Trans-acting genetic variation affects the expression of adjacent genes

Identification of regulatory variants of carboxylesterase 1 (CES1): A proof-of-concept study for the application of the Allele-Specific Protein Expression (ASPE) assay in identifying cis-acting regulatory genetic polymorphisms

It is challenging to study regulatory genetic variants as gene expression is affected by both genetic polymorphisms and non-genetic regulators. The mRNA allele-specific expression (ASE) assay has been increasingly used for the study of cis-acting regulatory variants because cis-acting variants affect gene expression in an allele-specific manner. However, poor correlations between mRNA and protein expressions were observed for many genes, highlighting the importance of studying gene expression regulation at the protein level. In the present study, we conducted a proof-of-concept study to utilize a recently developed allele-specific protein expression (ASPE) assay to identify the cis-acting regulatory variants of CES1 using a large set of human liver samples. The CES1 gene encodes for carboxylesterase 1 (CES1), the most abundant hepatic hydrolase in humans. Two cis-acting regulatory variants were found to be significantly associated with CES1 ASPE, CES1 protein expression, and its catalytic activity on enalapril hydrolysis in human livers. Compared to conventional gene expression-based approaches, ASPE demonstrated an improved statistical power to detect regulatory variants with small effect sizes since allelic protein expression ratios are less prone to the influence of non-genetic regulators (e.g., diseases and inducers). This study suggests that the ASPE approach is a powerful tool for identifying cis-regulatory variants.

Identification of essential genes in Mycobacterium avium subsp. paratuberculosis genome for persistence in dairy calves

To cause disease Mycobacterium avium subsp. paratuberculosis needs to enter mammalian cells, arrest phagosomal maturation and manipulate the host immune system. The genetic basis of the bacterial capacity to achieve these outcomes remains largely unknown. Identifying these genes would allow us to gain a deeper understanding of MAP's pathogenesis and potentially develop a live attenuated Johne's disease vaccine by knocking out these genes. MAP genes demonstrated to be essential for colonization in the natural host, ruminants, are unknown. Genome-wide transposon mutagenesis and high-throughput sequencing were combined to evaluate the essentiality of each coding region in the bacterial genome to survive in dairy calves. A saturated library of 3,852 MAP Tn mutants, with insertions in 56% of TA sites, interrupting 88% of genes, was created using a MycoMarT7 phagemid containing a mariner transposon. Six calves were inoculated with a high dose of a library of MAP mutants, 1011 CFUs, (input) at 2 weeks of age. Following 2 months of incubation, MAP cells were isolated from the ileum, jejunum, and their associated lymph nodes of calves, resulting in approximately 100,000 colonies grown on solid media across 6 animals (output). Targeted next-generation sequencing was used to identify the disrupted genes in all the mutants in the input pool and the output pool recovered from the tissues to identify in vivo essential genes. Statistical analysis for the determination of essential genes was performed by a Hidden Markov Model (HMM), categorizing genes into essential genes that are devoid of insertions and growth-defect genes whose disruption impairs the growth of the organism. Sequence analysis identified 430 in vivo essential and 260 in vivo growth-defect genes. Gene ontology enrichment analysis of the in vivo essential and growth-defect genes with the highest reduction in the tissues revealed a high representation of genes involved in metabolism and respiration, cell wall and cell processing, virulence, and information pathway processes. This study has systematically identified essential genes for the growth and persistence of MAP in the natural host body.

Maintenance of quantitative genetic variance in complex, multi-trait phenotypes: The contribution of rare, large effect variants in two Drosophila species

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.