Unveiling the genetic basis of Fusarium wilt resistance in chickpea using GWAS analysis and characterization of candidate genes

Introduction: Chickpea is a legume crop that thrives in regions with semi-arid or temperate climates. Its seeds are an excellent source of proteins, carbohydrates, and minerals, especially high-quality proteins. Chickpea cultivation faces several challenges including Fusarium wilt (FW), a major fungal disease that significantly reduces productivity.Methods: In this study, a Genome-wide Association Analysis (GWAS) was conducted to identify multiple genomic loci associated with FW resistance in chickpea. We conducted a comprehensive evaluation of 180 chickpea genotypes for FW resistance across three distinct locations (Ethiopia, Tunisia, and Lebanon) during the 2-year span from 2015 to 2016. Disease infection measurements were recorded, and the wilt incidence of each genotype was calculated. We employed a set of 11,979 single nucleotide polymorphisms (SNPs) markers distributed across the entire chickpea genome for SNP genotyping. Population structure analysis was conducted to determine the genetic structure of the genotypes.Results and Discussion: The population structure unveiled that the analyzed chickpea germplasm could be categorized into four sub-populations. Notably, these sub-populations displayed diverse geographic origins. The GWAS identified 11 SNPs associated with FW resistance, dispersed across the genome. Certain SNPs were consistent across trials, while others were specific to particular environments. Chromosome CA2 harbored five SNP markers, CA5 featured two, and CA4, CA6, CA7, and CA8 each had one representative marker. Four SNPs demonstrated an association with FW resistance, consistently observed across a minimum of three distinct environments. These SNPs included SNP5826041, SNP5825086, SNP11063413, SNP5825195, which located in CaFeSOD, CaS13like, CaNTAQ1, and CaAARS genes, respectively. Further investigations were conducted to gain insights into the functions of these genes and their role in FW resistance. This progress holds promise for reducing the negative impact of the disease on chickpea production

Genome-wide association study reveals SNP markers controlling drought tolerance and related agronomic traits in chickpea across multiple environments

Chickpea, renowned for its exceptional nutritional value, stands as a crucial crop, serving as a dietary staple in various parts of the world. However, its productivity faces a significant challenge in the form of drought stress. This challenge highlights the urgent need to find genetic markers linked to drought tolerance for effective breeding programs. The primary objective of this study is to identify genetic markers associated with drought tolerance to facilitate effective breeding programs. To address this, we cultivated 185 chickpea accessions in two distinct locations in Lebanon over a two-year period, subjecting them to both irrigated and rain-fed environments. We assessed 11 drought-linked traits, including morphology, growth, yield, and tolerance score. SNP genotyping revealed 1344 variable SNP markers distributed across the chickpea genome. Genetic diversity across populations originating from diverse geographic locations was unveiled by the PCA, clustering, and structure analysis indicating that these genotypes have descend from five or four distinct ancestors. A genome-wide association study (GWAS) revealed several marker trait associations (MTAs) associated with the traits evaluated. Within the rainfed conditions, 11 significant markers were identified, each associated with distinct chickpea traits. Another set of 11 markers exhibited associations in both rainfed and irrigated environments, reflecting shared genetic determinants across these conditions for the same trait. The analysis of linkage disequilibrium (LD) highlighted two genomic regions with notably strong LD, suggesting significant interconnections among several investigated traits. This was further investigated by the correlation between major markers associated with these traits. Gene annotation of the identified markers has unveiled insights into 28 potential genes that play a role in influencing various chickpea drought-linked traits. These traits encompass crucial aspects such as blooming organ development, plant growth, seed weight, starch metabolism, drought regulation, and height index. Among the identified genes are CPN60-2, hsp70, GDSL(GELP), AHL16, NAT3, FAB1B, bZIP, and GL21. These genes collectively contribute to the multifaceted response of chickpea plants to drought stress. Our identified genetic factors exert their influence in both irrigated and rainfed environments, emphasizing their importance in shaping chickpea characteristics

Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma

IntroductionOsteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease.MethodsIn our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks.ResultsOur study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc.ConclusionTo develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets

