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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

Table S1. Differentially expressed genes for c_1 vs c_1^* Table S2. Differentially expressed genes for c_2 vs c_1^* Table S3. Differentially expressed genes for c_3 vs c_1^* Table S4. Differentially expressed genes for c_2 vs c_1 Table S5. Differentially expressed genes for c_3 vs c_1 Table S6. Differentially expressed genes for c_3 vs c_2 Tables S7-S10. Differentially expressed genes for early, transition, persistent, and leukemia events. Tables S11-S14. GO terms enriched for early, transition, persistent, and leukemia events. Table S15. Top 1% of eigengenes.

Figure S1. Details of animal model and fusion gene CM, Figure S2. Ex vivo flow cytometry analysis of bone marrow, Figure S3. Details of principal component analysis, Figure S4. Comparison of state-space construction with different dimension reduction methods, Figure S5. Hierarchical clustering of time-series RNA-seq data and relation to state-space, Figure S6. Details of critical point estimation, construction of quasi-potential, and state-space dynamics, Figure S7. Correlation of state-space geometry with Kit and CM gene expression. Sensitivity of state-space geometry to inclusion of Kit and CM, Figure S8. Volcano plots of critical-point based differential gene expression analysis, Figure S9. Heatmaps of selected GO pathways in early, transition, persistent, and leukemic events, Figure S10. Computation of eigengene angle in state-space, Figure S11. Details of principal component analysis of validation cohorts and state-space dynamics, Figure S12. Mean-squared displacement analysis of particle trajectories in state-space and calibration of Fokker-Planck diffusion coefficient with training cohort, Figure S13. Sensitivity analysis of state-space construction to sample and normalization thresholds, Figure S14. Bootstrap cross validation of state-space construction.

In this supplement we provide additional data and methods to support our results. In particular, we show: details of our mouse model of AML; demonstrate the robustness of our state-space construction to normalization methods and gene selection criteria; compare dimension reduction methods; show bootstrap cross-validation of our predictions; compare hierarchical clustering with our state-space approach; and finally, provide full lists of differentially expressed genes and gene ontology (GO) term enrichment.

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.

MiR-126 and miR-155 are key microRNAs (miRNAs) that regulate, respectively, hematopoietic cell quiescence and proliferation. Herein we showed that in acute myeloid leukemia (AML), the biogenesis of these two miRNAs is interconnected through a network of regulatory loops driven by the FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD). In fact, FLT3-ITD induces the expression of miR-155 through a noncanonical mechanism of miRNA biogenesis that implicates cytoplasmic Drosha ribonuclease III (DROSHA). In turn, miR-155 down-regulates SH2-containing inositol phosphatase 1 (SHIP1), thereby increasing phosphor-protein kinase B (AKT) that in turn serine-phosphorylates, stabilizes, and activates Sprouty related EVH1 domain containing 1 (SPRED1). Activated SPRED1 inhibits the RAN/XPO5 complex and blocks the nucleus-to-cytoplasm transport of pre-miR-126, which cannot then complete the last steps of biogenesis. The net result is aberrantly low levels of mature miR-126 that allow quiescent leukemia blasts to be recruited into the cell cycle and proliferate. Thus, miR-126 down-regulation in proliferating AML blasts is downstream of FLT3-ITD–dependent miR-155 expression that initiates a complex circuit of concatenated regulatory feedback (i.e., miR-126/SPRED1, miR-155/human dead-box protein 3 [DDX3X]) and feed-forward (i.e., miR-155/SHIP1/AKT/miR-126) regulatory loops that eventually converge into an output signal for leukemic growth.

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.

The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?

1AbstractAggregate Hi-C maps centered on several genetic elements show significant DNA extrusion features. However, these features are missing from the component Hi-C images. To resolve this discrepancy, we present a new computational framework that reconstructs DNA extrusion scenarios (DESs) and explains the enhancement of DNA extrusion features by aggregation. We showed that the reconstructed DES is essential to understand the site interactions of the relevant genetic elements. For example, CCCTC-binding factor (CTCF) binding elements (CBE) with a specific motif direction are only affected by DNA extrusion activities initiated from one side, manifested as a directional bias. About half of promoters search for enhancers in a specific direction, as indicated by the corresponding DES. Moreover, since DNA extrusion does not pause at promoter or enhancer elements, direct promoter-enhancer contacts are not long-lasting; rather, promoters and enhancers are brought in proximity by continuous, dynamic DNA extrusion.

