Comparative interactome mapping of Tau-protein in classical and rapidly progressive Alzheimer's disease identifies subtype-specific pathways.

AIMS: Tau is a key player in Alzheimer's disease (AD) and other Tauopathies. Tau pathology in the brain directly correlates with neurodegeneration in AD. The recent identification of a rapid variant of AD demands an urgent need to uncover underlying mechanisms leading to differential progression in AD. Accordingly, we aimed to dissect the underlying differential mechanisms of toxicity associated with the Tau protein in AD subtypes and to find out subtype-dependent biomarkers and therapeutic targets. METHODS: To identify and characterise subtype-specific Tau-associated mechanisms of pathology, we performed comparative interactome mapping of Tau protein in classical AD (cAD) and rapidly progressive AD (rpAD) cases using co-immunoprecipitation coupled with quantitative mass spectrometry. The mass spectrometry data were extensively analysed using several bioinformatics approaches. RESULTS: The comparative interactome mapping of Tau protein revealed distinct and unique interactors (DPYSL4, ARHGEF2, TUBA4A and UQCRC2) in subtypes of AD. Interestingly, an analysis of the Tau-interacting proteins indicated enrichment of mitochondrial organisation processes, including negative regulation of mitochondrion organisation, mitochondrial outer membrane permeabilisation involved in programmed cell death, regulation of autophagy of mitochondrion and necroptotic processes, specifically in the rpAD interactome. While, in cAD, the top enriched processes were related to oxidation-reduction process, transport and monocarboxylic acid metabolism. CONCLUSIONS: Overall, our results provide a comprehensive map of Tau-interacting protein networks in a subtype-dependent manner and shed light on differential functions/pathways in AD subtypes. This comprehensive map of the Tau-interactome has provided subsets of disease-related proteins that can serve as novel biomarkers/biomarker panels and new drug targets.

Interplay of oxidative stress, cellular communication and signaling pathways in cancer.

Cancer remains a significant global public health concern, with increasing incidence and mortality rates worldwide. Oxidative stress, characterized by the production of reactive oxygen species (ROS) within cells, plays a critical role in the development of cancer by affecting genomic stability and signaling pathways within the cellular microenvironment. Elevated levels of ROS disrupt cellular homeostasis and contribute to the loss of normal cellular functions, which are associated with the initiation and progression of various types of cancer. In this review, we have focused on elucidating the downstream signaling pathways that are influenced by oxidative stress and contribute to carcinogenesis. These pathways include p53, Keap1-NRF2, RB1, p21, APC, tumor suppressor genes, and cell type transitions. Dysregulation of these pathways can lead to uncontrolled cell growth, impaired DNA repair mechanisms, and evasion of cell death, all of which are hallmark features of cancer development. Therapeutic strategies aimed at targeting oxidative stress have emerged as a critical area of investigation for molecular biologists. The objective is to limit the response time of various types of cancer, including liver, breast, prostate, ovarian, and lung cancers. By modulating the redox balance and restoring cellular homeostasis, it may be possible to mitigate the damaging effects of oxidative stress and enhance the efficacy of cancer treatments. The development of targeted therapies and interventions that specifically address the impact of oxidative stress on cancer initiation and progression holds great promise in improving patient outcomes. These approaches may include antioxidant-based treatments, redox-modulating agents, and interventions that restore normal cellular function and signaling pathways affected by oxidative stress. In summary, understanding the role of oxidative stress in carcinogenesis and targeting this process through therapeutic interventions are of utmost importance in combating various types of cancer. Further research is needed to unravel the complex mechanisms underlying oxidative stress-related pathways and to develop effective strategies that can be translated into clinical applications for the management and treatment of cancer. Video Abstract.

Targeted therapy using nanocomposite delivery systems in cancer treatment: highlighting miR34a regulation for clinical applications.

