Uncovering potentials of an association panel subset for nitrogen fixation and sustainable chickpea productivity

Abstract

Background

Chickpea (Cicer arietinum L.) is a nutritious legume that fixes atmospheric nitrogen through Rhizobium symbiosis, enhancing growth and yield. Sustainable agriculture is essential to address food security, climate change, and environmental sustainability. Plant Growth-Promoting Rhizobacteria (PGPR) improve crop productivity, reduce chemical fertilizer use, and support eco-friendly farming. This study highlights PGPR’s role in enhancing chickpea yield, resilience, and resource efficiency while minimizing environmental impacts. PGPR reduces chemical dependency, preserves ecosystems, and supports global sustainability goals. Findings demonstrate PGPR’s potential to improve food security and promote equitable distribution. This research offers innovative strategies for advancing sustainable agriculture.

Results

The study revealed significant variability in nodulation, nitrogen fixation, and yield among 20 chickpea genotypes under different treatments, including Rhizobium, vesicular-arbuscular mycorrhiza (VAM), and chemical fertilizers (NPK), applied alone and in combinations. Genotype ICC9085 consistently outperformed others, exhibiting the highest nodules per plant (22.67), nitrogen content (3.65%), and protein content (22.85%), while ICC1083 and ICC6579 showed minimal nodulation (< 5 nodules). Rhizobium treatment proved the most effective, achieving the highest nitrogenase activity (mean: 35.806; max: 189.2) and yielding superior growth-promoting results compared to VAM and control treatments. Molecular genotyping identified 20 out of 128 SSR markers as polymorphic, with an average polymorphic information content (PIC) of 0.35 and two alleles per marker, revealing moderate polymorphism and significant genetic diversity. Cluster analysis grouped the genotypes into four primary clusters, reflecting geographical and genetic diversity in nodulation traits. Environmental factors such as soil nutrients, temperature, and water stress, alongside genetic traits like root architecture and nitrogen-fixing efficiency, contributed to variations in growth and productivity. This interplay of genotype and environment underscores the adaptability and superior performance of certain varieties like ICC9085 under specific conditions.

Conclusion

These observations suggest that the identified superior genotypes can be used to introduce desirable traits into allied chickpea cultivars through marker-assisted selection and crop improvement programs.The research of the chickpea, a crop with significant agricultural and industrial value, will eventually help develop efficient methods for the production of climate-smart food crops to meet the food and feed needs of future generations for a sustainable environment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-06244-z.

Background

Chickpea (Cicer arietinum L., Fabaceae, 2n = 2x = 16) ranks third in the global production of edible legumes. Chickpea genome size is approximately 738 Mb [1]. The total area used for chickpea production worldwide is roughly 13.98 Mha, with a 13.7 Mt annual production and productivity of about 967.6 kg/ha. The seeds are rich in protein (20–30%), dietary fibers, 40% carbohydrates and 3–6% oil and are low in fats (mostly polyunsaturated fatty acids) [2]. They are regarded as one of the nutritionally best-composed dry legumes for human consumption and are good suppliers of vital minerals like calcium, magnesium, potassium, phosphorus, iron, zinc, and manganese [3]. Moreover, being nutritionally enriched to satisfy the requirements as an alternative to animal sources for proteins is of significant importance due to increasing malnutrition among children and food insecurity [4, 5]. Therefore, much emphasis is placed on improving nutritional status, sustainable agriculture, and food security.

Chickpeas like other legumes, specialized organs, nodules those inhabits Mesorhizobium ciceri. The symbiotic association between legumes and rhizobia leads to the nodule formation and in turn fixation of atmospheric nitrogen called biological nitrogen fixation.(BNF). The bacteria receive carbon and other nutrients from the host (plant), which in turn helps in the fixation of atmospheric nitrogen, which can be converted and used later by the host. The importance of BNF in the present context is sthat global demand for nitrogen fertilizer is predicted to increase by 1.4% annually. The loss of billions of dollars in farm profit has drawn attention to the need for alternative sources of N. Chickpeas, like other legumes, can fix atmospheric nitrogen through root nodule symbiosis on an average of 60 kg/ha under suitable crop growth conditions [6] and 19–24 kg/ ha under drought stress conditions [7]. It has also been reported that in the case of the pulses-cereals crop rotation, cereals yield 1.5 tonnes more yield per hectare than those not preceded by pulses, in addition to role of biological nitrogen fixationissoil conservation by effectively mainatningbenifical microorganisms in plants. Chickpea nodules are mostly indeterminate, and there is a lot of diversity in nodulation. Recently, based on the root nodule transcriptome of chickpea, many genic SSR markers and candidate genes for nodulation have been reported[8]. Further, chickpea lines PM233 and PM405 have been undertaken to find out the genes for the nodulation, identified through mutation for the nodulation genes namely RN1 and RN4, respectively [9]. However, little is known about the relationship between nodule size and growth attributes in chickpea. This study aims to provide new insights into the role of biological nitrogen fixation in chickpeas by elucidating the genetic and molecular mechanisms governing the chickpea-rhizobia symbiosis, investigating the impact of environmental factors on nitrogen fixation efficiency. However, several knowledge gaps including genetic factors, microbial interactions, environmental influences and agronomic practices hinder the full realization of chickpeas’ potential in nitrogen fixation. Understanding the genetic basis of nitrogen fixation in chickpeas is still in its infancy. Identifying and manipulating genes associated with efficient nitrogen fixation could lead to the development of superior chickpea varieties [10]. In microbial interactions, the complex symbiotic relationship between chickpeas and nitrogen-fixing bacteria (rhizobia) is influenced by numerous environmental factors. More research is needed to better understand these interactions and how they can be optimized under different agronomic conditions. Environmental influences such asclimate change and soil degradation pose significant threats to the nitrogen fixation process. Studying the impact of abiotic stressors, such as drought and salinity, on nitrogen fixation can provide insights into developing resilient chickpea varieties [11].Further, there is limited knowledge on best agronomic practices that can enhance nitrogen fixation in chickpeas. Research into crop rotation, intercropping, and soil management techniques can provide practical solutions for farmers.

