A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes

Pan-cancer proteogenomics expands the landscape of therapeutic targets

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

The BulkECexplorer compiles endothelial bulk transcriptomes to predict functional versus leaky transcription

Transcriptomic data can be mined to understand the molecular activity of cell types. Yet, functional genes may remain undetected in RNA sequencing (RNA-seq) experiments for technical reasons, such as insufficient read depth or gene dropout. Conversely, RNA-seq experiments may detect lowly expressed mRNAs thought to be biologically irrelevant products of leaky transcription. To represent a cell type's functional transcriptome more accurately, we propose compiling many bulk RNA-seq datasets into a compendium and applying established classification models to predict whether detected transcripts are likely products of active or leaky transcription. Here, we present the BulkECexplorer (bulk RNA-seq endothelial cell explorer) compendium of 240 bulk RNA-seq datasets from five vascular endothelial cell subtypes. This resource reports transcript counts for genes of interest and predicts whether detected transcripts are likely the products of active or leaky gene expression. Beyond its usefulness for vascular biology research, this resource provides a blueprint for developing analogous tools for other cell types.

How many submissions are needed to discover friendly suggested reviewers?

It is common in scientific publishing to request from authors reviewer suggestions for their own manuscripts. The question then arises: How many submissions are needed to discover friendly suggested reviewers? To answer this question, as the data we would need is anonymized, we present an agent-based simulation of (single-blinded) peer review to generate synthetic data. We then use a Bayesian framework to classify suggested reviewers. To set a lower bound on the number of submissions possible, we create an optimistically simple model that should allow us to more readily deduce the degree of friendliness of the reviewer. Despite this model's optimistic conditions, we find that one would need hundreds of submissions to classify even a small reviewer subset. Thus, it is virtually unfeasible under realistic conditions. This ensures that the peer review system is sufficiently robust to allow authors to suggest their own reviewers.

Folding Features and Dynamics of 3D Genome Architecture in Plant Fungal Pathogens

The folding and dynamics of three-dimensional (3D) genome organization are fundamental for eukaryotes executing genome functions but have been largely unexplored in nonmodel fungi. Using high-throughput sequencing coupled with chromosome conformation capture (Hi-C) data, we generated two chromosome-level assemblies for Puccinia striiformis f. sp. tritici, a fungus causing stripe rust disease on wheat, for studying 3D genome architectures of plant pathogenic fungi. The chromatin organization of the fungus followed a combination of the fractal globule model and the equilibrium globule model. Surprisingly, chromosome compartmentalization was not detected. Dynamics of 3D genome organization during two developmental stages of P. striiformis f. sp. tritici indicated that regulation of gene activities might be independent of the changes of genome organization. In addition, chromatin conformation conservation was found to be independent of genome sequence synteny conservation among different fungi. These results highlighted the distinct folding principles of fungal 3D genomes. Our findings should be an important step toward a holistic understanding of the principles and functions of genome architecture across different eukaryotic kingdoms. IMPORTANCE Previously, our understanding of 3D genome architecture has mainly come from model mammals, insects, and plants. However, the organization and regulatory functions of 3D genomes in fungi are largely unknown. In this study, we comprehensively investigated P. striiformis f. sp. tritici, a plant fungal pathogen, and revealed distinct features of the 3D genome, comparing it with the universal folding feature of 3D genomes in higher eukaryotic organisms. We further suggested that there might be distinct regulatory mechanisms of gene expression that are independent of chromatin organization changes during the developmental stages of this rust fungus. Moreover, we showed that the evolutionary pattern of 3D genomes in this fungus is also different from the cases in mammalian genomes. In addition, the genome assembly pipeline and the generated two chromosome-level genomes will be valuable resources. These results highlighted the unexplored distinct features of 3D genome organization in fungi. Therefore, our study provided complementary knowledge to holistically understand the organization and functions of 3D genomes across different eukaryotes.

Natural variation in the maternal and zygotic mRNA complements of the early embryo in Drosophila melanogaster

Background

Maternal gene products supplied to the egg during oogenesis drive the earliest events of development in all metazoans. After the initial stages of embryogenesis, maternal transcripts are degraded as zygotic transcription is activated; this is known as the maternal to zygotic transition (MZT). Recently, it has been shown that the expression of maternal and zygotic transcripts have evolved in the Drosophila genus over the course of 50 million years. However, the extent of natural variation of maternal and zygotic transcripts within a species has yet to be determined. We asked how the maternal and zygotic pools of mRNA vary within and between populations of D. melanogaster. In order to maximize sampling of genetic diversity, African lines of D. melanogaster originating from Zambia as well as DGRP lines originating from North America were chosen for transcriptomic analysis.

Results

Generally, we find that maternal transcripts are more highly conserved, and zygotic transcripts evolve at a higher rate. We find that there is more within-population variation in transcript abundance than between populations and that expression variation is highest post- MZT between African lines.

Conclusions

Determining the natural variation of gene expression surrounding the MZT in natural populations of D. melanogaster gives insight into the extent of how a tightly regulated process may vary within a species, the extent of developmental constraint at both stages and on both the maternal and zygotic genomes, and reveals expression changes allowing this species to adapt as it spread across the world.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08839-4.

