The research, led by Dalia Youssef El-Berawey of the University of Alexandria together with colleagues from the university’s Faculty of Education and Faculty of Science, focused on four widespread native species: prickly lettuce (Lactuca serriola), London rocket (Sisymbrium irio), nettle-leaved goosefoot (Chenopodium murale), and swallow-wort (Cynanchum acutum). These plants were not chosen at random. All four thrive in disturbed, degraded habitats and are known to tolerate harsh growing conditions, making them natural candidates for phytoremediation — the use of plants to extract, stabilize, or detoxify pollutants from soil and water. Unlike conventional remediation techniques, which rely on excavation, chemical washing, or landfilling of contaminated earth, phytoremediation is inexpensive, solar-powered, and environmentally benign, though its success depends entirely on selecting the right plant for the right pollutant.

To identify those candidates, the researchers sampled both soils and plants from industrial locations that they classified into two categories: low-pollution and highly polluted sites. Soil and plant tissues were analyzed for three of the most problematic metals — cadmium, lead, and copper — establishing a gradient of exposure across the study sites. The team then measured a suite of biochemical indicators in the plant tissues, including soluble sugars, proteins, secondary metabolites, total phenolics, malondialdehyde (MDA), and hydrogen peroxide (H2O2). Each of these molecules tells a story about stress. Hydrogen peroxide and MDA, for example, are classic signatures of oxidative damage: when heavy metals interfere with cellular metabolism, plants produce reactive oxygen species that attack membranes and proteins, and MDA accumulates as a byproduct of that lipid peroxidation.

The results showed a clear and consistent pattern. In the heavily polluted areas, all four species exhibited elevated levels of soluble sugars, proteins, hydrogen peroxide, MDA, and secondary metabolites compared with plants growing in less contaminated soil. Rising sugar and protein content reflects the plants’ attempt to fuel repair mechanisms and maintain osmotic balance under stress, while the surge in secondary metabolites points to an intensified antioxidant arsenal. The increase in H2O2 and MDA confirms that even these hardy weeds were experiencing genuine oxidative stress at the most contaminated sites — they were not immune to the toxicity, but they were actively fighting it. This combination of damage markers and defense responses provides a physiological portrait of plants pushed to their metabolic limits yet still functioning.

One of the most intriguing findings concerned total phenolic compounds, which displayed a unique interaction between plant species and location. Cynanchum acutum, the vigorous vine sometimes regarded as an invasive weed, responded to severe contamination with what the authors describe as hyper-stress phenolic accumulation — a massive surge in these antioxidant compounds to cope with the toxicity. Chenopodium murale, by contrast, maintained a balanced phenolic level while preserving the integrity of its genome. This divergence suggests two distinct survival strategies: one species throws overwhelming chemical defenses at the problem, while the other appears to buffer the stress more gently, avoiding both runaway oxidative damage and genetic injury. Understanding these strategies matters because a plant that keeps its genome stable under pollution pressure is more likely to grow, reproduce, and accumulate metals reliably across seasons.

That genetic dimension is where the study becomes particularly novel. Beyond the biochemical assays, the team employed two molecular marker systems — inter simple sequence repeats (ISSR) and start codon targeted (SCoT) markers — to probe the plants’ DNA directly. These polymerase chain reaction-based techniques generate banding patterns that reveal genetic variation and, crucially, detect DNA damage caused by environmental stress. From the marker profiles, the researchers calculated genomic template stability (GTS), a measure of how much the DNA banding pattern of stressed plants deviates from that of unstressed references. A high GTS means the genome has remained essentially intact; a low GTS signals genotoxic damage, with altered or lost bands reflecting mutations, rearrangements, or strand breaks induced by metal exposure.

The molecular data added a decisive layer to the physiological picture. While all species showed biochemical signs of stress in the highly polluted sites, Chenopodium murale stood out for maintaining genomic stability alongside its balanced phenolic profile. This is significant because DNA damage in a remediating plant can compromise growth and seed production, undermining the long-term viability of a phytoremediation program. A species that can tolerate heavy metals without suffering genetic erosion offers both immediate cleanup capacity and the resilience to persist across generations. The ISSR and SCoT markers thus serve a dual purpose: they act as sensitive biosensors of pollution-induced genotoxicity, and they help identify which plants possess the underlying genetic robustness needed for sustained field deployment.

