Morphological and Genetic Characterization of Jackfruit (Artocarpus heterophyllus) in Kayunga and Luwero districts of Uganda

doi:10.21203/rs.3.rs-2705430/v1

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Morphological and Genetic Characterization of Jackfruit (Artocarpus heterophyllus) in Kayunga and Luwero districts of Uganda

https://doi.org/10.21203/rs.3.rs-2705430/v1

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published 09 May, 2024

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Background

Jackfruit (Artocarpus heterophyllus), is an economically valuable fruit tree. Despite its potential, the production of jackfruit in Uganda is currently low, and may face further reduction due to deforestation. As a result, genetic erosion of the tree resource is predicted, before its documentation and exploitation for possible crop improvement and sustainable conservation. As a prerequisite for crop improvement and conservation, 100 A. heterophyllus tree isolates from Kayunga and Luwero districts in Uganda were characterized using 16 morphological and 10 microsatellite markers.

Results

The results from the morphological analysis showed variation in the tree height, diameter at breast height (DBH), and crown diameter traits, with coefficients of variation (CV) values of 20%, 41%, and 33% respectively. Except for taste, various forms of the 13 assessed qualitative traits were observed. Genotyping showed that the number of alleles amplified per microsatellite locus ranged from 2 to 5, with an average of 2.90, and totalling to 29. The mean observed (H_o) and expected (H_e) heterozygosity were 0.71 and 0.57 respectively. Analysis of molecular variance (AMOVA) indicated that 81% of the variation occurred within individual trees, 19% among trees within populations and 0% between the two populations. The gene flow (Nm) in the two populations was 88.72. The results from further analysis using ‘Partitioning Around Medoids’ (PAM), Principal Coordinate Analysis (PCoA) and genetic cluster analysis further revealed no differentiation of the A. heterophyllus populations. In addition, the Mantel test revealed no significant correlation between the genetic and geographic distances, and the morphological and geographic distances, and a negligible correlation between the morphological and genetic distances.

Discussion

Diversity was observed in the assessed A. heterophyllus because the crop is crossly pollinated. However, there was no differentiation of the jackfruit populations probably because of continuous gene flow in the adjacently located districts. Morphological resemblance did not imply genetic similarity probably because morphology is influenced by the environment.

Conclusions

Both morphological and genetic analyses revealed variation of jackfruit within a single interbreeding population. The observed diversity can be exploited to establish breeding and conservation strategies to increase production of jackfruit. However, selecting germplasm depending on morphological assessment alone may be misleading.

Artocarpus heterophyllus

morphological markers

microsatellite markers

crop improvement

conservation

The jackfruit (A. heterophyllus) tree belongs to the Artocarpus genus, which also comprises breadfruit (A. altilis), dugdug (A. mariannensis), breadnut (A. camansi), champedak (A. integer) and many other important species [1, 2]. The hardy drought-resistant tree adapts to a wide range of agro-climate conditions and survives in the poorest of soils making it a suitable crop in the face of climate change [3, 4]. Although the origin of jackfruit is believed to be the West Ghats forest of India, the tree is presently cultivated in the India subcontinent, Southeast Asia, the Neotropics, northern Australia, the evergreen forest zone of West Africa and in East Africa [5–9]. In Uganda, jackfruit is predominantly grown in the eastern, central and western regions, although a few trees are found in gardens of homesteads in other parts of the country [10]. In the past, jackfruit was grown as a shade tree in homesteads and plantations, and hardly on a large scale [11, 12]. In 1992, the Commonwealth Science Council (CSC) and the International Centre for Underutilized Crops (ICUC) identified jackfruit as one of the underutilised, but diverse crops, whose cultivation and conservation required priority promotion [7]. Apparently, the demand for jackfruit is increasing worldwide [13].

Jackfruit trees bear their first fruit at three to eight years after planting and live up to 60 or 70 years [14, 15]. The tree is able to yield 700 fruits weighing between 2 to 50 kilograms, every year, making it the biggest edible fruit in the world. The productivity of the fruit is estimated at 50–80 tons per hectare (ha) of land [8, 13]. In India, one of the world’s leading producers of jackfruit, production was approximately 1.4 million tons, cultivated on approximately 102,000 ha, in 1992 [13]. In 2010, Bangladesh, another high producer of the fruit yielded 1.5 million tons cultivated under an area of 160,000 ha [13]. In Uganda, there is scarce information about production levels of jackfruit, because the fruit has been given little attention. Nonetheless, [10] estimated production of 0.3 million metric tons per year, per district, in some of the highly producing regions of the fruit in Uganda.