The clinical application of microRNAs in modern therapeutics holds great promise to uncover molecular limitations and conquer the unbeatable castle of cancer metastasis. miRNAs play a decisive role that regulating gene expression at the post-transcription level while controlling both the stability and translation capacity of mRNAs. Specifically, miR34a is a master regulator of the tumor suppressor gene, cancer progression, stemness, and drug resistance at the cell level in p53-dependent and independent signaling. With changing, trends in nanotechnology, in particular with the revolution in the field of nanomedicine, nano drug delivery systems have emerged as a prominent strategy in clinical practices coupled with miR34a delivery. Recently, it has been observed that forced miR34a expression in human cancer cell lines and model organisms limits cell proliferation and metastasis by targeting several signaling cascades, with various studies endorsing that miR34a deregulation in cancer cells modulates apoptosis and thus requires targeted nano-delivery systems for cancer treatment. In this sense, the present review aims to provide an overview of the clinical applications of miR34a regulation in targeted therapy of cancer.

Cellular signaling modulated by miRNA-3652 in ovarian cancer: unveiling mechanistic pathways for future therapeutic strategies.

MicroRNAs (miRNAs) are small non-coding RNA molecules that play pivotal roles in regulating gene expression and have been implicated in the pathogenesis of numerous cancers. miRNA-3652, though relatively less explored, has recently emerged as a potential key player in ovarian cancer's molecular landscape. This review aims to delineate the functional significance and tumor progression role of miRNA-3652 in ovarian cancer, shedding light on its potential as both a diagnostic biomarker and therapeutic target. A comprehensive literature search was carried out using established databases, the focus was on articles that reported the role of miRNA-3652 in ovarian cancer, encompassing mechanistic insights, functional studies, and its association with clinical outcomes. This updated review highlighted that miRNA-3652 is intricately involved in ovarian cancer cell proliferation, migration, and invasion, its dysregulation was linked to altered expression of critical genes involved in tumor growth and metastasis; furthermore, miRNA-3652 expression levels were found to correlate with clinical stages, prognosis, and response to therapy in ovarian cancer patients. miRNA-3652 holds significant promise as a vital molecular player in ovarian cancer's pathophysiology. Its functional role and impact on tumor progression make it a potential candidate for diagnostic and therapeutic applications in ovarian cancer. Given the pivotal role of miRNA-3652 in ovarian cancer, future studies should emphasize in-depth mechanistic explorations, utilizing advanced genomic and proteomic tools. Collaboration between basic scientists and clinicians will be vital to translating these findings into innovative diagnostic and therapeutic strategies, ultimately benefiting ovarian cancer patients. Video Abstract.

The Electronic Properties of Cadmium Naphthalene Diimide Coordination Complex.

The computational simulations for electronic properties of cadmium (Cd) coordinated L-alanine NDI ligand (H2-l-ala NDI) based complex are the focus of this research. For the first time, the Cd-NDI complex (monomer) has been produced using water as the solvent; this is a new approach to synthesizing the Cd-NDI complex that has not been reported yet. Along with crystallography and Hirsch field analysis, CAM-B3LYP/LANL2DZ and B3LYP/LANL2MB basis sets were used, and in-depth characterisation of the Cd-NDI complex by following DFT and TD-DFT hypothetical simulations. Hyperpolarizabilities, frontier molecular orbitals (FMOs), the density of states (DOS), dipole moment (µ), electron density distribution map (EDDM), transition density matrix (TDM), molecular electrostatic potential (MEP), electron-hole analysis (EHA), and electrical conductivity (σ) have all been studied regarding the Cd-NDI complex. The vibrational frequencies and types of interaction are studied using infrared (IR) and non-covalent interaction (NCI) analysis with iso-surface. In comparison to the Cd-NDI complex with 2.61, 2.42 eV Eg (using CAM-B3LYP/LANL2DZ and B3LYP/LANL2MB basis sets, respectively) and 376 nm λmax, (in case of B3LYP/LANL2MB λmax is higher), H2-l-ala NDI have 3.387 eV Eg and 375 nm λmax, metal-ligand coordination in complex dramatically altered charge transfer properties, such as narrowing band gap (Eg). Based on the electronic properties analysis of Cd-NDI complex, it is predicted that the Cd-NDI complex will have a spectacular (nonlinear optical) NLO response. The Cd-NDI complex is discovered to be advantageous for the creation of future nanoscale devices due to the harmony between the Cd metal and H2-l-ala NDI, in addition to their influences on NLO characteristics.

