Identification of Treculia africana L. varieties using Internal Transcribed Spacer Region 1 (ITS 1) and Internal Transcribed Spacer Region 2 (ITS 2) DNA barcodes

doi:10.21203/rs.3.rs-4355610/v1

Background

Treculia africana L. (African breadfruit), is an underutilized, underexploited, and endangered species of southern Nigeria. It has been identified and classified using anatomical features, but there is insufficient information on its molecular identification and classification. There is a need to complement the morphological identification of the plant with molecular methods.

Results

To identify 86 accessions of Treculia africana var inversa and Treculia africana var africana, Internal Transcribed Spacer Region ITS-2 and Internal Transcribed Spacer Region lTS- 1 DNA barcodes were used. In this study, we observed that to determine the homology between sequences obtained and the Genbank database, the National Center for Biotechnology Information (NCBI) basic alignment search tool (BLAST) did not reveal any match. An alignment of the accessions with KU855474.1 Artocarpus altilis showed similarities via molecular evolutionary genetic analysis (mega 11).

Conclusions

The alignment revealed that the Treculia accessions were related and genetically similar to Artocarpus species, members of the Moraceae family, indicating that the accessions belong to the same family. However, the two varieties of Treculia could not be distinguished with ITS Barcodes. The molecular data of Treculia species need to be populated on the gene bank to support future molecular studies and also a combination of DNA barcodes is recommended for identification purposes.

Treculia

endangered

molecular identification

ITS barcode

alignment

Treculia africana is a highly endangered and scarce food tree species in Nigeria, Zambia, Uganda, Malawi and Tanzania. [1] described it as an underutilized plant with potential for food and nutrition security and as a medicinal food. [2] highlighted the urgency of conserving this species. It has been identified and classified using anatomical features [3], but there is insufficient information on its molecular identification and classification. The genus Treculia Decaisne is characterized in West Africa by three species namely T. africana Decaisne, T. obovoidea N. E. Br. which are trees and T. acuminate Ballion (T. zenkeri Engl.) which is a slender forest shrub [3]. There are three additional varieties of T. africana namely var. africana, var mollis and var inversa which were subsequently observed. T. africana var africana has large fruit heads, T. africana var mollis has medium heads, and T. africana var inversa has small heads [3]. There is a need to complement the morphological identifications of the plant with molecular methods.

Identification and characterization of plant materials are vital for ensuring the sustainable use of plant resources and achieving efficient conservation of these resources [4]. Morphological markers use phenotypic characteristics but are limited in that they can be identified in whole or adult plants [5]. Phenotypic characteristics are time-consuming, and may not provide precise identification, and separating closely related species that have certain physical traits in common, does not give precision in species identification [6]. These challenges are not obtainable with molecular tools. Molecular tools are uncomplicated, straightforward means for identifying investigated and uninvestigated plant taxa. These methods provide many novel taxonomic and evolutionary questions, which were previously impossible with solely phenotypic techniques [4]. This technique is an accelerated and more accurate means of ascertaining relationships among familiar species than techniques relying on morphological investigation [7]. This is because morphological traits are altered by environmental influences and detailed studies of mature plants are essential for taxonomic grouping [8].

Recently, DNA barcoding has become an emerging molecular technique that offers an adequate system of identification and classification technology and has been seen as a rebirth of taxonomy. Molecular characterization using DNA barcodes is an approach to species identification that requires short and standardized DNA fragments of standard genome regions to enable quick, precise, and definitive discrimination and identification of species [9, 10]. Short-standardized sequences can differentiate individuals of species based on the fact that genetic variation between species exceeds that within species [11]. Species identification is seen as the basic aspect of biodiversity description and recognition [12].

It has several applications in identifying unclear plant species [9], authenticating herbal products [13], identifying herbarium specimens [14], and identifying intraspecific ecotypes and ornamental species for horticulture [15]. There are several DNA regions or DNA sequences that are known for their universality and discriminatory power in barcoding plants. These include rbcL, mat K and trnH- psbA, atpF-atpH from the plastid genome (chloroplast DNA sequences) and the internal transcribed spacer (ITS) region of nuclear ribosomal DNA. The use of these barcodes as barcodes for land plants has been recommended by the consortium on the barcode of life [16]. Studies have shown that the nuclear marker ITS has high discriminatory power compared with these plastid markers, due to the rapid evolution of the nrDNA region which has led to genetic changes that allow the differentiation of closely related cogeneric species. Additionally, it is appropriate for species identification due to its short length, high variability, and high efficiency for PCR amplification. Therefore, nuclear DNA has been targeted for molecular identification and investigations [17].