Jackfruit is a highly nutritious fruit containing proteins, energy, fat, carbohydrates, fibre, moisture, vitamin A, thiamine, potassium, calcium, phosphorus, iron, β carotene, riboflavin, niacin, vitamin C, sodium and magnesium [14, 16, 17]. This makes it an ideal food supplement during times of food scarcity especially in rural communities. In Uganda, jackfruit is not only grown for home consumption, as farmers are making products like dried chips and wine to add value to the fruit which has a short shelf-life [10]. Also, the jackfruit leaves serve as fodder for livestock, and the seeds provide essential nutrients for poultry [18]. In addition, various jackfruit parts have several medicinal properties like anti-cancer, anti-HIV, anti-inflammatory, and also treat ailments like anaemia, asthma, ulcers, indigestion, dermatitis, diarrhoea, cough, tension and high blood pressure [6, 14, 19–22]. The tree also offers agroforestry functions [1, 11], and is a source of latex for making rubber [20], good quality timber and fuel [7].

Despite the benefits of the tree, jackfruit production in Uganda still faces challenges. The present production of the fruit cannot meet the increasing market demands within the country, for export and for industrial processing [10]. Nonetheless, there is potential to improve production, because farmers have the desire to plant the tree [10], although they lack elite germplasm. Also, as deforestation rises due to increased demand for fuel, timber and land area for agriculture, loss of tree genetic resource, including jackfruit, is anticipated [23, 24]. In Uganda, there is no jackfruit core collection, as the case is for India, at the Indian Agricultural Research Institute. Having a core collection is paramount for effective management and preservation of the existing genetic resources and, as a source of planting material. There is, hence, need to create planting material having farmer-desired and consumer-preferred traits, as well as sustainably utilize and conserve the existing germplasm. These strategies, however, require prior characterization of jackfruit.

This study combined both morphological and genetic markers (SSRs) to characterize 100 jackfruit trees from two major producing districts (Luwero and Kayunga) of the fruit in Uganda [4, 10]. Traditionally, morphological markers have been used for diversity analysis and crop characterization, even before the discovery of DNA-based markers. This is because they are readily available, inexpensive, require simple equipment to screen and are essential in understanding geographic and ecological distributions, evolution and conservation status [25–27]. Morphological markers have been successfully used to characterize jackfruit in India [11, 16, 17, 28, 29] and Bangladesh [5, 7, 8, 24]. As well, several studies using genetic markers, including Amplified Fragment Length Polymorphism (AFLP), Random Amplified Polymorphic Markers (RAPDs), and microsatellites have indicated diversity in jackfruit from various parts of the world [2, 9, 30–32]. In our study, genetic markers were used in combination with morphological descriptors because they are powerful tools in studying phylogenetic relationships. The genetic markers are more reliable, unlike the morphological ones, which exhibit continuous variation and a lot of phenotypic plasticity as a result of environment influence. This is because the genetic markers rely on direct analysis of polymorphisms in the species genome enabling direct measurement of genetic diversity [33, 34]. Microsatellites/SSRs were chosen because they are highly polymorphic, abundant, co-dominant and reproducible [33, 35]. In this study, therefore, jackfruit was characterised with the aim of generating knowledge for designing effective conservation and breeding programmes.

Morphological variations of jackfruit

Regarding the quantitative traits, the highest and lowest variations were observed in the DBH and tree height respectively, according to the coefficients of variation values, (Table 1). Based on characterization by qualitative traits, various forms of the characters as described by [36] were observed. However, for the pulp taste, only sweet jackfruit was observed. There was no jackfruit with an insipid, acidic, nor bitter taste, as described by [36] (Table 2).

Table 1

Variation of quantitative morphological traits of jackfruit in Kayunga and Luwero districts of Uganda
Plant material	Quantitative trait	Mean	SD	Min	Max	CV (%)
Plant/tree	Height (m)	12.4	2.5	7.0	20.0	20.5
	DBH (cm)	44.9	20.5	9.7	105.1	45.5
	Crown diameter (cm)	3.2	1.2	1.0	5.9	33.9
SD = standard deviations, CV = coefficients of variation