Previous studies have shown that approximately 40% of estrogen receptor positive (ER+) breast cancer (BC) patients harbor immune signaling defects in their blood at diagnosis that predicts overall survival. The aim of this work is to apply communication theory and signal processing methods to model cytokine-mediated signaling errors in immune cells in the peripheral blood. Our approach uses communication theory concepts to develop a communication system model consisting of a transmitter, receiver, and channel, applied to the JAK/STAT signaling pathway and tested using multi-color flow cytometry analysis of peripheral blood mononuclear cells stimulated with IFNγ, IL-2, IL-6, IL-4, IL-10, IL-12, or TGFβ in samples from 2 BC patients and 4 healthy controls. We use our model to estimate the detection error rate when one of the 7 cytokines is being transduced through the JAK/STAT signaling pathway, but its signal is being received on multiple phosphorylated signal transducers and activators of transcription (pSTAT) molecules. In addition, we extend the measured error rate value to an error rate curve by varying the signal-to-noise ratio available to the receiver in our system model. Our results show a trend towards higher detection error rates in immune cells from BC patients as compared to healthy controls, which may indicate altered immune signaling and incorrect cell decisions in these patients. Also, considering different cell subtypes, our results show two orders of magnitude increase in error rate in CD4+ memory T cells compared to CD4+ naïve T cells in BC patients. With further development, we believe that our communication theory model and quantification of optimal signal detection in cells will provide a better understanding into how immune cells can accurately “decode” information to respond properly to various signals and how the immune signaling is altered in BC patients, causing better or worse outcomes. The benefit of our novel approach is that it integrates all the transmitted (cytokines) and received signals (pSTATs) into a single communications framework that can be efficiently analyzed together. Citation Format: Adina Matache, Aleksandra Karolak, Sergio Branciamore, Andrei Rodin, Peter P. Lee, Russell C. Rockne. A communication theory framework for modeling cytokine-mediated signaling in healthy and breast cancer derived peripheral blood immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2741.

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

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of hematological cancers and is currently being investigated for solid tumors including high-grade glioma brain tumors. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumors. In this work we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor t-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyzer to parameterize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted as compared to higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumor growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumor characteristics including tumor growth rate and antigen level to maximize therapeutic benefit.Statement of SignificanceWe utilize a mathematical model to deconvolute the nonlinear contributions of CAR T-cell proliferation and exhaustion to predict therapeutic efficacy and dependence on CAR T-cell dose and target antigen levels.

We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual information to perform variable selection; during the second stage, we use scalable Bayesian network (BN) modeling to identify candidate genes and their interactions. Two crucial features of the proposed approach are (i) the ability to handle mixed data types (continuous and discrete, genomic, epigenomic, etc.), and (ii) a flexible boundary between the variable selection and network modeling stages --- the boundary that can be adjusted in accordance with the investigators’ BN software scalability and hardware implementation. These two aspects result in high generalizability of the proposed analytical framework. We apply the above strategy to three different TCGA datasets (LGG, Brain Lower Grade Glioma; HNSC, Head and Neck Squamous Cell Carcinoma; STES, Stomach and Esophageal Carcinoma), linking multimodal molecular information (SNPs, mRNA expression, DNA methylation) to two clinical outcome variables (tumor status and patient survival). We identify 11 candidate genes, of which 6 have already been directly implicated in the cancer literature. One novel LGG prognostic factor suggested by our analysis, methylation of TMPRSS11F type II transmembrane serine protease, presents intriguing direction for the follow-up studies.

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.