ITS DNA barcodes are preferred because other DNA barcodes are uniparentally inherited and cannot reveal the variations that are found in nature, moreover, based on organellar DNA derived from one parent, nuclear genes yield more information than does barcoding [18]. In pre-rRNA processing, this region is vital for controlling the transcription of active ribosomal units. These conserved regions enable the design of universal primers, PCR amplification and DNA sequencing of amplicons which reveals the genetic variability needed for distinguishing closely related species [10]. They have been amplified using universal primers and have been applied in understanding species relationships and providing evidence of evolution. ITS DNA barcodes have been applied in the identification of flowering plants [19], the genus Vigna [20], medicinal plants in Fabaceae [21], species within the Euphorbiaceae [22], Chinese medicinal plants, Pueraria spp. [23], and the Australian invasive weed Conyza spp, [24] and to differentiate Terminalia species [25]. Therefore, this study aimed to identify and distinguish between T. africana var africana and T. africana var inversa varieties using ITS 1 and ITS 2 DNA barcodes.

Germplasm collection.

Fruit heads of eighty-six accessions of Treculia africana varieties (var inversa and var africana) were collected from Enugu, Anambra, Ebonyi, Abia, Imo, Delta, Ogun, Osun, Oyo, and Edo States in Nigeria as shown in Tables 1a and 1b. The plants were identified by Mr Felix Okafor, a plant taxonomist in the Department of Plant Science and Biotechnology. Voucher specimens of some of the accessions have been deposited in the University of Nigeria Herbarium. The fruit heads were allowed to decompose and the seeds were extracted by washing from the pulp and allowed to dry.

S/No	Accession	State	Variety	Voucher no
Ab1	Aba branch	Abia	africana	UNN11721Ab1
Ab2	Aba branch	Abia	africana	UNN11721Ab2
Ab3	Achara ndawa	Abia	africana	UNN11721Ab3
Ab4	Achara	Abia	africana	UNN11721Ab4
Ab5	Mbala	Abia	inversa	UNN11722Ab5
Ab6	Mbala	Abia	africana	UNN11721Ab6
Ab7	Isiukwuato	Abia	africana	UNN11721Ab7
Ab8	Isiukwuato	Abia	africana	UNN11721Ab8
Ab9	Ibeku, Umuahia	Abia	africana	UNN11721Ab9
Ab10	Isialangwa)	Abia	africana	UNN11721Ab10
Ab11	Nsulu	Abia	africana	UNN11721Ab11
Ab12	Ngodo Isuochi	Abia	africana	UNN11721Ab12
Ab13	Amuda Isiochi	Abia	africana	UNN11721Ab13
Ab14	Ngodo Isiochi)	Abia	africana	UNN11722Ab14
Ab15	Isuochi Mbala	Abia	inversa	UNN11722Ab15
G16	Umualumaku, Mbano	Imo	africana	UNN11721Im16
G17	Umualumaku Oriagu, Mbano	Imo	africana	UNN117211m17
G18	Umualumaku Oriendu, Mbano)	Imo	inversa	UNN117221m18
Ab19	Umuezeke (Ukwa West LGA)	Abia	africana	UNN11721Ab19
Ab20	Umuekechi (Ukwa West LGA)	Abia	africana	UNN11721Ab20
Ab21	Obingwu (Ukwa West LGA)	Abia	africana	UNN11721Ab21
B22	Ezioka	Anambra	africana	UNN11721An22
B23	Ezioka	Anambra	inversa	UNN11722An23
B24	Ezioka	Anambra	africana	UNN11721An24
B25	Ezioka	Anambra	africana	UNN11721An25
B26	Ezioka	Anambra	africana	UNN11721An26
B27	Ezioka	Anambra	africana	UNN11721An27
B28	Ezioka	Anambra	africana	UNN11721An28
B29	Ezioka	Anambra	africana	UNN11721An29
B30	Uhuruakwa	Anambra	africana	UNN11721An30
B31	Uhuruakwa	Anambra	inversa	UNN11722An31
B32	Uhuruakwa	Anambra	africana	UNN11721An32
B33	Uhuruakwa	Anambra	africana	UNN11721An33
B34	Uhuruakwa	Anambra	africana	UNN11721An34
B35	Uhuruakwa	Anambra	africana	UNN11721An35
B36	Nkpeshi, Ugwu-Awgbu	Anambra	africana	UNN11721An36
B37	Nkpeshi, Ugwu-Awgbu	Anambra	africana	UNN11721An37
B38	Nkpeshi, Ugwu-Awgbu	Anambra	africana	UNN11721An38
B39	Nkpeshi, Ugwu-Awgbu	Anambra	africana	UNN11721An39
B40	Edoji, Nnewi Ichi	Anambra	inversa	UNN11722An40
B41	Ichi, Ekwusigo Nnewi	Anambra	africana	UNN11721An41
B42	Ichi. Ekwusigo Nnewi	Anambra	africana	UNN11721An42

