Biology subjects

Sherman, M. S.

Publications and source records attributed to Sherman, M. S..

5 recordsLinked to original sources

bioRxiv2025

Rapid hypothalamic-pituitary recovery after chronic glucocorticoid therapy enables strategies that prevent adrenal suppression

Glucocorticoid-induced adrenal insufficiency (GIAI) can persist for months after discontinuation of chronic corticosteroid therapy, placing patients at risk for life-threatening adrenal crises. This prolonged suppression has been attributed primarily to delayed restoration of hypothalamic-pituitary signaling based on indirect measures of central axis activity. To identify the rate-limiting site of hypothalamic-pituitary-adrenal (HPA) axis recovery, we evaluated the timing of functional and histologic recovery at each node of the axis following 8 weeks of dexamethasone (DEX) treatment in adult, male mice. DEX administration fully suppressed HPA axis activity. Unexpectedly, within one week of DEX withdrawal, hypothalamic Crh mRNA and plasma ACTH rebounded above control levels, whereas corticosterone (CORT) remained suppressed for an additional seven weeks. DEX-treated adrenals were markedly atrophic and contained large clusters of lipid-filled macrophages. Even after adjusting for macrophage content, CORT secretion was disproportionately low relative to the remaining adrenocortical cell mass despite supraphysiologic ACTH stimulation. The adrenal is thus the principal site of post-withdrawal GIAI, involving adrenocortical cell loss and a superimposed defect in steroidogenesis. We next tested whether preserving adrenal trophic signaling during glucocorticoid exposure could prevent GIAI. Adrenal function recovered more slowly in mice treated with DEX and daily cosyntropin (a synthetic ACTH analog) compared to those treated with DEX alone. In contrast, mice with non-suppressible endogenous ACTH due to targeted hypothalamic deletion of the glucocorticoid receptor maintained normal adrenal architecture and steroidogenic capacity despite prolonged DEX treatment. Pharmacologic treatments that mimic sustained trophic signaling to the adrenal during chronic glucocorticoid treatment may thus prevent GIAI.

physiology↗

bioRxiv2024

Single-cell multi-omic analysis of fibrolamellar carcinoma reveals rewired cell-to-cell communication patterns and unique vulnerabilities

Fibrolamellar carcinoma (FLC) is a rare malignancy disproportionately affecting adolescents and young adults with no standard of care. FLC is characterized by thick stroma, which has long suggested an important role of the tumor microenvironment. Over the past decade, several studies have revealed aberrant markers and pathways in FLC. However, a significant drawback of these efforts is that they were conducted on bulk tumor samples. Consequently, identities and roles of distinct cell types within the tumor milieu, and the patterns of intercellular communication, have yet to be explored. In this study we unveil cell-type specific gene signatures, transcription factor networks, and super-enhancers in FLC using a multi-omics strategy that leverages both single-nucleus ATAC-seq and single-nucleus RNA-seq. We also infer completely rewired cell-to-cell communication patterns in FLC including signaling mediated by SPP1-CD44, MIF-ACKR3, GDF15-TGFBR2, and FGF7-FGFR. Finally, we validate findings with loss-of-function studies in several models including patient tissue slices, identifying vulnerabilities that merit further investigation as candidate therapeutic targets in FLC.

cancer biology↗

bioRxiv2024

Harmonizing terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) with multiplexed iterative immunofluorescence enriches spatial contextualization of cell death

