, the evolutionary conservation of SL pairs is controversial [29, 73–75], even though a recent study inclines the balance towards a significant conservation of synthetic lethal interactions between eukaryotes . In addition, the existence of conserved SL associated to particular functions [29, 74, 76] is promising evidence for the inference methodology presented here. Even though not all yeast SL pairs are expected to be conserved in distant organisms, those associated to essential functions have a higher conservation probability. The identification of only a few of such partners could constitute alone an invaluable information in the strategy of drug design.
As Lawrence Loeb stated with his mutator-phenotype scenario for cancer evolution [7, 9, 77], some genetic instability but not too much is required for cancer progression. An illustrative example, the BUB1-TUBB* SL pair, is closely related with the strategy of forcing instability in order to kill cancer cells. It is reasonable to assume that an attack to tubulins by drugs in tumors where BUB1 appears mutated may drift the tumor population towards extinction by exceeding the limits of mutation tolerance. In this particular case, we speculate that treatments with vinca alkaloids should be more efficient in those cancers where BUB1 is mutated. Analogously, our results reveal the suitability of an attack to MMS19 helicase component when CDC73 is mutated. As argued by , compensatory helicase-dependent DNA repair pathways may represent a suitable target for anti-cancer therapy strategies that are designed to introduce DNA damage to tumors with pre-existing defined DNA repair deficiencies. In this context, we provide among our candidates the FEN1-BLM pair and the already confirmed MUS81-BLM , both suggested by  as potential targets in cancer therapy. However, other anti-cancer strategies such as the attack to protein degradation function by blocking PSMB2 proteasome component has been also uncovered by the presented methodology. In this case, our result suggests that this therapy should be more efficient in tumors where PPP6C is mutated.
We have proposed by the present study a tool for phylogenetic inference of candidates for future experimental validation as drug targets in anti-cancer therapy. Once more, we stress that we do not argue in favor of a methodology of SL-genes inference across distant species, as it has been already discussed in the literature to be a controversial step [73, 75]. Rather our study has a pharmacological utility and constitutes an alternative for massive drug screenings. In addition, the arguments brought forward in favor of the proposed candidates above justify their consideration for future experimental validation.
Furthermore, we provide an additional file on the results discussed above in order to foster the bioinformatic and pharmacological communities towards further analysis of this methodology.
inferred Human Synthetic Lethal Network
Yeast Synthetic Lethal Network
HUGO gene nomenclature committee.
This work was supported by the EU 6th Framework projects SYNLET (NEST-043312, AM and NCP) and ComplexDis (NEST-043241, CRC), NIH (CA 113004, RVS) and Santa Fe Institute (RVS). We are grateful to Bernd Mayer for inspiring this work, and to the SYNLET consortium for useful discussions. We also thank Nuria Lopez Bigas, Jordi Mestres Lopez, Miguel Angel Medina Torres and Josep Conde Colom for providing expertise and advice on this subject.
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