Table 2

Variation of qualitative morphological traits of jackfruit in Kayunga and Luwero districts of Uganda
Plant material described	Qualitative trait	Categories of the jackfruit trait (%) [36]
Plant/tree	Tree vigour	Low (5.0), Medium (30.0), High (65.0)
	Trunk surface	Smooth (35.0), Rough (56.0), Very rough (9.0)
	Branching density	Sparse (19.0), Medium (50.0), Dense (31.0)
	Tree growth habit	Erect (37.0), semi-erect (12.0), spreading (51.0)
	Crown shape	Irregular (39.0), Pyramidal (31.0), Broadly pyramidal (21.0), Semi-circular (5.0), Spherical (3.0), Oblong (1.0), Elliptical (0.0)
Leaf	Leaf blade shape	Elliptic (64.0), Narrowly elliptic (26.0), Broadly elliptic (4.0), Oblong (3.0), Obovate (3.0), Lyrate (0.0)
Fruit	Fruit shape	Ellipsoid (43.0), Oblong (20.0), Clavate (18.0), Irregular (11.0), Obloid (4.0), Spheroid (4.0)
	Fruit surface	Smooth (6.0), Spiny (94.0)
	Flake shape	Twisted (29.0), Cordate (20.0), Spheroid (13.0), Rectangular (12.0), Irregular (17.0), Oblong with curved tip (5.0), Obovate (4.0).
	Flake colour	Yellow (84.0), Coppery red (9.0), White (7.0)
	Flake flavour	Strong (39.0), Intermediate (34.0), Weak (27.0).
	Pulp consistency	Medium (48.0), Soft (34.0), Firm (14.0), Slimy (4.0).
	Pulp taste	Sweet (100.0), Insipid (0.0), Acidic (0), Bitter (0.0).

Relationships And Clustering Pattern Of Jackfruit Accessions

The levels of similarities amongst the assessed samples varied as shown by the Gower dissimilarity matrix (Fig. 1). The mean Gower distance of the jackfruit accessions was 0.32. The most similar accessions, which were both from Luwero district, had a distance of 0.05 between them, while the most dissimilar, (both from Kayunga) had a distance of 0.61. Nonetheless, the similitudes or variations amongst the accessions were irrespective of their districts of origin, as close relations were observed in jackfruit from different districts and vice versa (Fig. 1). Also, the Mantel test revealed that there was no significant linear correlation between the morphological and geographic distances (R = 0.034, p = 0.170) (Fig. 2.).

The three methods which were used for determining the optimal number of clusters (k) to be generated by PAM [37], proposed, k = 2 (Fig. 3). The PAM clustering revealed that the two clusters were heterogeneous and each of them contained accessions from both Kayunga and Luwero districts (Fig. 4).

Genetic Diversity Of Jackfruit

The assessed diversity measures revealed variation in the jackfruit accessions (Table 3). All the 10 microsatellite/SSR markers were polymorphic and a total of 29 alleles were amplified across all loci for 100 samples (Fig. 5). The number of alleles at a locus (N_a) ranged from two at loci MAA54a, MAA196b, MAA178a and MAA140 to five at locus at MAA105. The mean N_a per locus was 2.90 ± 0.31. The number of effective alleles (N_e) ranged from 1.74 at locus MAA54a to 3.42 at locus MAA105, with a mean of 2.42 ± 0.19. The Shannon information index (I) was lowest at locus MAA54a (0.62) and highest at locus MAA105 (1.35) and the mean was 0.91 ± 0.08. The least observed heterozygosity (H_o) was 0.38 at locus MAA145, while the highest was 0.99 at loci MAA196b and MAA178a. Locus MAA54a had the least expected heterozygosity (H_e) and unbiased expected heterozygosity (uH_e) (0.43 for both measures), while locus MAA105 had the highest (0.71 for both measures). The levels of heterozygosity were higher than expected (Mean H_o was 0.71 ± 0.07, while Mean H_e and uH_e was 0.57 ± 0.03 for both measures). The mean inbreeding coefficient (F_is) was − 0.31 ± 0.16. All loci significantly deviated from the Hardy-Weinberg equilibrium (HWE) at p < 0.05. Jackfruit variation in Kayunga district did not significantly differ from that of Luwero, as shown by the mean values of the diversity indices for both districts (Table 3).

The least frequent allele (frequency = 0.01), which was also private to Kayunga district and rare (with a frequency < 0.05), was “310” of locus MAA196a (Fig. 5). The most frequent allele was “290” of locus MAA145 (frequency = 0.72) (Fig. 5). Twenty eight alleles had frequencies above 0.05 and hence were polymorphic, and no fixed alleles (those with frequency > 0.90) were detected (Fig. 5).

