Uncovering divergence in gene expression regulation in the adaptation of yeast to nitrogen scarcity

Saccharomyces cerevisiae rewires its transcriptional output to survive stressful environments, such as nitrogen scarcity under fermentative conditions. Although divergence in nitrogen metabolism has been described among natural yeast populations, the impact of regulatory genetic variants modulating gene expression and nitrogen consumption remains to be investigated. Here, we employed an F1 hybrid from two contrasting S. cerevisiae strains, providing a controlled genetic environment to map cis factors involved in the divergence of gene expression regulation in response to nitrogen scarcity. We used a dual approach to obtain genome-wide allele-specific profiles of chromatin accessibility, transcription factor binding, and gene expression through ATAC-seq and RNA-seq. We observed large variability in allele-specific expression and accessibility between the two genetic backgrounds, with a third of these differences specific to a deficient nitrogen environment. Furthermore, we discovered events of allelic bias in gene expression correlating with allelic bias in transcription factor binding solely under nitrogen scarcity, where the majority of these transcription factors orchestrates the Nitrogen Catabolite Repression regulatory pathway and demonstrates a cis x environment-specific response. Our approach allowed us to find cis variants modulating gene expression, chromatin accessibility and allelic differences in transcription factor binding in response to low nitrogen culture conditions.

A long read mapping method for highly repetitive reference sequences

About 5-10% of the human genome remains inaccessible for functional analysis due to the presence of repetitive sequences such as segmental duplications and tandem repeat arrays. To enable high-quality resequencing of personal genomes, it is crucial to support end-to-end genome variant discovery using repeat-aware read mapping methods. In this study, we highlight the fact that existing long read mappers often yield incorrect alignments and variant calls within long, near-identical repeats, as they remain vulnerable to allelic bias. In the presence of a non-reference allele within a repeat, a read sampled from that region could be mapped to an incorrect repeat copy because the standard pairwise sequence alignment scoring system penalizes true variants.To address the above problem, we propose a novel, long read mapping method that addresses allelic bias by making use of minimal confidently alignable substrings (MCASs). MCASs are formulated as minimal length substrings of a read that have unique alignments to a reference locus with sufficient mapping confidence (i.e., a mapping quality score above a user-specified threshold). This approach treats each read mapping as a collection of confident sub-alignments, which is more tolerant of structural variation and more sensitive to paralog-specific variants (PSVs) within repeats. We mathematically define MCASs and discuss an exact algorithm as well as a practical heuristic to compute them. The proposed method, referred to as Winnowmap2, is evaluated using simulated as well as real long read benchmarks using the recently completed gapless assemblies of human chromosomes X and 8 as a reference. We show that Winnowmap2 successfully addresses the issue of allelic bias, enabling more accurate downstream variant calls in repetitive sequences. As an example, using simulated PacBio HiFi reads and structural variants in chromosome 8, Winnowmap2 alignments achieved the lowest false-negative and false-positive rates (1.89%, 1.89%) for calling structural variants within near-identical repeats compared to minimap2 (39.62%, 5.88%) and NGMLR (56.60%, 36.11%) respectively.Winnowmap2 code is accessible at https://github.com/marbl/Winnowmap

Loss of Imprinting in Human Placentas Is Widespread, Coordinated, and Predicts Birth Phenotypes

Genomic imprinting leads to mono-allelic expression of genes based on parent of origin. Therian mammals and angiosperms evolved this mechanism in nutritive tissues, the placenta, and endosperm, where maternal and paternal genomes are in conflict with respect to resource allocation. We used RNA-seq to analyze allelic bias in the expression of 91 known imprinted genes in term human placentas from a prospective cohort study in Mali. A large fraction of the imprinted exons (39%) deviated from mono-allelic expression. Loss of imprinting (LOI) occurred in genes with either maternal or paternal expression bias, albeit more frequently in the former. We characterized LOI using binomial generalized linear mixed models. Variation in LOI was predominantly at the gene as opposed to the exon level, consistent with a single promoter driving the expression of most exons in a gene. Some genes were less prone to LOI than others, particularly lncRNA genes were rarely expressed from the repressed allele. Further, some individuals had more LOI than others and, within a person, the expression bias of maternally and paternally imprinted genes was correlated. We hypothesize that trans-acting maternal effect genes mediate correlated LOI and provide the mother with an additional lever to control fetal growth by extending her influence to LOI of the paternally imprinted genes. Limited evidence exists to support associations between LOI and offspring phenotypes. We show that birth length and placental weight were associated with allelic bias, making this the first comprehensive report of an association between LOI and a birth phenotype.

Unperturbed Expression Bias of Imprinted Genes in Schizophrenia

How gene expression correlates with schizophrenia across individuals is beginning to be examined through analyses of RNA-seq from post-mortem brains of individuals with disease and control brains. Here we focus on variation in allele-specific expression, following up on the Common Mind Consortium (CMC) RNA-seq experiments of nearly 600 human dorsolateral prefrontal cortex (DLPFC) samples. Analyzing the extent of allelic expression bias—a hallmark of imprinting—we find that the number of imprinted human genes is consistent with lower estimates (≈0.5% of all genes) and thus contradicts much higher estimates. Moreover, the handful of putatively imprinted genes are all in close genomic proximity to known imprinted genes. Joint analysis of the imprinted genes across hundreds of individuals allowed us to establish how allelic bias depends on various factors. We find that age and genetic ancestry have gene-specific, differential effect on allelic bias. In contrast, allelic bias appears to be independent of schizophrenia.

FORGe: prioritizing variants for graph genomes

There is growing interest in using genetic variants to augment the reference genome into a “graph genome” to improve read alignment accuracy and reduce allelic bias. While adding a variant has the positive effect of removing an undesirable alignment-score penalty, it also increases both the ambiguity of the reference genome and the cost of storing and querying the genome index. We introduce methods and a software tool called FORGe for modeling these effects and prioritizing variants accordingly. We show that FORGe enables a range of advantageous and measurable trade-offs between accuracy and computational overhead.

Consistent Reanalysis of Genome-wide Imprinting Studies in Plants Using Generalized Linear Models Increases Concordance across Datasets

ABSTRACTGenomic imprinting leads to different expression levels of maternally and paternally derived alleles. Over the last years, major progress has been made in identifying novel imprinted candidate genes in plants, owing to affordable next-generation sequencing technologies. However, reports on sequencing the transcriptome of hybrid F1 seed tissues strongly disagree about how many and which genes are imprinted. This raises questions about the relative impact of biological, environmental, technical, and analytic differences or biases. Here, we adopt a statistical approach, frequently used in RNA-seq data analysis, which properly models count overdispersion and considers replicate information of reciprocal crosses. We show that our statistical pipeline outperforms other methods in identifying imprinted genes in simulated and real data. Accordingly, reanalysis of genome-wide imprinting studies in Arabidopsis and maize shows that, at least for the Arabidopsis dataset, an increased agreement across datasets can be observed. For maize, however, consistent reanalysis did not yield in a larger overlap between the datasets. This suggests that the discrepancy across publications might be partially due to different analysis pipelines but that technical, biological, and environmental factors underlie much of the discrepancy between datasets. Finally, we show that the set of genes that can be characterized regarding allelic bias by all studies with minimal confidence is small (~8,000/27,416 genes for Arabidopsis and ~12,000/39,469 for maize). In conclusion, we propose to use biologically replicated reciprocal crosses, high sequence coverage, and a generalized linear model approach to identify differentially expressed alleles in developing seeds.