Search

The homeostasis of vascular endothelium is crucial for cardiovascular health and endothelial cell (EC) aging and dysfunction could negatively impact vascular function. Leveraging transcriptome profiles from ECs subjected to various stimuli, including time-series data obtained from ECs under physiological pulsatile flow vs. pathophysiological oscillatory flow, we performed principal component analysis (PCA) to identify key genes contributing to divergent transcriptional states of ECs. Through bioinformatics analysis, we identified that a long non-coding RNA (lncRNA) RAMP2-AS1 encoded on the antisense of RAMP2, a determinant of endothelial homeostasis and vascular integrity, is a novel regulator essential for EC homeostasis and function. Knockdown of RAMP2-AS1 suppressed RAMP2 expression and caused EC functional changes promoting aging, including impaired angiogenesis and increased senescence. Our study demonstrates an integrative approach to quantifying EC aging based on transcriptome changes, which also identified a number of novel regulators, including protein-coding genes and many lncRNAs involved EC functional modulation, exemplified by RAMP2-AS1.

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.

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.

In addition to genetic variation, epigenetic variation and transposons can greatly affect the evolutionary fitnesses landscape and gene expression. Previously we proposed a mathematical treatment of a general epigenetic variation model that we called Stochastic Epigenetic Modification (SEM) model. In this study we follow up with a special case, the Transposon Silencing Model (TSM), with, once again, emphasis on quantitative treatment. We have investigated the evolutionary effects of epigenetic changes due to transposon (T) insertions; in particular, we have considered a typical gene locus A and postulated that (i) the expression level of gene A depends on the epigenetic state (active or inactive) of a cis- located transposon element T, (ii) stochastic variability in the epigenetic silencing of T occurs only in a short window of opportunity during development, (iii) the epigenetic state is then stable during further development, and (iv) the epigenetic memory is fully reset at each generation. We develop the model using two complementary approaches: a standard analytical population genetics framework (di usion equations) and Monte-Carlo simulations. Both approaches led to similar estimates for the probability of fixation and time of fixation of locus TA with initial frequency P in a randomly mating diploid population of effective size Ne. We have ascertained the e ect that ρ, the probability of transposon Modification during the developmental window, has on the population (species). One of our principal conclusions is that as ρ increases, the pattern of fixation of the combined TA locus goes from "neutral" to "dominant" to "over-dominant". We observe that, under realistic values of ρ, epigenetic Modifications can provide an e cient mechanism for more rapid fixation of transposons and cis-located gene alleles. The results obtained suggest that epigenetic silencing, even if strictly transient (being reset at each generation), can still have signi cant macro-evolutionary effects. Importantly, this conclusion also holds for the static fitness landscape. To the best of our knowledge, no previous analytical modeling has treated stochastic epigenetic changes during a window of opportunity.

Here we present a theoretical and mathematical perspective on the process of aging. We extend the concepts of physical space and time to an abstract, mathematically-defined space, which we associate with a concept of “biological space-time” in which biological dynamics may be represented. We hypothesize that biological dynamics, represented as trajectories in biological space-time, may be used to model and study different rates of biological aging. As a consequence of this hypothesis, we show how dilation or contraction of time analogous to relativistic corrections of physical time resulting from accelerated or decelerated biological dynamics may be used to study precipitous or protracted aging. We show specific examples of how these principles may be used to model different rates of aging, with an emphasis on cancer in aging. We discuss how this theory may be tested or falsified, as well as novel concepts and implications of this theory that may improve our interpretation of biological aging.

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.

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.

The identity/recognition of tRNAs, in the context of aminoacyl tRNA synthetases (and other molecules), is a complex phenomenon that has major implications ranging from the origins and evolution of translation machinery and genetic code to the evolution and speciation of tRNAs themselves to human mitochondrial diseases to artificial genetic code engineering. Deciphering it via laboratory experiments, however, is difficult and necessarily time- and resource-consuming. In this study, we propose a mathematically rigorous two-pronged in silico approach to identifying and classifying tRNA positions important for tRNA identity/recognition, rooted in machine learning and information-theoretic methodology. We apply Bayesian Network modeling to elucidate the structure of intra-tRNA-molecule relationships, and distribution divergence analysis to identify meaningful inter-molecule differences between various tRNA subclasses. We illustrate the complementary application of these two approaches using tRNA examples across the three domains of life, and identify and discuss important (informative) positions therein. In summary, we deliver to the tRNA research community a novel, comprehensive methodology for identifying the specific elements of interest in various tRNA molecules, which can be followed up by the corresponding experimental work and/or high-resolution position-specific statistical analyses.

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.

Background The hypothesis of an RNA-based origin of life, known as the "RNA world", is strongly affected by the hostile environmental conditions probably present in the early Earth. In particular, strong UV and X-ray radiations could have been a major obstacle to the formation and evolution of the first biomolecules. In 1951, J. D. Bernal first proposed that clay minerals could have served as the sites of accumulation and protection from degradation of the first biopolymers, providing the right physical setting for the evolution of more complex systems. Numerous subsequent experimental studies have reinforced this hypothesis. Results The ability of the possibly widespread prebiotic, clay mineral montmorillonite to protect the catalytic RNA molecule ADHR1 (Adenine Dependent Hairpin Ribozyme 1) from UV-induced damages was experimentally checked. In particular, the self-cleavage reaction of the ribozyme was evaluated after UV-irradiation of the molecule in the absence or presence of clay particles. Results obtained showed a three-fold retention of the self-cleavage activity of the montmorillonite-protected molecule, with respect to the same reaction performed by the ribozyme irradiated in the absence of the clay. Conclusion These results provide a suggestion with which RNA, or RNA-like molecules, could have overcame the problem of protection from UV irradiation in the RNA world era, and suggest that a clay-rich environment could have favoured not only the formation of first genetic molecules, but also their evolution towards increasingly complex molecular organization.