Diffuse gliomas are malignant neoplasms originating in the parenchyma of the central nervous system whose cellular origin remains elusive. To determine the cellular and spatiotemporal origin of gliomas, we devised a three-dimensional reconstruction of tumor lineage. We used neuronavigation to acquire eight to twelve image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma, which we characterized using DNA methylation arrays. A total of 133 samples were obtained from regions with and without imaging abnormalities. Methylation profiles were analyzed to construct phyloepigenetic trees and subsequently projected on 3D image-derived tumor maps. Lineage analysis of these evolutionary trees indicated that the sampled gliomas largely evolved stochastically, suggesting that critical tumor drivers were acquired early in time. These results were further validated using 102 multi-region samples from 24 independent patients. Patristic (evolutionary) and cartesian (spatial) distances between pairs of tumor samples from the same patient demonstrated strong correlations, suggesting that this information could be used to determine trajectories of tumor evolution. Evolutionary and spatial distance metrics were combined with histologically obtained and computationally quantified cellularity and proliferation rates to model the direction and magnitude of tumor growth. In order to relate tumor lineage to brain anatomy we mapped patient imaging to a reference space (Montreal Neurological Institute, MNI). Samples mapping to regions of white matter were earlier in tumor lineage when compared to samples mapping to regions of gray matter, suggesting that white matter involvement is an early feature of tumor development. Samples early in tumor lineage were located closer to the ventricles when compared with samples late in lineage, suggesting that tumors grow outward from the ventricular lining. Finally, we used a tumor probability map constructed using imaging from over 500 unrelated patients to associate lineage to tumor probability. Results from this analysis indicated that periventricular areas of high tumor probability coincided with samples early in tumor lineage and cortical areas of low tumor probability coincided with samples late in tumor lineage. Taken together, our phylogeographic analysis of tumor development supports a neural progenitor cell-of-origin model, where neural stem cells in the subventricular zone of the lateral ventricles give rise to mature tumors. Citation Format: Floris P. Barthel, Niels Verburg, Russell Rockne, Roelant Eijgelaar, Kevin Anderson, Domenique Müller, Sergio Branciamore, Kevin C. Johnson, Pieter Wesseling, Philip C. de Witt Hamer, Roel G. Verhaak. Stereotactic image-guided epigenome profiling reveals a neural stem cell evolutionary origin of diffuse gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3147.

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.

Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensional temporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identified step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development.Graphical AbstractIn briefThe theory of state-transition is applied to acute myeloid leukemia (AML) to model transcriptome dynamics and trajectories in a state-space, and is used to identify critical points corresponding to critical transcriptomic perturbations that predict leukemia development.HighlightsLeukemia transcriptome dynamics are modeled as movement in transcriptome state-spaceState-transition model and critical points accurately predicts leukemia developmentCritical point-based approach identifies step-wise transcriptome events in leukemiaState-based geometric analysis provides quantification of leukemogenic contribution

Significance We report here that a recently developed Bayesian network (BN) methodology and software platform yield useful information when applied to the analysis of intrachromosomal interaction datasets combined with Encyclopedia of DNA Elements publicly available datasets for the B-lymphocyte cell line GM12878. Of 106 variables analyzed, interaction strength between DNA segments was found to be directly dependent on only four types of variables: distance, Rad21 or SMC3 (cohesin components), transcription at transcription start sites, and the number of CCCTC-binding factor (CTCF)–cohesin complexes between interacting DNA segments. The importance of directionally oriented ctcf motifs was confirmed not only for loops but also for enhancer–promoter interactions. Purely data-driven BN analyses also identified known critical, lineage-determining transcription factors (TFs) as well as some potentially new dependencies between TFs., Long-range intrachromosomal interactions play an important role in 3D chromosome structure and function, but our understanding of how various factors contribute to the strength of these interactions remains poor. In this study we used a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly available datasets for intrachromosomal interactions. We investigated how 106 variables affect the pairwise interactions of over 10 million 5-kb DNA segments in the B-lymphocyte cell line GB12878. Strictly data-driven BN modeling indicates that the strength of intrachromosomal interactions (hic_strength) is directly influenced by only four types of factors: distance between segments, Rad21 or SMC3 (cohesin components),transcription at transcription start sites (TSS), and the number of CCCTC-binding factor (CTCF)–cohesin complexes between the interacting DNA segments. Subsequent studies confirmed that most high-intensity interactions have a CTCF–cohesin complex in at least one of the interacting segments. However, 46% have CTCF on only one side, and 32% are without CTCF. As expected, high-intensity interactions are strongly dependent on the orientation of the ctcf motif, and, moreover, we find that the interaction between enhancers and promoters is similarly dependent on ctcf motif orientation. Dependency relationships between transcription factors were also revealed, including known lineage-determining B-cell transcription factors (e.g., Ebf1) as well as potential novel relationships. Thus, BN analysis of large intrachromosomal interaction datasets is a useful tool for gaining insight into DNA–DNA, protein–DNA, and protein–protein interactions.

