Although Iph1 cleaved hallmark IDE substrates including insulin efficiently, its role in the ER stress response was independent of its catalytic activity since expression of inactive Iph1 restored normal sensitivity. Importantly, wild type as well as inactive human IDE complemented gene-invalidated yeast cells when expressed at the genomic locus under the control of +iph1 promoter. These results suggest that IDE has a previously unknown function unrelated to substrate cleavage, which links sensitivity to ER stress to a pro-survival role of the TORC1 pathway.Insulin Degrading Enzyme (IDE) is a protease conserved through evolution with a role in diabetes and Alzheimer's disease. The reason underlying its ubiquitous expression including cells lacking identified IDE substrates remains unknown. Here we show that the fission yeast IDE homologue (Iph1) modulates cellular sensitivity to endoplasmic reticulum (ER) stress in a manner dependent on TORC1 (Target of Rapamycin Complex 1). Reduced sensitivity to tunicamycin was associated with a smaller number of cells undergoing apoptosis. Wild type levels of tunicamycin sensitivity were restored in  Human Insulin Degrading Enzyme (hIDE) or insulinase belongs to the M16A family of peptidases, which comprises large zinc-dependent metalloproteases found in all prokaryotic and eukaryotic organisms examined The budding yeast orthologue of hIDE, Ste23p, displays similar substrate specificity as mammalian IDE and, together with Axl1, the second yeast M16A metalloprotease, cleaves the precursor of the mating pheromone a-factor Cellular proteins are subjected to continuous damage and maintenance of protein homeostasis is central to all biological processes. A cellular compartment particularly susceptible to protein damage is the endoplasmatic reticulum (ER). Accumulation of misfolded proteins in the ER induces the Unfolded Protein Response (UPR) that increases the level of chaperones, stimulates retro-translocation of misfolded proteins to the cytosolic proteolytic system and attenuates general translation and transcription. If this response cannot resolve the ER stress, apoptotic pathways are engaged Schizosaccharomyces pombe has two: the non-essential +tor1 gene encodes the kinase forming the TORC2 complex, while the essential +tor2 gene encodes the kinase present in the TORC1 complex tor2-S1837E mutant, which is predicted to prevent interaction with the FKBP12-rapamycin complex in vitro, similarly to other organisms The generation and resolution of cellular stress is intimately linked to the evolutionary conserved target of rapamycin (TOR) kinase, which regulates cell growth according to nutrient and energy availability Unbridled activation of mammalian TOR exacerbates cellular stress and is linked to diabetes, cancer and a shorter life span S. pombe homologue of mammalian IDE as model.Several facts concerning IDE – its preference for amyloidogenic substrates able to cause cellular stress, its high expression in beta cells subjected to permanent ER stress, its dead-end chaperone function, indirect evidence for unidentified functions that might underlie its ubiquitous expression – prompted us to speculate that IDE might be implicated in the response to proteotoxic stress. To address this hypothesis, we took advantage of its evolutionary conservation and used the Strains are listed in iph1 with KanR marker: (5′TGGCCTCTAAACAGTAATGCCTACGTACTGTGTGTATGTAAACACATAATTCAACCTATTGCCATATTTCTTACATATTACGGATCCCCGGGTTAATTAA-3′) and (5′CTAGCAGAAGAGTAGGTCTCGTCACACTTGTTTGGATAGCGAGAAAAACCGCAGTGCCAGAATGCAAAACTGAAATTAAGGAATTCGAGCTCGTTTAAAC3′).Gene disruption and replacement were performed by PCR-based gene targeting iph1 with ura4+ marker:Primers used to to disrupt (5′TATTACCCTTTTTTTGGGTGTAATAGCAGTAGTCAGAATTCTGGGTTGTTTTATCTTTTCCTTTCATAAATAAAAACGCCAGGGTTTTCCCAGTCACGAC-3′) and (5′GTGCCAGAATGCAAAACTGAAATTAAGATGAGAATATAAAATCAGTAAATTTGAGAATCGGATTAGGGAAAAAAAAAGCGGATAACAATTTCACACAGGA).tor1 with ura4+ marker in strain iph1-d ura4-D18: (5′TGGAAGAATTGAACACCGCGACTATTAGAAAGTCTATCGTTTCACTCGCTCTCTTTGATTCATGGAGTATTTTAGTCGCCAGGGTTTTCCCAGTCACGAC 3′) and (5′TAAATTAATAACAACACGAAAAAAATTATCATAATCTCAAAAAACAGAAAACATCATTACCAAAAACTACACCATCAGCGGATAACAATTTCACACAGGA3′).Primers used to disrupt iph1 coding sequence between position 188 and position 280 by the ura4+ gene to obtain strain iph1-M:Primers used to replace the (5′GAGAGATCCGGAAACAGATAATGCAAGTGCAGCTATTGACGTTCACATCGGCAGTCAAAGCAATCCACGAGAGTTGCGCCAGGGTTTTCCCAGTCACGAC 3′) and (5′ATAGAGCATCATGAGACACTTCGAAGTAATAATTTGTATTATTAGAGGCTGTATAGGCGTTTGAAATTCCATTATGAGCGGATAACAATTTCACACAGGA 3′).iph1-E71D was obtained by replacing the ura4+ gene in strain iph1-M with a DNA fragment of iph1 gene mutated at codon 71 (GAA to GAT). This fragment was synthesized, cloned and sequenced by GeneArt .Mutant The cDNA coding the hIDE protein was cloned into pCRBlunt plasmid. Site direct mutagenesis was used to change nt A390 to C to obtain the hIDE-E111D mutant as described in (5′ATTCAACCTATTGCCATATTTCTTACATATTACCCTTTTTTTGGGTGTAATAGCAGTAGTCAGAATTCTGGGTTGTTTTATCTTTTCCTTTCATAAATAAAAAATGCGGTACCGGCTAGCG 3′) and (5′TGTTTGGATAGCGAGAAAAACCGCAGTGCCAGAATGCAAAACTGAAATTAAGATGAGAATATAAAATCAGTAAATTTGAGAATCGGATTAGGGAAAAAAAACTAGAGTTTTGCAGCCATGAAGTTAATATG-3′).iph1-d ura+ strain and transformants were selected on standard 5 fluoroorotic acid (5-FOA) medium. Stable transformants expressing hIDE and hIDE-E111D from the iph1 promoter were selected.PCR- fragments were used to transform the Alignment was performed with Clustalw software. The tridimensional structure of Iph1 was performed using the Modeller software A full length cDNA of 2911 bp encoding Iph1 was amplified using a high fidelity enzyme  and primers encoding a C-terminal extension by six histidine residues, inserted into pCRBlunt , sequenced completely to confirm the absence of errors, and transferred as XbaI/PstI fragment into the baculovirus transfer vector pVL1393 (Invitrogen). To produce the E71D mutant, site-directed mutagenesis was performed directly on the pVL1393-Iph1 plasmid. The primers used for the mutagenesis PCR were (5′GGATTGGCGCACTTTTGTGATCATCTGTTGTTTATGGGGAC3′) and (5′GTCCCCATAAACAACAGATGATCACAAAAGTGCGCCAATCC3′). After the PCR, the reaction mixture was digested with Dpn I and this material was used for transformation. Successful mutagenesis was confirmed by sequencing. Recombinant baculoviruses encoding wt Iph1 and Iph1-E71D were produced by co-transfection of Sf9 insect cells with the resulting plasmids and BaculoGold™ virus DNA, followed by a plaque assay and plaque selection by PCR. Recombinant Iph1 and Iph1-E71D were produced in Hi5 insect cells after infection with the recombinant baculoviruses. Cells were harvested 72 h post-infection and lysed on ice for 30 min in 25 mM Tris, 50 mM phosphate, 300 mM NaCl, 10 mM imidazole, 1% Triton X-100, pH 8.0 in the presence of protease inhibitors. The lysis supernatant was harvested by centrifugation for 10 min at 14000 rpm. Then it was transferred on Ni-NTA beads (Invitrogen) equilibrated in 50 mM phosphate, 300 mM NaCl, 10 mM imidazole, pH 8.0 and left to bind on a turning wheel at 4°C overnight. The resin was washed with the same buffer containing 10 mM imidazole, then with 25 mM imidazole and the protein was eluted with buffer containing 300 mM imidazole. The recombinant protein was used directly for enzymatic assays. Insect cell-expressed recombinant hIDE carrying an N-terminal extension by 7 His residues was purchased from R&D Systems .Enzymatic activity towards the fluorogenic substrate Mca-RPPGFSAFK(Dnp)  was measured by following the time-dependent increase in the fluorescence signal at 405 nm after excitation at 340 nm on a Mithras LB 940 plate reader . 10 µM of substrate were incubated with 10 ng of hIDE or 20 ng of Iph1 in 50 mM Tris, 150 mM NaCl pH 7.4 at 25°C and fluorescence was recorded for 10 min. The resulting time slope was used to calculate the digestion rate of the fluorogenic substrate.10 µg of insulin were incubated with 2–500 ng hIDE or 8–800 ng Iph1 in 50 mM Tris, 150 mM NaCl pH 7.4 for 3 h 20 min or 16 h in 300 µl final volume. Reactions were stopped by the addition of 30 µl 10% formic acid. Analysis of insulin digestions was performed by reversed phase HPLC on a μRPC C2/C18 ST 4.6/100 column . Digestion products were eluted using a 20–50% acetonitrile gradient, while monitoring the absorbance at 215 nm. For analysis by SDS-PAGE on 15% Tris-Tricine gels, 185 ng insulin were digested for 16 h with Iph1 WT, hIDE or Iph1 E71D . Bands were visualized using SYPRO Orange Protein Gel Stain .2O and spotted onto the indicated medium. TU, dithiotreitol  and rapamycin  treatments were performed on mid-log cultures grown in YNB. For each time point or drug concentration two to three dilutions were plated on YNB in triplicates and plates incubated at the appropriated temperature for 3–5 days. Colonies formed were counted and percent of survival calculated against time 0. All experiments shown were performed at least three times. TU was suspended in dimethyl sulfoxide (DMSO) at 10 mg/mL and used at 10 µg/mL otherwise stated. RA was suspended in DMSO at 0.5 mg/mL, and used at the final concentration of 300 ng/mL. DTT was suspended in H2O at 1 M and used at the final concentration of 50 mM.In drop tests, cells from exponentially growing cultures in either EMM or YNB were treated or not with tunicamycin  for 45 min, serially diluted in HProtein extracts were prepared as described in Cells were treated with 10 µg/ml of TU for 45 min, collected, suspended in fresh medium without TU and incubated at 30°C with agitation. At 0, 4 and 6 hours from release, cells were processed as described in S. pombe encodes five putative metallopeptidases belonging to the M16 peptidase family that are potential orthologues of budding yeast Ste23p. Among these, the SPACUNK4.12c ORF on chromosome 1 encodes a protein sharing the highest degree of identity (37%) with hIDE .In order to compare the protease activity of Iph1 with hIDE we expressed recombinant wt Iph1 and a mutant Iph1 protein with a substitution of residue Glu71 in the catalytic site by Asp, both tagged by six C-terminal His residues, using the baculovirus system. The equivalent mutation in hIDE (E111D) decreases enzyme activity to<1% without affecting substrate binding Next, we sought to determine whether Iph1 is able to cleave insulin, the hallmark natural substrate of hIDE cleaved with high efficiency by it. We performed insulin digestions with various amounts of enzyme and analyzed the results by column and gel chromatography. After 16 h of digestion with either 500 ng hIDE or 800 ng Iph1, the insulin peak was no longer detectable in reversed phase chromatography and various new product peaks appeared on the chromatogram . Analysiiph1 ORF was replaced by the selectable marker KanR. Cells disrupted for iph1 (iph1-d) did not show any obvious phenotype and, in contrast to budding yeast ste23 null mutant, cells were not sterile.To study Iph1 function we constructed a haploid strain where the iph1-d) to wild-type (wt) cells. TU is an ER stressor that blocks protein N-glycosylation in the ER, leading to accumulation of misfolded proteins.IDE is highly expressed in murine pancreatic beta cells (our unpublished observation), in which the UPR is constitutively activated to handle glucose-triggered bursts of insulin synthesis that challenge the ER protein folding capacity iph1-d cells were more resistant to induced ER stress than wt  was constructed by replacing in the haploid strain the +iph1 sequence with iph1-E71D. As shown in iph1-d cells is due to lack of the protein but not to lack of its protease activity. Thus, Iph1 has a function in the ER stress response that is unrelated to substrate degradation.Given that proteins belonging to the insulinase family have protease activity, we asked if increased survival to ER stress of tor1 (tor1-d) were slightly more sensitive than wt cells to TU in all experiments, however differences in survival between wt and tor1-d were at the limit of statistical significance (p = 0.05) , indicating that the observed phenotype might be specific to ER stress.Fission yeast Tor1 kinase is required for the response to a wide range of stresses including heat stress and DNA damaging conditions  = 0.05) . In contng agent . The hig-d cells , indicat-d cells . Thus, rwt and tor1-d cells by iph1 deletion might depend on the Tor2 kinase that participates in the formation of the RA sensitive TORC1 complex as shown in wt cells and of tor1-d cells .Next to activating the UPR, ER stress is an inducer of autophagy that plays a cytoprotective role by preventing accumulation of misfolded proteins in apoptosis proficient cells We have shown that hIDE shares the protease independent function of Iph1 at least in yeast cells. Our results suggest that knock-out of mammalian IDE might also lead to ER stress resistance at least under certain metabolic conditions. This might be particularly relevant for pancreatic beta cells, in which excessive ER stress is linked to functional failure and diabetes development. The known capacity of IDE to degrade insulin is commonly viewed as mechanistic underpinning of its genetic link to diabetes. Our results suggest considering an additional or alternative possibility, namely that an implication of IDE in the response to proteotoxic stress might affect the survival of pancreatic beta cells in this pathology.