Table 3

Diversity measures calculated for 10 microsatellites, in 100 jackfruit accessions from Luwero and Kayunga districts in Uganda
Locus	N	N_a	N_e	I	H_o	H_e	uH_e	F	HWE
Locus	N	N_a	N_e	I	H_o	H_e	uH_e	F	Chisq	p-value
MAA54a	75	2	1.74	0.62	0.61	0.43	0.43	-0.44	14.67	< 0.001
MAA196a	80	4	2.81	1.11	0.90	0.64	0.65	-0.40	70.37	< 0.001
MAA122	71	3	2.92	1.09	0.51	0.66	0.66	0.23	39.73	< 0.001
MAA182	90	3	3.00	1.10	0.68	0.67	0.67	-0.02	180.00	< 0.001
MAA145	76	3	1.79	0.78	0.38	0.44	0.44	0.13	81.38	< 0.001
MAA105	89	5	3.42	1.35	0.62	0.71	0.71	0.13	116.00	< 0.001
MAA196b	69	2	2.00	0.69	0.99	0.50	0.50	-0.97	65.11	< 0.001
MAA156	85	3	2.58	1.01	0.52	0.61	0.62	0.15	12.64	= 0.005
MAA178a	72	2	2.00	0.69	0.99	0.50	0.50	-0.97	68.11	< 0.001
MAA140	80	2	1.99	0.69	0.94	0.50	0.50	-0.88	62.28	< 0.001
Mean	78.70	2.90	2.42	0.91	0.71	0.57	0.57	-0.30
SE	2.35	0.31	0.19	0.08	0.07	0.03	0.03	0.16
Kayunga
Mean	40.00	2.90	2.38	0.90	0.71	0.56	0.56	-0.30
SE	1.47	0.31	0.18	0.08	0.08	0.03	0.04	0.17
Luwero
Mean	38.70	2.80	2.41	0.91	0.71	0.57	0.57	-0.31
SE	1.16	0.29	0.17	0.08	0.07	0.03	0.03	0.16

HWE = Hardy-Weinberg equilibrium, Chisq = chi square test, SE = Standard Error.

Genetic Differentiation Of Jackfruit Populations (Districts) And Cluster Analysis

The results from AMOVA revealed that 81% of the observed variation was due to heterozygosity within individual accessions. The variation due to differences among accessions within populations/districts was 19%. There was no differentiation among the Kayunga and Luwero jackfruit populations as shown by the insignificant Fst value (Fst = 0.003, P = 0.19 after 999 random permutations). The average estimate of gene flow (Nm) was 88.72.

The lack of distinction of Kayunga and Luwero jackfruit populations was further shown by overlapping of the accessions from both districts on the PCoA plot. However, the accessions were scattered on the plot depicting genetic variation (Fig. 6). The combined coordinates explained 38.02% of the total variation, whereby the first coordinate explained 18.28%, the second explained 10.66% and the third, 9.09%.

Like the PCoA, the dendrogram generated using the genetic distances showed that the clustering of jackfruit was irrespective of the district of origin (Fig. 7). In addition, the Mantel test revealed insignificant correlation of the genetic and geographic distances (R = 0.035, p = 0.240) (Fig. 2). As well, the correlation of the morphological and genetic distances was negligible (R = 0.073, p = 0.020) (Fig. 2).

Table 4

Molecular variance of jackfruit from Kayunga and Luwero districts/populations
Source	Degrees of freedom	Sum of squares	Mean squares	Estimated variance	Variation (%)
Among Populations	1	5.07	5.07	0.01	0
Among Individuals	98	401.37	4.10	0.66	19
Within Individuals	100	278.50	2.79	2.79	81
Total	199	684.94		3.45	100

Genetic characterization of jackfruit using molecular markers (microsatellites) was undertaken to generate useful information for breeding programs and for designing sustainable conservation programs.

Diversity in the jackfruit accessions was observed based on the assessed quantitative and qualitative morphological characters. Similar studies in India [11, 12, 16, 17, 28, 29] and Bangladesh [5, 7, 8] revealed morphological variation of jackfruit. This variation is attributed to cross-pollination and predominance of seed propagation over a long period of time [17]. The variation observed in the different morphological characters is fundamental for germplasm improvement since breeders utilize the existing genetic pool, to create crops with desirable traits. For instance, variation in tree height is an important aspect while selecting superior trees, because control of pest and diseases by spraying, and harvesting of fruits, is more difficult in tall trees than short ones [11]. In this study, trees that were as short as 7.9 m were observed. Also, DBH is an important agronomic attribute to consider while selecting superior trees. The higher the DBH values, the more productive the tree is since the stem supports the branches, which are the fruit bearing zones. In Kayunga and Luwero trees with DBH values as high as 105.1 cm were observed. In addition, trees with high vigour, which prevailed most (65%) in the current study, survive longer than those with less vigour [7]. In the current study, all categories of the tree growth habit character, as described by [36], were observed. The tree growth habit is very important in deciding whether the tree is suitable for normal or high-density planting. The latter gives more yields per unit area and ‘semi-erect’ growth habit is suitable for this kind of planting [11]. Additionally, the existence of various flake colours, flavours and pulp consistency categories, as observed in the current study, is important for jackfruit marketability. Most consumers prefer brightly coloured and strongly flavoured jackfruit, with crunchy/hard pulps, for ready-to-eat snacks [40]. On the contrary, soft pulps are preferred for industrial utilization because they require less time and energy to process [10]. There was no variation in the jackfruit pulp taste. The fact that the studied trees were domesticated explains the reason why only sweet flakes of jackfruit were observed. The insipid, acidic and bitter jackfruit was probably selected against because the taste is not desirable. Nonetheless, the jackfruit in Kayunga and Luwero exhibited desirable agronomic traits, which are useful for jackfruit improvement. The observed variation is also important when deliberating on creation of a core collection and other conservation strategies.

