Complete mitogenome analysis of Fasciola gigantica from Sudan

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Mohamed, Kamal Ibrahim, Saeed Alasmari, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3848681/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Complete mitochondrial (mt) genomes are increasingly being used as molecular markers for investigating phylogenetic relationships. We sequenced the complete mt genome of the Fasciola gigantica of 16 samples from cattle, sheep and goats from Sudan using Illumina MiSeq platform. The complete mt genome of F. gigantica was 14,483 bp in length. Its genome is circular, and consists of 36 genes, including 12 protein-coding genes, 2 subunit ribosomal RNA genes (rRNA), and 22 genes for tRNA. The start and stop codons of the12 protein-coding genes are ATG and TAG respectively, which are identical to reference mt genomes except for the ND5 the start codon GTG and the stop codon of the ND4 which was TAA. Additionally, the lengths of the the12 protein-coding genes were identical in 10 genes, however, the ND4L of the reference was 12 bp longer with 273 bp as compared to the Sudan isolates which was 261 bp long and COX1 in the reference was 9 bp shorter which was 1,533 bp long as compared to Sudan isolates which 1,542 bp long. In contrast, the non-coding regions differed by 20 bp and 4 bp length in the long and the short non-coding regions of Sudan isolates. Nucleotide variability in the mt genome among F. gigantica from Sudan is quite different from the reference as revealed by the sliding window analysis. Phylogenetic analysis of the concatenated amino acid sequence data for all 12 protein-coding genes showed that all F. gigantica from Sudan clustered separately from the available F. gigantica . More interestingly, based on stem-loop (non-coding regions) it revealed better resolution on how the evolutionary process has affected host specificity and in particular for the sheep and goats. It is concluded that these novel complete mt genomes of F. gigantica from different host species provide additional genetic markers for studying epidemiology, population genetics, and phylogeographics of F. gigantica , as well as for understanding interplay and the host species. Fasciola gigantica mitochondrial genome genomic phylogeny Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Fascioliasis is an important neglected food-borne parasitic disease caused by trematodes of the genus Fasciola , affecting people and animals worldwide (FAO/WHO 2014 ). Fasciola hepatica and/or Fasciola gigantica infection is prevalent in over 600 million domestic ruminants worldwide (cattle, sheep, goats, buffalo, donkey, and pig), causing major economic losses of about US $ 3 billion per annum (Mas-Coma et al. 2005 ). The mitochondrial genome has been used widely to investigate phylogenetic relationships. As a consequence of its well-established organization, this genome has been sequenced in many organisms, including F. gigantica (Liu et al. 2014 ). The advancement of high-throughput, next-generation sequencing technologies has led to the recent focus on whole-genome sequencing and complete nuclear genomes of parasitic trematodes (e.g. Fasciola hepatica McNulty et al. 2017 ; Fasciola gigantica Luo et al. 2021 ; Paragonimus westermani by Gu et al. ( 2020 ). Availability of which facilitates detailed comparative phylogenetic analyses across many species complexes and major taxonomic groups of trematodes. Consequently, sequences generated from the mt genome provide excellent molecular markers for defining population groups, tracing the genetic history of an individual or a particular group of related individuals, and for constructing deep-branch taxonomic phylogenies (Rollinson et al., 1997 ; Boore, 1999 ). Fasciola gigantica and F. hepatica are thought to have diverged about 11.8 Ma, according to a recent work by Luo et al. ( 2021 ) that provides the reference genome of F . gigantica . They hypothesized that actin and aquaporin genes were responsible for the liver flukes' rapid evolution, large body size, and parasitic adaptability that led to their spread. To date, there is no available information known about the genetic characterization of F. gigantica in Sudan except the recent study by Ibrahim et al . (2023) in which COI and ND1 genes were used to characterize F. gigantica from different regions and hosts from Sudan. This study has provided a holistic and in depth analysis of complete mitochondrial genome, phylogenetic relationships that help extrapolate the evolutionary biology of this important neglected parasite. 2. Materials and methods 2.1 Sample collection and DNA extraction Sample collection and DNA extraction were carried out as described in detail in the previous study (Ibrahim et al. 2024 ). A total of sixteen samples, comprising ten from cattle, five from goats, and one from sheep, were utilized for generating complete mitochondrial genome sequences. In summary, these samples were sourced from slaughterhouses, subjected to thorough washing with distilled water, morphologically identified, and then squashed prior to genomic DNA extraction. Approximately 10 mg of tissue from the lateral zone was employed for DNA extraction using the QIAamp DNA extraction Kit (Qiagen, INC), following the manufacturer's guidelines. 2.2 PCR and Illumina MiSeq sequencing To obtain the complete mitochondrion genome sequences, two overlapping long PCR reactions were used; the first set with forward primer P101: 5’-TCTAGTGCTGCTTTGGTTGCTATGTC-3’ and reverse primer P265: 5’-GAAGCTACAATCCCACGCACTAAACT-3’ amplified approximately 7 kb and the second set with forward primer P247: 5’-TGTGGGTTATTGTACGGAGTTGTGTG-3’ and reverse primer P121: 5’-CATATAACCCAACACCTTACGCTCACC-3’ amplified approximately 9 kb. The PCR reactions were conducted in a 25 µl reaction mixture containing 12.5 µl of 2×Gflex PCR Buffer (Mg2+, dNTP plus), 0.5 µl of Tks Gflex DNA Polymerase 1.25 units/ µl (TaKaRa Bio Inc., Shiga, Japan), 200 nM of each primer, 1.0 µl of template DNA and molecular grade water. The cycling conditions for both PCRs were set with an initial denaturation at 94°C for 1 min, followed by 35 cycles of denaturation at 98°C for 10 sec, annealing at 60°C for 15 sec, extension at 68°C for 7 min and final extension at 68°C for 5 min. The amplicons were electrophoresed in 1% agarose gel stained with Gel-Red (Biotium, Hayward, CA, USA) and visualized under UV light. The amplicons were purified using NucleoSpin Gel and PCR Clean-Up Kit (TaKaRa Bio Inc.). The purified amplicons were sequenced on an Illumina MiSeq platform using the MiSeq Reagent Kit v3 (600 cycles) (Illumina, San Diego, USA) according to the manufacturer’s protocol. The reads obtained from the Illumina MiSeq run were mapped against published mitochondrial genomes of F. gigantica (NC_024025) using CLC Genomics Workbench v20.0.4 (QIAGEN, Hilden, Germany). The sequences obtained in this study were submitted to the NCBI GenBank database under accession numbers LC649568 - LC649583. 2.3 Mitochondrial genome organization: The complete mt genome of F. gigantica was compared with the F. gigantica reference sequence (NC_024025) to illustrate genes and transfer ribosomal RNA arrangements using the online GenomeVx software ( http://wolfe.ucd.ie/GenomeVx/ ). 2.4 Sliding window analysis of nucleotide variation To detect variable nucleotide sites, pairwise alignments of the 16 complete mt genomes of F. gigantica from Sudan, were performed using MUSCLE implanted in MEGA 7. The sliding window analysis of the aligned complete mt genomes sequences of F. gigantica was performed using DnaSP v.5. A sliding window of 300 bp (in 10 bp overlapping steps) was used to estimate nucleotide diversity Pi (π) across the alignment. Nucleotide diversity was plotted against mid-point positions of each window, and gene boundaries were identified. 