Seed inoculation with plant growth promoting rhizobacterium (PGPR)and vesicular arbuscular-mycorrhiza (VAM) are encouraged and fostered in agricultural practices for the improving growth of plants [12–14], including legumes [15, 16] to enhance the nitrogen fixation ability thereby yield and productivity. These practices enhance nutrient availability in hosts and promote growth and production through symbiotic relationships between the roots and beneficial microorganisms. Thus, improves yield in a sustainable approach without the utilization and application of chemical fertilizers which pose ill effects on both soil and consumers [17–19]. Chickpea should be properly inoculated to decrease or eliminate the requirement for nitrogen fertilizer. Arbuscular-mycorrhizal-fungus (AMF) collaborate with a variety of plant types. By enhancing the, controlling the host plant’s structure, and controlling the host plant’s mineral nutrition, AMF promotes the growth of its host plants [20]. VAM used as a biofertilizer along with chemical fertilizers aids the plant with additional supplements as growth materials. AMF maximises the rate of photosynthesis in host plants by altering their physiological condition by increasing their leaf area and phosphorus content [21, 22].The efficiency of nodulation and biological nitrogen fixation (BNF) can be measured through the nitrogenase and acetylene reduction assay/test (ARA) to examine the nitrogenase activity. Therefore, a better understanding of the factors that could improve the benefit of this symbiotic association with Rhizobium and VAM and the identification of genotypes which will respond better to these PGPR would be advantageous in agriculture.Understanding how genotype supports nodulation and N-fixation in chickpea is also important to maximise the benefit of N-fixation and reduce reliance on nitrogenous fertilisers. Earlier studies have reported that using transcriptomic analysis during the early stages of rhizobial colonization, genotypes with opposing levels of nodule formation, having generally overlapping gene expression profiles, show a sub-set of genes involved in signalling and transport pathways that vary by nodulation level, suggesting a potential role for these genes in chickpea rhizobial symbios, the genotypes capable of expressing high nodulation may exhibit an increased level of biological nitrogen fixation. Understanding molecular mechanism behind root nodulation is very important for further nodule improvement.

In this research profound significance for sustainable agriculture and global food security by addressing critical challenges such as feeding the growing population, adapting to climate change, and minimizing environmental impact. By developing innovative strategies to enhance crop productivity, resilience, and resource efficiency, it paves the way for reducing dependency on chemical inputs and preserving natural ecosystems. The findings not only contribute to advancing agricultural practices but also align with global efforts to ensure equitable food distribution and safeguard the planet for future generations. This broader perspective underscores the transformative potential of the research, making it a cornerstone for sustainable development.

Nitrogen fixation is globally significant for sustainable agriculture, as it reduces reliance on synthetic fertilizers, lowers greenhouse gas emissions, and improves soil health. Chickpeas, as nitrogen-fixing legumes, play a crucial role in enhancing soil fertility while serving as a nutritious, protein-rich crop. Their ability to thrive in arid and semi-arid regions makes them invaluable for climate-resilient agriculture, supporting food security and sustainable farming practices in the face of growing climate challenges.

Thus, in the current study, we have accomplished the molecular genotyping of the genotypes selected from the association panel for identification of nodulation-specific molecular markers and to further understand how PGPRs (Rhizobium and VAM), inorganic fertilizers NPK and their combinations interact amongst them and differed in their impact on nodulation, nodule function, host genotype and gene regulation affecting symbiotic nodule formation in order to identify the genotypes those enable the process to benefit from rhizobia and VAM in inoculated chickpea genotypes by studying the number of nodules per genotypes, the nitrogen content of the seed and nitrogense acivity of the nodulesas an indirect measurement of biological nitrogen fixation. The exploration of genetic mechanisms behind nodulation and nitrogen fixation is essential for improving crop productivity, particularly in leguminous plants like chickpeas. The symbiotic relationship between leguminous plants and nitrogen-fixing rhizobia bacteria governs nodulation and nitrogen fixation. Key genetic mechanisms include the nod genes (nodA, nodB, nodC) in rhizobia, which synthesize nod factors that trigger root hair curling and nodule formation, and the plant’s NFR receptor genes that initiate this process. Inside the nodules, nitrogen-fixing bacteria express nif genes encoding nitrogenase, which converts atmospheric nitrogen into ammonia, while plant genes like leghemoglobin maintain the oxygen balance necessary for nitrogenase activity. Understanding these genetic activities enables the breeding of crops with enhanced nodulation and nitrogen fixation efficiency, improving soil fertility and reducing reliance on synthetic fertilizers, promoting sustainable agricultural practices.

Results

Number of nodules of the studied genotypes

The number of nodules was found to be highest in the genotype ICC9085 followed by ICC1891 and BG3022 while the rest of the genotypes showed comparatively significantly lesser number of nodules. The number of nodules per plant was assessed for each genotype. The number of nodules ranged from 1.566 in genotype ICC 1083 to 21.355 in genotype ICC9085 (Table 1).

Table 1.

Number of nodules, nitrogen content and protein content among 20 genotypes of chickpea

Genotypes	N%	SD	Mean	SE	P Con.	SD	Mean	SE	NON	SD	Mean	SE
ICC 1083	1.69	0.21	1.69	0.15	18.73	0.08	18.73	0.05	2.90	1.88	2.90	1.33
ICC 1172	1.65	0.47	1.65	0.33	16.60	0.08	16.60	0.06	4.06	1.38	4.06	0.97
ICC3093	1.44	0.24	1.44	0.17	19.30	0.13	19.30	0.09	4.02	0.91	4.02	0.64
ICC3631	1.19	0.06	1.19	0.05	16.85	0.15	16.85	0.11	4.62	0.98	4.62	0.69
ICC6579	1.35	0.14	1.35	0.10	18.30	0.10	18.30	0.07	4.60	0.42	4.60	0.30
ICC6995	2.16	0.08	2.16	0.05	15.70	0.07	15.70	0.05	4.63	0.33	4.63	0.24
ICC7167	1.59	0.09	1.59	0.06	17.41	0.13	17.41	0.09	5.20	0.13	5.20	0.09
ICC7305	2.06	0.21	2.06	0.15	19.10	0.64	19.10	0.45	6.03	0.39	6.03	0.28
ICC13185	1.78	0.65	1.78	0.46	19.40	0.11	19.40	0.08	4.74	1.55	4.74	1.09
ICC1852	2.37	1.03	2.37	0.73	18.84	0.08	18.84	0.06	6.88	1.73	6.88	1.22
ICC1891	2.20	0.64	2.20	0.46	19.30	0.10	19.30	0.07	7.94	4.01	7.94	2.84
ICC1896	1.78	0.28	1.78	0.20	22.46	0.07	22.46	0.05	12.74	1.07	12.74	0.75
ICC2083	2.34	0.03	2.34	0.02	18.58	0.49	18.58	0.35	9.31	7.04	9.31	4.98
ICC3696	2.34	0.28	2.34	0.20	22.46	0.08	22.46	0.06	6.99	2.38	6.99	1.68
ICC4638	2.55	0.33	2.55	0.24	21.46	0.11	21.46	0.08	11.07	6.12	11.07	4.33
ICC6661	2.62	0.68	2.62	0.48	20.94	0.10	20.94	0.07	11.16	7.35	11.16	5.20
ICC9002	2.25	0.15	2.25	0.11	21.71	0.08	21.71	0.06	13.13	5.68	13.13	4.01
ICC9085	3.65	0.01	3.65	0.01	22.85	0.13	22.85	0.10	22.67	1.86	22.67	1.32
BG 372 (Check)	2.45	0.13	2.45	0.09	19.34	0.15	19.34	0.11	11.33	6.10	11.33	4.31
BG 3022 (Check)	2.52	0.53	2.52	0.38	20.92	0.07	20.92	0.05	14.89	3.91	14.89	2.77

