Natural genetic variation as a tool for discovery in Caenorhabditis nematodes

Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.

Substrate-Specific Effects of Natural Genetic Variation on Proteasome Activity

The bulk of targeted cellular protein degradation is performed by the proteasome, a multi-subunit complex consisting of the 19S regulatory particle, which binds, unfolds, and translocates substrate proteins, and the 20S core particle, which degrades them. Protein homeostasis requires precise, dynamic control of proteasome activity. To what extent genetic variation creates differences in proteasome activity is almost entirely unknown. Using the ubiquitin-independent degrons of the ornithine decarboxylase and Rpn4 proteins, we developed reporters that provide high-throughput, quantitative measurements of proteasome activity in vivo in genetically diverse cell populations. We used these reporters to characterize the genetic basis of variation in proteasome activity in the yeast Saccharomyces cerevisiae. We found that proteasome activity is a complex, polygenic trait, shaped by variation throughout the genome. Genetic influences on proteasome activity were predominantly substrate-specific, suggesting that they primarily affect the function or activity of the 19S regulatory particle. Our results demonstrate that individual genetic differences create heritable variation in proteasome activity and suggest that genetic effects on proteasomal protein degradation may be an important source of variation in cellular and organismal traits.

Natural variation in gene expression and Zika virus susceptibility revealed by villages of neural progenitor cells

Variation in the human genome contributes to abundant diversity in human traits and vulnerabilities, but the underlying molecular and cellular mechanisms are not yet known, and will need scalable approaches to accelerate their recognition. Here, we advanced and applied an experimental platform that analyzes genetic, molecular, and phenotypic heterogeneity across cells from very many human donors cultured in a single, shared in vitro environment, with algorithms (Dropulation and Census-seq) for assigning phenotypes to individual donors. We used natural genetic variation and synthetic (CRISPR-Cas9) genetic perturbations to analyze the vulnerability of neural progenitor cells to infection with Zika virus. These analyses identified a common variant in the antiviral IFITM3 gene that regulated IFITM3 expression and explained most inter-individual variation in NPCs' susceptibility to Zika virus infectivity. These and other approaches could provide scalable ways to recognize the impact of genes and genetic variation on cellular phenotypes.

Natural variation further increases resilience of sorghum bred for chronically drought–prone environments

Climate–change–associated shifts in rainfall distribution together with a looming worldwide water crisis make drought resilience of central importance to food security. Even for relatively drought resilient crops such as sorghum, moisture stress is nonetheless one of the major constraints for production. Here, we explore the potential to use natural genetic variation to build on the inherent drought tolerance of an elite cultivar (Teshale) bred for Ethiopian conditions including chronic drought, evaluating a backcross nested-association mapping (BC–NAM) population using 12 diverse founder lines crossed with Teshale under three drought-prone environments in Ethiopia. All twelve populations averaged higher head exsertion and lower leaf senescence than the recurrent parent in the two highest-stress environments, reflecting new drought resilience mechanisms from the donors. A total of 154 QTLs were detected for eight drought responsive traits – the validity of these were supported in that 100 (64.9%) overlapped with QTLs previously detected for the same traits, concentrated in regions previously associated with ′stay-green′ traits as well as the flowering regulator Ma6 and drought resistant gene P5CS2. Allele effects show that some favorable alleles are already present in the Ethiopian cultivar, however the exotic donors offer rich scope for increasing drought resilience. Using model-selected SNPs associated with eight traits in this study and three in a companion study, phenotypic prediction accuracies for grain yield were equivalent to genome-wide SNPs and were significantly better than random SNPs, indicating that these studied traits are predictive of sorghum grain yield. Rich scope for improving drought resilience even in cultivars bred for drought–prone regions, together with phenotypic prediction accuracy for grain yield, provides a foundation to enhance food security in drought-prone areas like the African Sahel.

Evolved Bmp6 enhancer alleles drive spatial shifts in gene expression during tooth development in sticklebacks

Mutations in enhancers have been shown to often underlie natural variation but the evolved differences in enhancer activity can be difficult to identify in vivo. Threespine sticklebacks (Gasterosteus aculeatus) are a robust system for studying enhancer evolution due to abundant natural genetic variation, a diversity of evolved phenotypes between ancestral marine and derived freshwater forms, and the tractability of transgenic techniques. Previous work identified a series of polymorphisms within an intronic enhancer of the Bone morphogenetic protein 6 (Bmp6) gene that are associated with evolved tooth gain, a derived increase in freshwater tooth number that arises late in development. Here we use a bicistronic reporter construct containing a genetic insulator and a pair of reciprocal two-color transgenic reporter lines to compare enhancer activity of marine and freshwater alleles of this enhancer. In older fish the two alleles drive partially overlapping expression in both mesenchyme and epithelium of developing teeth, but the freshwater enhancer drives a reduced mesenchymal domain and a larger epithelial domain relative to the marine enhancer. In younger fish these spatial shifts in enhancer activity are less pronounced. Comparing Bmp6 expression by in situ hybridization in developing teeth of marine and freshwater fish reveals similar evolved spatial shifts in gene expression. Together, these data support a model in which the polymorphisms within this enhancer underlie evolved tooth gain by shifting the spatial expression of Bmp6 during tooth development, and provide a general strategy to identify spatial differences in enhancer activity in vivo.