The morphological distance between two jackfruit accessions, which varied from 0.04 to 0.61 (M = 0.32) (Fig. 1) further confirmed the diversity of the jackfruit. The clustering of the jackfruit accessions was independent of the district of origin (Fig. 4), and closely related jackfruit from either district, as well as, distantly related accessions form the same district were observed (Fig. 1). In addition, the correlation between the morphological and geographic distances was insignificant (R = 0.034, p = 0.170). This means that geographic location of jackfruit did not influence its morphological appearance. Therefore, other factors like cross pollination, natural selection, and genetic drift, rather than geographic distance were responsible for the jackfruit morphologic differences, as was observed in similar studies [17, 29]. In addition, the overlapping clusters (Fig. 4) suggested that there was no clear partitioning of accessions into distinct groups, and hence the jackfruit in the two districts belonged to one interbreeding population. Since the two districts are geographically close, with no physical boundary separating them, germplasm exchange may have led to morphologically similar jackfruit in the locations. Also, considering that morphological appearance is influenced by the environment, similar environmental conditions in Luwero and Kayunga districts [41, 42] could have influenced the similar appearance of jackfruit in the two locations. Morphological similarity was observed in jackfruit from locations with the same environmental conditions in a study done in India by [30]. Also, cluster analysis of jackfruit in areas of different agro-climatic regions, in West Ghats of India [17] and in Bangladesh [5], generated clear partitioning.

Genetic diversity was revealed by the number of alleles detected, which was 29 alleles with a mean of 2.90 ± 0.31 per locus across the 10 loci, for 100 samples. Therefore, as expected of populations with diverse alleles, Kayunga and Luwero districts’ jackfruit trees have long-term potential to adapt and persist to changing environments [43]. However, [2] who developed the microsatellite primers used in the present study, observed a much higher total number of alleles (116 alleles, with a mean of 8.9 alleles per locus). The more number of alleles could be attributed to the bigger sample size (426 accessions assessed), and a bigger geographic area covered (samples were from Thailand, Indonesia, Malaysia, Jamaica, Singapore, Australia, India, Miami and Bangladesh) in the latter study. The difference could also be due to the different electrophoresis methods used by the two studies. The Beckman Coulter CEQ 8000 and ABI 3730xl DNA Analyzer platforms used in the study by [2] have a more resolving power than the PAGE technique used in the current study.

The Shannon information index (I) values of 0.91 ± 0.08 and 0.90 ± 0.08 in Luwero and Kayunga districts respectively, revealed rich and even distribution of jackfruit genotypes in the two districts. In agreement, the mean expected heterozygosity (H_e) of 0.57 ± 0.03 and 0.56 ± 0.03 in Luwero and Kayunga districts respectively, showed that the diversity levels were the same in both districts. Considering the mean H_e of 0.57 ± 0.03 in this study, similar levels of genetic diversity were observed by [2], in whose study a mean H_e of 0.59 was reported. The diversity of jackfruit is attributed to the cross pollination nature of the tree.

In our study, the mean observed heterozygosity, H_o (0.71 ± 0.07) was higher than the mean H_e (0.57 ± 0.03), pointing to abundance of heterozygotes. The high heterozygosity could be due to excess out-breeding as shown by the negative value of the F_is index (-0.31 ± 0.16) in the accessions assessed. All loci deviated from the Hardy-Weinberg equilibrium (HWE) at p < 0.05 (Table. 3). Similar results were observed for some of the loci by [2]. This observation is explained by the fact that jackfruit is a cultivated crop, and hence violates several assumptions for HWE, due to forces like natural and artificial selection, non-random mating, and gene flow.

The presence of a private allele (allele ‘310’ of SSR marker MAA196a) in Kayunga, was one feature that distinguished the district from Luwero (Fig. 5). Private alleles are indicative of a population’s distinctiveness [43]. Probably, Kayunga is unique from Luwero district in regards to locus MAA196a, if the observation is not as a result of missing the allele ‘310’ in the latter district, due to an insufficient sample size [44]. It is, therefore, worth investigating the allele, and also ascertaining if there are more private ones, using a larger sample size and in more populations. This will not only validate any genetic differentiation among the studied populations, but also establish the linkage of the private alleles to important functions that can be targeted in the improvement of the crop.