Synthesizing the physiological, biochemical, and molecular evidence, the authors conclude that two of the four species demonstrate superior potential for phytoremediation: Cynanchum acutum and Chenopodium murale. Cynanchum’s aggressive phenolic response and robust growth habit suggest it can withstand and perhaps sequester high metal loads, while Chenopodium’s combination of stress tolerance and genomic stability makes it an exceptionally reliable candidate. The finding aligns with earlier field evidence from other regions — including studies in Pakistan that identified Chenopodium murale as a promising cadmium accumulator — and reinforces a growing consensus that native weeds, often dismissed as agricultural nuisances, may be among the most practical tools for restoring contaminated land in resource-limited settings.

The implications extend beyond Egypt. Rapid industrialization across many emerging economies has outpaced environmental regulation, leaving a legacy of metal-contaminated soils that conventional cleanup cannot afford to address at scale. Phytoremediation with locally adapted native species offers a low-cost alternative that also restores vegetation cover, prevents erosion, and can be combined with careful harvesting and disposal of metal-laden biomass. The Egyptian study’s integrated approach — pairing classic stress biochemistry with modern DNA markers — provides a template other researchers can follow to screen regional floras quickly and rigorously, identifying the plants best suited to each pollution profile before committing to large-scale restoration projects.

There remain important caveats and next steps. The authors themselves emphasize that Cynanchum acutum and Chenopodium murale warrant further investigation for ecological restoration, and field-scale trials will be needed to confirm accumulation rates, biomass yields, and safe disposal of contaminated plant material. Questions about whether these species hyperaccumulate metals or merely stabilize them in their roots, and how their performance varies with soil chemistry and climate, remain open. Still, the study delivers a compelling message: the solution to industrial pollution may already be growing in the contaminated soil itself. By listening to the molecular signals of these unassuming weeds, scientists are learning which plants can turn toxic ground into living, self-repairing landscape — a quiet revolution in green technology rooted in some of nature’s most persistent survivors.

Subject of Research: Phytoremediation potential of native plant species under heavy metal stress assessed through physiological and ISSR/SCoT molecular markers

Article Title: Heavy metal stress in native plant species: investigating phytoremediation potential through physiological and ISSR/SCoT molecular assessments

Article References: El-Berawey, D. Y., Nouby, A. E., Megahed, S. M., & Fawzy, E. E. (2026). Heavy metal stress in native plant species: investigating phytoremediation potential through physiological and ISSR/SCoT molecular assessments. Environmental Science and Pollution Research, 33(30), 15728-15742. https://doi.org/10.1007/s11356-026-38207-8

Image Credits: AI Generated

DOI: 10.1007/s11356-026-38207-8

Keywords: phytoremediation, heavy metals, Chenopodium murale, Cynanchum acutum, ISSR markers, SCoT markers, genomic template stability, oxidative stress, cadmium, lead, copper, soil pollution

The research team, led by Mohamed R. Asaad of the Department of Agronomy at Minia University, evaluated nine mung bean genotypes under two contrasting irrigation regimes across two growing seasons. This created a matrix of genotype-by-environment combinations designed to expose how each line responded when water was plentiful and when it was deliberately withheld. The scale of the environmental influence was striking: environmental effects accounted for more than half of the total variation in both seed yield and biological yield. In practical terms, this means that more than half of what a breeder sees in the field is driven by where and when the plant is grown rather than by the plant’s own genetics, a sobering reminder of why single-location, single-season trials so often mislead selection decisions.

Drought stress, as expected, took a measurable toll. Across all nine genotypes, water limitation reduced seed yield by an average of 25.66 percent compared with well-irrigated controls. That figure is significant not only for what it reveals about the crop’s vulnerability but also because it establishes the baseline against which individual genotypes could be judged. A line that lost far less than a quarter of its yield under stress, while still maintaining respectable absolute production, would be a genuine candidate for drought-prone environments. The challenge was to separate those lines from the ones that simply performed well in the favorable treatment and then happened to decline less dramatically for reasons unrelated to true tolerance.