Note: Nitrogen is expressed as percentage of nitrogen per 0. 5 g seed dry matter

*N %- NitrogenPercentage, P Con- Proten Conent, NON- Number of Nodules

Estimation of nitrogen

Nodules are structures formed on the roots of legume plants, such as chickpeas, by nitrogen-fixing bacteria (e.g., Rhizobium). The ability to fix nitrogen can be directly correlated with the presence of many nodules. The genotypes of chickpeas with more nodules have higher nitrogen contents, which can benefit the plant’s general growth and development. The bacteria increase the plant’s supply of nitrogen by converting air nitrogen into a form that the plant can utilise. By examining the data on the number of nodules, it can be inferred that each genotype establishes a symbiotic relationship with nitrogen-fixing bacteria.However, the degree of association is different due to the presence of nodule numbers.

The percentage of nitrogen (%N) represents the proportion of nitrogen present in the chickpea plants, which is an essential nutrient for their growth and development. The higher the %N, the more nitrogen the contains, which generally indicates better nitrogen uptake and utilization.

The Kjeldahl nitrogen estimation method was employed to determine the total nitrogen (%N) and protein content of various chickpea genotypes. Table 1 summarizes these parameters alongside the number of nodules (NON) per plant, supported by additional data showing a range of 2.8 to 21.8 nodules per plant. Among the genotypes, ICC9085 demonstrated superior performance with the highest %N (3.65), protein content (22.85), and NON (22.67), with minimal variation (SD: 0.01 for %N). Moderate performers like ICC1896, ICC3696, and ICC4638 exhibited high protein content (21.46–22.46) and NON values (6.99–12.74). Check varieties BG 372andBG 3022 showed consistent reliability, particularly in NON (11.33 and 14.89, respectively). Low performers, such asICC3631 (1.19%N) and ICC6579 (1.35%N), displayed significant variability and lower NON values (< 5). Overall, ICC9085emerged as the most robust genotype for optimizing nitrogen and protein content.

Nitrogenase activity

The acetylene reduction assay (ARA) is a commonly employed method to quantify the potential for biological nitrogen fixation (BNF) in legume crops. The ARA technique evaluates the activity of the nitrogenase enzyme, which facilitates acetylene’s conversion to ethylene [24, 25].An indirect indicator of nitrogen fixation activity is provided by this reduction process [26]. In this ARA assay, a total of 20 genotypes are mentioned above, and this activity is done from the nodules of all these 20 genotypes with different biological treatments of Rhizobium, VAM, and control (Table 2).

Table 2.

ARA analysis of control, Rhizobium and VAM

Treatment	Minimum	Maximum	Range	SD	SE	Mean
C	2.9	90.32	87.42	29.805	6.665	20.207
VAM	1.85	103.4	101.55	27.833	5.413	15.712
RZ	2.07	189.2	187.13	45.518	12.26	35.806

In Table 2, According to the ARA, for every N2 fixed by the nitrogenase enzyme, acetylene is converted to ethylene for every N2 fixed by the nitrogenase enzyme. ARA analysis of all 20 genotypes of the control group, the treated group with Rhizobium and VAM. was 1.85, while the maximum was 90.32. The mean ARA value for this group was 20.207, with a standard deviation of 29.805. In the case of the VAM, the minimum ARA value recorded was 1.850, and the maximum was 103.4. The mean ARA value for this group was 15.712, with a relatively high standard deviation of 27.833. was 2.07, and the maximum was 189.2. The mean ARA value for this group was 35.806, with a higher standard deviation of 45.518. These results suggest that the Rhizobium treatment had the highest variability in ARA values, as indicated by the larger standard deviation. Additionally, the Rhizobium treatment group exhibited a wider range of ARA values compared to the control and VAM groups. Overall, the Rhizobium treatment appears to have the most significant impact on ARA values among the three treatments (Fig. 1).

Fig. 1.

Comparison of nitrogenase activity among control, VAM and Rhizobium

A clear trend emerges regarding the activity of the enzyme (nitrogenase) levels among the different strains of microorganisms tested. Specifically, ICC9085 exhibited the highest level of activity, indicating that it is highly proficient in fixing nitrogen. On the other hand, ICC1083 displayed the lowest activity, suggesting that it may be less effective in carrying out nitrogen fixation. Furthermore, the comparison of the effects of different treatments on nitrogenase activity can also be observed (Fig. 1). Among the treatments examined, the one involving the use of Rhizobium stands out as particularly effective, showing significantly superior results compared to the treatment involving VAM and the control group (which likely received no specific treatment). These observations highlight the importance of the Rhizobium treatment in enhancing nitrogenase activity and, consequently, nitrogen fixation. This could have significant implications for agricultural practices and soil fertility enhancement, as nitrogen fixation plays a vital role in plant growth and ecosystem nutrient cycling.

The 20 chickpea genotypes were categorized into five primary clusters based on the ARA estimates (Fig. 2) using Euclidean distance clustering of the genotypes. The results demonstrated a certain level of dissimilarity among the genotypes (Fig. 2). The first cluster comprised the largest number of genotypes, totalling 13. The second cluster consisted of two genotypes, while the third cluster contained three genotypes, and the remaining clusters (fourth and fifth) each consisted of one genotype.

Fig. 2.

Euclidean distance clustering of the genotypes based on ARA dissimilarity analysis

Principal component analysis (PCA) based on ARA estimates

PCA allows you to visualize relationships between variables and observations in a reduced-dimensional space while retaining the maximum amount of information. The scree plot based cumulative variability (Fig. 3A) in a biplot refers to the proportion of total variance explained by the principal components plotted on the axes of the biplot (Fig. 3B).

Fig. 3.

(A): Scree plot representing the eigenvalue and the cumulative variability. (B): Biplot of 2 axes (F1 and F2) between the active variables and active observations

The PCA results based on ARA indicated that the axes F1 and F2 explain a cumulative percentage of 69.98% of the variability between the active variables (genotypes) and active observations (treatments) F1 alone accounts for 36.75% of the variability, while F2 explains 33.24% of the variability. This suggests that F1 captures a larger proportion of the variability compared to F2. In other words, the first principal component (F1) carries more information and explains a higher percentage of the dataset’s overall variability than the second principal component (F2).For the 1st PCA the genotypes ICC3696,ICC2083, BG3022, ICC4896 and ICC9058 are the major contributors to the treatment, control and Rhizobium are the major variables F1 and remaining genotypes and the VAM majorly contribute to the F2.