According to AMOVA, there was no differentiation between Luwero and Kayunga jackfruit populations (Table 4). This was supported by the overlapping of accessions from both districts on the PCoA plot (Fig. 6) and clustering of accessions from either district on the dendrogram (Fig. 7). Further, the Mantel test revealed no isolation by distance (R = 0.035, p = 0.240) (Fig. 2) implying that genotypes were shared across the two districts. The lack of differentiation of Kayunga and Luwero populations of jackfruit is due to continuous gene flow between the districts, which was reported to be at an average rate of 88.72. Gene flow, in the absence of natural selection and genetic drift, leads to genetic uniformity in populations, due to movement of alleles from one population to another [43]. In this case, the gene flow may be attributed to sharing germplasm, in the two locations which are geographically close, with no physical boundary, and unintentional seed dispersal by humans [44, 45]. Similar results have been observed in out-crossing species like Annona cherimola Mill. [46] and Lycopersicon species [47].

Lastly, the correlation between morphological and genetic distance matrices was negligible (R = 0.073, p = 0.020) (Fig. 2), implying that morphological similarity/difference did not guarantee genetic similarity/difference. An insignificant correlation between the morphological and the molecular (SSR) similarity matrices of Persian walnuts was observed by [48]. Such a finding is explained by the fact that morphological traits are influenced by environmental other than hereditary factors. Another explanation could be due to the fact that SSR markers represent neutral diversity (along the DNA sequence in regions whose function is unknown), and have little association with adaptive variation (diversity based on known genes, within the coding regions of the DNA sequence), unless closely linked to genes which control traits [44, 48].

Results from this study indicate that jackfruit in Kayunga and Luwero districts is genetically diverse, although the two districts belong to one interbreeding population, which is attributed to continuous gene flow in the two areas. The diversity observed is useful for accurate selection of parents for hybridization, which, consequently, improves the chances of obtaining superior genotypes, when individuals with more highly dissimilar genetic background are crossed. Also, for the case of in-situ conservation of crops, characterization is essential for identifying populations for preservation and best possible sites for germplasm collection. The alelle“310” of locus MAA196a, which was rare and private to Kayunga may represent unique germplasm which also requires urgent conservation interventions. Both morphological and genetic cluster analyses did not discriminate jackfruit according to geographic origin. The study also clearly showed that the morphological differences seen in jackfruit, are not based on differences in their genetic make-up. Caution should, therefore, be taken while selecting germplasm depending on morphological assessment alone, because it may be misleading. Findings from this study will be helpful for setting up setting up breeding and conservation programs, which will then boost jackfruit production.

Study area

This study was conducted in Kayunga and Luwero districts, which are located in the Lake Victoria basin of Uganda (Fig. 8). The basin is found in the central region of Uganda which is known to produce jackfruit in large quantities [4]. The region has a modified equatorial climate type, which is characterised by rainfall throughout the year, but with two major seasons (March - May & October - December) [49]. The mean annual rainfall and temperature range from 1125 mm to 1250 mm, and 27.5°C to 30°C respectively. The region has fertile soils, which can sustain most crops and agriculture is the main occupation in the area. Farming in the two districts is mostly subsistence and the crops grown include banana, coffee, sugarcanes, maize, sweet potatoes, beans, cassava, tomatoes, pineapples, vanilla, upland rice, groundnuts and vegetables [41, 50].

Plant Materials And Morphological Data Collection

The data on morphological descriptors was collected using 16 morphological traits that describe the tree, leaf and fruit, with reference to ‘descriptors for jackfruit’ [36]. A total of 100 jackfruit trees (50 from each district) were randomly selected, ensuring a minimum distance of 30 metres apart. Only trees that were reported by the farmers to have been bearing fruits for seven years and more were chosen. Also, among these, only those that had well-developed fruits that were ready for harvest at the time of the visit were selectedFrom each farmer, only one tree was considered. The assessment of leaf characters was done using fully expanded vigorous leaves, with no disease or nutritional imbalance.

Three quantitative traits including the tree height, DBH, and crown diameter, were measured. The tree height was measured (in metres (m)) from ground to the top, using a suunto clinometer. The DBH was measured (in centimetres (cm)) using a diameter tape. The crown diameter was measured (in cm) as the mean diameter of two directions of the tree crown (North to South and East to West), using a measuring tape. The qualitative traits were assessed by observation, apart from the pulp consistency and taste, for which farmers’ perception was considered. Mature fruits which had just ripened were considered for the pulp taste and consistency, since these characters change depending on the time period after harvest. The tree locations were recorded in decimal degrees using the Geographical Positioning System (GPS), for geographic distance calculation.

Sample Collection, Storage And Transportation

Two young tender, healthy, and undamaged leaves were collected from each tree at the time of morphological data collection, and preserved on herbarium presses. The samples were transported to the molecular biology laboratory at the National Crops Resources Research Institute (NaCRRI). In the laboratory, the samples were kept at room temperature on a bench to ensure air circulation for proper drying of the leaves.