Soil Improvement Using Deep Dynamic Compaction

The use of a conventional shallow foundation system in the construction of a baseball stadium on the Delaware River waterfront was only possible by improvement of existing soils using deep dynamic compaction (DDC). Subsurface conditions at the site consisted of 5 ft to 15 ft of miscellaneous fill materials overlying up to 10 ft of soft river sediments over dense sand. Numerous obstructions and old foundations were found in the fill making pile driving difficult and extremely expensive. Several foundation designs and ground improvement alternatives were evaluated for building support. A deep dynamic compaction soil improvement plan was designed to allow for the use of a conventional shallow foundation and slab-on-grade system. Prior to construction, a full-scale plate load test was performed at a dynamically compacted area to verify soil behavior under maximum column loads. The load test was monitored using precise survey methods to determine settlements as well as using piezometers to monitor pore water pressure changes due to the DDC impacts. Soil borings were also drilled to verify soil improvement. Analysis of the load test and boring results showed that the soil improvement using DDC was effective and would allow the use of a shallow foundation system to support the stadium’s loads. The use of deep dynamic compaction proved to be an economical alternative resulting in significant savings in construction cost and a shorter construction schedule

Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial intelligence-based approach

The progressive, chronic nature of Alzheimer's disease (AD), a form of dementia, defaces the adulthood of elderly individuals. The pathogenesis of the condition is primarily unascertained, turning the treatment efficacy more arduous. Therefore, understanding the genetic etiology of AD is essential to identifying targeted therapeutics. This study aimed to use machine-learning techniques of expressed genes in patients with AD to identify potential biomarkers that can be used for future therapy. The dataset is accessed from the Gene Expression Omnibus (GEO) database (Accession Number: GSE36980). The subgroups (AD blood samples from frontal, hippocampal, and temporal regions) are individually investigated against non-AD models. Prioritized gene cluster analyses are conducted with the STRING database. The candidate gene biomarkers were trained with various supervised machine-learning (ML) classification algorithms. The interpretation of the model prediction is perpetrated with explainable artificial intelligence (AI) techniques. This experiment revealed 34, 60, and 28 genes as target biomarkers of AD mapped from the frontal, hippocampal, and temporal regions. It is identified ORAI2 as a shared biomarker in all three areas strongly associated with AD's progression. The pathway analysis showed that STIM1 and TRPC3 are strongly associated with ORAI2. We found three hub genes, TPI1, STIM1, and TRPC3, in the network of the ORAI2 gene that might be involved in the molecular pathogenesis of AD. Naive Bayes classified the samples of different groups by fivefold cross-validation with 100% accuracy. AI and ML are promising tools in identifying disease-associated genes that will advance the field of targeted therapeutics against genetic diseases.Open Access funding provided by the Qatar National Library

Prediction of Drilled Shafts Axial Capacities Using CPT Results

Results from nineteen full-scale axial load tests are reviewed to compare methods for predicting axial capacities of drilled shafts using results of Cone Penetration Tests (CPT). The three methods to estimate failure loads are: (1) Nottingham method, (2) Laboratoire des Ponts et Chaussees method (LPC), and (3) the Poulos and Davis method. Comparisons are made to assess the accuracy and dependability of each predictive method. Analyses of results indicate that the LPC method provides the most reliable predictions for axial capacities of drilled shafts in clay

Airway and Oral microbiome profiling of SARS-CoV-2 infected asthma and non-asthma cases revealing alterations-A pulmonary microbial investigation

New evidence strongly discloses the pathogenesis of host-associated microbiomes in respiratory diseases. The microbiome dysbiosis modulates the lung's behavior and deteriorates the respiratory system's effective functioning. Several exogenous and environmental factors influence the development of asthma and chronic lung disease. The relationship between asthma and microbes is reasonably understood and yet to be investigated for more substantiation. The comorbidities such as SARS-CoV-2 further exacerbate the health condition of the asthma-affected individuals. This study examines the raw 16S rRNA sequencing data collected from the saliva and nasopharyngeal regions of pre-existing asthma (23) and non-asthma patients (82) infected by SARS-CoV-2 acquired from the public database. The experiment is designed in a two-fold pattern, analyzing the associativity between the samples collected from the saliva and nasopharyngeal regions. Later, investigates the microbial pathogenesis, its role in exacerbations of respiratory disease, and deciphering the diagnostic biomarkers of the target condition. LEfSE analysis identified that Actinobacteriota and Pseudomonadota are enriched in the SARS-CoV-2-non-asthma group and SARS-CoV-2 asthma group of the salivary microbiome, respectively. Random forest algorithm is trained with amplicon sequence variants (ASVs) attained better classification accuracy, ROC scores on nasal (84% and 87%) and saliva datasets (93% and 97.5%). Rothia mucilaginosa is less abundant, and Corynebacterium tuberculostearicum showed higher abundance in the SARS-CoV-2 asthma group. The increase in Streptococcus at the genus level in the SARS-CoV-2-asthma group is evidence of discriminating the subgroups.Scopu

Genetic Dissection of Heat Stress Tolerance in Faba Bean (Vicia faba L.) Using GWAS