Biosensing chips for cancer diagnosis and treatment: a new wave towards clinical innovation.

Recent technological advances in nanoscience and material designing have led to the development of point-of-care devices for biomolecule sensing and cancer diagnosis. In situ and portable sensing devices for bedside, diagnosis can effectively improve the patient's clinical outcomes and reduce the mortality rate. Detection of exosomal RNAs by immuno-biochip with increased sensitivity and specificity to diagnose cancer has raised the understanding of the tumor microenvironment and many other technology-based biosensing devices hold great promise for clinical innovations to conquer the unbeatable fort of cancer metastasis. Electrochemical biosensors are the most sensitive category of biomolecule detection sensors with significantly low concentrations down to the atomic level. In this sense, this review addresses the recent advances in cancer detection and diagnosis by developing significant biological sensing devices that are believed to have better sensing potential than existing facilities.

Myricetin: targeting signaling networks in cancer and its implication in chemotherapy.

The gaps between the complex nature of cancer and therapeutics have been narrowed down due to extensive research in molecular oncology. Despite gathering massive insight into the mysteries of tumor heterogeneity and the molecular framework of tumor cells, therapy resistance and adverse side effects of current therapeutic remain the major challenge. This has shifted the attention towards therapeutics with less toxicity and high efficacy. Myricetin a natural flavonoid has been under the spotlight for its anti-cancer, anti-oxidant, and anti-inflammatory properties. The cutting-edge molecular techniques have shed light on the interplay between myricetin and dysregulated signaling cascades in cancer progression, invasion, and metastasis. However, there are limited data available regarding the nano-delivery platforms composed of myricetin in cancer. In this review, we have provided a comprehensive detail of myricetin-mediated regulation of different cellular pathways, its implications in cancer prevention, preclinical and clinical trials, and its current available nano-formulations for the treatment of various cancers.

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future.

Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Apigenin role as cell-signaling pathways modulator: implications in cancer prevention and treatment.

Cancer is a complex disease orchestrated by various extrinsic and intrinsic pathways. In recent years, there has been a keen interest towards the development of natural extracts-based cancer therapeutics with minimum adverse effects. In pursuit of effective strategy, a wide variety of natural products-derived compounds have been addressed for their anticancer effects. Apigenin is a naturally-occurring flavonoid present abundantly in various fruits and vegetables. Decades of research have delineated the pharmacological and biological properties of apigenin. Specifically, the apigenin-mediated anticancer activities have been documented in various types of cancer, but the generalized scientific evidence encompassing various molecular interactions and processes, such as regulation of the apoptotic machinery, aberrant cell signaling and oncogenic protein network have not been comprehensively covered. In this sense, in this review we have attempted to focus on the apigenin-mediated regulation of oncogenic pathways in various cancers. We have also addressed the cutting-edge research which has unveiled the remarkable abilities of apigenin to interact with microRNAs to modulate key cellular processes, with special emphasis on the nano-formulations of apigenin that can help their targeted delivery and can be a therapeutic solution for the treatment of various cancers.

Genistein as a regulator of signaling pathways and microRNAs in different types of cancers.