To do this, the researchers turned to a family of tools known as stress tolerance indices, mathematical transformations of yield data that compare a genotype’s performance under stress with its performance under normal conditions and with the average performance of the whole set. Three indices in particular stood out: the stress tolerance index, or STI, the geometric mean productivity, or GMP, and the mean productivity, or MP. All three were strongly associated with grain yield under drought conditions, meaning that genotypes scoring high on these indices were also the ones delivering the most seed when water was limited. This alignment matters because it gives breeders a single, easily calculated number that captures both the ability to yield under stress and the ability to yield when conditions are good, rather than forcing a trade-off between the two goals.

Heritability analysis added another layer of insight. Broad-sense heritability estimates the proportion of observed variation in a trait that is attributable to genetic rather than environmental causes, and when paired with expected genetic advance, it signals whether selection will actually move a population in the desired direction. In this study, three traits emerged as especially promising for breeders: green seed fresh weight per pod, green pod fresh weight, and thousand-seed weight. Each showed high broad-sense heritability coupled with high genetic advance, indicating that a large share of the variation in these traits is genetically controlled and that selecting the best-performing plants would produce meaningful improvement in the next generation. For a crop in which yield itself is notoriously difficult to select for directly, these traits offer reliable surrogate targets.

The multi-environment analysis then confronted the genotype-by-environment interaction, the statistical phenomenon in which genotypes rank differently across locations and seasons. The interaction was significant, confirming that the nine lines genuinely did not behave consistently across the four environment combinations. The researchers deployed the AMMI model, which combines analysis of variance for main effects with principal component analysis of the interaction term, and found that the first two interaction principal components together explained 95.04 percent of the interaction variance. In other words, nearly all of the complexity in how genotypes responded differently to environments could be compressed into two dimensions, making the patterns far easier to visualize and interpret. A complementary GGE biplot analysis, which plots both genotypes and environments in the same space to reveal which lines win where, reinforced the picture.

When the dust settled, two genotypes emerged with distinct but complementary virtues. G8 was identified as the highest-yielding genotype overall, the line that delivered the most seed across the trial. G6, by contrast, was the most stable across environments, maintaining its performance level regardless of whether it faced full irrigation or drought stress in either season. Stability statistics applied alongside the biplot analyses consistently supported this distinction, and the combined assessment showed that G8, G3, and G6 managed to pair superior productivity with favorable yield stability, a combination that is rarer than it might sound. Many high-yielding genotypes owe their numbers to one or two exceptional environment combinations, and their averages conceal poor performance elsewhere; the lines identified here avoided that trap.

What elevates the study beyond a conventional field trial is its molecular component. The team used SCoT markers, short for start codon targeted markers, a PCR-based fingerprinting technique that amplifies genomic regions flanking start codons and often targets gene-rich portions of the genome. The analysis generated 44 amplification products across the nine genotypes, of which 42 were polymorphic, a polymorphism rate of 95.45 percent. That figure confirms substantial genetic diversity within the evaluated material, which is exactly what a breeding program wants to see in its working collection, because diversity is the raw material from which new combinations of traits are assembled. Just as importantly, the molecular profiles supported the phenotypic differentiation of the superior genotypes, providing independent genetic evidence that the field-based groupings reflected real biological differences rather than environmental noise.

The molecular data proved especially illuminating for G6, a mutant-derived genotype. The SCoT analysis detected clear molecular differentiation between G6 and its parental genotype, G1, strengthening confidence that the mutation work had genuinely introduced novel genetic variation and that G6’s stability and broad adaptation were rooted in a distinct genetic makeup rather than residual similarity to its parent. This convergence of evidence, in which the yield trials, the stability statistics, and the DNA fingerprints all point toward the same conclusion, is precisely the kind of triangulation that modern plant breeding advocates have called for, and it demonstrates the advantage of combining phenotypic and molecular approaches when breeding decisions must be made under water-limited conditions.

