Multiple genetic loci affect place learning and memory performance in <i>Drosophila melanogaster</i>

Williams-Simon, Patricka A.; Posey, C.; Mitchell, Samuel J.; Ng’oma, Enoch; Mrkvicka, James A; Zars, Troy; King, Elizabeth G.

doi:10.1101/580092

Cited by 3 publications

(7 citation statements)

References 118 publications

(173 reference statements)

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“…S5E), suggesting potential co-regulation of the coding and non-coding transcript in cis as previously reported for other lncRNAs-mRNA pairs (Engreitz et al, 2016). Drosophila wdp is a transmembrane protein with known functions in the wing disc (Takemura et al, 2020) and the trachea (Huff et al, 2002), and it has also been implicated in synaptic target recognition (Kurusu et al, 2008) and learning (Williams-Simon et al, 2019). While the sequence of the lncRNA LOC112590028 itself is not conserved, Drosophila has a lncRNA, CR44758 in the same position as LOC112590028, between wdp and Gp150 (Fig.…”

Section: Long Non-coding Rnas In Single-cell Sequencing Analysis Revealed By Iso-seqsupporting

confidence: 66%

Genome annotation with long RNA reads reveals new patterns of gene expression in an ant brain

Shields

Sorida

Sheng

et al. 2021

Preprint

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Functional genomic analyses rely on high-quality genome assemblies and annotations. Highly contiguous genome assemblies have become available for a variety of species, but accurate and complete annotation of gene models, inclusive of alternative splice isoforms and transcription start and termination sites remains difficult with traditional approaches. Here, we utilized full-length isoform sequencing (Iso-Seq), a long-read RNA squencing technology, to obtain a comprehensive annotation of the transcriptome of the ant Harpegnathos saltator. The improved genome annotations include additional splice isoforms and extended 3' untranslated regions for more than 4,000 genes. Reanalysis of RNA-seq experiments using these annotations revealed several genes with caste-specific differential expression and tissue- or caste-specific splicing patterns that were missed in previous analyses. The extended 3' untranslated regions afforded great improvements in the analysis of existing single-cell RNA-seq data, resulting in the recovery of the transcriptomes of 18% more cells. The deeper single-cell transcriptomes obtained with these new annotations allowed us to identify additional markers for several cell types in the ant brain, as well as genes differentially expressed across castes in specific cell types. Our results demonstrate that Iso-Seq is an efficient and effective approach to improve genome annotations and maximize the amount of information that can be obtained from existing and future genomic datasets in Harpegnathos and other organisms.

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Section: Long Non-coding Rnas In Single-cell Sequencing Analysis Revealed By Iso-seqsupporting

confidence: 66%

Genome annotation with long RNA reads reveals new patterns of gene expression in an ant brain

Shields

Sorida

Sheng

et al. 2021

Preprint

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“…QTL Mapping and Heritability -We performed a genome scan on 741 RILs using a custom R script as described in Williams-Simon et al (2019). We used the average incapacitation times from each RIL as phenotype and applied the Haley-Knott regression (Haley and Knott 1992).…”

Section: A)mentioning

confidence: 99%

“…However, mapping approaches lack the resolution to identify single genes on their own (Mackay 2001). More recently researchers have been pairing mapping approaches with transcriptomics to identify candidate genes that influence complex traits (Cubillos et al 2017;Highfill et al 2017;Williams-Simon et al 2019;Wen et al 2019;Ng'oma et al 2020;Lecheta et al 2020), which was first introduced by Wayne and McIntyre (2002). While it is critically important to validate candidate genes that have been identified to be associated with complex traits, it has been a daunting task, and therefore few genetic studies of complex traits have definitively identified causal variants (Ding et al 2016;Highfill et al 2017;Gaspar et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Naturally segregating genetic variants contribute to thermal tolerance in aD. melanogaster model system

Williams-Simon,

Oster,

Moaton

et al. 2023

Preprint

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Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants of the genes that control this trait is of high importance if we want to better comprehend how this trait evolves in natural populations. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