Table 1a. T. africana accessions collected from different locations

S/No	Accession	State	Variety	Voucher no
B43	Umueze	Anambra	africana	UNN11721An43
B44	Ikenga	Anambra	africana	UNN11721An44
C45	Ohofia-Agba	Ebonyi	inversa	UNN11722Eb45
C46	Ezza agu	Ebonyi	inversa	UNN11722Eb46
C47	Agba Ebonyi	Ebonyi	inversa	UNN11722Eb47
D48	Olokemeji	Ogun	africana
D49	Olokemeji	Ogun	africana
D50	Olokemeji	Ogun	africana
D51	Olokemeji	Ogun	africana
D52	Olokemeji	Ogun	africana
D53	Olokemeji	Ogun	africana
D54	Unknown	Ogun	africana
D55	Unknown	Ogun	africana
D56	Ibeji Agbeji	Ogun	africana
E57	Abigi	Osun	africana
E58	Ikire	Osun	africana
F59	Oyo	Oyo	africana
F60	Oyo	Oyo	africana
F61	Oyo	Oyo	africana
F62	Oyo	Oyo	africana
F63	Oyo	Oyo	africana
G64	Umuebe Umuebi, amuzu Nwafor	Imo	africana	UNN11721Im64
G65	Umuebe Umuebi, amuzu Nwafor	Imo	africana	UNN11721Im65
G66	Agunebere, Mbaise	Imo	africana	UNN117211m66
G67	Agunebere , Mbaise	Imo	africana	UNN117211m67
G68	Ahiazu, Mbaise	Imo	africana	UNN117211m68
G69	Eluama, Orlu	Imo	africana	UNN117211m69
G70	Ndi Owere, Orlu	Imo	africana	UNN117211m70
H71	Ugwashiukwu	Delta	inversa	UNN11722De71
I72	Ohumora, Sabongida Ora	Edo	inversa	UNN11722Ed72
J73	Botany Garden University of Nigeria, Nsukka	Enugu	inversa	UNN11722 En73
J74	Imilike, Nsukka	Enugu	africana	UNN11721En74
J75	Okpukpa, Nsukka	Enugu	inversa	UNN11722En75
J76	Danfodio street, University of Nigeria Nsukka	Enugu	africana	UNN11721En76
J77	Enugu-Ezike	Enugu	inversa	UNN11722En77
J78	Odenigbo Nsukka	Enugu	africana	UNN11721En78
J79	Lejja, Nsukka	Enugu	africana	UNN11721En79
J80	Lejja, Nsukka	Enugu	africana	UNN11721En80
J81	Lejja, Nsukka	Enugu	africana	UNN11721En81
J82	Ugwueme	Enugu	africana	UNN11721En82
J83	Ugwueme	Enugu	africana	UNN11721En83
J84	Ogwu town	Enugu	africana	UNN11721En84
J85	Amogbo, Onuiyi	Enugu	africana	UNN11721En85
F86	Oyo	Oyo	africana	UNN11721En86

Table 1b. T. africana accessions collected from different locations Cont.

DNA extraction

The molecular analysis was carried out at Inqaba Biotec West Africa Ltd, Ibadan. Zymo Research (ZR) mini–prep DNA extraction kit protocol (catalogue number D6020) was used for DNA extraction. Beta-mercaptoethanol (250 µl per 50 ml) was added to the genomic lysis buffer for the best reaction. To a ZR BashingBead™ Lysis Tube (2.0 mm). finely ground dried seeds (150 g) were added. Then 750 µl of BashingBead™ Buffer was added and the tube was capped tightly, and processed for 20 minutes in a bead beater (Disruptor Genie). The lysis tube was centrifuged at 10 000 x g for 1 minute. A total of 400 µl of the supernatant above was transferred to a Zymo-Spin™ III-F Filter in a collection tube and centrifuged at 8 000 x g for 1 minute. The Zymo-Spin™ III-F Filter was removed and poured out. A total of 1200 µl of genomic lysis buffer was added to the filtrate in the collection tube from the above step and was carefully mixed. Eight hundred microliters (800 µl) of the mixture was then transferred to a Zymo-Spin™ IICR Column² in a collection tube and centrifuged at 10 000 x g for 1 minute. The flow through from the Collection tube was removed and the procedure was repeated. The Zymo-Spin™ IICR column was transferred to a new collection Tube, 200 µl of DNA pre-wash buffer was added to the tube, and the column was centrifuged at 10 000 x g for 1 minute. To the Zymo-Spin™ IICR column, 500 µl of g-DNA wash buffer was added and centrifuged at 10,000 x g for 1 minute. To elute the DNA, the Zymo-Spin™ IICR column was then transferred to a clean 1.5 ml microcentrifuge tube and 100 µl (at least 50 µl) of DNA elution buffer was added slowly and directly to the column matrix and centrifuged at maximum speed for 1 minute. In a clean collection tube, the Zymo-Spin™ III-HRC filter was placed and 600 µl of prep solution was added to it and centrifuged at 8 000 x g for 3 minutes. The eluted DNA was then transferred to a prepared Zymo-Spin™ III-HRC Spin filter in a clean 1.5 ml microcentrifuge tube and centrifuged at exactly 16 000 x g for 3 minutes. The filtered DNA was then stored at -20^oC.