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.

MiRNAs are small noncoding RNAs that control gene expression by binding to cognate sites in the 3′ untranslated region (3′ UTR) of target messenger RNAs (mRNAs). MiRNAs are transcribed as primary (pri) mRNAs and processed into precursor (pre) miRNAs by nuclear DROSHA before being exported into the cytoplasm via the Exportin 5 (XPO5)/RAN-GTP complex. Once in the cytoplasm, pre-miRNAs are further processed by Dicer into mature miRNAs. The FMS-like tyrosine kinase 3 internal tandem duplication (FLT3-ITD) is one of the most common mutations in acute myeloid leukemia (AML) and confers growth and survival advantages to leukemia blasts. Among miRNAs, miR-155 has emerged as one of the most significantly upregulated in the FLT3-ITD+ AML and has been shown to play a pivotal role in uncontrolled blast hyperproliferation and survival. Herein, we report a previously unrecognized activity of FLT3-ITD that leads to deregulation of miR-155 expression by providing the evidence for the existence of FLT3-dependent non-canonical mechanisms of miRNA biogenesis. We initially observed that FLT3-ITD blocked the biogenesis of intronic miRNAs via inhibition of the "gatekeeper" XPO5/RAN-GTP complex that allow nucleus-to-cytoplasm pre-miRNA transport. By using in vitro phosphorylation assay with [32P]-ATP labeling, we demonstrated that FLT3-ITD phosphorylates a Sprouty related EVH1 domain-containing protein 1 (SPRED1), and that phospho-SPRED1 in turn inhibited the XPO5/RAN-GTP complex thereby halting transportation of pre-miRNAs from the nucleus to cytoplasm. This resulted in a decrease of several intronic miRNAs involved in normal hematopoiesis (i.e., miR-29b, miR-181a, miR-146b, miR-126). Accordingly, knocking down of SPRED1 expression in FLT3-ITD+ AML cells resulted in increased production of mature intronic miRNAs. Since the XPO5/RAN-GTP complex is the main gatekeeper for miRNA biogenesis, these results appeared in contradiction with the miR-155 upregulation which has been invariably observed in FLT3-ITD+ AML blasts. Differently from intronic miRNAs, miR-155 is hosted at the genomic site of long non-coding RNA (lnc-RNA) and we therefore hypothesized that it follows a different path of biogenesis. In fact forced expression of FLT3-ITD in lin-Sca1+kit+ (LSK) cells decreased intronic miRNA biogenesis (i.e., miR-126) and increase production of lnc-RNA hosted miRNAs (i.e., miR-155). Accordingly, we demonstrated that upon FLT3-ITD activation, AKT phosphorylated DDX3X, a DEAD-box RNA helicase protein thereby reducing its ability to bind heterogeneous nuclear Ribonucleoprotein U (hnRNP U), a component of the hnRNP complex associated with pre-mRNA splicing. Consequently, in FLT3-ITD cells, BIC-155 splicing is decreased. In fact, we measured significant increased levels of phospho-DDX3X and unspliced BIC-155 in primary FLT3-ITD+ blasts compared with FLT3-ITD- AML blasts. In consistent, DDX3X knock-down (KO) in FLT3-ITD- cells decreased BIC-155 splicing whereas DDX3X re-expression reversed these effects. The excess of unspliced BIC-155 RNA then bound to nuclear RNA export factor 1 (NXF1), a shuttle protein that transports poly A+ RNAs from the nucleus to the cytoplasm. In consistent, NXF1 KO blocked unspliced BIC-155 nucleus-to-cytoplasm transportation. In FLT3-ITD+ blasts, once in the cytoplasm, the unspliced BIC-155 RNA was then processed by cytoplasmic isoforms of DROSHA, as demonstrated using immunostaining, RNA Immunoprecipitation (RIP), and other gain- and loss-of-function experiments. Furthermore, overexpression of cytoplasmic DROSHA but not nuclear DROSHA isoform increased the processing of unspliced BIC-155 to mature miR-155. None of the intronic miRNAs studies interacted with cytoplasmic DROSHA. The bound of BIC-155 RNA with cytoplasmic DROSHA was unique of FLT3-ITD+ blasts, as it was not observed in FLT3-ITD- blasts. Thus, our results indicate a two-fold activity of FLT3-ITD that leads to decreased levels of intronic miRNAs via XPO5/RAN-GTP blockage and upregulation of other lnc-RNA hosted miRNAs via cytoplasmic DROSHA activation. The net result is suppression of intronic miRNAs that participates to the regulation of normal hematopoiesis and upregulation of lnc-RNA-hosted miRNA, especially miR-155 that contribute to aberrant blast hyperproliferation in the FLT3-ITD AML phenotype (Figure 1). Disclosures No relevant conflicts of interest to declare.