This paper aims to present a new genetic approach that uses rank distance for solving two known NP-hard problems, and to compare rank distance with other distance measures for strings. The two NP-hard problems we are trying to solve are closest string and closest substring. For each problem we build a genetic algorithm and we describe the genetic operations involved. Both genetic algorithms use a fitness function based on rank distance. We compare our algorithms with other genetic algorithms that use different distance measures, such as Hamming distance or Levenshtein distance, on real DNA sequences. Our experiments show that the genetic algorithms based on rank distance have the best results. In many important problems in computational biology a common task is to compare a new DNA sequence with sequences that are already well studied and annotated. Sequences that are similar would probably have the same function, or, if two sequences from different organisms are similar, there may be a common ancestor sequence In computational biology the problem that deals with this task is known as the closest string problem (CSP): given a set The standard method used in computational biology for sequence comparison is by sequence alignment. Sequence alignment is the procedure of comparing two sequences  or more sequences  by searching for a series of individual characters or characters patterns that are in the same order in the sequences. Algorithmically, the standard pairwise alignment method is based on dynamic programming; the method compares every pair of characters of the two sequences and generates an alignment and a score, which is dependent on the scoring scheme used, i.e. a scoring matrix for the different base-pair combinations, match and mismatch scores, or a scheme for insertion or deletion (gap) penalties.Although dynamic programming for sequence alignment is mathematically optimal, it is far too slow for comparing a large number of bases, and too slow to be performed in a reasonable time.Also, since some of the search solutions are inaccurate from a biological point of view, alternative approaches periodically are explored in computational biology. This important problem, known also as DNA sequence comparison, is ranked in the top of two lists with major open problems in bioinformatics The standard distances with respect to the alignment principle are edit (Levenshtein) distance rank distance (RD)To measure the similarity between strings Dinu proposes a new distance measure, termed To measure the distance between two strings with RD we scan (from left to right) both strings and for each letter from the first string we count the number of elements between its position in the first string and the position of its first occurrence in the second string. Finally, we sum up all these scores and obtain the rank distance. In other words, the rank distance measures the “gap” between the positions of a letter in the two given strings, and then sums up these values. Intuitively, the rank distance gives us the total non-alignment score between two sequences.ad hoc extension to arbitrary strings, without affecting the low computational complexity. In contrast, the extensions of Hamming distance are mathematically optimal but computationally too heavy, and lead to the edit-distance, which is the base of the standard alignment principle. Thus, the rank distance sides with Hamming distance rather than Levenshtein distance as far as computational complexity is concerned: a significant indicator is the fact that in the Hamming and rank distance case the median string problem is tractable Clearly, the rank distance gives a score zero only to letters which are in the same position in both strings, as Hamming distance does . On the other hand, an important aspect is the reduced sensitivity of the rank distance with respect to deletions and insertions. Reduced sensitivity is of paramount importance, since it allows the RD is easy to implement, does not use the standard alignment principle, and has an extremely good computational behavior. Another advantage of RD is that it imposes minimal hardware demands: it runs in optimal conditions on modest computers, reducing the costs and increasing the number of possible users. For example, the time needed to compare a DNA string of Traditionally, the Closest String Problem (CSP) is related to Hamming distance and it tries to find a minimal integer In When CSP emerged in bioinformatics, the problem was investigated from many points of view. These investigations implied the use of different distances. The most intensive studied approach was the one based on edit distance. In In many practical situations the alphabet is of fixed constant size . For some applications, one needs to encode the DNA or protein sequences on a binary alphabet that expresses only a binary property of the molecule, e.g. hydrophoby  via rank distance are NP-hard. In this paper we use an approach based on genetic algorithms to propose an approximation of CSP and CSSP via rank distance.In In this section we introduce notation and mathematical preliminaries. We first introduce the rank distance and then we define closest string and closest substring problems.A ranking is an ordered list and is the result of applying an ordering criterion to a set of objects. Formally,Definition 1. Let A ranking defines a partial function on full rankings, while the others are partial rankings. We define the order of an object The rankings that contain all the objects of an universe Definition 2. Given two partial rankings In Definition 3. If The rank distance is naturally extended to strings. The following observation is immediate: if a string does not contain identical symbols, it can be transformed directly into a ranking (the rank of each symbol is its position in the string). Conversely, each ranking can be viewed as a string, over an alphabet equal to the universe of the objects in the ranking. The next definition formalizes the transformation of strings that have identical symbols into rankings.Definition 4. Let Example 1. If Observe that given Definition 5. Given Example 2. Consider the following two strings The computation of the RD between two rankings can be done in linear time in the cardinality of the universe. Our universe has precisely Let Problem 1 (Closest string via rank distance). Let The CSSP is a generalization of CSP where the objective is to find a string similar to substrings of the input.Problem 2 (Closest substring via rank distance). Let Homo sapiens, V00662), common chimpanzee , gorilla , donkey , rat , mouse , fat dormouse , and cow . Mitochondrial DNA (mtDNA) is the DNA located in organelles called mitochondria. The DNA sequence of mtDNA has been determined from a large number of organisms and individuals, and the comparison of those DNA sequences represents a mainstay of phylogenetics, in that it allows biologists to elucidate the evolutionary relationships among species. In mammals, each double-stranded circular mtDNA molecule consists of We test the genetic algorithm using mitochondrial DNA sequences extracted from several mammals available in the EMBL database: human  we design two similar experiments. We have another artificial experiment for CSSP, and another experiment for CSP with great interest for biologist.For the first experiment we use the human, chimpanzee and donkey genomes. We want to find the closest string (or substring) of nucleotides between the human and chimpanzee DNAs on one hand, and between the human and donkey DNAs on the other hand. The goal of this experiment is to compare the distances obtained for the two strings (or substrings). Note that the donkey belongs to the Perissodactylae branch, while the human and the chimpanzee belong to the Primates branch. Since the human and the chimpanzee are both primates, the human-chimpanzee distance should be smaller than the human-donkey distance. In other words, we expect the biological classification of mammals to be reflected in the DNA.For the second experiment we use the rat, house mouse, fat dormouse and cow genomes. As in the former case, we want to find the closest string (or substring) of nucleotides between the rat and house mouse DNAs, between the rat and fat dormouse DNAs, and between the rat and cow DNAs. The goal of this experiment is to compare the distances obtained for the three strings (or substrings). Note that the cow belongs to the Cetartiodactylae branch, while the rat, the house mouse, and the fat dormouse belong to the Rodentia branch. We expect the rat-house mouse distance and the rat-fat dormouse distance to be smaller than the rat-cow distance. We have chosen this experiment because in We also use an artificial test case for the closest substring problem to point out our optimization of the genetic algorithm presented in Note that another study that shows experiments using Hamming distance for CSP and CSSP is Each of our experiments are performed using three different metrics: rank distance, Hamming distance and Levenshtein distance. We want to compare the results for each distance measure. We show graphs of the best candidate evolution for each metric used.After we determine the metric that has the best results, we will perform another experiment (using only this metric) with great interest for biologists. At present, no definitive agreement on either the correct branching order or differential rates of evolution among the higher primates exists, despite the research in this area. Joining human with chimpanzee and the gorilla with the orangutan is currently favoured, but the alternatives that group humans with either gorillas or the orangutan rather than with chimpanzees also have support With two experiments and three distance measures for the closest string problem, we have six test cases with associated graphs. For the closest substring there is an extra artificial experiment, generating nine test cases and six graphs associated to the real DNA experiments. In our latest experiment we use the distance measure that has the best performance on the former test cases. We investigate only the closest strings for DNA sequences of variable lengths and we present three more graphs.For each experiment we give the input strings, then we present the results obtained by using rank distance, Hamming distance and Levenshtein distance, respectively. An input string is a DNA sequence. The algorithm designed for CSRD needs at least two DNAs (of same length) to produce an output DNA sequence. The output DNA is the closest string to the input strings computed with rank distance. Using Hamming or Levenshtein distance in the selection process of the genetic algorithm is analogous. The algorithm designed for CSSRD need two DNAs (not necessary of same length) to produce the output DNA that represents the closest substring.Let us describe the genetic algorithm parameters and the format of the input and output data. The population size represents the number of chromosomes in a single generation. The crossover probability represents the percent of chromosomes (from a single generation) that get involved in the crossover operation. The mutation probability is similar to the crossover probability only that the chromosomes are mutated. The number of strings (DNA sequences) gives the number of input strings. The size of each DNA sequence is the number of nucleotides in every DNA sequence. We use different input parameters for each problem that we are trying to solve. The input parameters used for one experiment are the same for every investigated metric. We want to compare only the