Molecular genotyping

In the use of PCR-based technologies for the investigation of genetic diversity and gene expression, primer design is a crucial step. Primers are now designed with genome-wide and high-throughput genotyping in mind as more plant genomes have been sequenced in recent years. In this study, we summarised recent developments in primer designing for profiling of DNA polymorphism and mRNA in chickpea, in addition to the development of certain new primer systems, and followed some standard criteria for the selection of primers for genotyping of nodule-specific gene (s). Out of the 128 primers used for performing polymerase chain reactions, 26 highly polymorphic markers were found and are listed in Table 3. The list of the 10 newly designed primers (Table 6) is based on the candidate’s genes for nodulation found in SSR regions along with those reported by [8].

Table 3.

List of the 26 polymorphic markers

Primer Number	Seq ID	Orientation	Seq	Len
4	IARI-4	FORWARD	GCGACTTGGCTCGAGTGATA	20
	IARI-4	REVERSE	GTCGTTTTGGTTGTCGTGGG	20
11	contig10053	FORWARD	GAAATGAATTAGCATTGCTTTAG	23
	contig10053	REVERSE	AACTCAATGTTCATCCAAACA	21
12	contig10856	FORWARD	TGATAGCAGCAAGAAGATGAT	21
	contig10856	REVERSE	AGAGGTTGTTTCAAGGAGAAC	21
17	contig1430	FORWARD	ATGCTGCTAAGTTGATTTCTG	21
	contig1430	REVERSE	TGATCAAGTTATTTCCACCAG	21
18	contig14945	FORWARD	TCCTCCTTTAATTCCTTCATC	21
	contig14945	REVERSE	TATTGAGCTTGAACAGCCATA	21
23	contig14945	FORWARD	CTTCAAGCACAGGTACACTTC	21
	contig14945	REVERSE	GGCAGTTTCTCTGTCTCTTTT	21
24	contig14945	FORWARD	AATGAAAGAAGGGGACAGTAG	21
	contig14945	REVERSE	ACCTACTTTGTGTTGTTGGAA	21
25	contig1514	FORWARD	TGTAAATGACACAGACGGAGT	21
	contig1514	REVERSE	AAGAATGCACATTTACGTGAG	21
26	contig15817	FORWARD	TCTCATCTCTATTTGGCATTC	21
	contig15817	REVERSE	ACTTGTCTGGTCAAAACAAAA	21
27	contig15935	FORWARD	GAGTTTTTAACCGCTCTTCTC	21
	contig15935	REVERSE	CCTTCAACTTTTCTTTTTCCT	21
28	contig15935	FORWARD	GAGTTGGTGGGTTTTCTAAAG	21
	contig15935	REVERSE	ACCAAACAGTTTCAAACAATC	21
42	contig2450	FORWARD	CCATCAAAATATTCTGCAATC	21
	contig2450	REVERSE	ATAGGTGGTTCTTTACCAAGC	21
44	contig28081	FORWARD	CTTTTCCTTCATCCTTTTGTT	21
	contig28081	REVERSE	GGAGAAAAGCTAGGTTAAACG	21
51	contig3726	FORWARD	ATTGCTTGTAACAAAAGAACG	21
	contig3726	REVERSE	CATTTATTTGTTAGGTTGTTCG	22
61	contig41987	FORWARD	ATTCGGAGAAGAAAAAGAAGA	21
	contig41987	REVERSE	CCTATTCCATCAACACTTCTC	21
74	contig5107	FORWARD	AACTGTTCCCAGTAACAAAGA	21
	contig5107	REVERSE	GACCCGATTGTAATGTAATGA	21
75	contig52636	FORWARD	AATTTTTGATGATGATGGAC	20
	contig52636	REVERSE	CTCAATTTTATTCCTAACAACCA	23
76	contig52636	FORWARD	GGAAAATTCAATGAAGTCACA	21
	contig52636	REVERSE	TCTTATTTGCACCAATAGTTTG	22
78	contig5610	FORWARD	CAAGACCATGTTGTTGACTTT	21
	contig5610	REVERSE	AAGAAGGAAAAGGCTCCAC	19
79	contig5625	FORWARD	GCCCTTCAAATGTAAACTTCT	21
	contig5625	REVERSE	GTGTTGGAATTGCCATATTTA	21
80	contig58230	FORWARD	GAATGTATCTGAGGCATGAAA	21
	contig58230	REVERSE	ATCCAGAGAATTTTCCTTTTG	21
90	contig69865	FORWARD	CATCAATTAAGAAGCAAATGG	21
	contig69865	REVERSE	GCAATATCAATATTACTCTGCAA	23
94	contig72698	FORWARD	GCAAAAGCATTACAAGAGAAA	21
	contig72698	REVERSE	TCCCTAGCTTAATTACCCACT	21
109	contig8014	FORWARD	ACAAGTGATCATAGTGTACAACG	23
	contig8014	REVERSE	CTTCTTGAAATTCCATCCTCT	21
110	contig8014	FORWARD	ACAAGTGATCATAGTGTACAACG	23
	contig8014	REVERSE	CTTCTTGAAATTCCATCCTCT	21
111	contig81518	FORWARD	CCATTCTCTATCTTGGTCTCC	21
	contig81518	REVERSE	GAGAAAGCCAAGAGAGTTTGT	21

*Naming based on all (128) the primers used in the study

Table 6.

List of newly designed primers used in the study

S No	Seq ID	Orientation	Sequence	Len
1	IARI-1	Forward	CTTCGGATTGCGAGAACATGG	21
	IARI-1	Reverse	TCGTTGGGTTGTGGTGATGAA	21
2	IARI-2	Forward	TGGTCGAGACCATTTGTTGG	20
	IARI-2	Reverse	ACCAATGTCCTCATCCCCG	19
3	IARI-3	Forward	CCCACGACAACCAAAACGAC	20
	IARI-3	Reverse	TTGTTGGAAGCTAGGGCCTG	20
4	IARI-4	Forward	GCGACTTGGCTCGAGTGATA	20
	IARI-4	Reverse	GTCGTTTTGGTTGTCGTGGG	20
5	IARI-5	Forward	TTAGGTGCGGCAGGGTTTAG	20
	IARI-5	Reverse	AACAAGTCCAAACGGAGGCA	20
6	IARI-6	Forward	AAAGCGGGAGACAACGTCTT	20
	IARI-6	Reverse	TGCTGCTCCCCAAACTCTTC	20
7	IARI-7	Forward	GGTGCCAATACAAAGCAGGC	20
	IARI-7	Reverse	GGTTCCTCCTTGCCCCAAAT	20
8	IARI-8	Forward	GACCAACTCCCAGGTTTGCT	20
	IARI-8	Reverse	TGCCTGCTTTGTATTGGCAC	20
9	IARI-9	Forward	GTCATGGAGGATGTCTCGGC	20
	IARI-9	Reverse	CCCGACAGTTACCTTGCACT	20
10	IARI-10	Forward	AGTGCAAGGTAACTGTCGGG	20
	IARI-10	Reverse	GAGGCCCGTTCACATGCTTA	20