Dna Extraction And Quantification

For each sample, 0.5 g of leaf tissue was ground in liquid nitrogen using a mortar and pestle. Total genomic DNA was extracted from the ground tissue following a modified cetyltrimethylammonium bromide method (CTAB) [51]. Each DNA sample was dissolved in 50 µl of sterile nuclease free water, and the concentration and purity were determined using the NanoDrop 2000C spectrophotometer (Thermo Fisher Scientific Inc., Pittsburgh, PA, USA).

Amplification And Electrophoresis Of Microsatellite Loci

Ten pairs of microsatellite markers, designed for breadfruit (Artocarpus altilis) by [2], were used in the genetic characterization of jackfruit accessions (Table 5). Polymerase chain reactions (PCR) for amplification of the microsatellite loci was done using the Biometra T-Professional thermal cycler (Biometra, Göttingen, Germany). A reaction volume of 10 µl constituting of 1 µl of 10X DreamTaq Green PCR buffer, 0.2 µl of 10 mM dNTP, 0.5 µl each of both 10 µM forward and reverse primers, 0.05 µl of 5 U/µl Dream Taq polymerase (Thermo Fisher Scientific, Vilnius, Lithuania), 1 µl of DNA (60ng/µl), and 6.75 µl of sterile nuclease-free water was used. The PCR conditions were; pre-denaturation at 95°C for 5 min, 40 cycles of denaturation at 94°C for 30 s, annealing for 90 s, at the temperature optimized for each primer pair (Table 5), extension at 72°C for 1 min and a final extension at 60°C for 30 min. For each sample, 4 µl of the PCR amplicon was electrophoresed along with a 50 bp DNA ladder (NBS Biological Limited, Huntingdon, UK), using 8% w/v non-denaturing polyacrylamide gels (PAGE), at 80 V for 8 h in 1X TAE buffer. The electrophoresed gels were stained by immersing in ethidium bromide (0.5µg/ml) for 30 min, and thereafter de-stained in distilled water for 5 min. The separated DNA bands were visualized and photographed under UV light, using the Syngene U:Genius 3 gel documentation system (Integrated Scientific Solutions, Inc., San Diego, CA). The PyElph software [52] was used to score the photographed PAGE gels by binary coding. For each locus, only clear bands/alleles having molecular weights within the expected size ranges as reported by [2] were considered.

Table 5

Primers used in the PCR amplification of jackfruit microsatellite loci [2]
Locus	Primer sequence (5’-3’)	EAS (bp)	T_a/ºC
MAA54a	F:AACCTCCAAACACTAGGACAAC R:AGCTACTTCCAAAACGTGACA	211–239	59
MAA105	F:GTTGGGACACTGTGAACTATTC R:AAAAGCTAGTGGATTAGATGCA	265–293	60
MAA122	F:CTGGCCTTCAGTTTTGTCAAC R:CACCAGGCTTCAAGATGAAA	254–312	55
MAA140	F: CCATCCCCCATCTTTCCT R: TCCTCGTTTGCCACAGTG	142 − 160	55
MAA145	F: CCAACGCATAGCCAAATC R: AAATCCCAAACCCAACGT	275–299	50
MAA156	F: CTGGTGCTTCAGCCTAATG R:TCAGCGTCAAAGATAACTCG	283–307	55
MAA178a	F:GATGGAGACACTTTGAACTAGC R:CACCAGGGTTTAAGATGAAAC	250–258	55
MAA182	F:TACTGGGTCTGAAAAGATGTCT R: CGTTTGCGTTTGGATAAAT	186–216	54
MAA196a	F:GGAATGTGGTAGATGAAACTCC R:GACAAAAAAACAAAGGAAGAC	283–315	57
MAA196b	F:GAATGTGAGAGATAAATCTCC R:GACAAAAAAACAAAGGAAGAC	337–377	46
EAS = Expected allele size, T_{a =} Annealing temperature

Data analysis

Morphological data analysis

The raw data was entered into Microsoft Office Excel sheets, from which it was exported to R software version 3.5.1 [53] for analysis. To describe the jackfruit variation patterns, means, standard deviations (SD), and coefficients of variation values for quantitative traits were calculated. Percentages for qualitative traits were also calculated.

Using the R package ‘cluster’ [54], a distance matrix to assess the dissimilarity among the jackfruit accessions was attained using the Gower algorithm [55]. The algorithm measures the distances of mixed data (both quantitative and qualitative) simultaneously, and also takes into account the different scales of measurement of the variables. Cluster analysis was then achieved by the PAM/‘K-medoids’ method [37], using the Gower distance matrix [55]. Prior to the partitioning, the optimal number of clusters (k) to be extracted were calculated using the average silhouette width [56], the elbow [57] and gap statistic [58] methods.