The implications extend well beyond a single crop in a single country. Mung bean cultivation is expanding in arid and semi-arid regions where climate change is making water availability less predictable, and the framework assembled here, integrating drought tolerance indices, AMMI and GGE stability analyses, and SCoT marker screening, offers a transferable template for other crops facing the same challenge. The researchers, whose team included Hassan A. H. Soltan of the Agricultural Research Center, Hanaa S. H. Bakry and Bahaa Abugammie of Minia University’s Department of Genetics, and corresponding author Islam M. Y. Abdellatif of the Department of Horticulture, describe their identified genotypes as valuable genetic resources for developing climate-resilient cultivars. For farmers on the front lines of water scarcity, the arrival of a mung bean line that yields heavily and holds steady from season to season could translate directly into more reliable harvests, and for the scientific community, the study is a compelling demonstration that the answer to drought lies not in any single measurement but in the disciplined integration of field, statistics, and genome.

Subject of Research: Identification of drought-tolerant and stable mung bean genotypes using stress tolerance indices, multi-environment stability analysis, and SCoT molecular markers

Article Title: Identification of drought-tolerant and stable mung bean genotypes using stress tolerance indices

Article References: Identification of drought-tolerant and stable mung bean genotypes using stress tolerance indices. (n.d.). https://doi.org/10.1186/s12870-026-09706-0

Image Credits: AI Generated

DOI: 10.1186/s12870-026-09706-0

Keywords: mung bean, drought tolerance, stress tolerance index, genotype by environment interaction, AMMI, GGE biplot, SCoT markers, plant breeding, yield stability, broad-sense heritability, Vigna radiata, climate resilience

Researchers at the National Horticultural Research Institute (NIHORT) in Ibadan set out to assess the genetic diversity of fifteen avocado accessions conserved in the institute’s genebank. The seeds behind these accessions were originally collected from smallholder farmers across four states in south-eastern Nigeria — Imo, Enugu, Anambra and Abia — during successive collection missions, and were registered with NIHORT accession numbers upon entry into the genebank. Because avocado is highly heterozygous and predominantly outcrossing, with a protogynous–protandrous flowering system that promotes cross-pollination, the seedlings raised from these seeds represent open-pollinated half-sib progenies rather than clones of the maternal trees, and were treated as independent accessions in the analysis.

The team, led by Omolara I. Akinyoola and published in the journal Discover Plants, employed two complementary DNA marker systems: inter-simple sequence repeats (ISSR) and start codon targeted (SCoT) markers. ISSR primers amplify DNA fragments lying between simple sequence repeats in the genome and require no prior knowledge of the species’ DNA sequence, making them accessible and inexpensive. SCoT markers, first developed in rice by Collard and Mackill, target regions surrounding the start codons of genes, giving them a functional bias that often makes them more informative. After a pilot screen of twenty ISSR and fifteen SCoT primers on three representative accessions, the researchers selected five primers of each type based on band clarity, polymorphism and reproducibility.

The laboratory work began with fresh young leaflets collected from the field and preserved in liquid nitrogen. Genomic DNA was extracted from approximately 100 milligrams of leaf tissue per accession using a modified CTAB protocol, in which polyvinylpyrrolidone and 2-mercaptoethanol were added to suppress polyphenols and oxidation, and the chloroform–isoamyl alcohol extraction step was repeated twice to remove residual protein and lipid. DNA was precipitated overnight with ice-cold isopropanol, washed with 70 percent ethanol and re-suspended in buffer containing RNase A. Quality checks on 1 percent agarose gels confirmed sharp, high-molecular-weight bands with no shearing, and Nanodrop spectrophotometry gave A260/A280 purity ratios between 1.80 and 2.00 before samples were diluted to a working concentration of 30 nanograms per microlitre.

Polymerase chain reactions were carried out in 25-microlitre volumes on an Applied Biosystems GeneAmp 9700 thermal cycler. ISSR amplification used a touchdown profile in which the annealing temperature dropped from 65 to 56 degrees Celsius over ten cycles before thirty standard cycles at 55 degrees, while SCoT amplification used thirty-five cycles with a 50-degree annealing temperature. Amplified fragments were separated on 2 percent agarose gels, visualised under ultraviolet transillumination and scored manually against a 50-base-pair ladder, with faint or ambiguous bands excluded. The convergence of results across two independent marker systems, and across multiple analysis platforms, provided a form of internal validation that strengthens confidence in the findings.

