Biology subjects

Shaik, A.

Publications and source records attributed to Shaik, A..

8 recordsLinked to original sources

bioRxiv2026

Optimizing resource allocation in Miscanthus breeding with sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

genomics↗

bioRxiv2026

Multi-trait Multi-environment Genomic Prediction Strategies for Miscanthus sacchariflorus Populations

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

genomics↗

bioRxiv2026

Weather Characterization for Optimizing Genomic Prediction in Miscanthus sacchariflorus

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

genomics↗

bioRxiv2026

ENHANCING GENOMIC PREDICTION MODELS IN MISCANTHUS POPULATIONS BY INCORPORATING THE GENOTYPE-BY-ENVIRONMENT INTERACTION

Giant Miscanthus giganteus (Mxg) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus (Msa) and Miscanthus sinensis (Msi) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (GxE). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa, 280 genotypes of Msi) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the GxE effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi) compared to the conventional approaches based on main effects only.

genomics↗

bioRxiv2025

The TONSL-MMS22L complex and FANCM form an interdependent complex on chromatin to counter replication stress

FANCM is branchpoint DNA translocase essential for cellular response to replication stress. Here, we show that replication stress stimulates FANCM and the TONSL-MMS22L heterodimer bound to histones H3-H4 to form an interdependent complex on chromatin. TONSL-MMS22L recruits FANCM and Fanconi anemia (FA) core complex to stalled and collapsed forks, maintains FANCM on replication-stressed chromatin, promotes FANCD2 monoubiquitination, facilitates both repair and replication traverse of DNA interstrand crosslinks (ICLs), and suppresses sister chromatid exchanges, through its interactions with FANCM and H3-H4. Reciprocally, both DNA translocase activity and phosphorylation of FANCM facilitate recruitment of TONSL-MMS22L and RAD51 to perturbed forks. Moreover, TONSL-MMS22L and FANCM function together to promote activation of the FA pathway, ICL repair, homologous recombination and replication traverse. Cancer patients with tumors with wildtype FANCM and low expression of TONSL-MMS22L have a more favorable prognosis than those with high expression. Thus, FANCM-TONSL-MMS22L acts coordinately as a complex on chromatin that resolves replication stress, and this complex may present a therapeutic target for wildtype FANCM-linked cancer.

cell biology↗

bioRxiv2024

A bifunctional antibody conjugate marks the location of DNA binding proteins on deproteinized DNA fibers.

Immunofluorescent foci of DNA Damage Response (DDR) proteins serve as surrogates for DNA damage and are frequently interpreted as denoting specific lesions. For example, Double Strand Breaks (DSBs) are potent inducers of the DDR, whose best-known factor is the phosphorylated histone variant H2AX ({gamma}-H2AX). The association with DSBs is so well established that the reverse interpretation that {gamma}-H2AX invariably implies DSBs is routine. However, this conclusion is inferential and has been challenged. The resolution of this question has been hampered by the lack of methods for distinguishing the location of DDR proteins relative to DSBs caused by sequence indifferent agents. Here, we describe an approach for marking the location of DDR factors in relation to DSBs on DNA fibers. We synthesized a two-arm "Y" conjugate containing biotin and trimethylpsoralen (TMP) coupled to a secondary antibody. After exposure to a DNA breaker, permeabilized mammalian cells were incubated with a primary antibody against the DDR factor followed by binding of the secondary antibody in the conjugate to the primary antibody. Exposure to longwave UV light covalently linked the psoralen to the DNA. DNA fibers were spread, and the immunofluorescence of the biotin tag denoted the location of the target protein. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/609705v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@15eaf5corg.highwire.dtl.DTLVardef@14ade70org.highwire.dtl.DTLVardef@51c83forg.highwire.dtl.DTLVardef@131cb58_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

biochemistry↗

bioRxiv2020

Ahr-bacterial diet interaction modulates aging and associated pathologies in C. elegans

Genetic and environmental factors concurrently shape the aging process. The nematode Caenorhabditis elegans has been instrumental in the past 30 years to identify most genes and interventions nowadays known to modulate aging (Lopez-Otin et al. 2013, Tigges et al. 2014, Dato et al. 2017). The aryl-hydrocarbon-receptor (AhR) was discovered as a dioxin-binding transcription factor involved in the metabolism of different environmental toxicants and xenobiotics (Poland et al. 1976). Since then, the variety of pathophysiological processes regulated by the AhR has rapidly grown, ranging from the immune response to cell death and metabolic pathways, and we disclosed it promotes aging phenotypes across species (Eckers et al. 2016, Esser et al. 2018). Interestingly, many AhR modulators may impact on aging and age-associated pathologies, but, whether their effects are actually AhR-dependent has never been explored. Here, we show for the first time that lack of C. elegans AHR-1 affects health and lifespan in a context-dependent manner. Using known mammalian AhR modulators we found that, similar to mammals, ahr-1 has protective effects against environmental insults and identified a new role for AhR-bacterial diet interaction in animal lifespan, stress resistance, and age-associated pathologies. We narrowed down the dietary factor involved in the observed AhR-dependent features to a bacterially-extruded metabolite likely involved in tryptophan metabolism. This is the first study clearly establishing C. elegans as a good model organism to investigate evolutionarily conserved functions of AhR-modulators and -regulated processes, indicating it can be exploited to contribute to the discovery of novel information about AhR in mammals. Significance StatementThe roundworm C. elegans has been instrumental for the identification of many of the genetic and environmental factors known to concurrently shape the aging process. The aryl-hydrocarbon-receptor (AhR) was originally discovered as a dioxin-binding transcription factor involved in the metabolism of environmental toxicants and, in the past, we found it promotes aging phenotypes across species. Many AhR modulators may impact on aging and age-associated pathologies, but, whether their effects are actually AhR-dependent has never been explored. We show here for the first time that C. elegans AhR has protective effects against environmental insults such as UVB radiations and the xenobiotic BaP and identified a new critical role for AhR-bacterial diet interaction in animal lifespan, stress resistance, and age-associated pathologies.

physiology↗

bioRxiv2019

Single-nucleus RNA-seq identifies Huntington disease astrocyte states

Huntington Disease (HD) is an inherited movement disorder caused by expanded CAG repeats in the Huntingtin gene. We have used single nucleus RNASeq (snRNASeq) to uncover cellular phenotypes that change in the disease, investigating single cell gene expression in cingulate cortex of patients with HD and comparing the gene expression to that of patients with no neurological disease. In this study, we focused on astrocytes, although we found significant gene expression differences in neurons, oligodendrocytes, and microglia as well. In particular, the gene expression profiles of astrocytes in HD showed multiple signatures, varying in phenotype from cells that had markedly upregulated metallothionein and heat shock genes, but had not completely lost the expression of genes associated with normal protoplasmic astrocytes, to astrocytes that had substantially upregulated GFAP and had lost expression of many normal protoplasmic astrocyte genes as well as metallothionein genes. When compared to astrocytes in control samples, astrocyte signatures in HD also showed downregulated expression of a number of genes, including several associated with protoplasmic astrocyte function and lipid synthesis. Thus, HD astrocytes appeared in variable transcriptional phenotypes, and could be divided into several different \"states\", defined by patterns of gene expression. Ultimately, this study begins to fill the knowledge gap of single cell gene expression in HD and provide a more detailed understanding of the variation in changes in gene expression during astrocyte \"reactions\" to the disease.

neuroscience↗