Cancers are complex diseases orchestrated by a plethora of extrinsic and intrinsic factors. Research spanning over several decades has provided better understanding of complex molecular interactions responsible for the multifaceted nature of cancer. Recent advances in the field of next generation sequencing and functional genomics have brought us closer towards unravelling the complexities of tumor microenvironment (tumor heterogeneity) and deregulated signaling cascades responsible for proliferation and survival of tumor cells. Phytochemicals have begun to emerge as potent beneficial substances aimed to target deregulated signaling pathways. Isoflavonoid genistein is an essential phytochemical involved in regulation of key biological processes including those in different types of cancer. Emerging preclinical evidence have shown its anti-cancer, anti-inflammatory and anti-oxidant properties. Testing of this substance is in various phases of clinical trials. Comprehensive preclinical and clinical trials data is providing insight on genistein as a modulator of various signaling pathways both at transcription and translation levels. In this review we have explained the mechanistic regulation of several key cellular pathways by genistein. We have also addressed in detail various microRNAs regulated by genistein in different types of cancer. Moreover, application of nano-formulations to increase the efficiency of genistein is also discussed. Understanding the pleiotropic potential of genistein to regulate key cellular pathways and development of efficient drug delivery system will bring us a step towards designing better chemotherapeutics.

Resveratrol, curcumin, paclitaxel and miRNAs mediated regulation of PI3K/Akt/mTOR pathway: go four better to treat bladder cancer.

Bladder cancer (BC) is a leading cause of death among urothelial malignancies that more commonly affect male population. Poor prognosis and resistance to chemotherapy are the two most important characteristics of this disease. PI3K/Akt/mTOR signaling pathway has been considered pivotal in the regulation of proliferation, migration, invasiveness, and metastasis. Deregulation of PI3K/Akt/mTOR signaling has been found in 40% of bladder cancers. Several microRNAs (miRNAs) have been reported to interact with the PI3K/Akt/mTOR signaling pathway with a different possible role in proliferation and apoptosis in bladder cancer. Thus, miRNAs can be used as potential biomarkers for BC. Natural compounds have been in the spotlight for the past decade due to their effective anti-proliferative capabilities. However, little is known of its possible effects in bladder cancer. The aim of this review is to discuss the interplay between PI3K/Akt/mTOR, miRNAs, and natural compounds and emphasize the importance of miRNAs as biomarkers and resveratrol, curcumin and paclitaxel as a possible therapeutic approach against bladder cancer.

ZnO-Templated Selenized and Phosphorized Cobalt-Nickel Oxide Microcubes as Rapid Alkaline Water Oxidation Electrocatalysts.

Oxygen electrocatalysis is of remarkable significance for electrochemical energy storage and conversion technologies, together with fuel cells, metal-air batteries, and water splitting devices. Substituting noble metal-based electrocatalysts by decidedly effective and low-cost metal-based oxygen electrocatalysts is imperative for the commercial application of these technologies. Herein, a novel strategy is presented to fabricate selenized and phosphorized porous cobalt-nickel oxide microcubes by using a sacrificial ZnO spherical template and the resulting microcubes are employed as an oxygen evolution reaction (OER) electrocatalyst. The selenized samples manifest desirable and robust OER performance, with comparable overpotential at 10 mA cm-2 (312 mV) as RuO2 (308 mV) and better activity when the current reaches 13.7 mA cm-2 . The phosphorized samples exhibit core-shell structure with low-crystalline oxides inside amorphous phosphides, which ensures superior activity than RuO2 with the same overpotential (at 10 mA cm-2 ) yet lower Tafel slope. Such a surface doping method possibly will provide inspiration for engineering electrocatalysts applied in water oxidation.

Natural Product Mediated Regulation of Death Receptors and Intracellular Machinery: Fresh from the Pipeline about TRAIL-Mediated Signaling and Natural TRAIL Sensitizers.

Rapidly developing resistance against different therapeutics is a major stumbling block in the standardization of therapy. Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL)-mediated signaling has emerged as one of the most highly and extensively studied signal transduction cascade that induces apoptosis in cancer cells. Rapidly emerging cutting-edge research has helped us to develop a better understanding of the signaling machinery involved in inducing apoptotic cell death. However, excitingly, cancer cells develop resistance against TRAIL-induced apoptosis through different modes. Loss of cell surface expression of TRAIL receptors and imbalance of stoichiometric ratios of pro- and anti-apoptotic proteins play instrumental roles in rewiring the machinery of cancer cells to develop resistance against TRAIL-based therapeutics. Natural products have shown excellent potential to restore apoptosis in TRAIL-resistant cancer cell lines and in mice xenografted with TRAIL-resistant cancer cells. Significantly refined information has previously been added and continues to enrich the existing pool of knowledge related to the natural-product-mediated upregulation of death receptors, rebalancing of pro- and anti-apoptotic proteins in different cancers. In this mini review, we will set spotlight on the most recently published high-impact research related to underlying mechanisms of TRAIL resistance and how these deregulations can be targeted by natural products to restore TRAIL-mediated apoptosis in different cancers.