Polymorphism analysis

A total of 128 SSR loci were examined in the study, covering diverse bin positions on various linkage groups. In the genetic material under examination, 26 SSR loci were found to be highly polymorphic and created with an average of 2 alleles per marker, with co-dominant markers identifying heterozygotes. The PIC values were determined based on the allele frequencies for the several SSR loci examined, with an average PIC of 0.35. The mean expected heterozygosity was 0.476 (Table 4). Similar results were obtained in chickpea by SSR marker by Tanya et al. [27]. The PIC content ranged from 0.305to 0.372 for the primer numbers 27 and 23. Several distinct individuals also reported a high PIC value in investigating chickpea microsatellites [28–35].

Table 4.

Analysis of SSR markers, polymorphism and genetic diversity studies

S.N.	Primer	No of alleles	PIC
1	P-4	2	0.351
2	P-11	2	0.368
3	P-12	2	0.318
4	P-17	2	0.365
5	P-18	2	0.321
6	P-23	2	0.372
7	P-24	2	0.365
8	P-25	2	0.351
9	P-26	2	0.351
10	P-27	2	0.305
11	P-28	2	0.365
12	P-42	2	0.365
13	P-44	2	0.369
14	P-51	2	0.318
15	P-61	2	0.337
16	P-74	2	0.374
17	P-75	2	0.365
18	P-76	2	0.362
19	P-78	2	0.374
20	P-79	2	0.329
21	P-80	2	0.372
22	P-90	2	0.371
23	P-94	2	0.352
24	P-109	2	0.346
25	P-110	2	0.375
26	P-111	2	0.374

The investigation yielded an overall genetic diversity of 0.654 using Shannon’s diversity index, showing that these genotypes exhibit high genetic diversity (Fig. 4). Values for gene diversity explained the likelihood that two randomly selected alleles had distinct ancestry and were identical to those of PIC. Figure 4 represents allelic patterns and variations across the population. We have not found any common alleles; it may be either because of small population size or the absence of common alleles indicating the presence of genetic variants, origination from different ancestry, or lack of genetic relatedness. On the other hand, presence (those found in only a few individuals) may indicate unique genetic lineages or recent mutations.

Fig. 4.

Allelic distribution among populations. I = Shannon’s Information Index, (Na = Number of Different Alleles, (Na (Freq > = 5%) = Number of Different Alleles with a Frequency > = 5%, (Ne = Number of Effective Alleles, Private Alleles = Number of Alleles Exclusive to a Single Population; Locally Common Alleles (= 25%) = Number of Locally Common Alleles (Freq. >= 5%), Found in 25% or Fewer Populations; Number of Locally Common Alleles (Freq. >= 5%) Found in 50% or Fewer Populations = Number of Locally Common Alleles (= 50%) He is the expected heterozygosity, and He is also the unaffected expected heterozygosity

Molecular similarity vs. dissimilarity analysis

One of the reasons for a narrow genetic basis resulting in smaller yield increases in chickpea is poor genetic diversity in farmed varieties [34]. Morphological, biochemical, and genetic indicators can all be used to determine diversity. Because morphological markers are phenotypic features, it might be difficult to compare phenotype and genotype because the same morphologies may have distinct genetic compositions. In contrast, molecular markers are able to detect changes at the DNA level and represent linkages between accessions and their common ancestry as well as true genetic variation more accurately than phenotypic markers. Four primary clusters formed from all 20 genotypes of chickpea (Fig. 5). Two sub-clusters of the first cluster, each containing four genotypes, were further separated from it. The second cluster, which has 13 genotypes, is the largest. The third and fourth clusters had 1 and 2 genotypes, respectively. The dendrogram in this study showed a clustering of the genotypes from the genetically related categories. This may be explained by the fact that SSR markers primarily monitor genetic variation in non-coding regions, which may not significantly affect the genotype’s morphology. This may result from substantial germplasm exchange across farms in various geographical areas. However, several accessions with similar geographic origins remained separate and were grouped together within themselves. It is generally acknowledged that Cicer arietinum is far less variable than its annual wild relatives. These findings show that the cultivable variants, despite intensive breeding attempts, are closely related to one another. This is possibly because there isn’t much genetic variety used in chickpea. To maximise the genetic variety available for introgression into cultivated species of chickpea, it is crucial to increase the number of accessions in the primary gene pool.

Fig. 5.

Clustering of genotypes based on Jaccard’s coefficient

Microsatellites are thought to be crucial in plant breeding because they are common, highly polymorphic, co-dominant, multi-allelic, and evenly distributed across the genome [35]. In this section, we present genetic variability among the genotypes based on the results of polymorphic markers. The similarity coefficient values were calculated using the combined data for the 26 polymorphic markers and the Pearson correlation matrix (Fig. 6). The hue of the square at the intersection of two variables reflects the degree of correlation between them. Bright green (strong positive association; r = 1.0) and bright red (strong negative correlation; r = 1.0) were the two most prominent hues. Positive correlations between markers indicated genetic linkage, meaning that they are physically close to the same chromosome. In linkage disequilibrium, certain marker alleles are found together more often than expected by chance. In Fig. 6, we can see that the primers which are represented in red dark green color are in high positive correlation and the primers which are in dark brown are in high negative correlation with each other for example, primer 4 has a high positive correlation with primer 25 and the primer 4 is having high negative correlation with primer 11.12 and 24. If two markers have a positive correlation, it means when one value increases, the other marker’s marker’s value also tends to increase. In molecular research, these associated markers might be used in place of one another, such as principal component analysis (PCA) loading plots. When variables are grouped together at an angle of 90 degrees or less, positive connections between the variables are found, with stronger associations being represented by smaller angles. Contrarily, a negative correlation exists between variables 180 degrees far from the origin. Variables that are 90 degrees apart from one another are not related. The extent of a variable’s impact on the PCA model is shown by the length of the loading queue for that variable.

Fig. 6.