2.5 Phylogenetic analysis 2.5.1 Phylogenetic relationship of F. gigantica , Trematodes and Cestodes Phylogenetic relationship of 16 mt genomes from Sudan and selected Fasciola, Fascioloides, Fasciolopsis, Echinostoma, Paragonimus, Opisthorchis, Clonorchis, Schistosoma, Trichobilharzia , and Orientobilharzia species (Table S1 ), was reconstructed based on amino acid sequences of 12 protein-coding genes using the Gyrodactylus species ( Gyrodactylus derjavinoides ) GenBank accession number NC_010976 as the outgroup. 2.5.2 Phylogenetic relationship based on the stem-loop (non-coding regions) The stem-loop structure referred to as AT-Loop according to the reference sequence (NC_024025) was analyzed separately for the 16 sequences in this study to investigate its role in genetic variability and diversity. Three scenarios of nucleotide sequences based NJ trees were constructed to examine the role of AT-Loop region in the evolution of F. gigantica : i) NJ complete mt genome of the 16 isolates, ii) complete mt genome without AT-loop, and iii) NJ tree based on the AT-loop sequences only. 3. Results and Discussion 3.1 Genome content and organization Sixteen complete mt genome sequences of F. gigantica were successfully obtained from (cattle, sheep, and goats), were 14,483 bp in length (Fig. 1 ). The F. gigantica mt genome contains 12 protein-coding genes (COXCOX1-3, ND1-6, ND4L, ATP6, and CYTB), a small subunit ribosomal RNA gene (rrnS), a large subunit ribosomal RNA gene (rrnL), and 22 transfer RNA genes, short and long non-coding regions (Table 1 ). The details of gene locations were given in (Table 2 ). In comparison with the reference sequence, we observed there is an addition of 5 bp in Sudan mt genome 14,483 bp as compared to the reference 14,478 bp see (Fig. 1 ). Table 1 Comparison of lengths of protein-coding genes and their start and stop codons in the mitochondrial genome of Fasciola hepatica from Sudan with the reference Fasciola hepatica from Sudan Reference (NC_024025) Genes/Proteins Length Codon Position 5' to 3' Genes/Proteins Length Codon Position 5' to 3' bp aa start stop bp aa start stop COX3 642 214 ATG TAG 1-642 COX3 642 213 ATG TAG 1-642 CYTB 1113 371 ATG TAG 715–1827 CYTB 1113 370 ATG TAG 715–1827 ND4L 261 87 GTG TAG 1836–2096 ND4L 273 90 GTG TAG 1836–2108 ND4 1272 424 GTG TAG 2057–3328 ND4 1272 423 GTG TAA 2069–3337 ATP6 519 173 ATG TAG 3545–4063 ATP6 519 172 ATG TAG 3554–4072 ND2 867 289 ATG TAG 4076–4942 ND2 867 288 ATG TAG 4085–4951 ND1 903 301 GTG TAG 5162–6064 ND1 903 300 GTG TAG 5171–6073 ND3 357 119 ATG TAG 6342–6698 ND3 357 118 ATG TAG 6364–6720 COX1 1542 514 GTG TAG 6834–8375 COX1 1533 510 GTG TAG 6856–8397 COX2 603 201 ATG TAG 10287–10889 COX2 603 200 ATG TAG 10310–10912 ND6 453 151 ATG TAG 10936–11388 ND6 453 150 ATG TAG 10959–11411 ND5 1569 523 ATG TAG 11724–13292 ND5 1569 522 GTG TAG 11744–13309 Table 2 Comparison of lengths and positions of transfer RNAs, ribosomal RNAs and non-coding regions in the mitochondrial genome of Fasciola hepatica from Sudan with the reference Genes (RNAs) Length (bp) Position 5' to 3' Genes (RNAs) Length (bp) Position 5' to 3' tRNA-His 62 650–713 tRNA-His 650–713 64 tRNA-Gln 64 3330–3395 tRNA-Gln 3339–3404 66 tRNA-Phe 63 3408–3472 tRNA-Phe 3417–3481 65 tRNA-Met 64 3479–3544 tRNA-Met 3488–3553 66 tRNA-Val 62 4948–5011 tRNA-Val 4957–5020 64 tRNA-Ala 63 5026–5090 tRNA-Ala 5035–5099 65 tRNA-Asp 63 5094–5158 tRNA-Asp 5103–5199 65 tRNA-Asn 66 6070–6137 tRNA-Asn 6084–6153 70 tRNA-Pro 66 6143–6210 tRNA-Pro 6163–6253 68 tRNA-Ile 59 6211–6271 tRNA-Ile 6231–6292 62 tRNA-Lys 64 6276–6341 tRNA-Lys 6297–6363 67 tRNA-Ser 54 6703–6758 tRNA-Ser 6725–6780 56 tRNA-Trp 61 6768–6830 tRNA-Trp 6790–6852 63 tRNA-Thr 66 8397–8464 tRNA-Thr 8419–8486 68 L-rRNA (16S) 984 8466–9451 l-rRNA 8488–9473 986 tRNA-Cys 63 9452–9516 tRNA-Cys 9474–9538 65 S-rRNA (12S) 768 9517–10286 s-rRNA 9539–10309 771 tRNA-Tyr 55 11396–11452 tRNA-Tyr 11419–11475 57 tRNA-Leu 63 11463–11527 tRNA-Leu 11486–11550 65 tRNA-Ser 57 11528–11586 tRNA-Ser 11551–11607 57 tRNA-Leu 62 11595–11658 tRNA-Leu 11616–11678 63 tRNA-Arg 67 11660–11728 tRNA-Arg 11680–11745 66 tRNA-Glu 66 13315–13382 tRNA-Glu 13332–13399 68 tRNA-Gly 62 13559–13622 tRNA-Gly 13574–13637 64 Non-coding region Short NR 176 13383–13621 AT-Loop 13400–13573 174 Long NR 860 13623–14483 AT-Loop 13638–14478 841 3.2 Protein-coding genes and codon usage patterns The boundaries between protein-coding genes in the mt genome of Sudan F. gigantica were determined by aligning their sequences and by identifying translation initiation and termination codons in comparison to the reference sequence (NC_024025). As shown in Table 1 , the start codon of the12 protein-coding genes was ATG codon, which is identical to reference mt genomes except for the ND5 which was GTG codon. On the other hand, the stop codon was TAG, which was identical to the reference, except in the ND4 which was TAA codon. Additionally, the lengths of the 12 protein-coding genes are identical in 10 genes and differ by 12 bp longer at the beginning of ND4L in the reference 273 bp as compared to Sudan isolates 261 bp and 9 bp shorter in COX1 in the reference 1,533 bp as compared to Sudan isolates 15,42 bp. The longer the branch, the more genetic change (or divergence) occurred. Furthermore, all the amino acids were different from the reference mt genome. This should be revised as suggested in the abstract. 3.3 Transfer RNA and ribosomal RNA genes and non-coding regions The 22 tRNA genes in F. gigantica mt genome vary in length from 56 to 69 nucleotides with differences (Table 2 ). The order and orientation of the gene arrangement pattern were identical to that of the reference. In contrast, the non-coding regions differed by 20 bp and 4 bp length in the long and the short non-coding regions of Sudan isolates that showed 861 bp and 178 bp, as compared to 841 and 174 bp in the reference, respectively. 3.4 Nucleotide variability in the mt genome among F. gigantica from Sudan An estimate of nucleotide diversity Pi (π) for individual mt genes was produced by sliding window analysis across the mt genomes of F. gigantica (Fig. 4 ). The sliding window revealed that the genes CYTB and ND1 had the highest and lowest levels of sequence variability based on the number of variable positions per unit length of the gene, respectively. Conserved regions were identified within ND1, COX 1, and ND5. In this study, the COX 1, ND 5 and ND3 genes are the most conserved protein-coding genes, and COX 3, ND4L and ATP 6 and ND2 and ND 1 are the least conserved. CYTB and ND4 have no conservations. This is in contrast to the reference genome (NC_024025) which showed that the lowest levels of sequence variability were within the genes ND6 and CYTB, respectively and the conserved regions were within ND1 and COX 1 genes. In addition, it has also been shown that the CYTB and ND1 genes are the most conserved protein-coding genes, while ND6, ND5 and ND4 are the least conserved. 3.5 Phylogenetic analysis Maximum likelihood phylogenetic analysis of the concatenated amino acid sequence data for all 12 mt proteins (Fig. 2 ) showed that F. gigantica from Sudan are well clustered from F. gigantica available in the GenBank forming one group and within themselves, these were further divided into two main subgroups. This tree is more or less comparable to those based COI and ND1 gene partial sequences of F. gigantica from Sudan (Ibrahim et al ., 2023). 