Biosynthesis of silver nanoparticles from leaf extract of Litchi chinensis and its dynamic biological impact on microbial cells and human cancer cell lines.

Green synthesis of metallic nanoparticles has attracted a great deal of attention from scientific community due to its biocompatibility and environment friendly nature. In the present study, silver nanoparticles were biologically synthesized using leave extracts of Litchi chinensis. Biosynthesized silver nanoparticles were characterized and their applications were observed by different methodologies. Bio-reduction reaction was confirmed by the surface plasmon resonance of silver nanoparticles at 417 nm through UV-VIS spectrophotometer. FTIR analysis revealed that the amine groups present in the leaf extracts were responsible for the reduction of silver ions to silver nanoparticles. X-ray diffraction analysis was used to determine the crystalline nature of silver nanoparticles and their diameter was noted in the range of 41-55 nm by scanning electron microscopy. Antibacterial activity was observed against gram positive and gram negative strains of bacteria. Furthermore, human epithelial type 2 cancer cells (HEp-2) and Human breast adenocarcinoma cells lines (MCF-7) were treated with the biosynthesized silver nanoparticles using MTT assay. The resulting cell death rate was noted up to 40.91+1.99%. This study concludes that plant mediated biosynthesis of nanoparticles is the superior alternative compared to chemical and physical approaches, to utilize them as drug delivery tool and need to conjugate apoptosis inducing biological agents with silver nanoparticles to suppress the uncontrolled division of cancer cells.

Efficient feature selection and classification of protein sequence data in bioinformatics.

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth.

Histological and Microscopic Analysis of Fats in Heart, Liver Tissue, and Blood Parameters in Experimental Mice.

The intake of various types and amounts of dietary fats influences metabolic and cardiovascular health. Hence, this study evaluated the impact of routinely consumed Pakistani dietary fats on their cardiometabolic impact. For this, we made four groups of mice, each comprising 5 animals: (1) C-ND: Control mice on a normal diet, (2) HFD-DG: High-fat diet mice on a normal diet plus 10% (w/w) desi ghee, (3) HFD-O: Mice on normal diet plus 10% (w/w) plant oil (4) HFD-BG: Mice on normal diet plus 10% (w/w) banaspati ghee. Mice were fed for 16 weeks, and blood, liver, and heart samples were collected for biochemical, histological, and electron microscopic analysis. The physical factors indicated that mice fed on HFD gained more body weight than the C-ND group. Blood parameters do not show significant differences, but overall, the glucose and cholesterol concentrations were raised in the mice fed with a fat-rich diet, with the highest concentrations in the HFD-BG group. The mice fed with HFD-BG and HFD-O had more lipid droplets in the liver, compared to HFD-DG and C-ND.

On Application of Lightweight Models for Rice Variety Classification and Their Potential in Edge Computing.

Rice is one of the fundamental food items that comes in many varieties with their associated benefits. It can be sub-categorized based on its visual features like texture, color, and shape. Using these features, the automatic classification of rice varieties has been studied using various machine learning approaches for marketing and industrial use. Due to the outstanding performance of deep learning, several models have been proposed to assist in vision tasks like classification and detection. Regardless of their best results on accuracy metrics, they have been observed as overly excessive for computational resources and expert supervision. To address these challenges, this paper proposes three deep learning models that offer similar performance with 10% lighter computational overhead in comparison to existing best models. Moreover, they have been trained for end-to-end flow to demonstrate minimum expert supervision for pre-processing and feature engineering sub-tasks. The results can be observed as promising for classifying rice among five varieties, namely Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The process and performance of the trained models can be extended for edge and mobile devices for field-specific tasks autonomously.