A natural genetic variation screen identifies insulin signaling, neuronal communication, and innate immunity as modifiers of hyperglycemia in the absence of Sirt1

ABSTRACTVariation in the onset, progression, and severity of symptoms associated with metabolic disorders such as diabetes impairs the diagnosis and treatment of at-risk patients. Diabetes symptoms, and patient variation in these symptoms, is attributed to a combination of genetic and environmental factors, but identifying the genes and pathways that modify diabetes in humans has proven difficult. A greater understanding of genetic modifiers and the ways in which they interact with metabolic pathways could improve the ability to predict a patient’s risk for severe symptoms, as well as enhance the development of individualized therapeutic approaches. In this study we use the Drosophila Genetic Reference Panel (DGRP) to identify genetic variation influencing hyperglycemia associated with loss of Sirt1 function. Through analysis of individual candidate functions, physical interaction networks, and Gene Set Enrichment Analysis (GSEA) we identify not only modifiers involved in canonical glucose metabolism and insulin signaling, but also genes important for neuronal signaling and the innate immune response. Furthermore, reducing the expression of several of these candidates suppressed hyperglycemia, making them ideal candidate therapeutic targets. These analyses showcase the diverse processes contributing to glucose homeostasis and open up several avenues of future investigation.

The Arabidopsis Accessions Selection Is Crucial: Insight from Photosynthetic Studies

Natural genetic variation in photosynthesis is strictly associated with the remarkable adaptive plasticity observed amongst Arabidopsis thaliana accessions derived from environmentally distinct regions. Exploration of the characteristic features of the photosynthetic machinery could reveal the regulatory mechanisms underlying those traits. In this study, we performed a detailed characterisation and comparison of photosynthesis performance and spectral properties of the photosynthetic apparatus in the following selected Arabidopsis thaliana accessions commonly used in laboratories as background lines: Col-0, Col-1, Col-2, Col-8, Ler-0, and Ws-2. The main focus was to distinguish the characteristic disparities for every accession in photosynthetic efficiency that could be accountable for their remarkable plasticity to adapt. The biophysical and biochemical analysis of the thylakoid membranes in control conditions revealed differences in lipid-to-protein contribution, Chlorophyll-to-Carotenoid ratio (Chl/Car), and xanthophyll cycle pigment distribution among accessions. We presented that such changes led to disparities in the arrangement of the Chlorophyll-Protein complexes, the PSI/PSII ratio, and the lateral mobility of the thylakoid membrane, with the most significant aberrations detected in the Ler-0 and Ws-2 accessions. We concluded that selecting an accession suitable for specific research on the photosynthetic process is essential for optimising the experiment.

Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design

Background Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower library preparation costs and reduced unwanted technological variation, such as batch effects. Computational demultiplexing tools based on natural genetic variation between individuals provide a simple approach to demultiplex samples, which does not require complex additional experimental procedures. However, to our knowledge these tools have not been evaluated in cancer, where somatic variants, which could differ between cells from the same sample, may obscure the signal in natural genetic variation. Results Here, we performed in silico benchmark evaluations by combining raw sequencing reads from multiple single-cell samples in high-grade serous ovarian cancer, which has a high copy number burden, and lung adenocarcinoma, which has a high tumor mutational burden. Our results confirm that genetic demultiplexing tools can be effectively deployed on cancer tissue using a pooled experimental design, although high proportions of ambient RNA from cell debris reduce performance. Conclusions This strategy provides significant cost savings through pooled library preparation. To facilitate similar analyses at the experimental design phase, we provide freely accessible code and a reproducible Snakemake workflow built around the best-performing tools found in our in silico benchmark evaluations, available at https://github.com/lmweber/snp-dmx-cancer.

Variation in the modality of a yeast signaling pathway is mediated by a single regulator

Bimodal gene expression by genetically identical cells is a pervasive feature of signaling networks, and has been suggested to allow organisms to hedge their "bets" in uncertain conditions. In the galactose-utilization (GAL) pathway of Saccharomyces cerevisiae, gene induction is unimodal or bimodal depending on natural genetic variation and pre-induction conditions. Here, we find that this variation in modality arises from regulation of two features of the pathway response: the fraction of cells that show induction, and their level of expression. GAL3, the galactose sensor, controls the fraction of induced cells, and titrating its expression is sufficient to control modality; moreover, all the observed differences in modality between different pre-induction conditions and amongst natural isolates can be explained by changes in GAL3's regulation and activity. The ability to switch modality by tuning the activity of a single protein may allow rapid adaptation of bet hedging to maximize fitness in complex environments.