metrics used, without changing the genetic algorithm parameters or the genetic operations involved.The average time represents the mean time for There are two different settings for this experiment corresponding to CSP and CSSP, respectively. We present the test cases and results separately for each setting.In this setting we use the first 1. RANK DISTANCE TEST CASE 1: Population size: 2500; number of generations: 300; crossover probability: 0.36; mutation probability: 0.002; size of each DNA sequence: 200.HUMAN-CHIMPANZEE RESULT: Average time: 22 seconds; Distance achieved: 3698; Closest string: G T A C T A C G C G T T T A C T C T A C C A A A C G C A T A C T G A C A A A T G T C T G T T A G A T G G A T C C A T C T C C G C G T G T A C T G T C T A A A A G C G T A G C G T C A C G T A C G T C A A G C A G T G T T T C A G T C C C A C A A T C C A T T G C A C A T T A C T G A G C T C T C C A T T C G T C T C A C T C T T T T T A C G A A C A A T A T T A T C A A T G C A A A C G T G G G C C C T C T T C.HUMAN-DONKEY RESULT: Average time: 22 seconds; Distance achieved: 5001; Closest string: T G A A G A G C A T T C C A T A T C T A A C T C C T G A A G T A C A C G A A C G G A T A T G C A C T T T G C T T C G T T A C A C T A G C G T G G A C G T A C A T T C T C G G C T G A C C T T G G G C A T A T A A T A T T A A A G T A A C G G A G T C T A C A T C T A A T A T C A T C G T A A C C C A T A G A A T G T T A T A C C C T C A T C G T C C T T C G C C C A A G T G C C C T G C T T A A C T T C T C A T.2. HAMMING DISTANCE TEST CASE 1: Population size: 2500; number of generations: 300; crossover probability: 0.36; mutation probability: 0.002; size of each DNA sequence: 200.HUMAN-CHIMPANZEE RESULT: Average time: 1 min 14 seconds; Distance achieved: 73; Closest string: G A T C A T G T G G C T A T C A C C C T C A A A G C C A C T C A C G G G A A C T G T T C A G A C A T T T T T A C A T T A C C C C A T G A A G A T A T G C G C G T G G T A C T A T T C T G T C A A G C A G C A G T C A G A A A A C T C A C T C T T G C A A T A A C T G T C T T T G C T T G C T T C A T C A T C T T A A T C T C T A T C A T C A C T A G G T A C A A T A A T A C A G G C C G A C G C A C T G C A G C.HUMAN-DONKEY RESULT: Average time: 1 min 13 seconds; Distance achieved: 77; Closest string: G A T C A C A G A G C T A A A A G A C A A C A A A C C A C G C A C C T G A A A A T G C C A A G A T T T T G G T T C C T A C G C C T T G G G C A T A T A C A C T C G A T C C C G T T C T G A T A C T C T G T A A C C G G T G C A A C C A C T C A T G C A A G A T T C G T C A T T C C T G C C T G A A T G A T C T C T T T A T T G A A C T C T C C G A T C T T A A G G A G C A G G T G C G A A C A A A A T T A C T A.3. LEVENSHTEIN DISTANCE TEST CASE 1: Population size: 2500; number of generations: 300; crossover probability: 0.36; mutation probability: 0.002; size of each DNA sequence: 200.HUMAN-CHIMPANZEE RESULT: Average time: 24 min 12 seconds; Distance achieved: 63; Closest string: G T A T A C A C A G C T C T A C C C C C T A A A G C A A T A C C A C G G A A G A T C T T C C A T G G A T T T A T A T C A T C C T C T A A G C A A C A T G C A T G G T A G C C T T G C G A T T C G A T T G A G C T C G T G A G A C C C T A T A T C G C A T A C T G A T C C C C G A T C C T G G T C A T C C T A T T A A T C A T C C A T G T A A A G T T A C A A G T A T T A C A G C G C G C A G C A A T T A C A A C.HUMAN-DONKEY RESULT: Average time: 24 min 11 seconds; Distance achieved: 59; Closest string: G T T C A A T G T A C T A T C A C G A T A T A A A T C A A G G A G C T G T C A A T G C A C T T G G T A G T T T C C T C T G C G C T A T G C A C A C A T A G G G C A T T G C G A C C T G G A G C C T T A T T A T T A C T A T G A A G C A G A T T A A C A T G C A T T G A T T C C T G C C T C C C C A T A T A A T C C T C T A A A T C G C A C T C T A G A T C A A A T T A C A G G C G A A C A A G A C T C T A C T A.In this setting we use the first 300 nucleotides extracted from the human, chimpanzee and donkey genomes. We want to determine the human-chimpanzee and human-donkey closest substrings of 24 nucleotides.Here the substring size input parameter represents the desired length of the best substring .1. RANK DISTANCE TEST CASE 2: Population size: 500; number of generations: 100; crossover probability: 0.36; mutation probability: 0.02; size of each DNA sequence: 300; substring size: 24.HUMAN-CHIMPANZEE RESULT: Average time: 39 seconds; Distance achieved: 26; Closest substring: C T T A G T A A C T A T A T C G A G A C A A G C.HUMAN-DONKEY RESULT: Average time: 40 seconds; Distance achieved: 34; Closest substring: A C A T G C C T A T C T A C C C G T A A T A C C.2. HAMMING DISTANCE TEST CASE 2: Population size: 500; number of generations: 100; crossover probability: 0.36; mutation probability: 0.02; size of each DNA sequence: 300; substring size: 24.HUMAN-CHIMPANZEE RESULT: Average time: 1 min 36 seconds; Distance achieved: 7; Closest substring: C T A C A C A C G C A A G C C T T C C C T G C A.HUMAN-DONKEY RESULT: Average time: 1 min 38 second; Distance achieved: 7; Closest substring: A C G T A C G A A C C A T A C T A C A A G C T A.3. LEVENSHTEIN DISTANCE TEST CASE 2: Population size: 500; number of generations: 100; crossover probability: 0.36; mutation probability: 0.02; size of each DNA sequence: 300; substring size: 24.HUMAN-CHIMPANZEE RESULT: Average time: 7 min 4 seconds; Distance achieved: 4; Closest substring: T T G A T T C C T G C C T A T C T A T T A G C T.HUMAN-DONKEY RESULT: Average time: 7 min 3 seconds; Distance achieved: 4; Closest substring: A T G C T A C T C T T A A T C G C A C C T A C G.First, we must point out that rank distance, Hamming distance and Levenshtein distance use different scales, i.e. a rank distance of 100 is not equivalent to a Hamming distance of 100, nor a Hamming distance of 100 to a Levenshtein distance of 100. We would also like to point out that rank distance has a finer scale, possibly being able to detect subtle differences between DNA strings.As one might expect, the results indicate that the human genome is closer to the chimpanzee genome, than it is to the donkey genome.In the CSP setting, rank distance shows a great difference between the human-chimpanzee closest string and the human-donkey closest string. Levenshtein distance indicates that humans are closer related to donkeys than to chimpanzees, while Hamming gives the expected result as rank distance does. Both Hamming and Levenshtein distances show small differences between the two analysed strings. The evolution of the best closest string candidate for each distance measure is given in In the CSSP setting, RD is the only distance that can catch the subtle difference between the human-chimpanzee closest substring and the human-donkey closest substring, even if we use only In both CSP and CSSP settings, rank distance clearly outperforms Hamming and Levenshtein distances see  and 2.As for the Human-Chimpanzee-Donkey experiment, there are two different settings corresponding to CSP and CSSP. We present the test cases and results separately for each setting.In this setting we use the first 150 nucleotides extracted from each of the rat, house mouse, fat dormouse and cow DNA sequences. We want to determine the rat-house mouse, rat-fat dormouse and rat-cow closest strings which also have 150 nucleotides.1. RANK DISTANCE TEST CASE 3: Population size: 1800; number of generations: 300; crossover probability: 0.36; mutation probability: 0.005; size of each DNA sequence: 150.RAT-HOUSE MOUSE RESULT: Average time: 12 seconds; Distance achieved: 454; Closest string: G T T G A A T C G T T A A T A T A C A A A G C A A G T A C A T G A A T C A G A A G T G A T A T T C T A A A A G C T T A G C A A C C A T C A A A T A T G T G G C C G T G T T C T A C A T T T A A G T G A A G A T G T A A A T C A A A C C T A A G C A T C A T G A C A T G C G A A T C A A G C A T A C C T A T T.RAT-FAT DORMOUSE RESULT: Average time: 12 seconds; Distance achieved: 1209; Closest string: G T A T A C T G T A G T A T A A A A A A T C T G A G A C C A T G A T A A T G T A C A G T A G G A T A C A T A C C T A A C C G C A A C A A T T G A T G C C G T G T A C G C T T A A T T T C A A T G T C T C T A G C A G G A A G A A A A T T T G C A A A C T T C C A A C G A A A G T C G C T A A A T G T C C A T.RAT-COW RESULT: Average time: 12 seconds; Distance achieved: 3321; Closest string: G T A T A A C A T G T C A C T G A A C C G A A T A C T A G T A A T G A A A A T T C G G C T C T T A T G C A A G A C T T A T A C T T T C A G G A G G A T C G A T T T T A G A A C A T G A A A A T G C T A G G C T G T A G T G G C G T A G A T C A C T A G G C A G C T G C T T G T T C T T T T G T C A A C T G G.2. HAMMING DISTANCE TEST CASE 3: Population size: 1800; number of generations: 300; crossover probability: 0.36; mutation probability: 0.005; size of each DNA sequence: 150.RAT-HOUSE MOUSE RESULT: Average time: 42 seconds; Distance achieved: 45; Closest string: G T T A A T G T A G C T T A T T A A C A A G G A A A G G A A T T G A A A A T G T T T A G T G G G T T C A A T A T T C C C A A T A A C C C A A A G G G T T G G T C C C G G G C C T G T A A A T A A A T T A A G G G T A G A A T A A A C A T T C A A A A C C C C C A A A A A C C G G G T T A A A A C C C T T T A.RAT-FAT DORMOUSE RESULT: Average time: 41 seconds; Distance achieved: 43; Closest string: G T T A A T G T A G C T T A T A A T A A G C A A A A C C A T T A A A A A G C T T T G G A T G G A A T C T A A A A C C C C T A A A A C A A A A A G T T T G G G C C C A G G C T T T T T A A T T G T T T G T A G G A A A A A T A A A C A T T G C A A C A A T C A C G A C A C C G G T A T A A A A C C C T T T A C.RAT-COW RESULT: Average time: 41 seconds; Distance achieved: 56; Closest string: A T T A A T G G A T A A T C T G C T A A T G C A A A G A C A T G A C A A T G C T G T G A T A G A T T T A G A A A T T C T A T A A T C A G G A A G G T T T T G G C A T T C A G C T A T G G T T G A C T G A G G G T A T G A T T C G A C A C A T A A A C T T C A A T A G G C C T T A G C A G A A T C T T T A G A.3. LEVENSHTEIN DISTANCE TEST CASE 3: Population size: 1800; number of generations: 300; crossover probability: 0.36; mutation probability: 0.005; size of each DNA sequence: 150.RAT-HOUSE MOUSE RESULT: Average time: 9 min 28 seconds; Distance achieved: 14; Closest string: G T T A A T G T A G C T T A T A A T A A A G C A A A G C A C T G A A A A G C T T A G A T G G A T C A A A T G A T C C C A T A A A C A C A A A G G T T T G G T C C T G G C C T A A A T A A T T A G A G G T A A A G A T C T A C A C A T G C A A A C C T C C A T A G A C C G G T G T A A A C A T C C C G T T A A.RAT-FAT DORMOUSE RESULT: Average time: 9 min 29 seconds; Distance achieved: 28; Closest string: T T A A T G A G C T T A A A A G C A A A G C A A C T G A A A T G C T T A G A T G G T A G C A A A T A T C C C A T A A A C A C A A A G G T T C T G G T C C C A G C C T T C T A T T A A T T A G A T T G T A T A G C A A G A T T A C A C A T G C A A C A T C A T G A A C C T G G T G T A A G A A T C C C T T A A.RAT-COW RESULT: Average time: 9 min 29 seconds; Distance achieved: 46; Closest string: G A C T A A T G G C T A T C A G A A T G C A A A G C A C A T G A A C A T G C T G C T G A G A T A G A T T T G A A A A T C T T T A A T A C T G G A A G G G T T G C T C C T G G A C T C A T A G C T A T G G A C G T A A G G C T T G A C A C A G C A T A C A T T G T A C C G G A G T A A A A T G C A C T T A A G.In this setting we use the first 300 nucleotides extracted from the rat, house mouse, fat dormouse and cow genomes. We want to determine the rat-house mouse, rat-fat dormouse and rat-cow closest substrings of 24 nucleotides.The substring size parameter is the desired length of the best substring.1. RANK DISTANCE TEST CASE 4: Population size: 700; number of generations: 110; crossover