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Biology subjects

Maestrini, D.

Publications and source records attributed to Maestrini, D..

5 recordsLinked to original sources

Xist-seeded nucleation sites form local concentration gradients of silencing proteins to inactivate the X-chromosome

The long non-coding RNA Xist exploits numerous effector proteins to progressively induce gene silencing across the X chromosome and form the inactive X (Xi)-compartment. The mechanism underlying formation of the chromosome-wide Xi-compartment is poorly understood. Here, we find that formation of the Xi-compartment is induced by [~]50 locally confined granules, where two Xist RNA molecules nucleate supra-molecular complexes (SMCs) of interacting proteins. Xist-SMCs are transient structures that concentrate rapidly recycling proteins in the X by increasing protein binding affinity. We find that gene silencing originates at Xist-SMCs and propagates across the entire chromosome over time, achieved by Polycomb-mediated coalescence of chromatin regions and aggregation, via its intrinsically disordered domains, of the critical silencing factor SPEN. Our results suggest a new model for X chromosome inactivation, in which Xist RNA induces macromolecular crowding of heterochromatinizing proteins near distinct sites which ultimately increases their density throughout the chromosome. This mechanism enables deterministic gene silencing without the need for Xist ribonucleoprotein complex-chromatin interactions at each target gene.

cell biology↗

A mathematical modeling approach to explore kinetics of Chimeric Antigen Receptor (CAR) T-cell Response in glioma: the CARRGO model

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.

immunology↗

Evolutionary exploitation of PD-L1 expression in hormone receptor positive breast cancer

Based on clinical data from hormone positive breast cancer patients, we determined that there is a potential tradeoff between reducing tumor burden and altering metastatic potential when administering combination therapy of aromatase inhibitors and immune checkpoint inhibitors. While hormone-deprivation therapies serve to reduce tumor size in the neoadjuvant setting pre-surgery, they may induce tumors to change expression patterns towards a metastatic phenotype. We used mathematical modeling to explore how the timing of the therapies affects tumor burden and metastatic potential with an eye toward developing a dynamic prognostic score and reducing both tumor size and risk of metastasis.

cancer biology↗

Transcriptome dynamics describe and predict state transition from health to leukemia

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.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC=\"FIGDIR/small/238923v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (55K):\norg.highwire.dtl.DTLVardef@12eeaccorg.highwire.dtl.DTLVardef@1392af9org.highwire.dtl.DTLVardef@34786dorg.highwire.dtl.DTLVardef@ce78bb_HPS_FORMAT_FIGEXP M_FIG C_FIG In 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.\n\nHighlightsO_LILeukemia transcriptome dynamics are modeled as movement in transcriptome state-space\nC_LIO_LIState-transition model and critical points accurately predicts leukemia development\nC_LIO_LICritical point-based approach identifies step-wise transcriptome events in leukemia\nC_LIO_LIState-based geometric analysis provides quantification of leukemogenic contribution\nC_LI

cancer biology↗

Aging in a relativistic biological space-time

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 clocks operate. 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 the dilation or contraction of 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 the implications of this theory, including novel concepts that may improve our interpretation of biological aging.

cancer biology↗