PCR amplification

The DNA extracts were quantified using Nanodrop spectrophotometer (Thermo Scientific™ NanoDrop™ One Microvolume UV-Vis Spectrophotometer) at various concentrations (ng/ul) of the A260/280 ratio; and the A260/230 ratio. The target regions were amplified using OneTaq® Quick-Load® 2X Master Mix (NEB, Catalogue No. M0486). The volumes used in the 12.5 µL reaction conditions were 1 µl of template DNA, 0.25 µl (10 nM) of 10 µM forward primer, 0.25 µl (10 nM) of 10 µM reverse primer, 6.25 µl of One Taq quick load 2X Master mix with standard buffer and 4.75 µl of nuclease-free water. The primer sequences used included ITS F – AACAAGGTTTCCGTAGGTGA and ITS R – TATGCTTAAAYTCAGCGGGT.

Thermal Cycling

The samples were subjected to thermal cycling using the Eppendorf Mastercycler nexus gradient 230. The PCR profile used consisted of an initial denaturation at 95^oC for 5 minutes, followed by 35 cycles of denaturation at 95⁰C for 30 seconds, annealing at 50^oC for 1 minute, extension at 68⁰C for I minute 30 seconds, final extension at 68⁰C for 10 minutes and held at 4⁰C.

DNA Sequencing

Nimagen, a Brilliant Dye™ Terminator Cycle Sequencing Kit V3.1, and BRD3-100/1000 were used to sequence the DNA fragments following the manufacturer’s instructions.

https://www.nimagen.com/products/Sequencing/Capillary-Electrophoresis/BrilliantDye-Terminator-Cycle-Sequencing-Kit/ .

The ZR-96 DNA sequencing Clean-up Kit was used in cleaning the labelled products (Catalogue No. 4053) http://www.zymoresearch.com/downloads/dl/file/id/52/d4052i.pdf

Thereafter, they were injected into the Applied Biosystems ABI 3500XL Genetic Analyser with a 50 cm array, using POP7:

https://www.thermofisher.com/order/catalog/product/4406016 and sequence data were collected.

Barcode analyses

The sequences were edited and visualised using FinchTV 1.4 (x86) software. Forward and reverse sequences were aligned to derive good and reliable consensus sequences using BioEdit 7.2 software. Each of the sequences was compared with nucleotide sequences in the GenBank database using the Basic Local Alignment Search Tool (BLAST). The top Blast results were determined using the highest percentage identity score or the lowest Expectation or E-value. Clustal W in Mega 11 software was used to perform multiple sequence alignments.

Sequence Analysis and Phylogenetic Tree Construction

The analysis involved 75 accessions of T. africana var africana and 13 accessions of T. africana var inversa nucleotide sequences using Mega 11 (Molecular Evolutionary Genetic Analysis). The nucleotide composition of the ITS gene sequences was subsequently calculated. The Clustal W tool was used with the default settings of gap opening penalty 15, to align sequences with estimated residue and pairwise distances; with a gap extension of 6.66 in pairwise alignment and 6.66 in multiple alignment. The number of conserved, variable, singleton, and parsimony informative sites was estimated. The maximum likelihood method of [26] was used to determine phylogenetic relationships. The initial tree for the heuristic search was derived automatically by inputting the Neighbor-Joining and BioNJ algorithms to a matrix of pairwise distances calculated using [27] and then choosing the topology with the superior log likelihood value.

DNA Sequence and Blast Analyses

DNA extraction was successful with good amplification. The PCR products of 86 samples were successfully sequenced. The length of the sequences varied from 300–670 bp for the inversa variety and 272–710 bp for the africana variety. The homology between these sequences and the gene bank database determined using the National Centre for Biotechnology Information (NCBI) basic alignment search tool (BLAST) did not reveal any matches. An alignment of KU855474 Artocarpus altilis, a related species,with var africana and var inversa accessions, showed similarities using mega 11 (Additional files 1 and 2). Accession G18 var africana and accession J73 var inversa were observed to align more strongly with Artocarpus altilis.

