Biology subjects

Sahay, S.

Publications and source records attributed to Sahay, S..

10 recordsLinked to original sources

bioRxiv2026

A simple and efficient CTAB plate-based protocol for genomic DNA extraction from crop plants

Modern plant breeding and molecular genetics rely on genotyping large populations for applications such as genome editing and genomic selection. However, current DNA extraction methods often require expensive equipment or signif-icant manual labor, which limits their scalability. The objective of this study was to develop a scalable, cost-effective DNA extraction method. A bead-beating protocol was used to homogenize small amounts of leaf tissue from Arabidopsis, camelina, maize, sorghum, soybean, tobacco, and wheat, as well as developing soybean seeds. Samples were processed in 96-well plates using a standard CTAB (cetyltrimethylammonium bromide)-based extraction solution. DNA concentration was measured, and DNA quality was estimated using the A260/A280 and A260/A230 ratios and gel electrophoresis. The extracted DNA was then used for PCR assays to amplify targeted endogenous sequences, detect Cas9-edited sequences, and estimate gene copy numbers. This workflow enabled a single worker to process up to 960 samples per day. The total DNA yield was 2-3 {micro}g for leaf and 0.5-1 {micro}g for soybean seeds with good quality, which was sufficient for multiple PCR reactions. The DNA was successfully used for the intended PCR assays. The extracted DNA was suitable for reliable, downstream PCR-based genotyping. This method supports diverse analyses, including routine amplification, identification of genome edits, and copy number analysis.

plant biology↗

bioRxiv2025

Nonphotochemical quenching changes with abiotic stressor and developmental stages

Nonphotochemical quenching (NPQ) is a critical photoprotective mechanism in plants, safeguarding photosystem II (PSII) and PSI from photodamage under abiotic stress. However, it is unclear if different stressors lead to similar NPQ phenotypes, and the magnitude of natural variation (between and within plant species) in NPQ response to abiotic stress is unknown. Testing a semi-high-throughput leaf-disc approach for examining the NPQ kinetics parameters, we investigated NPQ under chilling, drought and low nitrogen stress across multiple species and/or genotypes. Our results show substantial variation in NPQ phenotypes across species, genotypes and treatments. In C3 crops, tobacco and soybean, multiple NPQ parameters generally increased under chilling and drought, while in C4 crops, maize and sorghum, NPQ traits were more variable including a decrease of multiple NPQ parameters. Low-N stress revealed genotype- and developmental stage-specific effects on NPQ, potentially reflecting distinct adaptive strategies and regulatory changes in NPQ stress response. A significant effect of ecotype and stress treatment was detected on most NPQ kinetics traits in Arabidopsis thaliana, however, the interaction between ecotype and treatment was stronger in drought than in chilling. Differential regulation of NPQ could be associated with a combination of changes in proton motive, ATPase synthase activity, and PSI redox state. Our findings highlight that interpreting relative changes in NPQ under abiotic stress is inherently complex and demands a broader integration of physiological data across multiple regulatory layers.

physiology↗

bioRxiv2024

Quantitative genetics of photosynthetic trait variation in maize

Natural genetic variation in photosynthesis-related traits can aid both in identifying genes involved in regulating photosynthetic processes and developing crops with improved productivity and photosynthetic efficiency. However, rapidly fluctuating environmental parameters create challenges for measuring photosynthetic parameters in large populations under field conditions. We measured chlorophyll fluorescence and absorbance-based photosynthetic traits in a maize diversity panel in the field using an experimental design that allowed us to estimate and control multiple confounding factors. Controlling the impact of day of measurement and light intensity as well as patterns of two-dimensional spatial variation in the field substantially increased heritability with the heritability of 7 out of 14 traits measured exceeding 0.4. We were able to identify high confidence GWAS signals associated with variation in four spatially corrected traits (the quantum yield of photosystem II, non-photochemical quenching, redox state of QA, and relative chlorophyll content). Insertion alleles for Arabidopsis orthologs of three candidate genes exhibited phenotypes consistent with our GWAS results. Collectively these results illustrate the potential of applying best practices from quantitative genetics research to address outstanding questions in plant physiology and understand the mechanisms underlying natural variation in photosynthesis. Highlights[bullet] Controlling spatial and environmental confounding factors increased heritability of photosynthetic traits. [bullet]GWAS identified high confidence signals associated with variation in relative chlorophyll, {Phi}PSII, {Phi}NPQ, and qL. [bullet]Insertion alleles of the Arabidopsis orthologs of maize candidate genes exhibited photosynthesis related phenotypes consistent with the GWAS results.

plant biology↗

bioRxiv2024

SpatialLeiden - Spatially-aware Leiden clustering

Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is the algorithm of choice in the single-cell community. However, in the field of spatial omics, Leiden has been considered a non-spatial clustering method. Here, we show that by integrating spatial embeddings Leiden clustering is rendered into a computationally highly performant, spatially aware clustering method that compares well with state-of-the art clustering methods.

bioinformatics↗

bioRxiv2023

Genotype-specific nonphotochemical quenching responses to nitrogen deficit are linked to chlorophyll a to b ratios