3.6 Evolutionary relationships of F. gigantica based on stem-loop (non-coding regions) Comparative NJ trees based on with and without non-coding regions showed exact topology (Fig. 3 A, B), except for the information about the root which is clear in the NJ using the complete mt genome (with the non-coding region) (Fig. 3 A). It showed that goats samples (LC649580 and LC649579, LC649581) and cattle LC649569 were the oldest part of the tree and tell us the direction of evolution, with the flow of genetic information moving from the root, towards the tips with each successive generation. It also gave more information on the sheep sample (LC649583) which showed the branch length was affected by the presence or absence of the control region (see Fig. 3 A, B, C) which indicated more mutations and or polymorphism in the NJ without the non-coding regions. This non-coding region represents the mtDNA control region in higher vertebrates that is known to be the most polymorphic region of the mtDNA genome (Salim et al. 2014 ; Bronstein et al. 2018 ), rapidly evolving and that may contribute to the host's adaptation. Additionally, the NJ tree based on the non-coding regions alone revealed a similar topology to the with and without non-coding regions trees with the exception that the sheep sample (LC649583) and the two goat samples (LC649579 and LC649580), which have shown greater resolution of the polymorphisms in these small ruminant samples (Fig. 3 C). The goat sample LC649580 was shown to have been affected by the mt genome without the control region more than the control region which indicates the deep evolutionary process has shaped this sample as compared to the sheep (LC649583). The rates at which the coding areas accumulate mutations vary substantially, and as a result, their effectiveness for addressing certain concerns of biological importance is varied. The most conserved genes are those for tRNA and rRNA. The non-coding region (also called the control region or the displacement loop, or D-loop, in vertebrates) is the most variable portion of the genome both in terms of length and nucleotide composition and shows a high degree of variation between populations of the same species (Le et al. 2000 ). This region has been used for many intra-specific studies in vertebrates (Huang et al . 2016; Bronstein et al. 2018 ; Bernacki and Kilpatrick 2020 ) but little information is available for its structure and utility in many invertebrates except for a few studies (e.g. Jiang et al. 2018 , cephalopods – Loliginidae). This is true also for parasites and this variation must be studied because of its epidemiological relevance as clearly shown in this study. Variability can be a consequence of molecular evolutionary processes on species and populations, and the interpretation of experimental data must also bear this in mind. In this study, we took the advantage of complete mt genome sequences of F. gigantica from different host species and have provided evidence that non-coding of the parasite is a rich source of genetic markers for phylogenetic analysis and study of genetic variability in helminth. The mitochondrial control region also called the displacement loop, or D-loop is located between the genes coding for proline and phenylalanine tRNAs in vertebrates. It is responsible for the transcription of mitochondrial genes and contains the point of origin of heavy strand replication. Several regions of the mitochondrial D-loop show a high degree of variation between populations of the same species. Conclusion The complete mitogenome analysis of F. gigantica in this study provided a useful information for phylogenetic relatsionships and evolutionary biology of this important neglected parasite. The sequence of the waves of Fasciola that spread over the five continents will be supported by the material provided, as well as the knowledge created about the movements of domesticated hosts that were directed by humans. Equally important, we disclosed that the control region is a helpful marker in investigations of the population genetics of F. gigantica and that it may be utilized by itself to get outcomes that are more or less comparable to those obtained by the whole mitogenome. Declarations Ethics approval and consent to participate - All experimental protocols were approved with the experimental protocols and approval by the, Faculty of Veterinary Medicine, University of Khartoum, research review board. - All methods were carried out in accordance with the Faculty of Veterinary Medicine, University of Khartoum, according to their guidelines for sampling domestic animals in Sudan. Consent for publication Not applicable. Availability of data and materials The sequences obtained were deposited to the DNA Data Bank of Japan (http://www.ddbj.nig.ac.jp) under accession numbers LC649568 - LC649583. Competing interests The authors declare no competing interests. Funding Not applicable Authors' contributions BS, RN designed, conceptualized research and supervised the work. KI contributed samples. BS, EC, RN carried out the experiments. BS, NSM analyzed data. RN funding acquisition. BS, NSM, SA, YO, NN, RN, contributed to the analysis and the interpretation of the results. BS, RN administrated project. BS, NSM. SA writing original draft. BS, NSM. SA, FA and RN writing, reviewing and editing. All authors provided critical feedback and helped shape the research, analysis and manuscript. Acknowledgements We acknowledge the partial financial support from Japan Society for the Promotion of Science (JSPS) KAKENHI [22H02505] to the last author. References Bernacki LE, Kilpatrick CW. Structural Variation of the Turtle Mitochondrial Control Region. J Mol Evol. 2020;88(7):618–40. Boore JL. Animal mitochondrial genomes. Nucleic Acids Res. 1999;27(8):1767–80. Bronstein O, Kroh A, Haring E. Mind the gap! The mitochondrial control region and its power as a phylogenetic marker in echinoids. BMC Evol Biol. 2018;18(1):80. FAO/WHO. (2014). Multicriteria-Based Ranking for Risk Management of Food-Borne Parasites, p 287. Gu MJ, Huang WL, Li YS, Dong HF, Zhao QP. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3848681","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266268845,"identity":"b9b1e700-4fad-49b3-bc18-9b104a79ad6a","order_by":0,"name":"Bashir Salim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACAwYGNoYEIIOPmYHxAZDm4SNaCxszA7MBSAsbUVpAAEiySUAZ+IE5+/FnDx7uqZNnY+dOq/yaYyfDxsD88NENPFose3LMDRKeHTZsY+bddlt2WzLQYWzGxjn4HHYgh00i4cABRrAWyW3MQC08bNJ4tZx//gyopc4epKVYcls9EVpuJJgBtTAngrQwftx2mLAWyxlvQFoOJwO1bJZm3Hach42ZgF/M+dOfSf44UGfbz39248ef26rt+dmbHz7GpwUFMPOASWKVgwDjD1JUj4JRMApGwYgBACOuQfhfHVTSAAAAAElFTkSuQmCC","orcid":"","institution":"University of Khartoum","correspondingAuthor":true,"prefix":"","firstName":"Bashir","middleName":"","lastName":"Salim","suffix":""},{"id":266268846,"identity":"9c630b89-4167-4b04-a475-63066e98f247","order_by":1,"name":"Nouh S. Mohamed","email":"","orcid":"","institution":"Sirius Training and Research Center","correspondingAuthor":false,"prefix":"","firstName":"Nouh","middleName":"S.","lastName":"Mohamed","suffix":""},{"id":266268847,"identity":"6047ffb7-5ec0-46c1-9ff6-cd81385c0695","order_by":2,"name":"Kamal Ibrahim","email":"","orcid":"","institution":"Central Veterinary Research Laboratory, Khartoum, Sudan.","correspondingAuthor":false,"prefix":"","firstName":"Kamal","middleName":"","lastName":"Ibrahim","suffix":""},{"id":266268848,"identity":"24004efa-1b81-4bc3-9ea1-de39e0dcd4e1","order_by":3,"name":"Saeed Alasmari","email":"","orcid":"","institution":"Najran University","correspondingAuthor":false,"prefix":"","firstName":"Saeed","middleName":"","lastName":"Alasmari","suffix":""},{"id":266268849,"identity":"0461318f-3bee-4a0b-a51c-92f512523539","order_by":4,"name":"Elisha Chatanga","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Elisha","middleName":"","lastName":"Chatanga","suffix":""},{"id":266268852,"identity":"57ac59cd-e4c1-4edf-82a7-fc48ed61172d","order_by":5,"name":"Yuma