The coefficient of differentiation of the multiple sequence alignment of Artocarpus altilis with var africana yielded a total of 1174 sites, out of which 111 were conserved, and 906 were variable sites, containing 606 parsimony informative sites and 254 singleton sites (Additional file 3). The alignment with var inversa yielded a total of 834 sites, 191 conserved sites; 556 variable sites; 331 parsimony sites; and 204 singleton sites (Additional file 4). The yellow colour indicates the different sites of the coefficient of differentiation of the multiple alignments.

Nucleotide Constitution of ITS Gene sequences

The sequences were analysed to determine the nucleotide constitution using Mega 11. The nucleotides with maximum and minimum average values were C (28.6%) and A (20.9%). The maximum percentages of thymine and adenine were observed in Ab6 (27.9) and Ab7 (26.7), while their minimum percentages were observed in D55(19.1) and B24(17.7), respectively. The maximum percentage of guanine and cytosine (G+C) were observed in F59(30.2) and Ab8(30.9) while their minimum percentage was observed in Ab6 (23) and Ab1 (22) (Tables 2a and 2b). We detected the highest percentage of adenine and thymine (A + T, 53.0) in Ab7 and the highest percentage of guanine and cytosine (G + C, 59.2) was detected in B24. The G + C content was found to be higher than the A+T content.

Accessions	A	C	G	T(U)	A+T	G+C	Total
Ab1	25.3	29.3	22.0	23.4	48.7	51.3	304.0
Ab2	20.3	29.4	27.2	23.0	43.3	56.7	639.0
Ab3	25.0	26.6	25.3	23.1	48.1	51.9	320.0
Ab4	24.1	31.2	24.1	20.6	44.7	55.3	253.0
Ab5	20.9	29.2	26.4	23.5	44.4	55.6	277.0
Ab6	24.5	23.0	24.5	27.9	52.4	47.6	269.0
Ab7	26.7	24.0	23.0	26.3	53.0	47.0	509.0
Ab8	19.2	30.9	26.6	23.3	42.5	57.5	527.0
Ab9	20.5	30.6	26.5	22.4	42.9	57.1	585.0
Ab10	19.1	30.1	27.0	23.9	43.0	57.0	675.0
Ab11	22.4	28.1	29.1	20.3	42.8	57.2	615.0
Ab12	18.9	29.5	29.6	21.9	40.9	59.1	597.0
Ab13	22.7	29.3	27.2	20.9	43.5	56.5	556.0
Ab14	19.7	27.7	27.7	24.9	44.7	55.3	441.0
Ab15	19.5	29.1	27.7	23.6	43.1	56.9	656.0
Ab16	20.8	28.9	27.1	23.1	43.9	56.1	553.0
Ab17	18.9	27.7	27.5	25.9	44.8	55.2	433.0
Ab18	24.2	24.4	27.0	24.4	48.6	51.4	467.0
Ab19	18.8	29.2	28.0	24.0	42.9	57.1	658.0
Ab20	21.0	27.9	27.4	23.7	44.7	55.3	613.0
Ab21	22.2	29.4	26.3	22.2	44.4	55.6	514.0
B22	22.1	25.5	26.1	26.3	48.4	51.6	529.0
B23	20.0	30.1	28.4	21.5	41.5	58.5	571.0
B24	17.7	30.2	29.0	23.2	40.8	59.2	600.0
B25	21.0	28.5	27.7	22.9	43.9	56.1	643.0
B26	21.3	27.9	27.5	23.3	44.6	55.4	639.0
B27	20.1	29.7	27.2	23.0	43.1	56.9	573.0
B28	20.1	28.7	27.1	24.1	44.2	55.8	642.0
B29	19.4	30.1	28.7	21.9	41.2	58.8	599.0
B30	24.0	27.7	25.5	22.8	46.7	53.3	501.0
B31	19.8	28.5	28.0	23.7	43.5	56.5	596.0
B32	19.7	29.8	27.2	23.3	43.0	57.0	618.0
B33	18.7	29.9	27.6	23.9	42.6	57.4	700.0
B34	23.8	25.5	24.2	26.5	50.3	49.7	483.0
B35	24.0	29.5	21.1	25.4	49.4	50.6	492.0
B36	23.7	26.9	25.3	24.2	47.8	52.2	558.0
B37	20.0	29.8	28.1	22.1	42.1	57.9	655.0
B38	21.4	26.8	26.4	25.4	46.8	53.2	579.0
B39	23.4	27.8	25.1	23.6	47.1	52.9	529.0
B40	21.8	28.9	25.9	23.4	45.2	54.8	591.0
B41	22.3	27.7	26.9	23.0	45.4	54.6	573.0
B42	20.0	29.4	27.1	23.6	43.6	56.4	691.0
B43	19.5	28.8	28.1	23.7	43.1	56.9	473.0
B44	20.2	29.3	27.3	23.2	43.4	56.6	652.0