The interaction between electromagnetic radiation and bio-molecules in heterogeneous environments is a prebiotically relevant process. Minerals may have a pivotal role in the prebiotic evolution of complex chemical systems, mediating the effects of electromagnetic radiation, influencing the photostability of bio-molecules, catalyzing important chemical reactions and/or protecting molecules against degradation. In particular, nucleobases are relevant bio-molecules to investigate both in the prebiotic context, because they are coding components of nucleic acids, and from the standpoint of the survival of biological systems in space conditions. Several studies on the photodynamics of nucleobases suggest that their structure could have been naturally selected for the ability to dissipate electronic energy through ultrafast photophysical decay. Considering the putative involvement of minerals in the prebiotic chemistry, it is necessary to study the photostability of nucleobases under space conditions in the presence of mineral matrices, to investigate both the prebiotic processes that might have had a role in the development of the first living entities on Earth and the physical and chemical processes occurring in extraterrestrial environments. We focused our study on the characterization of the nature of the interaction between nucleobases and the surface of the minerals magnesium oxide and forsterite by infrared vibrational spectroscopy. We observed that most of the characteristic bands of pure nucleobases vanished when adsorbed on magnesium oxide. On the contrary, in the case of adenine and uracil adsorbed on forsterite, very intense nucleobase absorption peaks appeared. This phenomenon pertains to the surface selection rules changes related to molecular orientation. Moreover, based on the vibrational shifts, we deduced the molecular interaction sites with the mineral surfaces. Furthermore, we investigated the photostability of nucleobases adsorbed on such minerals through in situ UV irradiation experiments. Experimental results confirm the high intrinsic photostability of such molecules, because a rather low probability of interaction between UV radiation and nucleobases was estimated, and furthermore indicate that cytosine and hypoxanthine have a greater photostability compared to adenine and uracil, both pure and adsorbed onto magnesium oxide and forsterite.

Extraction experiments were performed to evaluate suitable sample-extraction and processing protocols that will be used by bioanalytical instruments like Life Marker Chip ( LMC ). These instruments will focus on the detection of molecules associated with life that will be extracted from the Martian soil. LMC is an antibody microarray biosensor instrument with optical readout, which uses fluorescently labeled antibodies, to detect and quantify the presence of polar and non-polar biomolecules, extracted from the Martian soil. The success of the LMC biomarkers detection experiment is dependent on the sample extraction protocol. The currently recommended extraction procedure, optimized for aliphatic biomarkers, consists in sonicating the samples using as solvent system 20:80 methanol:water with 1.5 mg mL −1 tween 80, which respects all the tight requirements associated with LMC biosensor. Examining this procedure for a particular class of biomarkers, the nucleobases adenine, cytosine, uracil and hypoxanthine adsorbed onto magnesium oxide (MgO), we observed low extraction efficiency, confirming that the selected solvent system is not able to extract with high efficiency aromatic biomarkers.