Ohari","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Yuma","middleName":"","lastName":"Ohari","suffix":""},{"id":266268855,"identity":"fb9acc35-b50a-4818-bffd-5b2bff56a2d0","order_by":6,"name":"Nariaki Nonaka","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Nariaki","middleName":"","lastName":"Nonaka","suffix":""},{"id":266268857,"identity":"e34572d3-b8fe-4735-97d4-a6ed2f8e75ec","order_by":7,"name":"Faisal Almathen","email":"","orcid":"","institution":"King Faisal University","correspondingAuthor":false,"prefix":"","firstName":"Faisal","middleName":"","lastName":"Almathen","suffix":""},{"id":266268858,"identity":"ae4cf6bf-3b68-48a3-8eb6-ce3883d26180","order_by":8,"name":"Ryo Nakao","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Nakao","suffix":""}],"badges":[],"createdAt":"2024-01-09 15:44:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3848681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3848681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49473111,"identity":"099457a6-ced0-42b7-a55e-eeb76f216089","added_by":"auto","created_at":"2024-01-11 12:27:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":432975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eF. gigentica\u003c/em\u003e complete mtDNA genome arrangement: comparison between Sudan and reference NC_024025.1. Gene scaling is only approximate. All genes have standard nomenclature including the 22 tRNA genes, which are designated purple color.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3848681/v1/2664456c4f3b7e2e4213f1b5.jpeg"},{"id":49473113,"identity":"03ffafa6-cf66-4829-baee-c7245c7dbdc5","added_by":"auto","created_at":"2024-01-11 12:27:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":318859,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum likelihood \u003c/strong\u003ephylogenetic tree of the concatenated amino acid sequence data for all 12 protein-coding genes showed that the all \u003cem\u003eF. gigantica\u003c/em\u003e from Sudan was clustered separately from the available \u003cem\u003eF. gigantica\u003c/em\u003e. complete mtDNA on nucleotide sequences. Genetic relationships of \u003cem\u003eFasciola gigantica\u003c/em\u003e from Sudan (Shaded) with \u003cem\u003eFasciola spp, Fascioloides\u003c/em\u003e, \u003cem\u003eFasciolpsis\u003c/em\u003e, and other trematodes.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3848681/v1/c1de98bf57a6b5b1c2c255e6.jpeg"},{"id":49473115,"identity":"fd47500a-90d2-4dec-95cb-55dc60c92df6","added_by":"auto","created_at":"2024-01-11 12:27:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":602107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eRooted neighbor joining tree based of complete mtDNA on nucleotide sequences showing the genetic relationships of 16 \u003cem\u003eFasciola gigantica \u003c/em\u003efrom Sudan B)\u003cstrong\u003e \u003c/strong\u003eRooted NJ tree based of complete mtDNA on nucleotide sequences without AT-loop\u003cstrong\u003e \u003c/strong\u003eC)\u003cstrong\u003e \u003c/strong\u003eRooted NJ tree based on AT-Loop only.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3848681/v1/44b004d7499171658073c6b8.jpeg"},{"id":49473417,"identity":"26cb3fc1-ed6f-4dd6-9cb4-57c1d0dd1214","added_by":"auto","created_at":"2024-01-11 12:35:10","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":821157,"visible":true,"origin":"","legend":"\u003cp\u003eSliding window analysis of the complete mtDNA sequences of \u003cem\u003eFasciola gigantica \u003c/em\u003efrom Sudan. The blue line indicates nucleotide diversity in a window of 300 bp (10 bp-steps). Gene regions were highlighted with grey. Genetic variability values are shown in the Y axis. X axis shows the length of the mtDNA. The vertical dashed lines indicate genes boundaries. Asterisks indicate where significant nucleotides diversity were observed in the mtDNA (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3848681/v1/f238211295a7f22498a657c1.jpeg"},{"id":55265182,"identity":"2b57ad6f-8909-4e97-b8dd-d13b2ca5006e","added_by":"auto","created_at":"2024-04-25 01:57:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1064316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3848681/v1/e7566765-ce25-48cd-8d7d-9552579c0f83.pdf"},{"id":49473112,"identity":"79211afa-8e7c-479f-bcd6-e7e199f7e8dc","added_by":"auto","created_at":"2024-01-11 12:27:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12933,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3848681/v1/472e4ee990e4612d082b60c1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Complete mitogenome analysis of Fasciola gigantica from Sudan","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFascioliasis is an important neglected food-borne parasitic disease caused by trematodes of the genus \u003cem\u003eFasciola\u003c/em\u003e, affecting people and animals worldwide (FAO/WHO \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). \u003cem\u003eFasciola hepatica\u003c/em\u003e and/or \u003cem\u003eFasciola gigantica\u003c/em\u003e infection is prevalent in over 600\u0026nbsp;million domestic ruminants worldwide (cattle, sheep, goats, buffalo, donkey, and pig), causing major economic losses of about US\u003cspan\u003e$\u003c/span\u003e3\u0026nbsp;billion per annum (Mas-Coma et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The mitochondrial genome has been used widely to investigate phylogenetic relationships. As a consequence of its well-established organization, this genome has been sequenced in many organisms, including \u003cem\u003eF. gigantica\u003c/em\u003e (Liu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The advancement of high-throughput, next-generation sequencing technologies has led to the recent focus on whole-genome sequencing and complete nuclear genomes of parasitic trematodes (e.g. \u003cem\u003eFasciola hepatica\u003c/em\u003e McNulty et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u003cem\u003eFasciola gigantica\u003c/em\u003e Luo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cem\u003eParagonimus westermani\u003c/em\u003e by Gu et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Availability of which facilitates detailed comparative phylogenetic analyses across many species complexes and major taxonomic groups of trematodes. Consequently, sequences generated from the mt genome provide excellent molecular markers for defining population groups, tracing the genetic history of an individual or a particular group of related individuals, and for constructing deep-branch taxonomic phylogenies (Rollinson et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Boore, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). \u003cem\u003eFasciola gigantica\u003c/em\u003e and \u003cem\u003eF. hepatica\u003c/em\u003e are thought to have diverged about 11.8 Ma, according to a recent work by Luo et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that provides the reference genome of \u003cem\u003eF\u003c/em\u003e. \u003cem\u003egigantica\u003c/em\u003e. They hypothesized that actin and aquaporin genes were responsible for the liver flukes' rapid evolution, large body size, and parasitic adaptability that led to their spread.\u003c/p\u003e \u003cp\u003eTo date, there is no available information known about the genetic characterization of \u003cem\u003eF. gigantica\u003c/em\u003e in Sudan except the recent study by Ibrahim \u003cem\u003eet al\u003c/em\u003e. (2023) in which COI and ND1 genes were used to characterize \u003cem\u003eF. gigantica\u003c/em\u003e from different regions and hosts from Sudan. This study has provided a holistic and in depth analysis of complete mitochondrial genome, phylogenetic relationships that help extrapolate the evolutionary biology of this important neglected parasite.