A correlation analysis of the morphologic and geographic distance matrices was done to assess the relationship between morphological appearance and geographic location. Geographic distances were attained by converting the trees’ GPS readings from decimal degrees to Universal Transverse Mercator (UTM) coordinates using the UTMConvDutch software [59], before changing them to kilometres in Genetic Analysis In Excel (GenAIEx 6.503) [60]. The correlation analysis was done using the Mantel test [61] GenAIEx 6.503 software [60] with 999 permutations.

Genetic Data Analysis

The scoring matrix of the alleles generated by the PyElph software [52], was used in GenAIEx 6.503 software [60] to calculate genetic diversity measures including total number of alleles, allele frequencies, number of alleles at each locus (N_a), effective number of alleles at each locus (N_e), Shannon’s Information Index (I), the proportion of N samples that are heterozygous at a given locus (Observed heterozygosity (H_o)), the proportion of heterozygosity expected under random mating (Expected heterozygosity (H_e)), Unbiased Expected Heterozygosity (uH_e) and Wright’s fixation index, F, [62]. The AMOVA based on F_st values was also computed, to understand the genetic differentiation and the rate of gene flow (Nm) in Kayunga and Luwero jackfruit populations. Significance levels for the variation estimates were calculated using 99 permutations.

In addition, a pairwise individual-by-individual genetic distance matrix was generated and used to perform a PCoA, for determining relationship patterns of the jackfruit accessions. The PCoA was complimented by a dendrogram, which was generated using the R packages “poppr version 2.9.3” [63] and “ggtree” [64]. The dendrogram was created using the Provesti distance [38] and the unweighted pair group method with arithmetic mean (UPGMA) [39], with 1000 bootstraps.

The Mantel test in GenAIEx 6.503 software [60], with 999 permutations was used to determine the isolation by distance (

IBD)/the relationship between genetic make-up and geographic location, by correlating the genetic and geographic distances. The same test was used to assess the relationship between morphological traits and genetic make-up, by correlating the morphological (Gower) and genetic (Provesti) matrices.

A heterophyllus Artocarpus heterophyllus

AMOVA Analysis of molecular variance

APAARI Asia-Pacific Association of Agricultural Research Institutions

CTAB cetyltrimethylammonium bromide method

DNA Deoxyribonucleic acid

dNTPs deoxyribonucleotide triphosphates

GenAIEx Genetic Analysis In Excel

GPS Geographical Positioning System

HWE Hardy-Weinberg equilibrium

PAGE Polyacrylamide gel

PCoA Principal Coordinate Analysis

PCR Polymerase Chain Reaction

SSR Simple Sequence Repeats

UTM Universal Transverse Mercator

Ethics approval and consent to participate

Data and samples from the jackfruit were collected in compliance with the guidelines by the International Plant Genetic Resources Institute (IPGRI). Also, the methods used in the study were in compliance with all relevant guidelines and regulations by the Uganda National Council for Science and Technology (UNCST).

Before data and sample collection, consent was sought from the jackfruit farmers after detailed explanation of the research and its objectives. They were required to sign the consent form, which was verbally translated to the local language, for clear understanding.

Consent for publication

Not applicable

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Agricultural Research Organization (NARO) -National Crops Resources Research Institute (NaCRRI).

Authors’ Contributions

R G, Research conception, design, morphological data collection, sample collection, data analysis, interpretation, manuscript writing.

A N Research design, data interpretation and manuscript revision.

G N Research design, data interpretation and manuscript revision.

H W Research design, data interpretation and manuscript revision.

I S K Data analysis and interpretation, manuscript revision.

D K Morphological data collection.

All authors read and approved the final manuscript.

Acknowledgments

We thank the farming communities and the local council personnel in Kayunga and Luwero districts, for their cooperation during the jackfruit sample collection exercise.

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No competing interests reported.

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Morphological and Genetic Characterization of Jackfruit (Artocarpus heterophyllus) in Kayunga and Luwero districts of Uganda

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Abstract

Figures

Background

Results

Morphological variations of jackfruit

Relationships And Clustering Pattern Of Jackfruit Accessions

Genetic Diversity Of Jackfruit

Genetic Differentiation Of Jackfruit Populations (Districts) And Cluster Analysis

Discussion

Conclusions

Materials And Methods

Study area

Plant Materials And Morphological Data Collection

Sample Collection, Storage And Transportation

Dna Extraction And Quantification

Amplification And Electrophoresis Of Microsatellite Loci

Data analysis

Morphological data analysis

Genetic Data Analysis

Abbreviations

Declarations

References

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