Correlation among the polymorphic markers

Discussion

Nitrogen is a crucial component for the growth and development of plants. It is essential for several physiological activities, such as protein synthesis, enzyme function, and chlorophyll synthesis. N was positively correlated with seed output, indicating that a higher seed yield was linked to more effective use of N. When the environment was favourable for high levels of early N accumulation, Legumes, like chickpeas, have the unusual capacity to develop a symbiotic association with rhizobia, a type of nitrogen-fixing bacterium. This symbiosis takes place in specialised organs known as root nodules. Nitrogen estimation of the 20 genotypes indicates that high nodulating genotypes result in high nitrogen content so, with an increase in BNF or nodulation, the nitrogen content can also be improved. The nitrogen content in chickpea plants is closely linked to the efficiency of root nodulation, which depends on factors like rhizobia strain, soil conditions, and nutrient management. By optimizing these factors, farmers can improve nitrogen fixation and ultimately enhance the growth and productivity of chickpea crops. Inoculating the chickpea seeds with specific rhizobia strains before planting can enhance nodulation and improve nitrogen fixation.

The ARA is widely used to assess the potential for BNF in legume crops, including chickpeas. This technique measures the activity of the nitrogenase enzyme, which is responsible for converting atmospheric nitrogen into a usable form through a symbiotic association between leguminous plants and nitrogen-fixing bacteria called rhizobia. The ARA assay measures the reduction of acetylene gas to ethylene, indirectly estimating nitrogen fixation activity in the root nodules [23]. The statistical analysis results, specifically the descriptive statistics, provide valuable insights into the comparative ARA data analysis for various treatments (Control, Rhizobium and VAM). These findings suggest that the Rhizobium treatment had the widest range(2.07–189.2) and highest mean ARA value (35.8), indicating a potentially greater nitrogen fixation activity compared to the other treatments. The Rhizobium is highly effective in optimizing biological nitrogen fixation compared to VAM mainly because legumes and Rhizobium had positive symbiosis can interact very well compared to other microorgagsims due their mutually benifical relationship as Rhizobium obtained their plant secondary metabolites such as flavonoids and plants can able to fix atmospheric nitrogen.so, legumes tend to be nodulated by competitive rhizobia that do not maximize nitrogen (N2) fixation, resulting in suboptimal yields. Rhizobial nodulation competitiveness and effectiveness at N2 fixation are independent traits, making their measurement extremely time-consuming with low experimental throughput. So in order to maximise the BNF instead of including two Rhizobium species, adding a combination of VAM and Rhzobium results in higher BNF. The genotypes such as ICC9085 and ICC1083 has showed higher and lower number of nodules per plant, the possible reasons may be genotype specific intercation in nodule formation. The genotype ICC9085 which is capable of expressing high number of nodules may generally have overlapping gene expression profiles of nodulating genes [36] and genes x genotypes produce secondary metabiltes in higher proportion in favour of Rhizobium compared to other genotypes and the genotype ICC 1083 may not be compeltey succesfull in inducing mutual beneficial relationship woth rhizobium or VAM and there opposing levels of nodule formation at the transcript level these can be further validated from the trascriptomic studies in the future.

In the future, the process of creating molecular markers and more effective tools for plant breeding will remain to be quite dynamic. In order to explore the genetic diversity among genotypes and landraces, which is the initial step for trait improvement through plant breeding programmes, these primers can be used to identify populations or plants that are high in agronomic and molecular assessments [37]. According to the, the moderate diversity index values(0.45 to 0.47) indicate that the genotypes analysed for the study lack strong genetic relatedness. Similar outcomes were previously reported by Yadav et al. [38], and moderate PIC values of the primers show that the microsatellites employed for diversity analysis are suitable [26, 29]. Various other researchers also corroborate the potential value of molecular markers in chaacterization [39]. As very few polymorphic markers are detected for root nodulation in chickpea, the polymorphic markers used in our study may facilitate the researchers for further utilization of these markers in future studies. There was acceptable polymorphism, indicating that these markers might be used further to enhance chickpea quality. An enormous possibility exists for the creation of an extensive fingerprint database when rare or unique SSR alleles are found. Numerous distinct SSR alleles may be a sign of the insertion or deletion of a few repeats [40], and polymerase slippage is the most logical explanation for the high mutation rate [41]. The sequence analysis at two microsatellite loci identified copy number variations at repeat motifs as the primary cause of microsatellite variability in the PIC values. This estimate of the discriminating power of a marker takes into account both the absolute and relative frequencies of the alleles present at a locus. Lower PIC values might be the result of closely related genotypes and vice versa. The estimates of nitrogen has shown ICC-9085 has the highest %N value of 4.2% while the genotype ICC1083 has the lowest with 1.3% in their seeds, suggesting that ICC9085 possesses a significantly higher amount of nitrogen as compared to ICC1083, it mainly due to higher biological nitrogen fixation due to high number of nodules. As we explained above, genotypes will also affect the rate of biological nitrogen fixation. Overall, it is agreed that C. arietinum is far less variable than its wild annual relatives. So, based on this study, it is confirmed that primers can be further utilized for chickpea nodulation improvement through marker-assisted selection and the genotypes can serve as a good source for improving nitrogen fixation and in turn productivity of the chickpea.The superior performance of genotypes like ICC 9085 can be attributed to traits such as enhanced root systems for better water and nutrient uptake, specific genetic adaptations for stress tolerance (e.g., osmotic adjustment, ABA signaling, nutrient use efficiency), biotic stress resistance, efficient carbon partitioning, epigenetic regulation, and improved photosynthetic efficiency. However, scaling such findings to field applications faces challenges, including environmental variability, seed multiplication hurdles, soil and biotic stress variability, and farmer adoption barriers. Addressing these challenges requires participatory breeding, government policy support, precision agriculture tools, integrated management practices, and public-private partnerships to ensure these superior genotypes’ practical and widespread application.

Conclusion

This study explores the impact of Rhizobium and Vesicular Arbuscular Mycorrhiza (VAM) treatments on biological nitrogen fixation (BNF) in chickpea crops, revealing that Rhizobium inoculation significantly enhances nitrogenase activity, nodulation, and growth compared to VAM and control treatments. Among 20 genotypes, ICC9085 showed the highest nodulation (22.67 nodules), nitrogen content (3.65%), and protein content (22.85%). Molecular genotyping identified genetic diversity, with 20 of 128 SSR markers being polymorphic, highlighting the potential for marker-assisted selection to improve nitrogen fixation efficiency. The findings emphasize the importance of both genetic factors and environmental conditions such as soil nutrients, temperature, and water stress in optimizing chickpea growth. These insights suggest that prioritizing superior genotypes like ICC9085 for breeding programs can enhance nitrogen fixation, improving chickpea productivity and contributing to sustainable agricultural practices. Tailored breeding and management strategies based on genotype-environment interactions could further optimize chickpea yield and nutritional value, addressing the growing demand for food and feed.