The adsorption of organic molecules on mineral matrices might have played a fundamental role in processes that led to the emergence of life. We investigated the adsorption properties of the nucleobases adenine, cytosine, uracil and hypoxanthine on magnesium oxide (MgO), determining the single solute batch equilibrium adsorption isotherms. Langmuir-type isotherms were fitted to data, assuming a rapid reversible equilibration of adsorption, demonstrated effectively through desorption experiments. The Langmuir equilibrium adsorption constantKand the amount of the solute per unit of adsorbent mass necessary to complete the monolayerbwere calculated. The results indicate that MgO is a good adsorbent for nucleobases (adenine > uracil > hypoxantine > cytosine), suggesting a role of metal oxides in concentrating biomolecules in prebiotic conditions that might have favoured the passage from geochemistry to biochemistry.

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.

Introduction: Daratumumab (Dara) is a human monoclonal antibody targeting the highly expressed multiple myeloma (MM) surface receptor CD38, with significant activity in relapsed MM. However, resistance to Dara develops in virtually all patients (pts). Thus, in order to maximize its clinical activity and prevent resistance, it is imperative to understand why pts stop responding. Our group recently reported that, in addition to the malignant cells, several immune-effector cells also express CD38. Thus, we hypothesized that the expression of CD38 or lack of it on non-cancer immune cell-subsets may also play a role in both clinical response and resistance to Dara. Results: To address our hypothesis, we analyzed CD38 surface expression in CD138+ MM cells isolated from 5 Dara-naïve (D-naïve) pts and from 8 Dara progressing pts (D-prg) who had discontinued Dara therapy for at least 4 weeks prior to analysis. We observed that the CD138+ MM-cells of D-prg displayed a lower median fluorescence intensity (MFI) of CD38 compared to the MFI of MM cells isolated from the D-naïve (p=0.04); no significant difference was however observed in the percentage of CD38+ MM cells between the two groups. In fact, all of the MM cells expressed CD38 in both D-naïve and D-prg, supporting the hypothesis that resistance to Dara was not mediated by loss of CD38 expression on MM cells. Next, we looked at whether CD38 on the surface of MM cells from D-prg was still recognizable by Dara. Thus, we treated MM cells ex-vivo with Dara at the clinically achievable concentration of 100 μg/ml. We showed that Dara was still able to bind CD38 on the surface of the D-prg CD138+ MM cells, since, after Dara treatment, no CD38+ signal was detected upon staining with a CD38 monoclonal antibody sharing the same targeting CD38 epitope (clone IB6) (Fig.1a). Conversely, we detected a CD38+ signal when a different anti-human CD38 multi-epitope antibody was used in a similar experiment (Cytognos) (Fig.1b). CD38 mRNA expression was observed in all MM cells isolated from D-prg, and we were not able to identify any missense mutations in CD38 mRNA from MM cells of D-prg for whom material was available for analysis. Thus, having demonstrated that resistance to Dara was not related to the expression of CD38 on the MM-cells, we turned our attention to the MM bone marrow (BM) microenvironment (ME). We performed RNA-sequencing of the BM-ME depleted of the CD138+ MM cells obtained from the D-prg (n=5) and D-naïve (n=5). A gene expression signature differentiated the two groups. In D-prg, we observed a significant downregulation (fold-change Conclusions: Our data are supportive of the hypothesis that loss of Dara binding to MM-cell surface CD38 or the complete loss of CD38 on MM cells may not necessarily be the cause of Dara treatment resistance. We instead propose that Dara binding on CD38+ immune effector cells may be critical and that the loss of CD38 expression on these cells can lead to mechanisms of Dara-acquired resistance. Disclosures Rosenzweig: Celgene: Speakers Bureau. Krishnan:Celgene: Consultancy, Equity Ownership, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Sutro: Speakers Bureau; Takeda: Speakers Bureau; Onyx: Speakers Bureau.