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample collection and DNA extraction\u003c/h2\u003e \u003cp\u003eSample collection and DNA extraction were carried out as described in detail in the previous study (Ibrahim et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A total of sixteen samples, comprising ten from cattle, five from goats, and one from sheep, were utilized for generating complete mitochondrial genome sequences. In summary, these samples were sourced from slaughterhouses, subjected to thorough washing with distilled water, morphologically identified, and then squashed prior to genomic DNA extraction. Approximately 10 mg of tissue from the lateral zone was employed for DNA extraction using the QIAamp DNA extraction Kit (Qiagen, INC), following the manufacturer's guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 PCR and Illumina MiSeq sequencing\u003c/h2\u003e \u003cp\u003eTo obtain the complete mitochondrion genome sequences, two overlapping long PCR reactions were used; the first set with forward primer P101: 5\u0026rsquo;-TCTAGTGCTGCTTTGGTTGCTATGTC-3\u0026rsquo; and reverse primer P265: 5\u0026rsquo;-GAAGCTACAATCCCACGCACTAAACT-3\u0026rsquo; amplified approximately 7 kb and the second set with forward primer P247: 5\u0026rsquo;-TGTGGGTTATTGTACGGAGTTGTGTG-3\u0026rsquo; and reverse primer P121: 5\u0026rsquo;-CATATAACCCAACACCTTACGCTCACC-3\u0026rsquo; amplified approximately 9 kb. The PCR reactions were conducted in a 25 \u0026micro;l reaction mixture containing 12.5 \u0026micro;l of 2\u0026times;Gflex PCR Buffer (Mg2+, dNTP plus), 0.5 \u0026micro;l of Tks Gflex DNA Polymerase 1.25 units/ \u0026micro;l (TaKaRa Bio Inc., Shiga, Japan), 200 nM of each primer, 1.0 \u0026micro;l of template DNA and molecular grade water. The cycling conditions for both PCRs were set with an initial denaturation at 94\u0026deg;C for 1 min, followed by 35 cycles of denaturation at 98\u0026deg;C for 10 sec, annealing at 60\u0026deg;C for 15 sec, extension at 68\u0026deg;C for 7 min and final extension at 68\u0026deg;C for 5 min. The amplicons were electrophoresed in 1% agarose gel stained with Gel-Red (Biotium, Hayward, CA, USA) and visualized under UV light.\u003c/p\u003e \u003cp\u003eThe amplicons were purified using NucleoSpin Gel and PCR Clean-Up Kit (TaKaRa Bio Inc.). The purified amplicons were sequenced on an Illumina MiSeq platform using the MiSeq Reagent Kit v3 (600 cycles) (Illumina, San Diego, USA) according to the manufacturer\u0026rsquo;s protocol. The reads obtained from the Illumina MiSeq run were mapped against published mitochondrial genomes of \u003cem\u003eF. gigantica\u003c/em\u003e (NC_024025) using CLC Genomics Workbench v20.0.4 (QIAGEN, Hilden, Germany). The sequences obtained in this study were submitted to the NCBI GenBank database under accession numbers LC649568 - LC649583.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mitochondrial genome organization:\u003c/h2\u003e \u003cp\u003eThe complete mt genome of \u003cem\u003eF. gigantica\u003c/em\u003e was compared with the \u003cem\u003eF. gigantica\u003c/em\u003e reference sequence (NC_024025) to illustrate genes and transfer ribosomal RNA arrangements using the online GenomeVx software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wolfe.ucd.ie/GenomeVx/\u003c/span\u003e\u003cspan address=\"http://wolfe.ucd.ie/GenomeVx/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sliding window analysis of nucleotide variation\u003c/h2\u003e \u003cp\u003eTo detect variable nucleotide sites, pairwise alignments of the 16 complete mt genomes of \u003cem\u003eF. gigantica\u003c/em\u003e from Sudan, were performed using MUSCLE implanted in MEGA 7. The sliding window analysis of the aligned complete mt genomes sequences of \u003cem\u003eF. gigantica\u003c/em\u003e was performed using DnaSP v.5. A sliding window of 300 bp (in 10 bp overlapping steps) was used to estimate nucleotide diversity Pi (π) across the alignment. Nucleotide diversity was plotted against mid-point positions of each window, and gene boundaries were identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Phylogenetic analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Phylogenetic relationship of \u003cem\u003eF. gigantica\u003c/em\u003e, Trematodes and Cestodes\u003c/h2\u003e \u003cp\u003ePhylogenetic relationship of 16 mt genomes from Sudan and selected \u003cem\u003eFasciola, Fascioloides, Fasciolopsis, Echinostoma, Paragonimus, Opisthorchis, Clonorchis, Schistosoma, Trichobilharzia\u003c/em\u003e, and \u003cem\u003eOrientobilharzia\u003c/em\u003e species (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), was reconstructed based on amino acid sequences of 12 protein-coding genes using the Gyrodactylus species (\u003cem\u003eGyrodactylus derjavinoides\u003c/em\u003e) GenBank accession number NC_010976 as the outgroup.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Phylogenetic relationship based on the stem-loop (non-coding regions)\u003c/h2\u003e \u003cp\u003eThe stem-loop structure referred to as AT-Loop according to the reference sequence (NC_024025) was analyzed separately for the 16 sequences in this study to investigate its role in genetic variability and diversity. Three scenarios of nucleotide sequences based NJ trees were constructed to examine the role of AT-Loop region in the evolution of \u003cem\u003eF. gigantica\u003c/em\u003e: i) NJ complete mt genome of the 16 isolates, ii) complete mt genome without AT-loop, and iii) NJ tree based on the AT-loop sequences only.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Genome content and organization\u003c/h2\u003e \u003cp\u003eSixteen complete mt genome sequences of \u003cem\u003eF. gigantica\u003c/em\u003e were successfully obtained from (cattle, sheep, and goats), were 14,483 bp in length (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003eF. gigantica\u003c/em\u003e mt genome contains 12 protein-coding genes (COXCOX1-3, ND1-6, ND4L, ATP6, and CYTB), a small subunit ribosomal RNA gene (rrnS), a large subunit ribosomal RNA gene (rrnL), and 22 transfer RNA genes, short and long non-coding regions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The details of gene locations were given in (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In comparison with the reference sequence, we observed there is an addition of 5 bp in Sudan mt genome 14,483 bp as compared to the reference 14,478 bp see (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of lengths of protein-coding genes and their start and stop codons in the mitochondrial genome of \u003cem\u003eFasciola hepatica\u003c/em\u003e from Sudan with the reference\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFasciola hepatica\u003c/em\u003e from Sudan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e \u003cp\u003eReference (NC_024025)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenes/Proteins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCodon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePosition 5' to 3'\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenes/Proteins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eLength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCodon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePosition 5' to 