Terminal deoxynucleotidyl transferase dUTP Nick End Labeling (TUNEL) is an essential tool for the detection of cell death in tissues. Although TUNEL is not known to be compatible with multiplexed spatial proteomic methods, harmonizing TUNEL with such methods offers the opportunity to delineate cell-type specific cell death labeling and precise spatial contextualization of cell death in complex tissues. Here we evaluated variations of the TUNEL assay for their compatibility with a multiplexed immunofluorescence method, multiple iterative labeling by antibody neodeposition (MILAN), in two different tissues and injury models for cell death, acetaminophen-induced hepatocyte necrosis and dexamethasone-induced adrenocortical apoptosis. Using a commercial Click-iT-based assay as a standard, TUNEL signal could be reliably produced independent of antigen-retrieval method, with tissue-specific minor differences in signal-to-noise. In contrast, proteinase K treatment consistently reduced or even abrogated protein antigenicity, while pressure cooker treatment consistently enhanced protein antigenicity for the targets tested. Antibody-based TUNEL protocols using pressure-cooker antigen retrieval were MILAN erasure-compatible thus enabling harmonization of TUNEL with MILAN. As many as four staining cycles could be performed without loss of subsequent TUNEL signal, while first-round TUNEL did not influence protein antigenicity in subsequent rounds. We conclude this harmonized assay performs comparably to an established commercial assay, but preserves protein antigenicity, thus enabling versatile integration with multiplexed immunofluorescence using MILAN. We anticipate this harmonized protocol will enable broad and flexible integration of TUNEL into multiplexed spatial proteomic assays, thus vastly enhancing the spatial contextualization of cell death in complex tissues.

pathology↗

bioRxiv2023

Chronic metabolic stress drives developmental programs and loss of tissue functions in non-transformed liver that mirror tumor states and stratify survival

Under chronic stress, cells must balance competing demands between cellular survival and tissue function. In metabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD/NASH), hepatocytes cooperate with structural and immune cells to perform crucial metabolic, synthetic, and detoxification functions despite nutrient imbalances. While prior work has emphasized stress-induced drivers of cell death, the dynamic adaptations of surviving cells and their functional repercussions remain unclear. Namely, we do not know which pathways and programs define cellular responses, what regulatory factors mediate (mal)adaptations, and how this aberrant activity connects to tissue-scale dysfunction and long-term disease outcomes. Here, by applying longitudinal single-cell multi-omics to a mouse model of chronic metabolic stress and extending to human cohorts, we show that stress drives survival-linked tradeoffs and metabolic rewiring, manifesting as shifts towards development-associated states in non-transformed hepatocytes with accompanying decreases in their professional functionality. Diet-induced adaptations occur significantly prior to tumorigenesis but parallel tumorigenesis-induced phenotypes and predict worsened human cancer survival. Through the development of a multi-omic computational gene regulatory inference framework and human in vitro and mouse in vivo genetic perturbations, we validate transcriptional (RELB, SOX4) and metabolic (HMGCS2) mediators that co-regulate and couple the balance between developmental state and hepatocyte functional identity programming. Our work defines cellular features of liver adaptation to chronic stress as well as their links to long-term disease outcomes and cancer hallmarks, unifying diverse axes of cellular dysfunction around core causal mechanisms.

systems biology↗

bioRxiv2022

Stochastic Spatiotemporal Simulation of a General Reaction System

Biological systems frequently contain biochemical species present as small numbers of slowly diffusing molecules, leading to fluctuations that invalidate deterministic analyses of system dynamics. The development of mathematical tools that account for the spatial distribution and discrete number of reacting molecules is vital for understanding cellular behavior and engineering biological circuits. Here we present an algorithm for an event-driven stochastic spatiotemporal simulation of a general reaction process that bridges well-mixed and unmixed systems. The algorithm is based on time-varying particle probability density functions whose overlap in time and space is proportional to reactive propensity. We show this to be mathematically equivalent to the Gillespie algorithm in the specific case of fast diffusion. We develop a computational implementation of this algorithm and provide a Fourier transformation-based approach which allows for near constant computational complexity with respect to the number of individual particles of a given species. To test this simulation method, we examine reaction and diffusion limited regimes of a bimolecular association-dissociation reaction. In the reaction limited regime where mixing occurs between individual reactions, equilibrium numbers of components match the expected values from mean field methods. In the diffusion limited regime, however, spatial correlations between newly dissociated species persist, leading to rebinding events and a shift the in the observed molecular counts. In the final part of this work, we examine how changes in enzyme efficiency can emerge from changes in diffusive mobility alone, as may result from protein complex formation.

biophysics↗