The results revealed a moderate but meaningful reservoir of variation. The ten loci detected a total of 41 polymorphic alleles, averaging 4.1 alleles per locus — higher than the 3.1 alleles per locus reported in a previous avocado study using EST-SSR primers. The polymorphism information content, a measure of a locus’s discriminating power, ranged from 0.509 to 0.825 with a mean of 0.654, and gene diversity averaged 0.708 across the ten loci. Genetic similarity coefficients between accessions ranged from 0.59 to 0.84. Notably, SCoT markers outperformed ISSR markers, generating a higher mean number of alleles per locus (4.4 versus 3.8) and higher mean PIC values (0.671 versus 0.637), leading the authors to recommend the combined use of both systems rather than either alone.

When the researchers clustered the accessions using the unweighted pair group method with arithmetic mean (UPGMA) on Jaccard dissimilarities, supported by 1000 bootstrap resamplings, the fifteen accessions split into two main clusters — one containing six accessions and the other nine — with membership broadly reflecting the south-eastern states of origin. A factorial coordinate analysis performed in DARwin software resolved four geographic groups along the first two factorial axes, with groups dominated respectively by accessions from Imo, Enugu and Anambra, while the single Abia accession stood apart. Some accessions, however, were interwoven between groups, a pattern the authors attribute to informal farmer-to-farmer seed exchange across state boundaries.

The most striking result came from the analysis of molecular variance. AMOVA partitioned 99.3 percent of the genetic variation within the UPGMA clusters and only 0.7 percent between them, with a Phi statistic of 0.007 that was statistically non-significant (P = 0.395, based on 9999 random permutations). Cross-validation in a second R package yielded an essentially identical result (P = 0.390), confirming the robustness of the partitioning. In other words, the two clusters represent dissimilarity gradients within a single, largely panmictic gene pool rather than genetically isolated subpopulations. The authors attribute this to two interacting forces: avocado’s strongly outcrossing reproductive biology, which maintains high heterozygosity within genotypes, and widespread informal seed exchange among smallholder farmers, which has homogenised allele frequencies across the sampled states.

The study is not without limitations, which the authors acknowledge candidly. The sample of fifteen accessions, while reflecting the current holdings of the NIHORT avocado germplasm at the time of sampling, is modest for inferring fine-scale population structure and limits the statistical power of subgroup analyses. Marker-trait associations were not pursued, and the dominant nature of ISSR and SCoT markers means each accession had to be treated as a single haploid genotype. Future work, the team suggests, should integrate SNP-based platforms such as genotyping-by-sequencing with phenotypic and biochemical fruit characterisation, to link the genetic variation documented here to agronomically relevant traits such as fruit quality, pest resistance and climate resilience.

Even so, the significance of the work extends well beyond a single genebank. Nigeria’s avocado industry is growing rapidly and holds considerable potential in the global market, but it faces threats from pests including the avocado lace bug, the Persea mite and the western avocado leaf roller, as well as diseases such as anthracnose and avocado black streak. The moderate diversity documented in the NIHORT collection provides a baseline molecular dataset for future curation, hybridisation and selection, and underscores a broader truth for crop science: genetic diversity is the raw currency of breeding, and knowing exactly what a genebank holds is the first step towards spending it wisely. The identified variation can now be deployed in breeding programmes aimed at developing better varieties with desirable traits, while the finding that most variation lies within rather than between geographic groupings offers a caution against assuming that provenance alone guarantees genetic distinctiveness in conserved germplasm.

Subject of Research: Genetic diversity assessment of avocado germplasm using ISSR and SCoT molecular markers

Article Title: Genetic diversity assessment of avocado (Persea americana Mill.) germplasm from NIHORT genebank

Article References: Akinyoola, O. I., Olagunju, Y. O., Matthew, J. O., Akin-Idowu, P. E., & Ajayi, E. O. (2026). Genetic diversity assessment of avocado (Persea americana Mill.) germplasm from NIHORT genebank. Discover Plants, 3(1), Article 414. https://doi.org/10.1007/s44372-026-00866-9

Image Credits: AI Generated

DOI: 10.1007/s44372-026-00866-9

Keywords: avocado, Persea americana, genetic diversity, ISSR markers, SCoT markers, germplasm, genebank, Nigeria, NIHORT, molecular markers, AMOVA, plant breeding