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Heterogeneity of endothelial cell (EC) populations reflects their diverse functions in maintaining tissue’s homeostasis. However, their phenotypic, molecular, and functional properties are not entirely mapped. We use the Tie2-CreERT2;Rosa26-tdTomato reporter mouse to trace, profile, and cultivate primary ECs from different organs. As paradigm platform, we use this strategy to study bone marrow endothelial cells (BMECs). Single-cell mRNA sequencing of primary BMECs reveals that their diversity and native molecular signatures is transitorily preserved in an ex vivo culture that conserves key cell-to-cell microenvironment interactions. Macrophages sustain BMEC cellular diversity and expansion and preserve sinusoidal-like BMECs ex vivo. Endomucin expression discriminates BMECs in populations exhibiting mutually exclusive properties and distinct sinusoidal/arterial and tip/stalk signatures. In contrast to arterial-like, sinusoidal-like BMECs are short-lived, form 2D-networks, contribute to in vivo angiogenesis, and support hematopoietic stem/progenitor cells in vitro. This platform can be extended to other organs’ ECs to decode mechanistic information and explore therapeutics.

MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual’s disease trajectory.

Syntaxin 4 (STX4), a plasma membrane-localized SNARE protein, regulates human islet β-cell insulin secretion and preservation of β-cell mass. We found that human type 1 diabetic (T1D) and non-obese diabetic (NOD) mouse islets show reduced β-cell STX4 expression, consistent with decreased STX4 expression as a potential driver of T1D phenotypes. To test this hypothesis, we generated inducible β-cell-specific STX4-expressing NOD mice (NOD-iβSTX4). Of NOD-iβSTX4 mice, 73% had sustained normoglycemia versus + Treg cells were also enriched in NOD-iβSTX4 islets. These results provide a deeper mechanistic understanding of STX4 function in β-cell protection and warrant further investigation of STX4 enrichment as a strategy to reverse or prevent T1D in humans or protect β-cell grafts.

Olfactory receptors (ORs) belong to class A G-protein coupled receptors (GPCRs) and are activated by a variety of odorants. To date, there is no three-dimensional structure of an OR available. One of the major bottlenecks in obtaining purified protein for structural studies of ORs is their poor expression in heterologous cells. To design mutants that enhance expression and thereby enable protein purification, we first identified computable physical properties that recapitulate OR and class A GPCR expression and further conducted an iterative computational prediction-experimental test cycle and generated human OR mutants that express as high as biogenic amine receptors for which structures have been solved. In the process of developing the computational method to recapitulate the expression of ORs in membranes, we identified properties, such as amino acid sequence coevolution, and the strength of the interactions between intracellular loop 1 (ICL1) and the helix 8 region of ORs, to enhance their heterologous expression. We identified mutations that are directly located in these regions as well as other mutations not located in these regions but allosterically strengthen the ICL1-helix 8 enhance expression. These mutants also showed functional responses to known odorants. This method to enhance heterologous expression of mammalian ORs will facilitate high-throughput “deorphanization” of ORs, and enable OR purification for biochemical and structural studies to understand odorant-OR interactions.

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. Significance: These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development. See related commentary by Kuijjer, p. 3072

Recent developments in sequencing and growth of bioinformatics resources provide us with vast depositories of protein network and single nucleotide polymorphism data. It allows us to re-examine, on a larger and more comprehensive scale, the relationship between protein–protein interactions and protein variability and evolutionary rates. This relationship has remained far from unambiguously resolved for quite a long time, reflecting shifting analysis approaches in the literature, and growing data availability. In this study, we utilized several public genomic databases to investigate this relationship in human, mouse, pig, chicken, and zebrafish. We observed strong non-linear relationship patterns (tending towards convex decreasing function shapes) between protein variability and the density of corresponding protein–protein interactions across all five species. To investigate further, we carried out stochastic simulations, modeling the interplay between protein connectivity and variability. Our results indicate that a simple negative linear correlation model, often suggested (or tacitly assumed) in the literature, as either a null or an alternative hypothesis, is not a good fit with the observed data. After considering different (but still relatively simple, and not overfitting) simulation models, we found that a convex decreasing protein variability–connectivity function (specifically, exponential decay) led to a much better fit with the real data. We conclude that simple correlation models might be inadequate for describing protein variability–connectivity interplay in vertebrates; they often tend towards false negatives (showing no more than marginal linear or rank correlation where there are in fact strong non-random patterns). Electronic supplementary material The online version of this article (10.1007/s00239-019-09899-z) contains supplementary material, which is available to authorized users.

Summary The challenges in recapitulating in vivo human T cell development in laboratory models have posed a barrier to understanding human thymopoiesis. Here, we used single-cell RNA sequencing (sRNA-seq) to interrogate the rare CD34+ progenitor and the more differentiated CD34– fractions in the human postnatal thymus. CD34+ thymic progenitors were comprised of a spectrum of specification and commitment states characterized by multilineage priming followed by gradual T cell commitment. The earliest progenitors in the differentiation trajectory were CD7– and expressed a stem-cell-like transcriptional profile, but had also initiated T cell priming. Clustering analysis identified a CD34+ subpopulation primed for the plasmacytoid dendritic lineage, suggesting an intrathymic dendritic specification pathway. CD2 expression defined T cell commitment stages where loss of B cell potential preceded that of myeloid potential. These datasets delineate gene expression profiles spanning key differentiation events in human thymopoiesis and provide a resource for the further study of human T cell development.