Table 2a: Nucleotide constitution of ITS gene sequences of accessions of Treculia varieties

Table 2b: Nucleotide composition of ITS gene sequences of accessions of Treculia varieties continued

Accessions	A	C	G	T(U)	A+T	G+C	Total
C45	23.7	29.1	26.1	21.1	44.8	55.2	464.0
C46	21.7	28.0	24.0	26.3	48.0	52.0	521.0
C47	20.3	29.4	29.8	20.5	40.8	59.2	483.0
D48	19.2	29.7	27.8	23.4	42.5	57.5	637.0
D49	20.7	28.7	28.4	22.2	42.9	57.1	634.0
D50	20.0	29.7	28.3	22.0	42.0	58.0	654.0
D51	24.2	27.3	25.2	23.3	47.6	52.4	433.0
D52	19.8	29.7	28.6	21.9	41.7	58.3	657.0
D53	19.1	29.4	28.1	23.4	42.4	57.6	629.0
D54	19.1	29.4	28.1	23.4	42.4	57.6	629.0
D55	23.4	28.1	29.4	19.1	42.4	57.6	629.0
D56	21.6	29.2	25.3	23.8	45.5	54.5	541.0
E57	19.7	29.9	28.5	21.9	41.6	58.4	649.0
E58	20.2	29.2	28.6	22.0	42.2	57.8	664.0
F59	19.8	29.4	30.2	20.6	40.4	59.6	530.0
F60	23.4	24.2	25.9	26.5	49.9	50.1	611.0
F61	19.1	30.0	28.4	22.4	41.5	58.5	633.0
F62	22.3	23.3	26.4	27.9	50.2	49.8	605.0
F63	19.9	30.2	28.6	21.3	41.2	58.8	653.0
G64	20.0	29.4	28.8	21.8	41.8	58.2	660.0
G65	18.5	30.6	28.6	22.3	40.8	59.2	552.0
G66	19.6	29.2	28.1	23.1	42.7	57.3	654.0
G67	20.9	27.4	27.7	24.0	44.9	55.1	541.0
G68	20.3	29.4	29.4	20.8	41.1	58.9	649.0
G69	19.8	29.3	27.2	23.7	43.5	56.5	655.0
G70	21.6	28.6	27.5	22.4	44.0	56.0	612.0
H71	19.9	29.6	27.5	23.1	43.0	57.0	670.0
I72	24.1	28.0	25.4	22.5	46.6	53.4	528.0
J73	22.5	27.0	28.5	21.9	44.4	55.6	729.0
J74	21.4	27.4	27.4	23.7	45.1	54.9	645.0
J75	21.8	26.4	26.5	25.3	47.1	52.9	592.0
J76	20.3	29.5	28.6	21.6	41.9	58.1	661.0
J77	20.2	29.7	28.6	21.4	41.7	58.3	653.0
J78	22.6	26.4	25.1	25.8	48.5	51.5	561.0
J79	21.4	28.7	26.3	23.6	45.0	55.0	631.0
J80	20.2	28.1	27.9	23.9	44.0	56.0	645.0
J81	23.1	25.5	29.6	21.8	44.9	55.1	628.0
J82	19.9	29.2	27.2	23.7	43.6	56.4	654.0
J83	20.1	29.6	27.3	23.0	43.1	56.9	656.0
J84	19.7	29.4	27.5	23.4	43.1	56.9	650.0
J85	20.3	28.9	27.5	23.4	43.6	56.4	637.0
J86	20.1	29.1	27.4	23.5	43.6	56.4	647.0
Avg.	20.9	28.6	27.3	23.2	44.1	55.9	578.1

Phylogenetic analysis

The phylogenetic trees comparing Artocarpus altilis with T. africana var inversa and var africana using the maximum likelihood method produced a tree with the highest log likelihoods of (-5433.23) and (-14133.81) respectively as shown in Figures 1 and 2. A total of 836 and 1174 positions were found in the final dataset. The trees consisted of 4 and 12 groups respectively. The tree comparing var africana with Artocarpus altilis revealed two major clusters of which Artocarpus altilis was observed to be in the second cluster. Artocarpus was observed to occur in group 6 and did not cluster with any of the accessions. However, the tree comparing var inversa with Artocarpus altilis revealed that accession J73 and Artocarpus altilis were in the same cluster and were the roots of the tree.Accession G18 was a direct descendant of the cluster and stood out from the remaining inversa accessions.