3'\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003estart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003estop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ebp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eaa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003estart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003estop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1-642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCOX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1-642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCYTB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e715\u0026ndash;1827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCYTB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e715\u0026ndash;1827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND4L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1836\u0026ndash;2096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eND4L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1836\u0026ndash;2108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2057\u0026ndash;3328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eND4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2069\u0026ndash;3337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3545\u0026ndash;4063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eATP6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3554\u0026ndash;4072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4076\u0026ndash;4942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eND2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4085\u0026ndash;4951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5162\u0026ndash;6064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eND1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5171\u0026ndash;6073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6342\u0026ndash;6698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eND3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6364\u0026ndash;6720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6834\u0026ndash;8375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCOX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6856\u0026ndash;8397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10287\u0026ndash;10889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCOX2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10310\u0026ndash;10912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10936\u0026ndash;11388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eND6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10959\u0026ndash;11411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eND5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11724\u0026ndash;13292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eND5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11744\u0026ndash;13309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of lengths and positions of transfer RNAs, ribosomal RNAs and non-coding regions in the mitochondrial genome of \u003cem\u003eFasciola hepatica\u003c/em\u003e from Sudan with the reference\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes (RNAs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition 5' to 3'\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenes (RNAs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLength (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePosition 5' to 3'\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-His\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e650\u0026ndash;713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-His\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e650\u0026ndash;713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Gln\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3330\u0026ndash;3395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Gln\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3339\u0026ndash;3404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Phe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3408\u0026ndash;3472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Phe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3417\u0026ndash;3481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Met\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3479\u0026ndash;3544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Met\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3488\u0026ndash;3553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Val\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4948\u0026ndash;5011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Val\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4957\u0026ndash;5020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Ala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5026\u0026ndash;5090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Ala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5035\u0026ndash;5099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Asp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5094\u0026ndash;5158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Asp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5103\u0026ndash;5199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Asn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6070\u0026ndash;6137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Asn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6084\u0026ndash;6153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Pro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6143\u0026ndash;6210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Pro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6163\u0026ndash;6253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Ile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6211\u0026ndash;6271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Ile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6231\u0026ndash;6292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Lys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6276\u0026ndash;6341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Lys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6297\u0026ndash;6363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Ser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6703\u0026ndash;6758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Ser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6725\u0026ndash;6780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6768\u0026ndash;6830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6790\u0026ndash;6852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Thr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8397\u0026ndash;8464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Thr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8419\u0026ndash;8486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL-rRNA (16S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e984\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8466\u0026ndash;9451\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003el-rRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8488\u0026ndash;9473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Cys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9452\u0026ndash;9516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Cys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9474\u0026ndash;9538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS-rRNA (12S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e768\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9517\u0026ndash;10286\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es-rRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9539\u0026ndash;10309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Tyr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11396\u0026ndash;11452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Tyr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11419\u0026ndash;11475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Leu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11463\u0026ndash;11527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Leu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11486\u0026ndash;11550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Ser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11528\u0026ndash;11586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Ser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11551\u0026ndash;11607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Leu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11595\u0026ndash;11658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Leu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11616\u0026ndash;11678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Arg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11660\u0026ndash;11728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Arg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11680\u0026ndash;11745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Glu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13315\u0026ndash;13382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Glu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13332\u0026ndash;13399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etRNA-Gly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13559\u0026ndash;13622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA-Gly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13574\u0026ndash;13637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-coding region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort NR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13383\u0026ndash;13621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAT-Loop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13400\u0026ndash;13573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong NR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13623\u0026ndash;14483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAT-Loop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13638\u0026ndash;14478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Protein-coding genes and codon usage patterns\u003c/h2\u003e \u003cp\u003eThe boundaries between protein-coding genes in the mt genome of Sudan \u003cem\u003eF. gigantica\u003c/em\u003e were determined by aligning their sequences and by identifying translation initiation and termination codons in comparison to the reference sequence (NC_024025). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the start codon of the12 protein-coding genes was ATG codon, which is identical to reference mt genomes except for the ND5 which was GTG codon. On the other hand, the stop codon was TAG, which was identical to the reference, except in the ND4 which was TAA codon. Additionally, the lengths of the 12 protein-coding genes are identical in 10 genes and differ by 12 bp longer at the beginning of ND4L in the reference 273 bp as compared to Sudan isolates 261 bp and 9 bp shorter in COX1 in the reference 1,533 bp as compared to Sudan isolates 15,42 bp. The longer the branch, the more genetic change (or divergence) occurred. Furthermore, all the amino acids were different from the reference mt genome. This should be revised as suggested in the abstract.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Transfer RNA and ribosomal RNA genes and non-coding regions\u003c/h2\u003e \u003cp\u003eThe 22 tRNA genes in \u003cem\u003eF. gigantica\u003c/em\u003e mt genome vary in length from 56 to 69 nucleotides with differences (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The order and orientation of the gene arrangement pattern were identical to that of the reference. In contrast, the non-coding regions differed by 20 bp and 4 bp length in the long and the short non-coding regions of Sudan isolates that showed 861 bp and 178 bp, as compared to 841 and 174 bp in the reference, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Nucleotide variability in the mt genome among \u003cem\u003eF. gigantica\u003c/em\u003e from Sudan\u003c/h2\u003e \u003cp\u003eAn estimate of nucleotide diversity Pi (π) for individual mt genes was produced by sliding window analysis across the mt genomes of \u003cem\u003eF. gigantica\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The sliding window revealed that the genes CYTB and ND1 had the highest and lowest levels of sequence variability based on the number of variable positions per unit length of the gene, respectively. Conserved regions were identified within ND1, COX 1, and ND5. In this study, the COX 1, ND 5 and ND3 genes are the most conserved protein-coding genes, and COX 3, ND4L and ATP 6 and ND2 and ND 1 are the least conserved. CYTB and ND4 have no conservations. This is in contrast to the reference genome (NC_024025) which showed that the lowest levels of sequence variability were within the genes ND6 and CYTB, respectively and the conserved regions were within ND1 and COX 1 genes. In addition, it has also been shown that the CYTB and ND1 genes are the most conserved protein-coding genes, while ND6, ND5 and ND4 are the least conserved.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Phylogenetic analysis\u003c/h2\u003e \u003cp\u003eMaximum likelihood phylogenetic analysis of the concatenated amino acid sequence data for all 12 mt proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that \u003cem\u003eF. gigantica\u003c/em\u003e from Sudan are well clustered from \u003cem\u003eF. gigantica\u003c/em\u003e available in the GenBank forming one group and within themselves, these were further divided into two main subgroups. This tree is more or less comparable to those based COI and ND1 gene partial sequences of \u003cem\u003eF. gigantica\u003c/em\u003e from Sudan (Ibrahim \u003cem\u003eet al\u003c/em\u003e., 2023).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Evolutionary relationships of\u003c/b\u003e F. gigantica\u003c/b\u003e based on stem-loop (non-coding regions)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eComparative NJ trees based on with and without non-coding regions showed exact topology (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B), except for the information about the root which is clear in the NJ using the complete mt genome (with the non-coding region) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). It showed that goats samples (LC649580 and LC649579, LC649581) and cattle LC649569 were the oldest part of the tree and tell us the direction of evolution, with the flow of genetic information moving from the root, towards the tips with each successive generation. It also gave more information on the sheep sample (LC649583) which showed the branch length was affected by the presence or absence of the control region (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, C) which indicated more mutations and or polymorphism in the NJ without the non-coding regions. This non-coding region represents the mtDNA control region in higher vertebrates that is known to be the most polymorphic region of the mtDNA genome (Salim et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bronstein et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), rapidly evolving and that may contribute to the host's adaptation. Additionally, the NJ tree based on the non-coding regions alone revealed a similar topology to the with and without non-coding regions trees with the exception that the sheep sample (LC649583) and the two goat samples (LC649579 and LC649580), which have shown greater resolution of the polymorphisms in these small ruminant samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The goat sample LC649580 was shown to have been affected by the mt genome without the control region more than the control region which indicates the deep evolutionary process has shaped this sample as compared to the sheep (LC649583).