Significance While most mammalian genes are expressed from both chromosomal copies, many autosomal genes randomly express only one allele in a given cell, resulting in somatic cellular mosaicism. To better understand the mechanisms, developmental aspects, and evolution of autosomal monoallelic expression (MAE), we used nucleotide polymorphism differences between hybrid mice to analyze MAE of clonal neural stem cell lines as they differentiated to astrocytes. We found that genes showing MAE are highly enriched among developmental stage-specific genes. Genes showing strong skewed expression are similarly enriched. We also found evidence suggestive of balancing selection not just for genes with MAE but also, for developmental stage-specific genes., Cellular mosaicism due to monoallelic autosomal expression (MAE), with cell selection during development, is becoming increasingly recognized as prevalent in mammals, leading to interest in understanding its extent and mechanism(s). We report here use of clonal cell lines derived from the CNS of adult female F1 hybrid (C57BL/6 X JF1) mice to characterize MAE as neural stem cells (nscs) differentiate to astrocyte-like cells (asls). We found that different subsets of genes show MAE in the two populations of cells; in each case, there is strong enrichment for genes specific to the respective developmental state. Genes that exhibit MAE are 22% of nsc-specific genes and 26% of asl-specific genes. Moreover, the promoters of genes with MAE have reduced CpG dinucleotides but increased CpG differences between the two parental mouse strains. Extending the study of variability to wild populations of mice, we found evidence for balancing selection as a contributing force in evolution of those genes showing developmental specificity (i.e., expressed in either nsc or asl), not just for genes showing MAE. Furthermore, we found that genes showing skewed allelic expression (SKE) were similarly enriched among cell type-specific genes and also showed a heightened probability of balancing selection. Thus, developmental stage-specific genes and genes with MAE or SKE seem to make up overlapping classes subject to selection for increased diversity. The implications of these results for development and evolution are discussed in the context of a model with stochastic epigenetic modifications taking place only during a relatively brief developmental window.

Evolution by gene duplication is generally accepted as one of the crucial driving forces for the gain of new complexity and functions, but the formation of pseudogenes remains a problem for this mechanism. Here we expand on earlier ideas that epigenetic modifications can drive neo- and subfunctionalization in evolution by gene duplication. We explore the effects of stochastic epigenetic modifications on the evolution (and thus development) of complex organisms in a constant environment. Modeling is done both using a modified genetic drift analytical treatment and computer simulations, which were found to agree. A transposon silencing model is also explored. Some key assumptions made include (i) stochastic, incomplete removal (or addition) of repressive epigenetic marks takes place during a window(s) of opportunity in the zygote and early embryo; (ii) there is no statistical variation of the marks after the window closes; and (iii) the genes affected are sensitive to dosage. Our genetic drift treatment takes into account that after gene duplication the prevailing case upon which selection operates is a duplicate/singlet heterozygote; to the best of our knowledge, this has not been considered in previous treatments. We conclude from our modeling that stochastic epigenetic modifications, with rates consistent with experimental observation, can both increase the rate of gene fixation and decrease pseudogenization, thus dramatically improving the efficacy of evolution by gene duplication. We also find that a transposon silencing model is advantageous for fixation of recessive genes in diploid organisms, especially with large effective population sizes.

We analyse 6,810 tRNAs, calculating the free energy of the corresponding double hairpin and 'cigar' secondary structures, for which we find a high thermodynamic and statistical significance. We also analyse these tRNAs for similarity and complementarity of their 5' and 3' halves or segments of them in intra-and inter-molecular comparisons. We find very clear signs that the two halves of tRNAs had an evident evolutionary relationship, although it is not totally clear whether this was a relationship of homology or complementarity between the 5' and 3' halves of tRNAs, even if there is strong evidence in favour of the homology hypothesis. Overall, these data favour models for the origin of the tRNA molecule postulating that a duplication event involving a hairpin structure as a precursor was involved in the origin of this molecule. Moreover, we interpret these results and favour the hypothesis that sees the assembly of two hairpin structures sharing a homology relationship as the intermediate evolutionary stage preceding the appearance of the cloverleaf structure of tRNA.

For the RNA-world hypothesis to be ecologically feasible, selection mechanisms acting on replicator communities need to be invoked and the corresponding scenarios of molecular evolution specified. Complementing our previous models of chemical evolution on mineral surfaces, in which selection was the consequence of the limited mobility of macromolecules attached to the surface, here we offer an alternative realization of prebiotic group-level selection: the physical encapsulation of local replicator communities into the pores of the mineral substrate. Based on cellular automaton simulations we argue that the effect of group selection in a mineral honeycomb could have been efficient enough to keep prebiotic ribozymes of different specificities and replication rates coexistent, and their metabolic cooperation protected from extensive molecular parasitism. We suggest that mutants of the mild parasites persistent in the metabolic system can acquire useful functions such as replicase activity or the production of membrane components, thus opening the way for the evolution of the first autonomous protocells on Earth.