Heat waves are expected to become more frequent and intense, which will impact faba bean cultivation globally. Conventional breeding methods are effective but take considerable time to achieve breeding goals, and, therefore, the identification of molecular markers associated with key genes controlling heat tolerance can facilitate and accelerate efficient variety development. We phenotyped 134 accessions in six open field experiments during summer seasons at Terbol, Lebanon, at Hudeiba, Sudan, and at Central Ferry, WA, USA from 2015 to 2018. These accessions were genotyped using genotyping by sequencing (GBS), and 10,794 high quality single nucleotide polymorphisms (SNPs) were discovered. These accessions were clustered in one diverse large group, although several discrete groups may exist surrounding it. Fifteen lines belonging to different botanical groups were identified as tolerant to heat. SNPs associated with heat tolerance using single-trait (ST) and multi-trait (MT) genome-wide association studies (GWASs) showed 9 and 11 significant associations, respectively. Through the annotation of the discovered significant SNPs, we found that SNPs from transcription factor helix–loop–helix bHLH143-like S-adenosylmethionine carrier, putative pentatricopeptide repeat-containing protein At5g08310, protein NLP8-like, and photosystem II reaction center PSB28 proteins are associated with heat tolerance

Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by altered brain connectivity and function. In this study, we employed advanced bioinformatics and explainable AI to analyze gene expression associated with ASD, using data from five GEO datasets. Among 351 neurotypical controls and 358 individuals with autism, we identified 3,339 Differentially Expressed Genes (DEGs) with an adjusted p-value (≤ 0.05). A subsequent meta-analysis pinpointed 342 DEGs (adjusted p-value ≤ 0.001), including 19 upregulated and 10 down-regulated genes across all datasets. Shared genes, pathogenic single nucleotide polymorphisms (SNPs), chromosomal positions, and their impact on biological pathways were examined. We identified potential biomarkers (HOXB3, NR2F2, MAPK8IP3, PIGT, SEMA4D, and SSH1) through text mining, meriting further investigation. Additionally, ‎we shed light on the roles of RPS4Y1 and KDM5D genes in neurogenesis and neurodevelopment. Our analysis detected 1,286 SNPs linked to ASD-related conditions, of which 14 high-risk SNPs were located on chromosomes 10 and X. We highlighted potential missense SNPs associated with FGFR inhibitors, suggesting that it may serve as a promising biomarker for responsiveness to targeted therapies. Our explainable AI model identified the MID2 gene as a potential ASD biomarker. This research unveils vital genes and potential biomarkers, providing a foundation for novel gene discovery in complex diseases

Identification, characterization, and validation of NBS-encoding genes in grass pea

Grass pea is a promising crop with the potential to provide food and fodder, but its genomics has not been adequately explored. Identifying genes for desirable traits, such as drought tolerance and disease resistance, is critical for improving the plant. Grass pea currently lacks known R-genes, including the nucleotide-binding site-leucine-rich repeat (NBS-LRR) gene family, which plays a key role in protecting the plant from biotic and abiotic stresses. In our study, we used the recently published grass pea genome and available transcriptomic data to identify 274 NBS-LRR genes. The evolutionary relationships between the classified genes on the reported plants and LsNBS revealed that 124 genes have TNL domains, while 150 genes have CNL domains. All genes contained exons, ranging from 1 to 7. Ten conserved motifs with lengths ranging from 16 to 30 amino acids were identified. We found TIR-domain-containing genes in 132 LsNBSs, with 63 TIR-1 and 69 TIR-2, and RX-CCLike in 84 LsNBSs. We also identified several popular motifs, including P-loop, Uup, kinase-GTPase, ABC, ChvD, CDC6, Rnase_H, Smc, CDC48, and SpoVK. According to the gene enrichment analysis, the identified genes undergo several biological processes such as plant defense, innate immunity, hydrolase activity, and DNA binding. In the upstream regions, 103 transcription factors were identified that govern the transcription of nearby genes affecting the plant excretion of salicylic acid, methyl jasmonate, ethylene, and abscisic acid. According to RNA-Seq expression analysis, 85% of the encoded genes have high expression levels. Nine LsNBS genes were selected for qPCR under salt stress conditions. The majority of the genes showed upregulation at 50 and 200 μM NaCl. However, LsNBS-D18, LsNBS-D204, and LsNBS-D180 showed reduced or drastic downregulation compared to their respective expression levels, providing further insights into the potential functions of LsNBSs under salt stress conditions. They provide valuable insights into the potential functions of LsNBSs under salt stress conditions. Our findings also shed light on the evolution and classification of NBS-LRR genes in legumes, highlighting the potential of grass pea. Further research could focus on the functional analysis of these genes, and their potential use in breeding programs to improve the salinity, drought, and disease resistance of this important crop