probability: 0.36; mutation probability: 0.03; size of each DNA sequence: 300; substring size: 24.RAT-HOUSE MOUSE RESULT: Average time: 1 min 25 seconds; Distance achieved: 0; Closest substring: A A A G C A A A G C A C T G A A A A T G C T T A.RAT-FAT DORMOUSE RESULT: Average time: 1 min 24 seconds; Distance achieved: 4; Closest substring: A T A A G A C A A G C A C T G A A A A T G C T T.RAT-COW RESULT: Average time: 1 min 25 seconds; Distance achieved: 22; Closest substring: A G A T A C G T T C A G T A C A T G A G T A C C.2. HAMMING DISTANCE TEST CASE 4: Population size: 600; number of generations: 110; crossover probability: 0.36; mutation probability: 0.03; size of each DNA sequence: 300; substring size: 24.RAT-HOUSE MOUSE RESULT: Average time: 2 min 5 seconds; Distance achieved: 0; Closest substring: T C A G C A G T G A T A A A T A T T A A G C A A.RAT-FAT DORMOUSE RESULT: Average time: 2 min 4 seconds; Distance achieved: 1; Closest substring: C C C C A T A A A C A C A A A G G T T T G G T C.RAT-COW RESULT: Average time: 2 min 4 seconds; Distance achieved: 7; Closest substring: G T A A T T G G A C A T A A A T T T T C A C A T.3. LEVENSHTEIN DISTANCE TEST CASE 4: Population size: 700; number of generations: 110; crossover probability: 0.36; mutation probability: 0.03; size of each DNA sequence: 300; substring size: 24.RAT-HOUSE MOUSE RESULT: Average time: 13 min 18 seconds; Distance achieved: 1; Closest substring: T A A A A A A G C A A A G C A C T G A A A A T G.RAT-FAT DORMOUSE RESULT: Average time: 13 min 19 seconds; Distance achieved: 1; Closest substring: T A A A C G A A A G T T T G A C T A A G C T A G.RAT-COW RESULT: Average time: 13 min 19 seconds; Distance achieved: 6; Closest substring: C A A A C A T C T A C C A C C C G G T T A A A A.The expected result for this experiment should indicate that the rat is closer to the house mouse and fat dormouse, than the cow. We would also like to catch even a finer difference between the rat-house mouse distance and the rat-fat dormouse distance.In the CSP setting, rank distance shows again a great difference between the rat-house mouse closest string, the rat-fat dormouse and the rat-cow closest string. Hamming is able to distinguish the rat from the cow genome, but it doesn’t catch the difference between the rat-house mouse closest string and the rat-fat dormouse closest string. The rat-fat dormouse Hamming distance appears to be smaller than the rat-house mouse Hamming distance, which is wrong. Levenshtein distance works as good as rank distance in this case, giving the expected result. Our observations are supported by the graphs shown in In the CSSP setting, all distances perform very good and are able to put the rat genome near the house mouse and fat dormouse genomes rather than the cow genome. However, the rat-house mouse Hamming distance is very close to the rat-fat dormouse Hamming distance (the closest substrings differ only by one letter). The Levenshtein distance is the same for rat-house mouse and rat-fat dormouse closest substrings. The associated graphs are given in In both CSP and CSSP settings, all distances are able to put the rat near the house mouse and fat dormouse rather than the cow, which is the expected result see  and 4. OFor this experiment we use only the CSSP setting. The goal of this experiment is to show the time improvement obtained by optimizing the genetic algorithm introduced in 1. TEST CASE 5: Population size: 500; number of generations: 100; crossover probability: 0.36; mutation probability: 0.02; size of DNA sequence 1∶90; size of DNA sequence 2∶90; substring size: 30.DNA Sequence 1: A A A A A A A A A A A A T T T T T T T T T T T T T T T T T T T T T T T T T G G G G G A A A A A A A A A A A A A A A A A A A A A A A A A A A G G G G G G G G G G T T T T T A A A A A A A A A A A A A A A A.DNA Sequence 2: C C C C C C C C C C G G G G G G G G G G T T T T T C C C C C C C C C C C C C C C C C C T T T T T T T T T T T T T T T T T T T T T T T T T G G G G G C C C C C C C C C C C C C C C C C.RANK DISTANCE RESULT: Average time: 10 seconds; Distance achieved: 0; Closest substring: T T T T T T T T T T T T T T T T T T T T T T T T T G G G G G.HAMMING DISTANCE RESULT: Average time: 35 seconds; Distance achieved: 0; Closest substring: T T T T T T T T T T T T T T T T T T T T T T T T T G G G G G.LEVENSHTEIN DISTANCE RESULT: Average time: 3 min 22 seconds; Distance achieved: 0; Closest substring: T T T T T T T T T T T T T T T T T T T T T T T T T G G G G G.Using an algorithm to compute rank distance in linear time and a hash table to store precomputed distances between DNA sequences, we are able to report a great improvement in terms of speed. The algorithm that computes rank distance in linear time was introduced in At the selection step, the genetic algorithm needs to sort the chromosomes in each generation by distance. In order to sort the chromosomes we must compare distances that are computed (or recomputed) between chromosomes and input sequences. Instead of computing the distances each time, we store the precomputed distances in a hash table. It is much faster to access a distance value stored in a hash table instead of computing it in linear time. Note that we also used the hash table optimization for Hamming and Levenshtein distances. This optimization helps us reduce the number of distances to be computed from For this test case, in We obtained the same closest substring for each of the three metrics. This result shows that if an exact common substrings exists, the genetic algorithm can find it disregading the metric used. This shows that the genetic algorithm is robust and it can find the optimal solution if the input parameters are properly set.We designed simple and clear experiments that can show the differences of the compared distances. In order to keep things simple, we used the genetic algorithms to determine the closest string or substring for only two DNA sequences. Of course, the algorithms work as well with multiple sequences at once, since the CSP and CSSP problems are generally defined for sets of strings.We mention that the results obtained are not influenced by the fact that the DNA strings are part of coding or non-coding sequences or within genes or part of intergenic regions. The DNA strings used in our experiments were selected without taking into consideration these aspects so the strings may be part of any kind of region. However, it is important for DNA strings used in the same experiment to be extracted from the same position because the alignment matters. In other words, it doesn’t have sense to compare DNA from different regions that have different significance.All our experiments show that RD can be computed 2 times faster than Hamming distance and 10 to 15 times faster than Levenshtein distance. As the closest string (or substring) size increases the Levenshtein distance takes more time to compute when compared to rank distance and Hamming distance.Although the Hamming distance computes almost as fast as rank distance, the downside is that is gives inaccurate results compared to RD. The Levenshtein distance can easily be dismissed because is takes longer to compute and it is also unable to detect the subtle differences that rank distance detects by having a finer scale.Neither Hamming distance or Levenshtein distance were able to give the right answer in all our experiments (Levenshtein distance is wrong in TEST CASE 1 and Hamming distance is wrong in TEST CASE 3). Only rank distance has the expected outcome in all the experiments. Overall, we believe that rank distance is best suited for finding closest strings or substrings on DNA sequences. Due to this observation we conducted the following experiment using only RD.The goal of this experiment is to see if the DNA information can lead to one of the three distinct unrooted phylogenetic trees of higher primates. For this experiment we use only the CSP setting: we want to find the human-chimp closest string and the human-gorilla closest string and compare the associated rank distances. We perform four tests using DNA sequences of variable length and different input parameters for the genetic algorithm. We show graphs for the last three test cases which are more relevant.In the first test case (TEST CASE 6) we use the first The difference between the human, chimpanzee and gorilla mtDNA is very small and We present only the distance achieved for each closest string, because the strings are too long to be presented here.1. RANK DISTANCE TEST CASE 6: Population size: 7000; number of generations: 500; crossover probability: 0.36; mutation probability: 0.001; size of each DNA sequence: 800.HUMAN-CHIMPANZEE RESULT: Average time: 7 min 3 seconds; Distance achieved: 43207.HUMAN-GORILLA RESULT: Average time: 7 min 6 seconds; Distance achieved: 45544.2. RANK DISTANCE TEST CASE 7: Population size: 33000; number of generations: 2000; crossover probability: 0.36; mutation probability: 0.0002; size of each DNA sequence: 5000.HUMAN-CHIMPANZEE RESULT: Average time: 13–14 hours; Distance achieved: 426232;HUMAN-GORILLA RESULT: Average time: 13–14 hours; Distance achieved: 358525.3. RANK DISTANCE TEST CASE 8: Population size: 40000; number of generations: 2400; crossover probability: 0.36; mutation probability: 0.0001; size of each DNA sequence: 7000.HUMAN-CHIMPANZEE RESULT: Average time: 27–28 hours; Distance achieved: 682664.HUMAN-GORILLA RESULT: Average time: 27–28 hours; Distance achieved: 656806;4. RANK DISTANCE TEST CASE 9: Population size: 55000; number of generations: 2800; crossover probability: 0.36; mutation probability: 0.00005; size of each DNA sequence: 16000.HUMAN-CHIMPANZEE RESULT: Average time: 5 days and 6–7 hours; Distance achieved: 2412780.HUMAN-GORILLA RESULT: Average time: 5 days and 6–7 hours; Distance achieved: 2089976.We adjusted the genetic algorithm parameters to obtain the best results disregarding the higher computational time needed to get these results for the first three test cases. The graphs show that the size of the population used in the genetic algorithm is much higher than necessary because the best chromosome evolves very fast during the first In TEST CASE 9 the parameters are rather ajusted for speed than accuracy. We can obtain better approximations of the closest strings by using a population larger than The results for TEST CASE 6 shows that according to rank distance the human is near the chimpanzee rather than the gorilla. The graphs corresponding to TEST CASE 7, 8 and 9 from It seems that Note that in our last test case we used almost all of the entire mtDNA which is approximately Overall, the DNA information that RD was able to extract during this experiment seems to support the theory favours the phylogenetic tree that joins the human with the gorilla In this paper we presented two genetic algorithms designed for solving the closest string problem and closest substring problem, respectively. The genetic operations for the closest string problem have a strong mathematical background and are only inspired from nature. The genetic algorithm designed for the closest substring problem uses standard genetic operations.We tested these two algorithms using several experiments that involve DNA sequences extracted from mammals genomes. Each of these experiments were performed using three different metrics: rank distance, Hamming distance and Levenshtein distance. In all our experiments rank distance clearly outperforms Hamming and Levenshtein distances. On top of this, rank distance is the only distance able to catch subtle differences between DNA strings.By comparing the results for each distance