Biology subjects

Ali, B.

Publications and source records attributed to Ali, B..

6 recordsLinked to original sources

bioRxiv2025

Unravelling the Genetic Architecture of Field Traits through Multi-Omics Platform Data integration

BackgroundIdentifying the genes and regulatory regions underlying a complex trait is a long-standing challenge. GWAS is generally used, but it suffers from insufficient power, lack of resolution and inefficiency to systematically screen for epistasis. We propose a systems genetics approach integrating multi-omics to overcome these limits. It was applied to a panel of maize with the objective to analyze the genetic determinism of yield in a multi-environment trials by combining genomics with transcriptomics measured on a platform. ResultsDespite the contrasted conditions between the platform and the fields, transcriptomics could be used to identify candidate genes. A presence-absence variation was in particular detected, and the transcripts allowed the identification of causal genes, increasing resolution in comparison to GWAS. In total, 47 genes were identified along the genome, and we could characterize their contrasted effect on yield according to environmental covariates. We demonstrated that the cis- and also the trans-eQTLs of these genes had an important contribution to genetic variance, suggesting a key role of epistatic interactions. In terms of predictive ability, the cis-eQTL resulted in an increase of 39 to 52% on average across the environments, in comparison to random SNP sets. ConclusionsBy efficiently combining multi-omics, it is possible to considerably increase our understanding of genetic architecture in comparison to standard GWAS. We demonstrated that omics data even measured on a phenotyping platform can be used for the analysis of field traits, opening the way for their routine use in plant breeding both for marker-assisted selection and bio-informed predictions.

genetics↗

bioRxiv2025

The experimentally evolved fluconazole-resistant Clade II isolates of Candidozyma auris exhibit a distinct lipid compositional landscape, highlighting intra-clade sphingolipid heterogeneity

The intrinsic resistance of Candidozyma auris to antifungal drugs poses a major therapeutic challenge, with conventional resistance mechanisms providing only partial explanations. Sphingolipids (SLs), known for their interclade heterogeneity, play a crucial role in antifungal resistance. This study examined the SL landscape in two drug-susceptible clade II isolates, C-line and P-line, from distinct geographical origins, which were experimentally evolved to develop stable fluconazole (FLC) resistance. The progenitors displayed distinct SL profiles, P1 had higher PhytoCer and OHPhytoCer, indicating a more active acidic SL biosynthesis branch, whereas C1 exhibited elevated OHGlcCer, OHCer, and LCBs, reflecting a greater role of the neutral biosynthesis branch. The principal component analysis (PCA) also confirmed distinct segregation of the two progenitors. Upon evolution, P1.1 and C1.1 adaptors showed significant SL alterations. P1.1 exhibited PhytoCer enrichment, while C1.1 showed reduced OHGlcCer alongside increased PhytoCer, dhCer and OHPhytoCer levels. Notably, OHGlcCer remained unchanged in P1.1, whereas LCBs and OHPhytoCer decreased compared to P1. Despite these lineage-specific differences between the progenitors, both evolved replicates exhibited increased PhytoCer as a common denominator like what is also observed in clinical FLC-resistant isolates. These findings highlight intra-clade SL variability and suggest that specific SLs contribute to FLC resistance in C. auris.

microbiology↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

genetics↗

bioRxiv2024

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions

The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plants response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in routine field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, with and without pedo-climatic data, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a hybrid panel of 244 maize Dent lines crossed to a Flint tester. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV1), and in the second one, partially observed hybrids are predicted (CV2). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. This highlights their efficiency in capturing regulation processes in relation to the growth conditions. For the field traits, we observed that only the additive components of omics were useful and only slightly improved predictive abilities for predicting new hybrids (CV1, model MegaGAO) and for predicting partially observed hybrids (CV2, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest for predicting productivity, if the omics costs were to drop significantly. Our study confirms the potential of omics to predict additive and non-additive genetic effects, resulting in a potentially high increase in predictive abilities compared to standard genomic prediction models. Key MessageTranscriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits.