Non-photochemical quenching (NPQ) protects plants from photodamage caused by excess light energy. The mechanism of NPQ appears to be conserved across flowering plants. However, substantial variation in NPQ has been reported within different genotypes of the same species grown under the environmental conditions. Individual maize genotypes from a diversity panel exhibited a range of responses to low nitrogen with some genotypes exhibiting increased NPQ between control and low nitrogen conditions and others exhibiting no change. These patterns were consistent for the same genotypes across multiple field seasons. NPQ increases under low nitrogen were correlated with shifts in the ratio of chlorophyll a to chlorophyll b consistent with a decrease in reaction centers. Both photosynthetic capacity and dry biomass accumulation decreased more in maize genotypes which were unable to maintain constant NPQ levels between control and low N conditions. Collectively these results suggest that the ability to maintain sufficient numbers of reaction centers under low nitrogen conditions and avoid dissipating a greater proportion of absorbed light energy via the NPQ pathway may play a key role in increasing carbon fixation and productivity in nitrogen-limited environments. HighlightsO_LISubstantial variation in NPQ kinetics exists in maize on both low and control N. C_LIO_LIIn early and late-vegetative stages a similar portion of genotypes increased, no-change or decreased NPQmax in response to low N while in the post-flowering stage substantially more genotypes decreased NPQmax. C_LIO_LIIn low nitrogen conditions, the NPQmax strongly correlates with shifts in Chl a/Chl b ratios. C_LI

plant biology↗

bioRxiv2023

Comparative GWAS identifies a role for Mendel green pea gene in the nonphotochemical quenching kinetics of sorghum, maize, and arabidopsis

Photosynthetic organisms must cope with rapid fluctuations in light intensity. Nonphotochemical quenching (NPQ) enables the dissipation of excess light energy as heat under high light conditions, whereas its relaxation under low light maximizes photosynthetic productivity. We quantified variation in NPQ kinetics across a large sorghum (Sorghum bicolor) association panel in four environments, uncovering significant genetic control for NPQ. A genome-wide association study (GWAS) identified 20 unique regions in the sorghum genome associated with NPQ. We detected strong signals from the sorghum ortholog of Arabidopsis thaliana SUPPRESSOR OF VARIEGATION3 (SVR3) involved in plastid-nucleus signaling and tolerance to cold. By integrating GWAS results for NPQ across maize (Zea mays) and sorghum association panels, we identified a second gene, NON-YELLOWING 1 (NYE1), originally identified by Gregor Mendel in pea (Pisum sativum) and involved in the degradation of photosynthetic pigments in light-harvesting complexes, along with OUTER ENVELOPE PROTEIN 37 (OEP37), that encodes a transporter in chloroplast envelope. Analysis of nye1 insertion alleles in A. thaliana confirmed the effect of this gene on NPQ kinetics across monocots and eudicots. We extended our comparative genomics GWAS framework across the entire maize and sorghum genomes, identifying four additional loci involved in NPQ kinetics. These results provide a baseline for engineering crops with improved NPQ kinetics and increasing the accuracy and speed of candidate gene identification for GWAS in species with high linkage disequilibrium.

plant biology↗

bioRxiv2023

An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells

Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets. Here we introduce a new method, ODeGP (Oscillation Detection using Gaussian Processes), which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene, thus highlighting our methods ability to discover unexpected patterns. In its current implementation, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.

systems biology↗

bioRxiv2022

The genomics and physiology of abiotic stressors associated with global elevation gradients in Arabidopsis thaliana

Phenotypic and genetic diversity in Arabidopsis thaliana may be associated with adaptation along its wide elevational range. We took a multi-regional view of elevational adaptation and in a diverse panel of ecotypes measured plant responses to high elevation stressors: low partial CO2 pressure, high light, and night freezing. We conducted genome-wide association studies (GWAS) and found evidence of contrasting locally adaptive clines between regions. Western Mediterranean ecotypes showed low {delta}13C/early flowering at low elevations to high {delta}13C/late flowering at high elevations, while Asian ecotypes showed the opposite pattern. We mapped different candidate genes for each region, and trait-associated SNPs often showed elevational clines likely maintained by selection. Antioxidants and pigmentation showed regional differentiation but rarely elevational clines. GWAS for antioxidants identified an ascorbate transporter PHT4;4 (AT4G00370), which we show alters non-photochemical quenching kinetics under high light and may be involved in local adaptation to Moroccan mountains. The low-antioxidant PHT4;4 GWAS allele was associated with lower PHT4;4 expression and this haplotype was characterized by binding sites of a transcription factor family, DOF, involved in light response. Our results highlight how physiological and genomic elevational clines in different regions can be unique, underlining the complexity of local adaptation in widely distributed species.

evolutionary biology↗

bioRxiv2021

SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

1The combination of a cells transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data. Availability and ImplementationSSAM-lite is an open-source browser-based web application with source code freely available on Github via https://github.com/HiDiHlabs/ssam-lite. Stable releases can be accessed via https://ssam-lite.bihealth.org and https://ssam-lite.netlify.app, and developmental releases can be accessed via https://dev--ssam-lite.netlify.app. The source code for a locally deployable server version, SSAM-lite-server, is available on GitHub via https://github.com/HiDiHlabs/ssam-lite-server. Both versions require a modern browser with JavaScript and WebGL support. Detailed user guides and documentation can be found at https://ssam-lite.readthedocs.io.

bioinformatics↗

bioRxiv2019

Reference-free deconvolution of complex DNA methylation data - a detailed protocol

Epigenomic profiling enables unique insights into human development and diseases. Often the analysis of bulk samples remains the only feasible option for studying complex tissues and organs in large patient cohorts, masking the signatures of important cell populations in convoluted signals. DNA methylomes are highly cell type-specific, and enable recovery of hidden components using advanced computational methods without the need for reference profiles. We propose a three-stage protocol for reference-free deconvolution of DNA methylomes comprising: (i) data preprocessing, confounder adjustment and feature selection, (ii) deconvolution with multiple parameters, and (iii) guided biological inference and validation of deconvolution results. Our protocol simplifies the analysis and integration of DNA methylomes derived from complex samples, including tumors. Applying this protocol to lung cancer methylomes from TCGA revealed components linked to stromal cells, tumor-infiltrating immune cells, and associations with clinical parameters. The protocol takes less than four days to complete and requires basic R skills.

bioinformatics↗