measure, we are able to conclude that RD is best suited for finding closest strings or substrings on DNA sequences.We used our genetic algorithm with rank distance to bring some light in a case disputed by biology scientists: which is the closest human relative, the chimpanzee or the gorilla? The DNA information extracted by rank distance supports the theory that says the human closest relative is the gorilla. We also showed the importance of using DNA sequences that are long enough to obtain conclusive results. Too short DNA sequences can lead to confusing results.In the near future we would like to compare our genetic algorithms based on RD with other approaches, such as dynamic programming techniques. We strongly believe that our approach is comparable, in terms of precision and speed, with other approaches.We also want to investigate a possible approach to obtain better results. This approach combines the results coming from several parallel executions of the genetic algorithm. The best candidates from these parallel executions may be taken to form the first generation of another genetic algorithm. The best candidates will evolve together until the final result is achieved. The final result is expected to be an optimal solution. This approach could work very good with very high-dimensional input data.Genetic algorithms are adaptive searching techniques based on the principles of genetics see . The firWe used the classic general form of the genetic algorithm. For each problem, we used a different set of operations. The set of operations used for the closest substring problem are classical. The crossover and the mutation operations are the same operations found in nature. For the closest string problem the operations are only inspired from biology, but they rely on a mathematical background. We will later describe the structure of the chromosomes and the operations applied on each generation.Algorithm 1General Form.Initialization: Generate a random population that represents the first generation.1: Loop: For a number of generations apply the next operations:2: 2.a 1em Apply the crossover according to the probability of having a crossover.2.b 1em Apply mutations according to the probability of having a mutation.2.c 1em Select the best candidates for the next generation using a density of probability.Termination: Choose the best individual from the last generation to be the optimal ranking.3: An individual chromosome is a permutation of ranks. Each chromosome is a possible candidate for the optimal ranking. We need to convert each input DNA to a permutation. Note that any string can be converted to a permutation. Each letter of the string can be annotated with an index that starts at There are three forms of crossover that are used by the algorithm. Each time the crossover must occur we apply all three forms of crossover.The first crossover operation keeps the first part (prefix) of the individuals and completes the rest of the permutation according to the order given by the complementary chromosome. The second operation uses the same principle, but applies it at the other end of the chromosomes. This crossover operation keeps the last part (suffix) of the individuals and completes the rest of the permutation according to the order given by the complementary chromosome.The third crossover is a natural combination of the previous two. This crossover keeps both the prefix and the suffix of the chromosomes but completes the middle part according to the order found in the complementary chromosome.In order to successfully apply the crossover operations a certain cutting point should be randomly generated. There are six new individuals after the recombination because each crossover operation generates two new individuals. The best two individuals are chosen to replace the parent chromosomes. The optimality condition is used as a criterion to choose the best individuals.We have chosen this model (with 3 types of crossover) because the use of a single crossover usually destroys certain parts of the two individuals involved in the operation. For example, the crossover that keeps the prefixes will have to reorder the components of the suffix. If this single type of crossover is used, we would be unable to evolve the suffix part of the chromosome. This will generate populations with similar individuals that tend to have a bad pattern. In this pattern a good part and a bad part always appear. With our model we ensure that individuals do not follow this pattern and get close to the optimal ranking, but in different ways.The mutation operation may be applied to any chromosome. The mutation only needs one chromosome. To apply a mutation on an individual two positions are randomly chosen. The values at the two positions are swaped.To select the individuals for the new generation from the current generation we use a density of probability function. The new generation is involved in the next iteration of the algorithm. The first step is to sort the individuals on the maximal distances from the input rankings criterion in descending order. Then we generate indexes from the top to the bottom of the list of candidates. The indexes close to the top of the list are more probable. Note that one index can be generated several times; this is the case with the best candidates. There are also indexes that may never be generated; this is the case of the candidates close to the bottom of the list. The density probability function used to select the candidates for the next generation is the normal distribution of mean The graph of this function is represented in Note that in the implementation of the algorithm the fitness function was statistically approximated.The motivation for using this fitness function is based on test results. This fitness functions reduces the number of generations that are required to obtain a close-to-optimal solution. Helped by the crossover and mutation operations, the fitness function has a good generalization capacity: it doesn’t favour certain chromosomes which could narrow the solution space and lead to local minima solutions.Each chromosome is a sequence of DNA of fixed length that represents a possible candidate for the closest substring. Note that a sequence of DNA is simply a strand of nucleotides  that appear randomly in a sequence.The crossover operation between two chromosomes for the closest substring problem is straightforward. First, we need to generate a random cutting point. The prefixes of the two chromosomes remain in place, while the suffixes of the two chromosomes interchange. This is the standard crossover operation inspired directly from nature.To apply a mutation to a certain chromosome, one position is randomly chosen. The nucleotide found at that position will be changed with a new one. Multiple mutations may appear at the same chromosome, although this is very unlikely. This is the classic mutation operation that can also be found in nature.The selection operation used here is similar to the selection used for closest string problem and is based on the normal distribution of mean


We show that snippets from this sequence, at 100 base pairs or longer, drive gene expression in vitro in a number of mammalian cells, and are thus candidates for use in protein production. We further show that expression is driven by the general transcription factors TFIIB and TFIID, both being ubiquitously present across cell types, which results in less tissue- and species-specific regulation compared to the viral promoter SV40. We lastly found that the strength of a promoter can be tuned up and down by modulating the counts of GC and CpGs in localized regions. These results constitute a “proof-of-concept” for custom-designing promoters that are suitable for biotechnological and medical applications.The choice of promoter is a critical step in optimizing the efficiency and stability of recombinant protein production in mammalian cell lines. Artificial promoters that provide stable expression across cell lines and can be designed to the desired strength constitute an alternative to the use of viral promoters. Here, we show how the nucleotide characteristics of highly active human promoters can be modelled via the genome-wide frequency distribution of short motifs: by overlapping motifs that occur  Artificially engineered promoter sequences have the potential for use in industrial and biotechnological applications, such as recombinant protein production of biopharmaceuticals. Some human proteins require mammalian cell lines for proper production, with e.g. the Chinese hamster ovary (CHO) cell line being a widely used system for EPO, Interferon-β, Factor VIII, IX, etc The promoter is the genomic region around the transcription start site (TSS) of a gene, and acts as an essential component in gene regulation and transcription, its role being to interface with transcription factors (TFs) through protein-DNA binding. The TFs anchor the pre-initiation complex (PIC), specifying the exact point of initiation, and recruit RNA polymerase (Pol) II to start transcription SSRCGCC’ General transcription factors (GTFs), organized in complexes TFIIA, TFIIB, TFIID, TFIIE, TFIIF, TFIIH and TFIIJ, form a special class of TFs, in that they are ubiquitously present and both necessary and sufficient to enable Pol II transcription at significant levels, making these proteins desirable candidates as drivers of expression of artificial promoters. Only TFIIB and TFIID have been shown to exhibit sequence preference: the TATA-Binding Protein  is most well characterized and binds to the TATA-Box, thereby establishing the TSS 25–30 base pairs downstream of its location. However, only about 10% of human promoters rely on a TATA-Box nucleotide composition, rather than sequence motifs, where we define the term “nucleotide composition” as the frequency patterns of mono-nucleotides, di-nucleotides, tri-nucleotides etc. This composition varies over the human genome on a large scale, recognizable as isochores in vitro in mammalian cells, as well as how expression levels depend on highly localized features in these sequences.Designing artificial sequences that attract TFIIB and TFIID requires determining the features that capture the interactions between these proteins and the DNA. Generally, a complicating factor is the lack of a one-to-one relationship between TFs and exact instances of binding motifs, so that neither motif consensus of short sequences nor Position Weight Matrices (PWM) are ideal representations of binding sites in vitro expression. Here, we first recapitulate how highly active promoters (mostly associated with both housekeeping and strong tissue-specific genes) differ from the genome-wide distribution in GC and CpG, as well as in other di-nucleotides. We then exploit these “un-genomic” features by devising a measure that tracks the genome-wide frequency of each short motif: the less common genome-wide, the more likely it is to reflect properties of a promoter. We subsequently overlap a set of uncommon motifs to build “promoter-like” contiguous sequence, which allows for editing existing promoters, as well as to construct entirely artificial ones that work in a number of mammalian cells. We last show that TFIIB and TFIID bind to these promoters, which is reflected in their stability of expression level across multiple cell lines.Highly active promoters exhibit nucleotide patterns that are different from the majority of the genome Trinity . The resulting assembly consists of 38 Mb of sequence, residing in 27,000 disjoint transcripts. We eliminated non-full-length