Biology subjects

Sharifi, H.

Publications and source records attributed to Sharifi, H..

3 recordsLinked to original sources

bioRxiv2025

Macrophage ferroptosis inhibits Aspergillus conidial killing in lung transplantation

Immune suppression heightens the risk for fungal infections, but the mechanisms that result in clinical disease are poorly understood. Here we demonstrate that macrophage ferroptosis, an iron-dependent form of regulated cell death, inhibits Aspergillus fumigatus (Af) killing. In a mouse tracheal transplant model of Af infection, we observed an increase in macrophage lipid peroxidation, a decreased expression of negative ferroptosis regulators Gpx4 and Slc7a11, and an increase in positive regulators Ptgs2 and Nox2, relative to syntransplants. Depletion of macrophages in transplant recipients decreased Af invasion. In vitro, iron overload reduced macrophage viability and decreased their capability to kill Af spores, through a decrease in lysosomal acidification and lysosomal loss. Treatment with ferrostatin-1, a ferroptosis inhibitor, and deferasirox (an iron chelator) restored Af killing. Ferroptotic alveolar macrophages isolated from lung transplant patients also showed a decreased ability to kill Af spores and the patients bronchoalveolar lavage was characterized by higher iron levels and markers of ferroptotic stress compared to non-lung transplants. These characteristics were strongly correlated with a clinical history of fungal infections, independent of immune suppressive medications. Our findings indicate that macrophage ferroptosis augments the risk of invasive aspergillosis, representing a novel mechanism for host immune dysfunction. Graphical AbstractSchematic of proposed mechanism underlying ferroptosis induced immune dysregulation and increased Af invasion in lung transplantation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/643092v1_ufig1.gif" ALT="Figure 1"> View larger version (104K): org.highwire.dtl.DTLVardef@1112102org.highwire.dtl.DTLVardef@18e2ec1org.highwire.dtl.DTLVardef@19f9c5forg.highwire.dtl.DTLVardef@1d325e8_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

A multiscale model of the cardiovascular system that incorporates baroreflex control of chronotropism, cell-level contractility, and vascular tone

Multiscale models of the cardiovascular system can provide new insights into physiological and pathological processes. PyMyoVent is a computer model that bridges from molecular to organ-level function and which simulates a left ventricle pumping blood through the systemic circulation. Initial work with PyMyoVent focused on the End Systolic Pressure Volume Relationship and ranked potential therapeutic strategies by their impact on contractility. This manuscript extends the PyMyoVent framework by adding closed loop feedback control of arterial pressure. The control algorithm mimics important features of the physiological baroreflex and was developed as part of a long-term program that focuses on growth and biological remodeling. Inspired by the underlying biology, the reflex algorithm uses an afferent signal derived from arterial pressure to drive a kinetic model that mimics the net result of neural processing in the medulla and cell-level responses to autonomic drive. The kinetic model outputs control signals that are constrained between limits that represent maximum parasympathetic and maximum sympathetic drive and which modulate heart rate, intracellular Ca2+ dynamics, the molecular-level function of both the thick and the thin myofilaments, and vascular tone. Simulations show that the algorithm can regulate mean arterial pressure at user-defined set-points as well as maintaining arterial pressure when challenged by changes in blood volume and/or valve resistance. The reflex also regulates arterial pressure when cell-level contractility is modulated to mimic the idealized impact of myotropes. These capabilities will be important for future work that uses computer modeling to investigate clinical conditions and treatments.

physiology↗

bioRxiv2019

BDKANN - Biological Domain Knowledge-based Artificial Neural Network for drug response prediction

MotivationOne of the main goals of precision oncology is to predict the response of a patient to a given cancer treatment based on their genomic profile. Although current models for drug response prediction are becoming more accurate, they are also black boxes and cannot explain their predictions, which is of particular importance in cancer treatment. Many models also do not leverage prior biological knowledge, such as the hierarchical information on how proteins form complexes and act together in pathways. ResultsIn this work, we use this prior biological knowledge to form the architecture of a deep neural network to predict cancer drug response from cell line gene expression data. We find that our approach not only has a low prediction error compared to baseline models but also allows meaningful interpretation of the network. These interpretations can both explain predictions made and discover novel connections in the biological knowledge that may lead to new hypotheses about mechanisms of drug action. AvailabilityCode at https://github.com/osnow/BDKANN Supplementary informationIncluded with submission

bioinformatics↗