Gene expression phylogenies and ancestral transcriptome reconstruction resolves major transitions in the origins of pregnancy

Structural and physiological changes in the female reproductive system underlie the origins of pregnancy in multiple vertebrate lineages. In mammals, the glandular portion of the lower reproductive tract has transformed into a structure specialized for supporting fetal development. These specializations range from relatively simple maternal nutrient provisioning in egg-laying monotremes to an elaborate suite of traits that support intimate maternal-fetal interactions in Eutherians. Among these traits are the maternal decidua and fetal component of the placenta, but there is considerable uncertainty about how these structures evolved. Previously, we showed that changes in uterine gene expression contributes to several evolutionary innovations during the origins of pregnancy (Mika et al., 2021b). Here, we reconstruct the evolution of entire transcriptomes ('ancestral transcriptome reconstruction') and show that maternal gene expression profiles are correlated with degree of placental invasion. These results indicate that an epitheliochorial-like placenta evolved early in the mammalian stem-lineage and that the ancestor of Eutherians had a hemochorial placenta, and suggest maternal control of placental invasiveness. These data resolve major transitions in the evolution of pregnancy and indicate that ancestral transcriptome reconstruction can be used to study the function of ancestral cell, tissue, and organ systems.

Molecular Evolution across Mouse Spermatogenesis

Genes involved in spermatogenesis tend to evolve rapidly, but we lack a clear understanding of how protein sequences and patterns of gene expression evolve across this complex developmental process. We used fluorescence-activated cell sorting (FACS) to generate expression data for early (meiotic) and late (postmeiotic) cell types across 13 inbred strains of mice (Mus) spanning ∼7 My of evolution. We used these comparative developmental data to investigate the evolution of lineage-specific expression, protein-coding sequences, and expression levels. We found increased lineage specificity and more rapid protein-coding and expression divergence during late spermatogenesis, suggesting that signatures of rapid testis molecular evolution are punctuated across sperm development. Despite strong overall developmental parallels in these components of molecular evolution, protein and expression divergences were only weakly correlated across genes. We detected more rapid protein evolution on the X chromosome relative to the autosomes, whereas X-linked gene expression tended to be relatively more conserved likely reflecting chromosome-specific regulatory constraints. Using allele-specific FACS expression data from crosses between four strains, we found that the relative contributions of different regulatory mechanisms also differed between cell types. Genes showing cis-regulatory changes were more common late in spermatogenesis, and tended to be associated with larger differences in expression levels and greater expression divergence between species. In contrast, genes with trans-acting changes were more common early and tended to be more conserved across species. Our findings advance understanding of gene evolution across spermatogenesis and underscore the fundamental importance of developmental context in molecular evolutionary studies.

The circadian oscillator analysed at the single-transcript level

The circadian clock is an endogenous and self-sustained oscillator that anticipates daily environmental cycles. While rhythmic gene expression of circadian genes is well-described in populations of cells, the single-cell mRNA dynamics of multiple core clock genes remain largely unknown. Here we use single-molecule fluorescence in situ hybridisation (smFISH) at multiple time points to measure pairs of core clock transcripts, Rev-erbα (Nr1d1), Cry1 and Bmal1, in mouse fibroblasts. The mean mRNA level oscillates over 24 h for all three genes, but mRNA numbers show considerable spread between cells. We develop a probabilistic model for multivariate mRNA counts using mixtures of negative binomials, which accounts for transcriptional bursting, circadian time and cell-to-cell heterogeneity, notably in cell size. Decomposing the mRNA variability into distinct noise sources shows that clock time contributes a small fraction of the total variability in mRNA number between cells. Thus, our results highlight the intrinsic biological challenges in estimating circadian phase from single-cell mRNA counts and suggest that circadian phase in single cells is encoded post-transcriptionally.

Polymorphism and Divergence of Novel Gene Expression Patterns in Drosophila melanogaster

Transcriptomes may evolve by multiple mechanisms, including the evolution of novel genes, the evolution of transcript abundance, and the evolution of cell, tissue, or organ expression patterns. Here, we focus on the last of these mechanisms in an investigation of tissue and organ shifts in gene expression in Drosophila melanogaster. In contrast to most investigations of expression evolution, we seek to provide a framework for understanding the mechanisms of novel expression patterns on a short population genetic timescale. To do so, we generated population samples of D. melanogaster transcriptomes from five tissues: accessory gland, testis, larval salivary gland, female head, and first-instar larva. We combined these data with comparable data from two outgroups to characterize gains and losses of expression, both polymorphic and fixed, in D. melanogaster We observed a large number of gain- or loss-of-expression phenotypes, most of which were polymorphic within D. melanogaster Several polymorphic, novel expression phenotypes were strongly influenced by segregating cis-acting variants. In support of previous literature on the evolution of novelties functioning in male reproduction, we observed many more novel expression phenotypes in the testis and accessory gland than in other tissues. Additionally, genes showing novel expression phenotypes tend to exhibit greater tissue-specific expression. Finally, in addition to qualitatively novel expression phenotypes, we identified genes exhibiting major quantitative expression divergence in the D. melanogaster lineage.

A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes

How do the cells of an organism—each with an identical genome—give rise to tissues of incredible phenotypic diversity? Key to answering this question is the transcriptome: the set of genes expressed in a given tissue. We would clearly benefit from the ability to identify qualitative differences in expression (whether a gene is active or inactive in a given tissue/species). Inferring the expression state of genes is surprisingly difficult, owing to the complex biological processes that give rise to transcriptomes and to the vagaries of techniques used to generate transcriptomic datasets. We develop a hierarchical Bayesian mixture model that—by describing those biological and technical processes—allows us to infer the expression state of genes from replicate transcriptomic datasets.

Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets—where we demonstrate its ability to correctly infer true (known) expression states—and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.