transcript assemblies of less prominently expressed genes by requiring sequences to contain open reading frames of 500 bp or more, aligned the remaining sequences to the human genome, and selected only transcripts with the 5′ end falling within 50 bp of an annotated TSS To capture a set of highly active promoters, we sequenced the mRNA of the most highly expressed genes in human cerebellum tissue. We constructed two cDNA libraries, one normalized and one un-normalized library (both filtered for poly-A tails), and sequenced both libraries on one lane of Illumina each, yielding a total of 2 billion base pairs in 71 bp long reads. We then assembled the reads from both libraries into contiguous transcripts using the transcriptome assembly program While only 127 (7.3%) of the 1,746 promoter sequences contain one or more instances of a TATA-Box in the correct orientation, the sequences are clearly distinct from the genome-wide average by their high G/C content (66% vs. 40% genome-wide) as well as the average frequency of CpGs (9% vs. 1% genome-wide). α score”, see α yields a potential spatial resolution of tens of nucleotides. annotated TSSWe defined a measure that tracks with G/C and CpG content see , incorpoα score for each possible 12-mer, including those not present in the human genome. We selected sequences from two quintiles: (a) the top 5% represent the most “un-genomic” (or promoter-like) 12-mers; and (b) the percentile between 45–50%, which contains 12-mers with di-, tri-nucleotide etc. frequencies close to the genomic median, representing more “normal” (or non-promoter-like) sequences. The 12-mers from each set were then independently assembled into “concatomers”  and non-promoter-like (for down-regulation) artificial constructs. As test case, we chose the promoter upstream of the TSS of the X-linked gene cancer/testis antigen 1A (CTAG1A), which exhibited strong in-vitro activity in human cell line HEK293. The CTAG1A promoter region contains three distinct regions of elevated α scores  with size-matched, but of higher α score, snippets from the promoter-like concatomer.We tested whether we could modulate in-vitro promoter activity in HEK293 cells relative to the original sequence, as does replacement with “non-promoter” sequences with α scores of approximately half the original (“hCTAG1A-replace”). Replacement with “promoter-like” sequences with an α score roughly twice that of the original (“hCTAG1A-UP”) increases activity beyond that of the original sequence. This indicates that highly localized changes in sequence composition can drive up- and down-regulation of in-vitro gene expression.in-vitro expression, we pulled sequences from the promoter-like concatomer using different criteria and of different lengths  in four mammalian cell lines: CHO (hamster ovary); P19 (mouse embryo); Vero (monkey kidney); and HEK293 (human kidney). Promoter strength of most constructs was comparable to, or exceeded activity of the SV40 core promoter, which is a routinely used viral promoter for recombinant protein expression in mammalian cell lines (Spodoptera frugiperda) for any of the constructs, suggesting fundamental differences in promoter mechanisms between insects and mammals .To create entirely artificial promoter constructs for ll lines . NotablyAll constructs contain at least one instance of a TATA-Box. In addition to two TATA-Boxes, ArS232 contains one perfect, and 12 imperfect  instances of BRE, and one Inr. To examine to what extent the TATA-box is needed to drive expression, we constructed three variations : (i) remD values, and that these readings are thus somewhat more difficult to interpret than the TFIIB binding results.The general transcription factors TFIIB and TBP bind to the artificial promoter constructs: we monitored real-time binding of TFIIB to constructs ArS110, ArS300, ArS201 and ArS232 through measurement of quantitative protein kinetics . Figure in vitro transcription depending of the abundance of the binding proteins that drive the promoter.Artificially engineered promoter sequences have potential for use in industrial, biotechnological and medical applications involving recombinant protein production and gene therapy, since they can be designed to have different activities and be adapted to the specific requirements (strong or weak expression). The method proposed here yields constructs that appear less variable and species specifically regulated than the viral promoter SV40, a feature that would increase stability across cell types and conditions. By extension, it should be possible to build artificial test beds to determine the behavior of known binding sites, and subsequently design promoters that are targeted and regulated by specific transcription factors. Preliminary results already show promise that this is, in fact, the case, and future experiments will help expand our “vocabulary” of promoter elements, and to predict their effect on in vivo expression, in presence of additional factors, such as methylation, degradation by miRNAs etc. While it might not be possible to accurately predict the behavior of artificially designed promoters in living organisms in the immediate short term, modifying short sub-sequences to adjust relative expression levels should be. In addition, emerging fields such as Synthetic Biology We do not address here how these results translate to This work might also be relevant for the study of expression regulation on a more general level: our findings suggest that different sequences can respond to transcription factors in very similar ways, even though they share no nucleotide sequence similarity; this would provide an explanation as to why promoters are generally not conserved across species over their entire length, but exhibit a pattern of conservation peaks and troughs α score measures the “un-genomicness” of short (12 nucleotides long) sequences, taking into account the genome-wide frequencies of di-nucleotides, tri-nucleotides etc. within the sequence. Let N denote the number of k-mers  in the genome, and kiϕ the genome wide occurrence count of the k-mer starting at position i (using zero-based counting) in the 12 base pair (bp) long sequence, then the score kα iskThe For existing promoter templates as well as the CTAG1A promoter, we computed the scores for all overlapping 12 bp long sequences within, and assigned the score to the base at position 6.α score, and extended the boundaries of each until its composite α score became negative, which yields a 2% false positive rate on the promoter template set  were designed as described in the Constructs ArS 50, 110, 201, 232 and 300  were selected from the 160,000 nucleotide (nt) long promoter-like concatomer. For identification and spacing of TATA Boxes, we used a promoter prediction tool trained on fruit fly E. coli and purification using NucleoSpin Extrakt II  all plasmids were sequenced to confirm the original sequence. In case of CHO dhfr-, HEK293 and P19, 4×10∧6 cells were transfected with 10 µg of the firefly luciferase plasmids and co-transfected with 1 µg of the Renilla luciferase reporter vector pRL-SV40  as an internal standard using Amaxa's Nucleofector Kit V  according to the manufacturer's instructions. 3×10∧5 VERO and MDCK cells were transfected with 1 µg DNA of the firefly luciferase constructs and co-transfected with 0,25 µg pRL-SV40 using Dreamfect Gold and CombiMag . 3×10∧6 Sf9 cells were transfected with 1 µg of the firefly luciferase constructs using Cellfectin II  according to the manufacturer's instructions. Instead of the SV40 promoter the baculovirus derived immediate early promoter OplE2, which is active in insect cells was used for the positive control. Luciferase expression was measured 48 h post transfection on a Synergy 2 microplate reader  with the Gen5 software using the Dual-Glo luciferase assay system . To normalize transfection efficiency, promoter activities are expressed as the ratio of firefly and Renilla luciferase activity. The pGL3-Promoter plasmid , containing the SV40 promoter served as positive control. The promoter activity of this viral promoter was set to 100%. All other measurements refer to this value within the same cell-line. The promoterless pGL3-Basic Vector  was used as a negative control.All inserts have been assembled either by oligo synthesis  followed by annealing and PCR or by gene synthesis  and cloning into the reporter vector pGL3 Basic  upstream of a firefly luciferase gene. After propagation in on and koff) and affinities (KD) were calculated using the Octet Data Analysis Software Version 6.3.For the identification of sequence-specific DNA binding of the transcription factors TFIIB and TBP the binding kinetics were measured by biolayer interferometry on an Octet QK instrument (ForteBio Inc.), which provides continuous real-time display of biomolecular interactions. Streptavidin biosensors were loaded with biotinylated DNA fragments (25 µg/ml) of the promoter constructs ArS110, ArS201, ArS232 and ArS300, or with the promoter constructs ArS232, ArS232 dT1, ArS232 dT2 and ArS232 dT12, generated by PCR amplification using 5′ biotinylated primer (Sigma-Aldrich). Binding was conducted in 1× Kinetics Buffer (ForteBio Inc.) with a protein concentration of 285 nM for TBP  and 270 nM for TFIIB . Kinetic parameters (k>ArS 50ACGCACGCGGTATAAACGCGCGACCTATTCGCGACCGTATAGCGACCGGA>ArS 110CTACGCCGCGTAAATATCGCGCGCTAACGGTGCGCGTTAAAACGCCGACGCGTCATAAAGCGCCGGCGTATAAGCGCGCCGTACGTCGTCGAACCACGTTAGTCCGGACC>ArS 201AACGGTGCGCGTTAAAACGGCCGACGCGTCATAACCGCGACTCGTCGACGCAGCGCCGGCGTATAAGCGCGCCGTACGTCAACCGTCGACGTTAGTCCGACGATCGCGGCGTCTATACGCCGCGTCAATCGCGCGCGGTTCAACGTCGCGCTACGGGCGCGTATAAGTCGCGCGTATGGACCGCGTACGTCCTACGAGCGT>ArS 232TCGACGCGCGTATAACACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGCCCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCGCCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGCGTAACGGTGCGCGTTAAAACGCCGACGCGTCATAACCGCGACTCGTCGACGC>ArS 300AACGGTGCGCGTTAAAACGCCGACGCGTCATAACCGCGACTCGTCGACGCAGCGCCGGCGTATAAGCGCGCCGTACGTCAACCGTCGACGTTAGTCCGACGATCGCGGCGTCTATACGCCGCGTCAATCGCGCGCGGTTCAACGTCGCGCTACGGGCGCGTATAAGTCGCGCGGTTAATACGCGCGGTGTACGCGGATGCCGGGGTCGCGTATAATCGGCGCGTATACCTCGCGCGTATACGCGGCGTATTACGGCCGCGTATAATTCGCGCGTATGGACCGCGTACGTCCTACGAGCGT>CTAG1A_originalCGGAGCACGTGACCGGTTCTCACCAACCCCGCCCCTCCCCAAGAGAGCCCGGGCCGGAAGGTGGCCGCAATGCCAGCTTGGACCCCTCACCCCTGAGCAGCCGGCTGTCCGCCGGACCCCTGTCCCGGGAGCCCTGCAGGGAGTCAGGCACTGCGGGGCCCAGCCTGTCCCATCCCCCGGGTCTCCCTCACATCGAGGAGCAAGACGGGCCTGGGAACACGGGGCCGGGACTGTGCGGCCATCGTCCCGGACCCTGCCTGCCCTGTCCGTCCTTGGGGGAGCGCCCAGGACAGACfCCCGGGGGGCAGGCCTCTAfACTGGGCTCAGCAGCCTCCGTCCCTGTCCTGGTCGCCCAGCTGGTGGGGTAGCTGGAACTGCATGTCTGGTCTCAGAGAGAAGGTCAGGGCCCACGAGGATGCGGAGGCAGAGAGGCTGCAGGAAGTTCCGCCCCCTGGCGTGAGATGGGCAGCCCGGGATCCTCAGGGCGCCTGCGCACAGGGGCCCTACTTCCGGCCCTGGGAGACCCCGAGTGAGCCC>CTAG1A_replaceTTACCTAAAACAGCCCAAAAGAGCAACCCCGCCCCTCCCCAAGAGAGCCCGGGCCGGAAGGTGGCCGCAATGCCAGCTTGGACCCCTCACCCCTGAGCCCACCACCACCTCCACCACCACTGTCCCGGGAGCCCTGCAGGGAGTCAGGCACTGCGGGGCCCAGCCTGTCCCATCCCCCGGGTCTCCCTCACATCGAGGAGCAAGACGGGCCTGGGAACACGGGGCCGGCCAAAGAAGCCCAAAAAGGCCCAGGAAACCCAAACTTTCCGTCCTTGGGGGAGCGCCCAGGACAGACCCCGGGGGGCAGGCCTCTAACTGGGCTCAGCAGCCTCCGTCCCTGTCCTGGTCGCCCAGCTGGTGGGGTAGCTGGAACTGCATGTCTGGTCTCAGAGAGAAGGTCAGGGCCCACGAGGATGCGGAGGCAGAGAGGCTGCAGGAAGTTCCGCCCCCTGGCGTGAGATGGGCAGCCCGGGATCCTCAGGGCGCCTGCGCACAGGGGCCCTACTTCCGGCCCTGGGAGACCCCGAGTGAGCCC>CTAG1A_deltaTCTCAGAGAGAAGGTCAGGGCCCACGAGGATGCGGAGGCAGAGAGGCTGCAGGAAGTTCCGCCCCCTGGCGTGAGATGGGCAGCCCGGGATCCTCAGGGCGCCTGCGCACAGGGGCCCTACTTCCGGCCCTGGGAGACCCCGAGTGAGCCCCAACCCCGCCCCTCCCCAAGAGAGCCCGGGCCGGAAGGTGGCCGCAATGCCAGCTTGGACCCCTCACCCCTGAGCTCCCGGGAGCCCTGCAGGGAGTCAGGCACTGCGGGGCCCAGCCTGTCCCATCCCCCGGGTCTCCCTCACATCGAGGAGCAAGACGGGCCTGGGAACACGGGGCCGGTCCGTCCTTGGGGGAGCGCCCAGGACAGACCCCGGGGGGCAGGCCTCTAACTGGGCTCAGCAGCCTCCGTCCCTGTCCTGGTCGCCCAGCTGGTGGGGTAGCTGGAACTGCATGTCTGG>CTAG1A_upCGTTTGACGGACGCCGTTCGCAGTCAACCCCGCCCCTCCCCAAGAGAGCCCGGGCCGGAAGGTGGCCGCAATGCCAGCTTGGACCCCTCACCCCTGAGCCGGAGCACGTGACCGGTTCTCACTCCCGGGAGCCCTGCAGGGAGTCAGGCACTGCGGGGCCCAGCCTGTCCCATCCCCCGGGTCTCCCTCACATCGAGGAGCAAGACGGGCCTGGGAACACGGGGCCGGATCGCGCAGCGATCGACGCCGGATCAACGCGATACGGTCCGTCCTTGGGGGAGCGCCCAGGACAGACCCCGGGGGGCAGGCCTCTAACTGGGCTCAGCAGCCTCCGTCCCTGTCCTGGTCGCCCAGCTGGTGGGGTAGCTGGAACTGCATGTCTGGTCTCAGAGAGAAGGTCAGGGCCCACGAGGATGCGGAGGCAGAGAGGCTGCAGGAAGTTCCGCCCCCTGGCGTGAGATGGGCAGCCCGGGATCCTCAGGGCGCCTGCGCACAGGGGCCCTACTTCCGGCCCTGGGAGACCCCGAGTGAGCCCTATAACACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGCTCGACGCGCG232  