Sentinel plot surveillance of cotton leaf curl disease in Pakistan- a case study at the cultivated cotton-wild host plant interface.

A sentinel plot case study was carried out to identify and map the distribution of begomovirus-betasatellite complexes in sentinel plots and commercial cotton fields over a four-year period using molecular and high-throughput DNA 'discovery' sequencing approaches. Samples were collected from 15 study sites in the two major cotton-producing areas of Pakistan. Whitefly- and leafhopper-transmitted geminiviruses were detected in previously unreported host plant species and locations. The most prevalent begomovirus was cotton leaf curl Kokhran virus-Burewala (CLCuKoV-Bu). Unexpectedly, a recently recognized recombinant, cotton leaf curl Multan virus-Rajasthan (CLCuMuV-Ra) was prevalent in five of 15 sites. cotton leaf curl Alabad virus (CLCuAlV) and cotton leaf curl Kokhran virus-Kokhran, 'core' members of CLCuD-begomoviruses that co-occurred with CLCuMuV in the 'Multan' epidemic were detected in one of 15 sentinel plots. Also identified were chickpea chlorotic dwarf virus and 'non-core' CLCuD-begomoviruses, okra enation leaf curl virus, squash leaf curl virus, and tomato leaf curl New Delhi virus. Cotton leaf curl Multan betasatellite (CLCuMuB) was the most prevalent CLCuD-betasatellite, and less commonly, two 'non-core' betasatellites. Recombination analysis revealed previously uncharacterized recombinants among helper virus-betasatellite complexes consisting of CLCuKoV, CLCuMuV, CLCuAlV and CLCuMuB. Population analyses provided early evidence for CLCuMuV-Ra expansion and displacement of CLCuKoV-Bu in India and Pakistan from 2012-2017. Identification of 'core' and non-core CLCuD-species/strains in cotton and other potential reservoirs, and presence of the now predominant CLCuMuV-Ra strain are indicative of ongoing diversification. Investigating the phylodynamics of geminivirus emergence in cotton-vegetable cropping systems offers an opportunity to understand the driving forces underlying disease outbreaks and reconcile viral evolution with epidemiological relationships that also capture pathogen population shifts.

Population structure and linkage disequilibrium in Lupinus albus L. germplasm and its implication for association mapping.

White lupin (Lupinus albus L.) has been around since 300 B.C. and is recognized for its ability to grow on poor soils and application as green manure in addition to seed harvest. The seed has very high levels of protein (33-47 %) and oil (6-13 %). It also has many secondary metabolites that are potentially of nutraceutical value to animals and humans. Despite such a great potential, lupins role in modern agriculture began only in the twentieth century. Although a large collection of Lupinus germplasm accessions is available worldwide, rarely have they been genetically characterized. Additionally, scarce genomic resources in terms of recombinant populations and genome information have been generated for L. albus. With the advancement in association mapping methods, the natural populations have the potential to replace the recombinant populations in gene mapping and marker-trait associations. Therefore, we studied the genetic similarity, population structure and marker-trait association in a USDA germplasm collection for their current and future application in this crop improvement. A total of 122 PI (Plant Inventory) lines were screened with 18 AFLP primer pairs that generated 2,277 fragments. A subset of 892 polymorphic markers with MAF >0.05 (minor allele frequency) were used for association mapping. The cluster analysis failed to group accessions on the basis of their passport information, and a weak structure and low linkage disequilibrium (LD) were observed indicating the usefulness of the collection for association mapping. Moreover, we were also able to identify two markers (a p value of 1.53 × 10(-4) and 2.3 × 10(-4)) that explained 22.69 and 20.5 % of seed weight variation determined using R (LR) (2) . The implications of lack of geographic clustering, population structure, low LD and the ability of AFLP to map seed weight trait using association mapping and the usefulness of the PI collections in breeding programs are discussed.