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe rates at which the coding areas accumulate mutations vary substantially, and as a result, their effectiveness for addressing certain concerns of biological importance is varied. The most conserved genes are those for tRNA and rRNA. The non-coding region (also called the control region or the displacement loop, or D-loop, in vertebrates) is the most variable portion of the genome both in terms of length and nucleotide composition and shows a high degree of variation between populations of the same species (Le et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This region has been used for many intra-specific studies in vertebrates (Huang \u003cem\u003eet al\u003c/em\u003e. 2016; Bronstein et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bernacki and Kilpatrick \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but little information is available for its structure and utility in many invertebrates except for a few studies (e.g. Jiang et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, cephalopods \u0026ndash; Loliginidae). This is true also for parasites and this variation must be studied because of its epidemiological relevance as clearly shown in this study. Variability can be a consequence of molecular evolutionary processes on species and populations, and the interpretation of experimental data must also bear this in mind. In this study, we took the advantage of complete mt genome sequences \u003cem\u003eof F. gigantica\u003c/em\u003e from different host species and have provided evidence that non-coding of the parasite is a rich source of genetic markers for phylogenetic analysis and study of genetic variability in helminth.\u003c/p\u003e \u003cp\u003eThe mitochondrial control region also called the displacement loop, or D-loop is located between the genes coding for proline and phenylalanine tRNAs in vertebrates. It is responsible for the transcription of mitochondrial genes and contains the point of origin of heavy strand replication. Several regions of the mitochondrial D-loop show a high degree of variation between populations of the same species.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":" \u003cp\u003eThe complete mitogenome analysis of \u003cem\u003eF. gigantica\u003c/em\u003e in this study provided a useful information for phylogenetic relatsionships and evolutionary biology of this important neglected parasite. The sequence of the waves of \u003cem\u003eFasciola\u003c/em\u003e that spread over the five continents will be supported by the material provided, as well as the knowledge created about the movements of domesticated hosts that were directed by humans. Equally important, we disclosed that the control region is a helpful marker in investigations of the population genetics of \u003cem\u003eF. gigantica\u003c/em\u003e and that it may be utilized by itself to get outcomes that are more or less comparable to those obtained by the whole mitogenome.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e- All experimental protocols were approved with the experimental protocols and approval by the, Faculty of Veterinary Medicine, University of Khartoum, research review board.\u003c/p\u003e\n\u003cp\u003e- All methods were carried out in accordance with the Faculty of Veterinary Medicine, University of Khartoum, according to their guidelines for sampling domestic animals in Sudan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequences obtained were deposited to the DNA Data Bank of Japan (http://www.ddbj.nig.ac.jp) under accession numbers LC649568 - LC649583.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Authors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBS, RN designed, conceptualized research and supervised the work. KI contributed samples. BS, EC, RN carried out the experiments. BS, NSM analyzed data. RN funding acquisition. BS, NSM, SA, YO, NN, RN, contributed to the analysis and the interpretation of the results. BS, RN administrated project. BS, NSM. SA writing original draft. BS, NSM. SA, FA and RN writing, reviewing and editing. All authors provided critical feedback and helped shape the research, analysis and manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the partial financial support from Japan Society for the Promotion of Science (JSPS) KAKENHI [22H02505] to the last author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBernacki LE, Kilpatrick CW. Structural Variation of the Turtle Mitochondrial Control Region. J Mol Evol. 2020;88(7):618\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoore JL. Animal mitochondrial genomes. 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Anim Genet. 2014;45(6):782\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fasciola gigantica, mitochondrial genome, genomic, phylogeny","lastPublishedDoi":"10.21203/rs.3.rs-3848681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3848681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComplete mitochondrial (mt) genomes are increasingly being used as molecular markers for investigating phylogenetic relationships. We sequenced the complete mt genome of the \u003cem\u003eFasciola gigantica\u003c/em\u003e of 16 samples from cattle, sheep and goats from Sudan using Illumina MiSeq platform. The complete mt genome of \u003cem\u003eF. gigantica\u003c/em\u003e was 14,483 bp in length. Its genome is circular, and consists of 36 genes, including 12 protein-coding genes, 2 subunit ribosomal RNA genes (rRNA), and 22 genes for tRNA. The start and stop codons of the12 protein-coding genes are ATG and TAG respectively, which are identical to reference mt genomes except for the ND5 the start codon GTG and the stop codon of the ND4 which was TAA. Additionally, the lengths of the the12 protein-coding genes were identical in 10 genes, however, the ND4L of the reference was 12 bp longer with 273 bp as compared to the Sudan isolates which was 261 bp long and COX1 in the reference was 9 bp shorter which was 1,533 bp long as compared to Sudan isolates which 1,542 bp long. In contrast, the non-coding regions differed by 20 bp and 4 bp length in the long and the short non-coding regions of Sudan isolates. Nucleotide variability in the mt genome among \u003cem\u003eF. gigantica\u003c/em\u003e from Sudan is quite different from the reference as revealed by the sliding window analysis. Phylogenetic analysis of the concatenated amino acid sequence data for all 12 protein-coding genes showed that all \u003cem\u003eF. gigantica\u003c/em\u003e from Sudan clustered separately from the available \u003cem\u003eF. gigantica\u003c/em\u003e. More interestingly, based on stem-loop (non-coding regions) it revealed better resolution on how the evolutionary process has affected host specificity and in particular for the sheep and goats. It is concluded that these novel complete mt genomes of \u003cem\u003eF. gigantica\u003c/em\u003e from different host species provide additional genetic markers for studying epidemiology, population genetics, and phylogeographics of \u003cem\u003eF. gigantica\u003c/em\u003e, as well as for understanding interplay and the host species.\u003c/p\u003e","manuscriptTitle":"Complete mitogenome analysis of Fasciola gigantica from Sudan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 12:27:06","doi":"10.21203/rs.3.rs-3848681/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"752e6cf5-20fc-4483-8ddd-f27995ed9bbe","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-22T08:32:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-11 12:27:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3848681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3848681","identity":"rs-3848681","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}