genetics↗

bioRxiv2023

Microevolution of clade II isolates of Candida auris highlights multifaceted intra-clade heterogeneity in acquiring resistance towards amphotericin B

Candida auris exhibits high-level resistance to amphotericin B (AmB). Mechanisms such as ergosterol biosynthesis malfunction, oxidative damage mismanagement, and increased drug efflux contribute to AmB resistance in C. auris. In this study, we experimentally evolved two East Asian drug-susceptible clade II isolates of C. auris (P2428 and CBS10913T) isolated from different geographical locations to develop resistance against AmB. We analysed alterations in karyotype, genome sequence, and gene expression profiles to uncover the mechanisms driving AmB resistance. The independently evolved clade II adaptors displayed up to 4-16-fold higher MIC50, as compared to the parental cells. AOX2 (alternative oxidase) and the cell wall integrity pathway have been identified as critical in the development of AmB resistance. However, we noted certain intra-clade heterogeneity in the associated mechanisms. While in P2428 adaptors (P-lines), the ergosterol and sphingolipid pathways appear to play a crucial role, this was not the case for CBS10913T adaptors (A-lines), which acquired resistance independent of lipid-associated changes. The transcriptomic, WGS, and phenotypic analyses also confirm that the evolved AmB-resistant isolates follow distinct trajectories for adaptation, Furthermore, unlike the fluconazole-resistant isolates, as reported previously, changes in ploidy do not seem to contribute to the differential mechanisms of AmB resistance. Overall, this study not only provides insights into the mechanisms and pathways involved in AmB resistance but also highlights intra-clade-heterogeneity that exists within clade II of C. auris. ImportanceCandida auris demonstrates significant resistance to amphotericin B (AmB) that stems from factors like alteration of ergosterol biosynthesis, perturbation of the oxidative damage response, etc. A comprehensive understanding of underlying mechanisms can be studied in a holistic manner by subjecting resistant as well as susceptible clinical isolates to a comparative genome-level analysis. An alternate and more dynamic approach is to expose susceptible isolates to a certain concentration of drug which is not lethal but can trigger the resistance mechanisms. In the present study, we evolved C. auris towards AmB and observed novel and differential mechanisms of resistance development, in two different isolates despite belonging to the same clade. This study provides insights into the intra-clade heterogeneous behavior of C. auris towards AmB.

microbiology↗

bioRxiv2019

Evaluation of rice wild relatives as a source of traits for adaptation to iron toxicity and enhanced grain quality

Rice wild relatives (RWR) constitute an extended gene pool that can be tapped for the breeding of novel rice varieties adapted to abiotic stresses such as iron (Fe) toxicity. Therefore, we screened 75 Oryza genotypes including 16 domesticated O. sativas, one O. glaberrima, and 58 RWR representing 21 species, for tolerance to Fe toxicity. Plants were grown in a semi-artificial greenhouse setup, in which they were exposed either to control conditions, an Fe shock during the vegetative growth stage (acute treatment), or to a continuous moderately high Fe level (chronic treatment). In both stress treatments, foliar Fe concentrations were characteristic of Fe toxicity, and plants developed foliar stress symptoms, which were more pronounced in the chronic Fe stress especially toward the end of the growing season. Among the genotypes that produced seeds, only the chronic stress treatment significantly reduced yields due to increases in spikelet sterility. Moreover, a moderate but non-significant increase in grain Fe concentrations, and a significant increase in grain Zn concentrations were seen in chronic stress. Both domesticated rice and RWR exhibited substantial genotypic variation in their responses to Fe toxicity. Although no RWR strikingly outperformed domesticated rice in Fe toxic conditions, some genotypes scored highly in individual traits. Two O. meridionalis accessions were best in avoiding foliar symptom formation in acute Fe stress, while an O. rufipogon accession produced the highest grain yields in both chronic and acute Fe stress. In conclusion, this study provides the basis for using interspecific crosses for adapting rice to Fe toxicity.

plant biology↗