This study was carried out to identify T. africana. var inversa and var africana. The successful amplification and sequencing of the ITS 1 and ITS 2 DNA barcode regions of Treculia species met the criterion of the suitability of the use of DNA barcodes. [28] reported that DNA barcodes should have the ability to yield high amplification and efficiency in sequencing and detect sufficient genetic variations to distinguish sequences among species and within individuals of the same species. Compared with those of the other DNA barcodes, high success rates have been reported for the use of ITS DNA barcodes by [10] and [29] for Musa species and medicinal plants respectively. According to [30] the relative variability and ease of PCR amplification make nuclear internal transcribed spacer (ITS) regions suitable as molecular markers.

There were no available matches from the BLAST results of the sequences of the ITS 1 and 2 barcodes using primers derived from studies on the relative Artocarpus [31] which may not have been appropriate for the identification of Treculia species. The lack of universal primers (which may be published or have the capacity to be developed using available information) and unsuccessful use of existing primers were outlined by [19] as problems facing the use of ITS DNA barcodes. The unsuccessful use of existing primers was likely due to difficulty in the amplification of genes with few copies of degenerate accessions and the necessity of constantly cloning PCR products before sequencing. Plant barcodes are limited by the use of a single locus approach [32] or a universal barcode that may suit a particular family or genus [33]. It has also been reported that a broad range of taxonomic groups and the shortage of comparative data for all candidate markers are limitations to the use of DNA barcodes [34]. Treculia africana has a few DNA barcodes available on the NCBI gene bank. However, there is a dearth of information available on DNA barcoding on the plant and its relatives. In addition, the lack of data on the ITS regions of Treculia species in the Genbank could be a factor that may have limited the use of ITS barcode regions in the identification of the plant. Thus, the ITS DNA barcodes used in the present study could not be used to identify the Treculia accessions. [35] and [36] opined that to accurately identify species, a wide range of molecular databases is required to compare unknown species. [37] suggested that DNA barcoding may not lead to species discovery since barcodes identify species using already existing barcodes. According to [38], other limitations facing ITS barcodes include incomplete concerted evolution which gives rise to paralogous copies within an individual, difficulty in amplifying and sequencing the marker in several sample sets, and fungal contamination [17].

According to [39], the unrelated matches observed are based on the limitations in the use of sequence homology in NCBI which is due to chance or comparison of non-homologous sequences, real sequences, and real but non-homologous sequences being shuffled to preserve constitutional properties. Therefore, according to [40] and [41] database search programs, such as the FASTA database generate the best local alignment scores when comparing the query sequence to every sequence in the database. Unrelated sequences may be involved and can be used in estimating K and lambda (where K and lambda are parameters used in scoring systems for evaluating the E value). This process involves the avoidance of the use of random sequence models but rather the use of real sequences despite the omission of estimation scores from pairs of closely related sequences. The BLAST programme was therefore described by [41] to obtain quick alignment scores by calculation of unrelated sequences and avoiding the calculation of optimal alignment scores while relying on parameters such as K and lambda for gap costs and a specific set of substitution matrices. These estimations are applicable based on real sequences but instead have been based on random sequence models [42] thereby yielding fairly correct results [41]. [43] added that although the statistical estimates provided by BLAST are very reliable, their statistical estimates and statistical significances sometimes fail not because of homology but because of statistical errors.

Hence, multiple alignments offer better structural, functional, and phylogenetic information than pairwise alignments [43] Therefore, the multiple alignments revealed that the Treculia accessions were related and genetically similar to a member of the Moraceae family Artocarpus species from the NCBI database, indicating that the accessions belong to the same family. The number of conserved sites is indicative of similar or the same patterns among them. The alignment data also suggested a level of genetic diversity based on the number of variable sites (singleton and parsimony informative) which could be used to distinguish the accessions. This finding agrees with studies evaluating of genetic variation among Mangifera species [44]. Based on the number of variable sites observed, ITS DNA barcodes were able to differentiate the accessions. The variable sites identified in recent studies are the most common SNP sites that have segregating sites that could be useful in barcode segment construction [45] [46]. [47] opined that they are also useful in the identification of species as was applied by [45] in the identification of Orchidaceae plants. The parsimony informative sites obtained in the study revealed the evolutionary relationships among the accessions.

A high G + C content is an area of complex gene regulation [48] and has greater thermal stability than AT base pairs due to the strong stacking and triple bonds between GC bases [49] thereby stabilizing DNA and RNA [48]. A higher GT content has also been attributed to a high rate of mutability due to cytosine methylation [50] and bendability resulting in BDNA and ZDNA conformations due to metabolically active DNA and the regulation of DNA transcription and gene expression respectively [51, 52]. It has also been suggested that a high GC content enables plants to respond to environmental stress thereby making them tolerant to both heat and precipitation [52]. A high G + C content can be applied in the taxonomic description of Treculia africana species by determining the G + C content values of genomic sequences and comparing them with DNA - DNA hybridization similarities. This was opined by [53] in studies evaluating the postulation that the G + C content can vary up to 3–5 % within species using genomic datasets. GC content varies in unlike organisms due to selection, mutational bias and biased recombination linked with DNA repair [54]. This approach has been applied in bacterial systematics [55].

