Biology subjects

Greenblum, S.

Publications and source records attributed to Greenblum, S..

4 recordsLinked to original sources

bioRxiv2025

Dopamine biosynthesis in plants

Dopamine, an essential human neurotransmitter, also plays important roles in plant-microbe interactions and abiotic stress tolerance. While dopamine biosynthesis is well-characterized in mammals, it remains largely unclear in plants. Through metabolite-based genome-wide association studies, we identified two putative polyphenol oxidase genes for dopamine biosynthesis in Brachypodium distachyon, located in the same chromosomal region as two tyrosine decarboxylase genes. Biochemical and knockout mutant analyses demonstrated that dopamine biosynthesis proceeds primarily via tyramine as an intermediate and contributes to plant tissue browning. Homology searches and functional assays indicate that this dopamine biosynthetic pathway is conserved across diverse plant species. This discovery provides valuable insights into plant dopamine biosynthesis with potential applications in reducing food browning, improving stress resilience, and bioengineering valuable compounds.

plant biology↗

bioRxiv2025

Single-cell-level response to drought in Sorghum bicolor reveals novel targets for improving water use efficiency

Increasing drought threatens global agriculture, especially in water-limited regions. Sorghum bicolor, a drought-tolerant C4 grass, is a promising bioenergy crop for cultivation on marginal lands, yet its molecular drought responses remain poorly understood. To uncover single-cell-level transcriptional responses to drought, we performed single-nucleus RNA sequencing on mature sorghum leaves under well-watered and drought conditions. We identified major cell types and analyzed differential gene expression across mesophyll, bundle sheath, epidermal, vascular, and stomatal cells. Surprisingly, drought effects on transcriptomes exceeded differences due to cell identity, revealing a shared response across cell types. We leveraged this convergence to identify candidate regulators of drought-responsive gene expression. These findings advance our understanding of sorghum drought adaptation and offer new targets for engineering enhanced water use efficiency in bioenergy crops.

plant biology↗

bioRxiv2024

Pervasive fitness trade-offs revealed by rapid adaption in large experimental populations of Drosophila melanogaster

Trade-offs are an inherent feature of organismal biology that are expected play a fundamental role in the evolution of natural populations. Efforts to quantify trade-offs are largely confined to phenotypic measurements and the identification of negative genetic-correlations among fitness-relevant traits. Here, we use time-series genomic data collected during experimental evolution in large, genetically diverse populations of Drosophila melanogaster to directly measure the manifestation of trade-offs in response to fluctuating selection on ecological timescales. Specifically, we first conducted a lab-based selection experiment to quantify a genome-wide signal of antagonistic pleiotropy elicited in response to shifting population densities and associated with reproduction and stress tolerance selection. In doing so, we identified a putative role of two cosmopolitan inversions in these trade-offs. We then conducted an independent experiment to show that a simple manipulation of increasing population density under controlled lab-based conditions identified loci that are relevant to selection during population expansion and collapse in a complex, semi-natural setting. In concert, our results reveal how adaptation in complex, natural environments can be coarse-grained in such a manner to drive repeatable and predictable patterns of genomic variation, and further add credence to models positing a role of generic fitness trade-offs in the maintenance of variation in natural populations.

evolutionary biology↗

bioRxiv2018

High accuracy haplotype-derived allele frequencies from ultra-low coverage pool-seq samples

Evolve-and-resequence (E+R) experiments leverage next-generation sequencing technology to track the allele frequency dynamics of populations as they evolve. While previous work has shown that adaptive alleles can be detected by comparing frequency trajectories from many replicate populations, this power comes at the expense of high-coverage (>100x) sequencing of many pooled samples, which can be cost-prohibitive. Here, we show that accurate estimates of allele frequencies can be achieved with very shallow sequencing depths (<5x) via inference of known founder haplotypes in small genomic windows. This technique can be used to efficiently estimate frequencies for any number of bi-allelic SNPs in populations of any model organism founded with sequenced homozygous strains. Using both experimentally-pooled and simulated samples of Drosophila melanogaster, we show that haplotype inference can improve allele frequency accuracy by orders of magnitude for up to 50 generations of recombination, and is robust to moderate levels of missing data, as well as different selection regimes. Finally, we show that a simple linear model generated from these simulations can predict the accuracy of haplotype-derived allele frequencies in other model organisms and experimental designs. To make these results broadly accessible for use in E+R experiments, we introduce HAF-pipe, an open-source software tool for calculating haplotype-derived allele frequencies from raw sequencing data. Ultimately, by reducing sequencing costs without sacrificing accuracy, our method facilitates E+R designs with higher replication and resolution, and thereby, increased power to detect adaptive alleles.

evolutionary biology↗