Materials and methods

Planting material, plant growth conditions, sterilization

Twenty genotypes were selected from the core germplasm, of which 9 genotypes have a higher number of nodules while another 9 have a smaller number of nodules with 2 genotypes as the checks (Desi and Kabuli). The genotypes were collected from the ICRISAT (International Crops Research Institute for the Semi-Arid Tropics), Hyderabad, India, genetic resources division. The seeds were stored in sealed polyethene bags at 4°C. The study was conducted during the Rabi season 2020-21 in the Indian condition where crops are sown in mid-October / November and harvested in April/Mayat28.708°N latitude, 77.108°E longitude and 219 MS latitude. The trials were conducted for two years, the first-year trial were conducted at the National Phytotron Facility (NPF), Indian Agriculture Research Institute (IARI), New Delhi and the second trial was conducted at the Nanaji Deshmukh Plant Phenomics Center (NDPPC), IARI, New Delhi. The list of the genotypes with their orgin used under study is presented in Table 5.

Table 5.

List of genotypes and their location used in the study

S. No	Genotype	Location	S. No	Genotype	Location
1	ICC-1083	Iran	11	ICC-1891	India
2	ICC-1172	India	12	ICC-1896	India
3	ICC-3093	Iran	13	ICC-2083	Mexico
4	ICC-3631	Iran	14	ICC-3696	Iran
5	ICC-6579	Iran	15	ICC-4638	India
6	ICC-6995	Iran	16	ICC-6661	Iran
7	ICC-7167	Turkey	17	ICC-9002	Iran
8	ICC-7305	Afghanistan	18	ICC-9085	Iran
9	ICC-13,185	Iran	19	BG-372	India
10	ICC-1852	India	20	BG-3022	India

Soil sterilization

Sandy clay loam (pH 6.9) with a moisture content of 12.4%, organic matter concentration of 1.69 mg/g, total nitrogen concentration of 0.16%, accessible phosphorus concentration of 14.2 mg/g, exchangeable potassium concentration of 6.0 mg/g, sodium concentration of 32.0 mg/g, and available nitrate concentration of 16.4 mg/g. Before sowing the seeds, the soil was subjected to sterilization. The soil was sterilized with a 4% formaldehyde solution. Then the soil was autoclaved for 1 h on alternate days, with two rounds of autoclaving performed on alternate days. Following the sterilization process, the pots (8 kg each of 12 12 size) were filled with the sterilized soil. Adequate moisture levels, equivalent to the field capacity, were maintained in the pots to ensure suitable conditions for sowing the chickpea seeds.

RDF (required dose of fertilizer)

The amount of fertilizer that was mixed to meet the specific requirements (1 kg soil sample requires DAP (diammonium phosphate) 44.65 mg/kg). Approximately 0.4 g DAP was added to the soil in each pot. It was carefully mixed into the top 5 cm of the soil in each pot, just before the sowing of seeds to ensure proper incorporation of the fertilizer for optimal plant growth and development.

Experimental design and treatments

The experimental plant materials were cultivated in Randomized Block Design (Fig. 7a) with triplicates in 16-inch diameter pots under various treatment combinations as follows: T1: Control (No fertilizer, No inoculant), T2: Required Dose of Fertilizer (RDF) (DAP 100 Kg/ha, equivalent to 20 Kg N and 40 Kg P), T3: Mesorhizobium (Mr) Reference Strain (IARI), T4: Vesicular Arbuscular Mycorrhiza (VAM), T5: VAM + RDF, T6: Mr (Reference Strain IARI) + RDF, T7: Mr (Reference Strain IARI) + VAM, and T8: Mr (Reference Strain IARI) + VAM + RDF. In total, 20 genotypes × 8 treatments × 3 replications = 480 pots were used for the experiment (Fig. 7b). Each pot initially contained nine plants: three for observations on root/shoot traits, three for RNA isolation (uprooted 60 days after sowing at 50% flowering), and three harvested at maturity for pod and seed-related data. The pots were placed at the Nanaji Deshmukh National Phenomic Facility (NDNPF), ICARI-IARI, Pusa, New Delhi, India. Plants were grown under natural light conditions with a temperature range of 32 °C / 28 °C (day/night) and 70–80% RH. Well-watered systems maintained them at saturated moisture conditions (25% V/V basis). Recommended practices for weed, pest, and disease control were followed.

Fig. 7.

(a): Layout of the pot experiment for two years (8 Treatments X 3 Replications X 20 Genotypes = 480 Pots) Pink colour denotes genotypes lower number of nodules, and Green colour denotes genotypes of higher number of nodules. (b): Experimental setup showing different treatments namely T1: control, T2: application of VAM and T3 Rhizobium

Seed inoculation with Rhizobium and VAM

Before sowing, the soil was sterilizedby 4% of formaldehyde by covering witha fine plastic sheet for two days. Sterilized soil was filled in plastic pots upto three-fourth level of the pot. The seeds were, washed 3 times with sterilized sterilized with 0.1% HgCl2 for 2 min, and washed 3 times with sterilized distilled water. During the Rhizobium treatment, visually uniform seeds were selected to nullify the effect of differential seed vigor on plant growth. For commercial use, 200 g of Rhizobium is used to treat 10 kg of seeds; accordingly, 1.2–1.5 g of Rhizobium was used for treating a 100-seed weight. During treatment, seeds were mixed with a CMC solution, sprinkled with Rhizobium (F-75 strain) culture, and dried in the shade for immediate sowing. VAM applications were made at sowing by creating a 5 cm deep hole, placing the VAM inoculum (a mix of Glomus mosseae, Glomus fusiculatum, Glomus entietatum, Gigspora margarita, and Scutellaspora species) in it, followed by the seeds, and covering with soil. All the Plant Growth-Promoting Rhizobacteria (PGPRs) were collected from the Division of Microbiology at the Indian Council of Agricultural Research - Indian Agricultural Research Institute (ICAR-IARI) in New Delhi, 110012. The Rhizobium culture used was the F-75 strain, prepared and dried in the shade to ensure its viability for immediate sowing. For Vesicular-ArbuscularMycorrhiza (VAM) applications, the inoculum was applied at the time of sowing. The process involved creating a 5 cm deep hole in the soil. The VAM inoculum, which comprised a mix of several species, including Glomus mosseae, Glomus fasciculatum, Glomus etunicatum, Gigaspora margarita, and Scutellospora species, was then placed into these holes. This method ensured that the VAM fungi were in close proximity to the chickpea roots, promoting effective colonization and subsequent enhancement of nutrient uptake and plant growth. Phenotyping of 20 diverse chickpea genotypes for nodulation-associated traits was conducted. Traits observed included the number of nodules, nodule fresh weight (mg), nodule dry weight (mg), root fresh weight (g), root dry weight (g), shoot fresh weight (g), shoot dry weight (g), plant height (cm), days to maturity, days to flowering, number of primary branches, number of secondary branches, number of pods per plant, number of seeds per plant, and seed weight per plant (g). Observations were made either at the 50% flowering stage, 55–65 days after sowing, or at harvest, depending on the trait. For example, since nodule formation typically starts 45 days after sowing, observations on nodulation traits were taken 55–65 days after sowing, one day before root trait observations. Selected pots were watered, plants were carefully uprooted using sharp-edged tools, thoroughly washed, and data recorded. The mean of three observations was computed for all measurements.The selection of specific genotypes like ICC 9085 and PGPR (Plant Growth-Promoting Rhizobacteria) strains aligns closely with the research objectives of improving stress resilience and enhancing crop productivity. ICC 9085 is known for its superior root system, drought tolerance, and nutrient efficiency, making it ideal for studying stress adaptation mechanisms. Similarly, PGPR strains are chosen to enhance nutrient uptake, promote root growth, and protect against biotic and abiotic stresses. These selections aim to synergize genetic potential with microbial benefits, ensuring improved plant performance, yield stability, and scalability of findings to field conditions, directly addressing the research goals.