A high G-C content of 63.2% was reported by [56] using the ITS of ribosomal DNA from Ficus carica. Their report differs from that of [57] who obtained lower G + C values (50%) in wild mushrooms in India using ITS1 and ITS 2. A high GC content has been associated with a reduced efficiency of amplification requiring higher temperatures and resulting in slow polymerase reactions [58]. This was observed to be due to the structural composition of the GC region [59], which is thermodynamically unfastened, requiring more energy or heat to separate the strands thereby making amplification or sequencing difficult [60]. However, [61] observed hindrances in sequencing workflows due to GC biases outside the 45–65% GC range leading to falsely low GC coverage and poor GC sequences. Hence, the GC contents of the Treculia species under study are within this range and may, not have generated such biases. G + C could be considered when designing primers for Treculia species for amplifying DNA of interest as suggested by [62, 63].

A phylogenetic tree comparing var africana and Artocarpus altilis showed that Artocarpus could be a mutational descendant of var africana after several evolutionary events. Only accessions in groups 1 to 5 could be said to be older than Artocarpus altilis. The clustering of accessions J73 of var inversa with that of Artocarpus alitilis could suggest that they are genetically similar or related. These genes were also observed to constitute an outgroup which may indicate that they are ancestors of the other inversa accessions. Thus, the other remaining var inversa may be their mutational descendants or products of evolutionary events over time.

To overcome the challenges associated with the use of ITS DNA barcodes, whole-genome sequencing through next-generation sequencing methods can provide novel molecular markers that can be used to characterize, and differentiate varieties and identify agronomic traits that will help improve the species. A combination of ITS and a noncoding plastid DNA barcode such as trnH-psbA can therefore be recommended for the identification of Treculia varieties with its accessions. There is also a need to populate molecular data for Treculia species in GenBank to improve the accuracy of future identifications.

The use of primers designed for a species may not be efficient in identifying other members of the same family. Hence, primers for ITS DNA barcodes for Treculia species should be designed for future evaluation of the species to prevent misidentification. Additionally, due to the gradual extinction of the plant and the endangered status of Treculia species, proper germplasm collections will ease challenges facing the use of the plant for future improvement programs.

ITS - 1 Internal Transcribed Spacer Region 1

ITS – 2 Internal Transcribed Spacer Region 2

pre- rRNA Pre ribosomal Ribose Nucleic Acid

nr-DNA nuclear ribosomal DNA

BLAST Basic Local Alignment Search Tool

MEGA Molecular Evolutionary Genetic Analysis

Bp Base Pair

PCR Polymerase Chain Reaction

SNP Single Nucleotide Polymorphism

G + C Guanine and Cytosine content

AT Adenine and Thymine content

ZR Zymo Research

Ethics approval and consent to participate: not applicable.

Consent for publication: not applicable.

Availability of data and materials: The datasets used and/ or analysed during the current study are not publicly available due to the sequences are yet to be deposited in the Gen database but are available from the corresponding author upon reasonable request. The sequence of Artocarpus alitlis (KU855474.1) referred to in this work was downloaded from GenBank of NCBI (https://www.ncbi.nlm.nih.gov/nuccore/1151016440). The ML tree was performed by the software Mega (Molecular Evolutionary Genetic Analysis) Version 11.0.13.

Competing interest: The authors declare that they have no financial and non-financial competing interests.

Funding: Research was partly funded by the Tertiary Education Trust Fund (Tetfund) in Nigeria and a greater part by CCI

Authors contribution: FIA conceived the project, supervised and edited the manuscript; CCI collected samples, designed the experiment, carried out the research, and bioinformatics analysis, wrote and edited the manuscript and funded the research; UCU participated in the design, research and bioinformatics analysis.

Acknowledgement:

We wish to acknowledge the Tertiary Education Trust Fund (Tetfund) Nigeria for sponsoring a part of the research.

Authors’ information: CCI- BSc, MSc. PhD Genetics and Plant Breeding

FIA - BSc, MSc, PhD, Professor of Genetics and Plant Breeding

UNU- BSc

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No competing interests reported.

Identification of Treculia africana L. varieties using Internal Transcribed Spacer Region 1 (ITS 1) and Internal Transcribed Spacer Region 2 (ITS 2) DNA barcodes

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Abstract

Background

Results

Conclusions

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Background

Materials and Methods

Results

Discussions

Conclusion

Abbreviations

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References

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