Biology subjects

Adler, F. R.

Publications and source records attributed to Adler, F. R..

3 recordsLinked to original sources

bioRxiv2020

What p53 sees: ATM and ATR activation through crosstalk between DNA damage response pathways

1Cells losing the ability to self-regulate in response to damage is a hallmark of cancer. When a cell encounters damage, regulatory pathways estimate the severity of damage and promote repair, cell cycle arrest, or apoptosis. This decision-making process would be remarkable if it were based on the total amount of damage in the cell, but because damage detection pathways vary in the rate and intensity with which they promote pro-apoptotic factors, the cells real challenge is to reconcile dissimilar signals. Crosstalk between repair pathways, crosstalk between pro-apoptotic signaling kinases, and signals induced by damage byproducts complicate the process further. The cells response to{gamma} and UV radiation neatly illustrates this concept. While these forms of radiation produce lesions associated with two different pro-apoptotic signaling kinases, ATM and ATR, recent experiments show that ATM and ATR react to both forms of radiation. To simulate the pro-apoptotic signal induced by{gamma} and UV radiation, we construct a mathematical model that includes three modes of crosstalk between ATM and ATR signaling pathways: positive feedback between ATM/ATR and repair proteins, ATM and ATR mutual upregulation, and changes in lesion topology induced by replication stress or repair. We calibrate the model to agree with 21 experimental claims about ATM and ATR crosstalk. We alter the model by adding or removing specific processes, then examine the effects of each process on ATM/ATR crosstalk by recording which claims the altered model violates. Not only is this the first mathematical model of ATM/ATR crosstalk, its implications provide a strong argument for treating pro-apoptotic signaling as a holistic effort rather than attributing it to a single dominant kinase.

systems biology↗

bioRxiv2020

Rolling Signal-based Ripley's K: A new algorithm to identify spatial patterns in histological specimens

The spatial distribution of cells within a tissue underlies organ function. However, these spatial distributions are often difficult to identify, making it challenging to evaluate how cells establish these patterns during development or how diseases may disrupt these patterns and impair function. To address this, we developed an image analysis tool based on a novel algorithm that identifies spatial patterns within tissues. This analytical tool was used to study the bone marrow, a specialized microenvironment in which spatial patterning of regulatory cells may influence the differentiation and survival of hematopoietic stem cells. Using this algorithm, we discovered clusters of regulatory cells within the bone marrow that suggest an organization of micro-niches, which may form the basis of the hematopoietic stem cell microenvironment. This work provides a new tool for the detection and analysis of tissue morphology that enables identification of spatial patterns within tissues that can lead to a deeper understanding of tissue function, provide clues for early onset of disease, and be used as a tool for studying the impact of pharmaceutics on tissue development and regeneration. In BriefThis work introduces a new statistic to analyze the patterning of cells and physiological features in histological images. This statistic was used on a published set of immunofluorescent images of murine bone to identify novel spatial structures within the bone marrow that may provide new inisghts to the organization of the hematopoietic stem cell microenvironment. HighlightsO_LIRSRK, a statistical tool for analyzing the spatial distribution of features in histological images, is introduced. C_LIO_LIRSRK incorporates the quantification of signal distribution to identify unique spatial patterns. C_LIO_LISpatial patterns in hematopoietic stem cell microenvironments are identified. C_LI Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

systems biology↗

bioRxiv2020

Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy

The extent that immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled on a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients, but not those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached, tumor burden decreases, similar to models of predator-prey interactions; these dynamic patterns were absent in non-responder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients tumor-immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T-cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy. One sentence summaryPeripheral immune cell differentiation and signaling, upon initiation of immunotherapy, reflects tumor attacking ability and patient response. Significance statementThe evolution of peripheral immune cell abundance and signaling over time, as well as how these immune cells interact with the tumor, may impact a cancer patients response to therapy. By developing an ecological population model, we provide evidence of a dynamic predator-prey like relationship between circulating immune cell abundance and tumor size in patients that respond to immunotherapy. This relationship is not found either in patients that are non-responsive to immunotherapy or during chemotherapy. Single cell RNA-sequencing (scRNAseq) of serial peripheral blood samples from patients show that the strength of tumor-immune cell interactions is reflected in T-cells interferon activation and differentiation early in treatment. Thus, circulating immune cell dynamics reflect a tumors response to immunotherapy.

cancer biology↗