Biochemical analyses: Acetylene reduction activity of root nodules

ARA was estimated from the method given by Drevon et al. [42]. Chickpea root samples intact with nodules were collected in 60 ml test tubes and immediately stopped with a rubber cap. Then 10% of the atmosphere of each test tube containing root samples was substituted with gaseous acetylene and incubated at 28 °C for 30 min. The amount of ethylene produced in each tube was measured using a gas chromatograph with a flame ionisation detector and a capillary column. Nodules were counted, and their dry weights were taken after being kept in an oven at 65(C for 48 h).

Estimation of nitrogen

The Kjeldahl method (AOAC) was used to calculate the total amount of crude protein. Briefly, a 40 mm screen was used to filter 0.5 g of finely ground sample flour. After moving the sample into a 500 mL Kjeldahl flask, 25 mL of concentrated H2SO4 was added, and the mixture was let to stand for 30 min. Then, 5 g of sodium thiosulphate was added, and the mixture was once again let to stand for 30 min. The catalyst mixture was then added, and the Kjeldahl flask was heated rapidly after first heating slowly until frothing continued. Following a clean digestion, the process was repeated for another 30 min. After cooling the flask, 150 mL of water was carefully added before cooling it once more. The flask was then filled with 120 mL of a 40% NaOH solution, a few glass beads, and a drop of mineral oil before being immediately connected to the distillation apparatus. A 25mL boric acid solution with a mixed indication was used to capture evolved ammonia. Up till 150 mL of distillate were obtained, distillation was continued. Ammonia was collected and titrated against 0.1 N sulfuric acid. In addition to the other reagents and a piece of paper used to exclude the sample, a blank was also run.

Phenotypic data analyses

To assess the variations in parameters under the treatments, a one-way ANOVA was performed using WindoStat 10 (ver 9.8). P 0.05 is regarded as statistically significant, and the data is shown as mean ± SE. Further, the phenotypic data dendrogram was constructed using the Euclidean UPGMA dissimilarity method with a dissimilarity and similarity threshold value of 5 and the genotypes with the lower value than 5 were considered similar.

Molecular analyses

DNA isolation, primer designing, gene amplification, scoring and molecular data analysis

Extraction

The DNA was extracted from the young leaves of F2 and parent plants ( 1.5 g) stored at -80 °C using the CTAB method [43]. The extracted DNA was quantified on a 0.8% gel, and a UV trans illuminator gel documentation system was used to see the gel.

Primer designing

Genes sequences involved in the nodulation pathways in chickpea were retrieved through NCBI (www.ncbi.mlm.nih.gov) namely nod factor export ATP-binding protein I-like (Accession No XM_027337700), nodulation signaling pathway 2 protein gene (Accession No KC534503) and chlorophyll(ide) b reductase NOL (Accession No XM_012718850). The sequences were saved in FASTA format and using the Primer3 software (https://primer3.ut.ee/) [44] was used to design the new primers (Table 6). Primers were designed with the following parameters, 18–22 bp length, annealing temperature range 45–65 °C, GC content (30–70%) and product of PCR size is (100–300 bp). The study also used other primers (Supplementary Table 1) as reported by [8].

Plymerase chain reaction (PCR)

The reaction was performed in 10 µl, 4 µl of the reaction mixture, 3 µl of diluted template DNA and 1.5 µl of each primer using the 128 primers (118 have been used as reported by Kant et al., 2016 and 10 new primers were reported in this study). The following conditions were used for the amplification: a first denaturation phase lasting 5 min at 95 °C, then 34 cycles of 1 min at 94 °C, 1 min of annealing at the temperatures (55 °C, 61 °C, and 67 °C), and finally, a final extension lasting 2 min at 72 °C. After gel electrophoresis and visualization of the amplified products for scoring, the PCR results were kept at -20 °C.

Analysis (PIC and genetic similarities)

Polymorphic products from microsatellite analyses were scored in comparison with 100 bp ladder and missing data (scored as ‘0’) for each marker allele and genotype. For statistical analysis, every band was considered as a single locus/allele. The entered data was transformed into discrete variables in a binary data matrix. According to the technique of, the polymorphic information content (PIC) [45] (Eq. 1):

Using the Jaccard’s coefficient clustering algorithm and the Darwin programme, genetic similarity was calculated from the binary data matrix. The UPGMA method’s cluster analysis of the chickpea genotypes used the similarity coefficients and and for ARA genetic dissimilarity dendrogram was calculated using eludian distance. Using NTSYS-version 2.2, the analysis and dendrogram construction were carried out [46]. For further confirmation, POPGENE software version 1.32 [47, 48] was used, and the genetic diversity was also analyzed using the Shannon diversity index (H) calculated by the following formula [49].

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

RK conceptualized, interpreted and refined the manuscript. RKM and CBS conducted experiments. performed data analysis, literature collections, drafting of the manuscript and share first authorship as equal contributors. RKS and V performed in silico designing of primers. AT, SK, AS and DD contributed to the experimental guidance and refinement of the manuscript. All authors meticulously perused and approved the manuscript.

Funding

This research was funded by Department of Science and Technology- Science Engineering and Research Board (DST-SERB), New Delhi, India under grant number SERB No: CRG/2019/006273.

Data availability

The data is included in the manuscript and as supplementary material.

Declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

Consent for publishing

Not applicable.