60TCGACGCGCG-----CACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGC 55dT1 TATAACACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGCTCGACGCGCGdT2  60TCGACGCGCG-----CACGCGAGCGGTTCGAACGTTGGCGCGCTAACGCGAGTCGTACGC 55dT12 ********** *********************************************CCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCG 120232 CCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCG 115dT1 CCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCG 120dT2 CCGTCAACGCGGATCAATCGCGCGACTTGTGCGCGACGTTAGACCGCCGATCGTCAAGCG 115dT12 ************************************************************CCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGC 180232 CCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGC 175dT1 CCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGC 180dT2 CCGATCGGTAATCGGACGATTCGGATACGCGAGTTCGGACGTACGAGCGTGATACGGCGC 175dT12 ************************************************************TTAAAACGCCGACGCGTCATAACCGCGACTCGTCGACGCGTAACGGTGCGCG232  232TTAAAACGCCGACGCGTCATAACCGCGACTCGTCGACGCGTAACGGTGCGCGdT1  227GTAACGGTGCGCG------CGCCGACGCGTCATAACCGCGACTCGTCGACGC 226dT2 GTAACGGTGCGCG------CGCCGACGCGTCATAACCGCGACTCGTCGACGC 221dT12 ************* *********************************


Fibroblast growth factor 19 (FGF19) is a hormone-like protein that regulates carbohydrate, lipid and bile acid metabolism. At supra-physiological doses, FGF19 also increases hepatocyte proliferation and induces hepatocellular carcinogenesis in mice. Much of FGF19 activity is attributed to the activation of the liver enriched FGF Receptor 4 (FGFR4), although FGF19 can activate other FGFRs in vitro in the presence of the coreceptor βKlotho (KLB). In this report, we investigate the role of FGFR4 in mediating FGF19 activity by using Fgfr4 deficient mice as well as a variant of FGF19 protein (FGF19v) which is specifically impaired in activating FGFR4. Our results demonstrate that FGFR4 activation mediates the induction of hepatocyte proliferation and the suppression of bile acid biosynthesis by FGF19, but is not essential for FGF19 to improve glucose and lipid metabolism in high fat diet fed mice as well as in leptin-deficient ob/ob mice. Thus, FGF19 acts through multiple receptor pathways to elicit pleiotropic effects in regulating nutrient metabolism and cell proliferation. FGF19 (fibroblast growth factor 19) and its murine ortholog Fgf15 are the founding members of the endocrine FGF subfamily that also includes FGF21 and FGF23 In addition to the effects on lipid and glucose metabolism, FGF19/Fgf15 has also been implicated in the regulation of hepatic BA metabolism and hepatocyte proliferation. FGF19/Fgf15 expression in the intestine is transcriptionally regulated by the nuclear BA receptor Farnesoid X Receptor (FXR) FGF19/Fgf15 is believed to act by activating FGF receptor (FGFR) homodimers complexed with a membrane bound protein βKlotho (KLB) In order to determine which of the metabolic effects elicited by FGF19 are mediated by FGFR4, we treated HFD-fed WT or Fgfr4 KO mice with recombinant FGF19 or vehicle control and studied metabolic phenotypes and gene expression. To achieve sustained exposure to FGF19, mice were implanted with osmotic pumps to continuously infuse FGF19 at 1 ng/hr. This achieved an average FGF19 serum concentration of 26 ng/ml, as determined by ELISA, about 50- to 250-fold higher than circulating FGF19 concentrations in humans To evaluate changes in systemic BA regulation, serum BA composition was determined by liquid chromatography-mass spectrometry . AlthougBased upon the results described above, we hypothesized that if we could generate FGF19 variants with specifically reduced FGFR4 activity, such molecules would retain beneficial metabolic effects while losing FGFR4-dependent actions such as the induction of hepatocyte proliferation and altered BA homeostasis. In order to quantitatively evaluate specific activation of FGFRs by FGF19, an FGF-responsive GAL-Elk1 luciferase reporter assay was introduced into rat L6 cells One chimeric construct classified as a class II molecule, consisting of amino acids 1-20 of FGF21 and 25-194 of FGF19 , was selected for large scale synthesis in CHO cells and this variant is referred to as “FGF19v”. When compared with FGF19 using the luciferase reporter assay, FGF19v protein exhibited a similar dose-dependent activity to FGF19 in L6 cells cotransfected with KLB and FGFR1c . HoweverActivity of FGF19v was further tested in vivo in comparison with FGF19 and FGF21 by intravenously injection into overnight fasted FVB mice. Livers were harvested at 4 hours post injection and hepatic mRNA expression was determined by qPCR. Genes that were acutely induced by FGF19 but not by FGF21, such as Egr-1 and c-Fos, were not efficiently induced by FGF19v, consistent with the reduced FGFR4 activity of FGF19v . FGF19v It has been previously proposed that FGFR4 mediates the induction of hepatocyte proliferation by FGF19 The in vitro and in vivo results described above raised the question as to whether FGF19v, a variant of FGF19 with reduced FGFR4 activity and proliferative potential, could improve hyperglycemia in diabetic animals similar to FGF21. FGF21, FGF19v (1ng/hr) or vehicle control was continuously infused subcutaneously into ob/ob mice using osmotic mini-pumps. While infusion did not significantly affect body weight , both FGThe mechanism by which FGF21 and FGF19 ameliorate hyperglycemia in diabetic animals is not well understood. Since FGF21 and FGF19v show very similar anti-diabetic effects in ob/ob mice, we hypothesize that commonly regulated pathways may contribute to their anti-diabetic effects. We identified a number of genes exhibiting commonly altered expression in ob/ob mice treated with FGF21 and FGF19v. In the liver, both proteins induced IGFBP2 (a recently demonstrated anti-diabetic protein) Although FGF19 has been shown to activate multiple FGFRs in the presence of the coreceptor KLB in vitro, contribution of each FGFR to the in vivo activity of FGF19 has been poorly defined. Our findings in Fgfr4 KO mice as well as using a FGF19 variant protein with reduced FGFR4 activity have delineated pathways downstream of FGF19. We have shown that FGFR4 is required for regulation of BA biosynthesis and hepatocyte proliferation as previously proposed Intriguingly, we found that both FGF19 and FGF21 acutely reduce hepatic expression of Cyp7a1 even in Fgfr4 KO mice . This FgFgfr4 has also been implicated in the regulation of lipid metabolism and glucose tolerance and may indeed mediate regulation of fat metabolism by endogenously produced Fgf15 protein Previously, therapeutic potential for FGF19 in the treatment of obesity and diabetes has been proposed Each FGF family protein consists of the structurally conserved central globular domain, and the flanking N-terminal and C-terminal segments that are structurally flexible and are divergent in primary sequence. In X-ray crystal structures of multiple FGF/FGFR complexes, the N-terminal segment of the FGF molecule makes specific contact with the FGFR and is believed to play an important role determining the specificity of the FGF-FGFR interaction In conclusion, our study demonstrates that FGFR4 is not required for beneficial pharmacological activity of FGF19, and that an engineered FGF19 variant mimicking the specificity of FGF21 could successfully be generated. Given the pleiotropic activities of FGF19 and FGF21 on multiple receptors , further exploration into altering receptor specificity of FGF19 or FGF21 to achieve specific activation of a particular FGFR may provide a safer and more predictable approach to exploit endocrine FGF pathways and provide new therapeutic options for the epidemic of obesity associated-disorders such as type 2 diabetes, nonalcoholic fatty liver disease and other manifestations of insulin resistance and the metabolic syndrome.The study protocols for all animal experiments were approved by the Genentech Institutional Animal Care and Use Committee (IACUC). The approval IDs for this study are: #08-1943, #08-2004, #08-2004A, #08-2004B, #08-2004C, #08-2136, #08-2136F, #09-1001, #09-1066, #10-1818.Unless otherwise noted, recombinant human FGF21, FGF19 and variants produced in transiently transfected CHO cell and purified to homogeneity in PBS were used for experiments. For some experiments, E. coli derived FGF21  was used. All the purified proteins were tested for activity by cell based GAL-Elk1 assays prior to use in for other assays. For experiments in All the constructs also possessed a signal sequence at the N-terminal end (cleaved upon secretion) and the flag tag (DYKDDDDK) at the C-terminal end. The sequences derived from FGF21 are shown in bold.1 (hFGF19): RPLAFSDAGPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPMLPMVPEEPEDLRGHLESDMFSSPLETDSMDPFGLVTGLEAVRSPSFEKLKPGTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPMLPMVPEEPEDLRGHLESDMFSSPLETDSMDPFGLVTGLEAVRSPSFEK2: RPLAFSDAGPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRADGVVDCARGQSAHSLLEIKAHPIPDSSPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPMLPMVPEEPEDLRGHLESDMFSSPLETDSMDPFGLVTGLEAVRSPSFEK3: HPIPDSSPLLQFGGQVRQRYLYTSGPHGLSSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPMLPMVPEEPEDLRGHLESDMFSSPLETDSMDPFGLVTGLEAVRSPSFEK4 (hFGF19v): HPIPDSSPLLQFGGQVRQRYLYTDDPHGLSSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPMLPMVPEEPEDLRGHLESDMFSSPLETDSMDPFGLVTGLEAVRSPSFEK5: HPIPDSSPLLQFGGQVRQRYLYTDDAQLSSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPMLPMVPEEPEDLRGHLESDMFSSPLETDSMDPFGLVTGLEAVRSPSFEK6: HPIPDSSPLLQFGGQVRQRYLYTDDAQQTSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPMLPMVPEEPEDLRGHLESDMFSSPLETDSMDPFGLVTGLEAVRSPSFEK7: LPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS8: RPLAFSDAGPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPLPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS9: RPLAFSDAGPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVQLKALKPGVIQILGVKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPLPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS10: RPLAFSDAGPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRADGVVDCARGQSAHSLLPESLLQLKALKPGVIQILGVKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPLPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS11: RPLAFSDAGPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRADGVVDCARGQSTVGGAADQSPESLLQLKALKPGVIQILGVKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPLPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS12: RPLAFSDAGPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRADGEDGTVGGAADQSPESLLQLKALKPGVIQILGVKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPLPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS13: RPLAFSDAGPHVHYGWGDPIRLRHLYTSGPHGLSSCFLRIRDDAQQTEAHLEIREDGTVGGAADQSPESLLQLKALKPGVIQILGVKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPLPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS14: RPLAFSDAGPHVHYGWGDPIRLRHLYTLLQFGGQVRQRYLYTDDAQQTEAHLEIREDGTVGGAADQSPESLLQLKALKPGVIQILGVKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPLPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS15: RPLAFSDAGPHPIPDSSPLLQFGGQVRQRYLYTDDAQQTEAHLEIREDGTVGGAADQSPESLLQLKALKPGVIQILGVKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPMLPMVPEEPEDLRGHLESDMFSSPLETDSMDPFGLVTGLEAVRSPSFEK16: HPIPDSSPLLQFGGQVRQRYLYTDDAQQTEAHLEIREDGTVGGAADQSPESLLQLKALKPGVIQILGVKTSRFLCQRPDGALYGSLHFDPEACSFRELLLEDGYNVYQSEAHGLPLHLPGNKSPHRDPAPRGPARFLPLPGLPPALPEPPGILAPQPPDVGSSDPLSMVGPSQGRSPSYAS17 (hFGF21): –8 hours in serum free media containing 25 mg/L porcine heparin (Sigma) and FGF protein at a various concentration. The cells were then lysed with PLB reagent (Promega) and luciferase activity in each well was determined using Dual-Glo Luciferase Assay System (Promega) and EnVision Multilabel Reader (PerkinElmer). Firefly luciferase activity was normalized to the co-expressed Renilla luciferase activity, and is shown as an average and standard error of the mean of the three replicas.All the cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) at 37°C under 5% CO2. Rat L6 myoblasts in a 96-well plate were transiently-transfected with expression vectors encoding Renilla luciferase , human KLB, appropriate human FGFR, GAL4-Elk-1 transcriptional activator , and firefly luciferase reporter driven GAL4 binding sites , using FuGENE HD Transfection Reagent (Roche Applied Science). On the next day, the transfected cells were cultured for an additional 6A 96-well-plate was filled with 50 µL/well of 0.5% molten agarose in growth media. After the base agarose had solidified, about 670 HepG2 cells suspended in 50 µL top molten agarose solution (0.35% agarose in growth media) were added to the base agar in each well, and allowed to solidify. Following solidification, 20 µL of growth medium containing an appropriate amount of FGF protein was added to each well on designated day 0. On each of the subsequent days 2, 4, 6 and 8, a further 20 µL of growth medium with an appropriate amount of FGF protein was added to each well. A subset of the sample wells was also treated with protein synthesis inhibitor Geneticin (Invitrogen) to provide a background fluorescence signal. On day 9, 10 µL AlamarBlue reagent (Invitrogen) was added to each sample well and the plate was further incubated for 5 hrs. The resulting fluorescent intensity was measured using EnVision Multilabel Reader (PerkinElmer) and used as an indication of the total metabolic activity in each well. Each condition was tested in quintuplicate.FGFR-binding activity of FGF19 and FGF19v were measured as described in Mice were maintained in a pathogen-free animal facility at 21°C under standard 12 hr light/12 hr dark cycle with access to chow  or a high fat, high carbohydrate diet  and water ad libitum. Male mice were used for all of the experiments described. FGFR4 KO mice in C57BL/6 background were previously described Total cholesterol, triglyceride, β-hydroxybutylate (BHB), lactate (Thermo DMA) and nonesterified fatty acid (Roche) were determined by using enzymatic reactions. Serum insulin levels were determined by ELISA . BA composition was determined by liquid chromatography-mass spectrometry analysis as previously described Tissue RNAs were isolated by using QIAzol reagent (Qiagen). cDNA was synthesize with the Quantitect Reverse Transcription Kit (Qiagen). For real time qPCR, samples were run in triplicate in the ABI Prism 7900HT (Applied Biosystems) by using SYBR green universal mix (Invitrogen) or by Taqman universal mix (Roche) and normalized by levels of 36B4. Pre-designed Quantitect primers for GK, SHP, Cyp8b1, IGFBP2, and AFP were obtained from Qiagen and all other primers were designed using primer express software (Applied Biosystems). Sequences of in-house designed primers will be provided upon request.p-value <0.05 was considered statistically significant. Values were presented as means+/− standard error of the mean.Unpaired student's t-test (two-tailed) was used for statistical analyses to compare treatment groups using Prism 5 software (Graphpad) or Excel (Microsoft). A Figure S1FGF21 and 19 activates FGFR2c and FGFR3c in the presence of KLB. GAL-Elk1 luciferase assay in L6 cells. L6 cells were cotransfected with expression vectors for KLB and the indicated FGFR together with GAL-Elk1, SV40-Renilla Luciferase, and Gal-responsive luciferase reporter. Transfected cells were incubated with media containing increasing concentrations of FGF19 (○) or FGF21(▴) for 6 hours before luciferase assays. Transcriptional activation was assessed by the relative luciferase activity normalized by Renilla luciferase activity and expressed as relative luciferase unit (RLU).(TIF)Click here for additional data file.Figure S2In vitro activity of FGF21, FGF19 and chimeric constructs. GAL-Elk1 luciferase assay in rat L6 cells. L6 cells were cotransfected with expression vectors for KLB and the indicated FGFR together with GAL-Elk1, SV40-renilla Luciferase, and Gal-responsive firefly luciferase reporter. Transfected L6 cells were incubated for 6 hours before luciferase assays with conditioned medium from 293 cells transiently transfected with each FGF construct indicated at the bottom. The number below each group corresponds to the number of the construct as indicated in (TIF)Click here for additional data file.


Escherichia coli, yeast and CHO have been exploited for producing recombinant human insulin and a variety of different recombinant insulin are extensively used.Plants are among promising and suitable platform systems for production of recombinant biopharmaceutical proteins due to several features such as safety, no need for fermentation, inexpensive investment, and fast and easy scale-up. Human insulin is one of the most widely used medicines in the world. Up to now different expression systems including This study reports on the transformation and expression of proinsulin gene in tomato plants for the first time in Iran.Agrobacterium tumefaciens strain LBA4404, and used for Agrobacterium mediated stable transformation of tomato plants. Presence of the desired gene in transgenic lines was confirmed through colony PCR and sequencing. The expression of the protein in transgenic lines was confirmed by immunodot blot assay.This study reports the cloning, transformation and expression of proinsulin gene in tomato plants. Specific primers were designed and used for PCR amplification and cloning of the proinsulin gene in the plant expression vector pCAMBIA1304. The recombinant construct was transferred into The presence of the proinsulin gene in the genomic DNA of transgenic tomato was confirmed by PCR. Also total protein of transgenic tomato was extracted and the expression of proinsulin was detected using dotblot assay.This survey addresses the possibility of proinsulin gene transfer and expression in tomato transgenic lines. This study can be used as a basis for future researches to produce human proinsulin in tomato and other candidate plants. Escherichia coli as insoluble inclusion bodies have been demonstrated and are commercially used. The major advantage of these approaches is that proinsulin can be produced in large scale, but the intricate process of refinement and the formation of accurate disulfide bonds during folding are critical cost factors  is one of the most important and favorable vegetable crops. It is an ideal candidate plant for the production and delivery of oral vaccines. Being a short-duration crop and having the ability to grow in greenhouses adds to its advantages for exploring the possibilities of using this crop for biopharmaceutical production  harboring the human proinsulin gene. This expression vector having a resistance gene for kanamycin (NPT II) was used for selection of the transformed bacteria, and the hygromycin resistance gene was used to create hygromycin resistance in plants to screen the transgenic line. CaMV35S (Cauliflower Mosaic Virus promoter which induces high level of transcription) promoter, NOS  terminator sequence, the reporter genes of GUS and GFP (as reporter genes) encoding beta-glucuronidase and green florescent protein was used in front of the 35S promoter, and the sequences for restriction enzymes of Bst EII and Nco I were used to replace the desired gene with the reporter genes (Staphylococcus aureus (pro A-Pins) were cloned instead of the reporter genes. Proinsulin gene was obtained from the Pasteur Institute, Iran. The coding sequence is:er genes . The humATGGCGGGATTNAACCAATTTAATAAGGAACAACAAAATGCTTTCTATGAAATCTTACATTTACCTAACTTAAATGAAGAACAACGCAATGGTTTCATCCAGAGCTTAAAAGATGACCCAAGCCAAAGCGCTAACCTTTTAGCAGAAGCTAAAAAGCTAAATGATGCACAAGCACCAAAAGCTGATAACAAAGGATCCCGTCGCTTTGTTAACCAACACCTGTGCGGTTCTCACCTGGTTGAAGCTCTGTACCTGGTTTGCGGTGAACGTGGTTTCTTCTACACCCCGAAGACCCGTCGTGAAGCTGAAGACCTGCAGGTTGGTCAGGTTGAACTGGGTGGTGGTCCGGGTGCTGGTAGCCTGCAACCGCTGGCTCTGGAAGGTTCTCTGCAGAAGCGTGGTATCGTTGAACAGTGCTGCACCTCTATCTGCTCTCTGTACTACCAACTGGAAAACTACTGCAACStaphylococcus aureus spa gene for immunoglobulin G binding protein A", and black is proinsulin  , which has been deleted because there was no need for targeting recombinant protein . Proinsulin is converted into the bioactive hormone insulin by removal of its connecting peptide (C-peptide). PROTEIN A can cause high expression of recombinant proinsulin gene and high stability of recombinant proinsulin protein produced in the Escherichia coli, and this protein causes easier purification of human proinsulin bound to it. Proinsulin is converted to bioactive hormone insulin by carboxypeptidase-H (CP-H) from Arg-Arg and Lys-Arg sites in secreting vesicle  for 30 minutes at room temperature. The explants were transferred into sterile paper to remove excess Agrobacterium, and were placed into coculture medium top side down (CM) for 2 days in the dark at 28˚C. Finally, inoculated explants were transferred into regeneration medium (RM)  were surface sterilized by 70% ethanol for 30 seconds, and then rinsed in distilled water for 1 minute. Then seed sterilization continued by immersing in 3% NaClO and rinsed three times with sterile distilled water. Sterile seeds were germinated in the dark at 25˚C on Murashige and Skoog (MS)  medium fium (RM) . The traGenomic DNA of transgenic plants was extracted, using the CTAB method . A quantFor the extraction of total soluble protein (TSP), 200 mg of young tomato leaves were used. The tomato leaves were ground in liquid nitrogen to a fine powder. Proteins were extracted by using 1000 µL of extraction buffer  and 0.04% (v/v) 2- Mercaptoethanol). Cell debris was removed by two rounds of centrifugation , and the supernatant was used for dot blot analysis. Twenty nanograms of protein samples were directly spotted onto a nitrocellulose membrane. BSA 1% solution was added after dying and incubated for 1 hour at 25˚C. Solution was poured out and washing with PBS-T for 5 minutes was performed; this step was repeated 3 times. Then the primary antibody  was added and incubated for 1 hour. The solution was poured out; washing with PBS-T was performed for 5 minutes, and repeated 3 times. Next, nitrocellulose membrane was incubated in anti-rabbit IgG conjugated with horseradish peroxidase as a secondary antibody for an hour with 1:4000 dilutions. Color development solution was DAB and 0.01% hydrogen peroxide in 50 mM Tris (pH = 7.5).E. coli strain DH5α. The constructed vector was confirmed by colony PCR, PCR, digestion and sequencing  and mammalian cell cultures (0.1% cost reduction) . The fir