We have conducted a check by substituting, in a previous phylogenetic analysis, an outgroup of sequences from hyperthermophilic archaea with another of mesophilic sequences of archaea. This should remove a possible compositional bias which might be responsible for the deep position of Thermotogales and Aquificales in the Bacteria domain, as observed in previous analyses. This check brought to light a weak compositional bias which does not seem, however, to entirely explain the deep position occupied by hyperthermophilic bacteria. The present analysis also seems to show that Planctomycetes are one of the deepest lines of divergence in the Bacteria domain, although they do not seem to be the very deepest.

The hypothesized RNA-based world would have required the presence of a protected environment in which RNA, or an RNA-like molecule, could originate and express its biological activity. Recent studies have indicated that RNA molecules adsorbed/bound on clay minerals are able to persist in the presence of degrading agents, to interact with surrounding molecules, and to transmit the information contained in their nucleotide sequences. In this study, we assessed the ability of RNA molecules with catalytic activity to perform a specific reaction in a mineral environment. For this purpose, we investigated the self-cleavage reaction of the hammerhead ribozyme of the Avocado Sun Blotch Viroid (ASBVd), both in the monomeric and in dimeric forms. The monomeric transcript was tightly bound on the clay mineral montmorillonite to form a stable complex, while the behaviour of the dimeric transcript was studied in the presence of the clay particles in the reaction mixture. The results indicated that the hammerhead ribozyme was still active when the monomeric transcript was adsorbed on the clay surface, even though its efficiency was reduced to about 20% of that in solution. Moreover, the self-cleavage of clay-adsorbed molecule was significantly enhanced (∼ four times) by the presence of the 5′ reaction product. The self-cleavage reaction of the dimeric transcript in the presence of montmorillonite indicated that the mineral particles protected the RNA molecules against aspecific degradation and increased the rate of cleavage kinetics by about one order of magnitude. These findings corroborate the hypothesis that clay-rich environments would have been a good habitat in which RNA or RNA-like molecules could originate, accumulate and undergo Darwinian evolutionary processes, leading to the first living cells on Earth.

All life forms on Earth share the same biological program based on the DNA/RNA genomes and proteins. The genetic information, recorded in the nucleotide sequence of the DNA and RNA molecule, supplies the language of life which is transferred through the different generations, thus ensuring the perpetuation of genetic information on Earth. The presence of a genetic system is absolutely essential to life. Thus, the appearance in an ancestral era of a nucleic acid-like polymer able to undergo Darwinian evolution indicates the beginning of life on our planet. The building of primordial genetic molecules, whatever they were, required the presence of a protected environment, allowing the synthesis and concentration of precursors (nucleotides), their joining into larger molecules (polynucleotides), the protection of forming polymers against degradation (i.e. by cosmic and UV radiation), thus ensuring their persistence in a changing environment, and the expression of the "biological" potential of the molecule (its capacity to self-replicate and evolve). Determining how these steps occurred and how the primordial genetic molecules originated on Earth is a very difficult problem that still must be resolved. It has long been proposed that surface chemistry, i.e. on clay minerals, could have played a crucial role in the prebiotic formation of molecules basic to life. In the present work, we discuss results obtained in different fields that strengthen the hypothesis of a clay-surface-mediated origin of genetic material.

A procedure is presented that is based on the detection of Cu(II)–asparagine complexes by quantitative ESR, and allows in a very simple and rapid manner to evaluate the changes of asparagine concentration during the entire time range of the growth of Bacillus subtilis in a typical growth medium. The analysis is carried out in terms of the decrease of the intensity of the ESR-active mono- and di-asparagine–copper(II) complexes. It is resulted that at the end of the exponential growth the asparagine concentration was reduced to values as low as 2% of the initial value. The procedure here reported may be the basis of similar methods to be used for other amino acids and prokaryote systems.