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“…Drosophila melanogaster is a model organism that has been extensively phenotyped and genotyped for a multitude of stress-resistance traits. These include but are not limited to: desiccation resistance, heat tolerance, resource acquisition, starvation resistance, and physiological changes Kennington et al 2001;Karan and Parkash 1998;Telonis-Scott et al 2016;Harbison et al 2005;Goenaga, Fanara, and Hasson 2013;Davidson 1990;King, Roff, and Fairbairn 2011;Williams-Simon et al 2019;Chippindale et al 1998;Burke et al 2010). Their widespread, historic use in genetic studies has made them a model organism for the elucidation of genetic mechanisms of quantitative traits (Roff and Mousseau 1987;Mackay et al 2012;Sokolowski 2001;Morgan 1910).…”

Section: Introductionmentioning

confidence: 99%

“…(Cridland et al 2013;Marriage et al 2014;Najarro et al 2015;Highfill et al 2016;Williams-Simon et al 2019;Singh et al 2022;Riddle and Farnoudi).…”

mentioning

confidence: 99%

The evolution and genetic basis of complex traits in Drosophila melanogaster

Elkins¹

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The current climate crisis, increasing habitat fragmentation, deforestation, the number of extreme weather events, contagious diseases, and more are major environmental stressors that threaten the survival of every species on Earth. It is necessary for organisms to evolve stress resistant traits and phenotypic plasticity to survive these catastrophic effects. To resist stress, organisms will need to invest some amount of energy, leading to trade-offs with other traits. In addition, organisms must develop behavioral adaptations to manage environmental stressors. Behaviors such as dispersal and migration, and stress-resistant traits such as starvation resistance, have evolved as a means of survival. While researchers have studied behavioral adaptations and stress-resistant traits, the genetic and evolutionary mechanisms and phenotypic plasticity of these traits are not fully known. For my dissertation research, I studied the genetic mechanisms and phenotypic plasticity of two traits: exploration behavior and starvation resistance, respectively, in evolved multiparent populations of D. melanogaster. I used a bulk-segregant analysis (BSA) approach using a multiparent population, the Drosophila Synthetic Population Resource (DSPR), to uncover the genetic basis of exploration tendency in D. melanogaster. I defined exploration as the tendency of female fruit flies to move from a starting chamber to a novel fly chamber through a narrow tube. To identify the source of genetic variability in exploration, I generated 17 pairs of "high exploration" and "low exploration" bulk segregant populations consisting of 40 - 100 female flies and performed whole genome pooled sequencing. I then compared allele frequency differences between these pools to identify regions of the genome implicated in exploration tendency. In my second chapter I studied starvation resistance in an experimentally evolved population of D. melanogaster. Our lab placed twelve replicate populations of D. melanogaster on three selection treatments: constant high nutritional diet (CHA), fluctuating nutritional diet (FA), and deteriorating nutritional diet (DA). These three treatments have been ongoing for over 50 generations. For my experiment, a duplicated set of flies from all replicates and treatments were placed on one of three diets for 10 days: high sugar, standard, and low yeast diets. After 22 days post-oviposition (p.o.), flies were placed on nutrition-less agar. Starvation resistance was measured as the time it takes for a fly to die starting the moment it is placed on nutrition-less agar. We link these phenotypic changes to variation in artificial selection pressure and environmental conditions. In my code appendix, I established a novel method of statistical error estimation due to variation in coverage in pooled sequencing experiments. Coverage is defined as the number of reads at a given location along the genome. Due to the law of large numbers, a higher sequencing coverage value leads to a more accurate allele estimation at that locus.

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Multiple genetic loci affect place learning and memory performance in Drosophila melanogaster

Cited by 3 publications

References 118 publications

Genome annotation with long RNA reads reveals new patterns of gene expression in an ant brain

Genome annotation with long RNA reads reveals new patterns of gene expression in an ant brain

Naturally segregating genetic variants contribute to thermal tolerance in aD. melanogaster model system

The evolution and genetic basis of complex traits in Drosophila melanogaster

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