Biology subjects

Pierobon, M.

Publications and source records attributed to Pierobon, M..

8 recordsLinked to original sources

bioRxiv2026

DIRAS3 broadly suppresses signaling from diverse oncogenic KRAS hotspot variants through an inhibitory interface

Despite major advances in KRAS-directed therapies, broadly suppressing oncogenic KRAS signaling across diverse hotspot variants remains an unmet challenge. Here, we identify an endogenous inhibitory mechanism that suppresses signaling across the major oncogenic KRAS hotspot classes while largely sparing basal wild-type KRAS, revealing activation-state-selective inhibition. We demonstrate that the imprinted tumor suppressor DIRAS3 directly engages activated KRAS through a functionally defined inhibitory interface that can be molecularly mimicked to broadly inhibit KRAS-driven signaling. Using genetically controlled RASless reconstitution models, complementary cancer cell validation, structure-function analyses, molecular dynamics-based membrane topology modeling, and ultrastructural imaging, we show that dual lipid modification constrains DIRAS3 membrane topology, enabling an 5-encoded inhibitory interface to engage activated KRAS, disrupt signaling-competent membrane assemblies, and suppress downstream signaling. Structure-function analyses establish this interface as a critical determinant of DIRAS3-mediated KRAS inhibition. A conformationally constrained 5 mimetic recapitulates full-length DIRAS3 activity, broadly suppressing MAPK and PI3K signaling across multiple oncogenic KRAS variants, including Q61 mutants with reduced sensitivity to current tri-complex RAS(ON) inhibitors. Mechanistically, immunoprecipitation shows that the 5 mimetic reduces KRAS association with both RAF1 and p110 similarly across G12 and Q61 mutants, whereas RMC-7977 reduces inhibition of p110 association with Q61 KRAS. The 5 mimetic also disrupts KRAS nanocluster organization and inhibits tumor growth in vivo. These findings identify a targetable inhibitory interface within DIRAS3 and establish mimicry of an endogenous KRAS suppressor as a mechanistically distinct strategy for broad KRAS inhibition.

cancer biology↗

bioRxiv2026

Vertical inhibition of the autophagy pathway impairs growth and enhances sensitivity to mTORC1 inhibition in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is dependent on autophagy for growth. Chloroquine/Hydroxychloroquine (CQ/HCQ), the sole FDA-approved autophagy inhibitors, have shown limited clinical efficacy as cancer therapies. To identify approaches to improve PDAC response to CQ, we performed a CQ-anchored, CRISPR-Cas9 mediated loss-of-function screen. We identified that the loss of genes encoding proteins upstream in the autophagy pathway enhanced CQ-mediated growth suppression. This indicated that simultaneous targeting of two distinct nodes of the same pathway, vertical inhibition, may be a more effective strategy than single node inhibition. We demonstrated that genetic loss or pharmacological inhibition of VPS34, a protein necessary for autophagosome nucleation, sensitized PDAC cells to inhibitors of the terminal stage of the autophagy pathway, including CQ and an inhibitor of PIKfyve. We extended this concept to the initiation complex and demonstrated that ULK1/2 inhibition synergized with CQ and PIKfyve inhibition to impair PDAC cell growth and increase apoptosis. Anticipating mechanisms of resistance to vertical autophagy inhibition, we performed reverse-phase protein array profiling and identified that vertical inhibition of the autophagy pathway resulted in enhanced activation of the PI3K-AKT-mTORC1 signaling pathway. Increased mTORC1 signaling resulted in heightened sensitivity to bi-steric mTORC1 inhibition in both cell line and organoid models of PDAC. This study identifies novel anti-autophagy inhibitor combinations that may improve the clinical efficacy of autophagy inhibition for PDAC treatment. IMPLICATIONSVertical inhibition of the autophagy pathway reduces pancreatic cancer cell growth, increases apoptosis, and enhances sensitivity to mTORC1 inhibition; thereby representing a novel therapeutic strategy for autophagy-driven pancreatic cancer.

Cancer Biology↗

bioRxiv2026

Direct Synthesis of Targeted Nanosized ICG J-aggregate for Photoacoustic Imaging

Indocyanine green (ICG) J-aggregates (JAs) are self-assembled particles characterized by a sharp and strong absorption peak in the near-infrared region ([~]890 nm), enhanced photostability, low fluorescence, and high photothermal conversion efficiency, compared to monomeric ICG. These attributes make ICG-JAs promising contrast agent candidates for photoacoustic imaging (PAI). However, traditional methods for synthesizing ICG-JAs often yield particles without targeting ability, which limit their applications. Thus, to synthesize targeted nanoscale JA, complex and multi-step encapsulation and filtration processes are generally required. To solve this issue, we introduce a robust and rapid strategy for direct synthesis of targeted nanoscale ICG-JA by co-assembling ICG and ICG-azide dyes under optimized formulation conditions that do not require encapsulation. The resulting nanoscale JAAZ particles (nJAAZ) exhibit diameters of [~]120-150 nm and are amenable to direct bio-orthogonal functionalization via copper-free click chemistry for the attachment of virtually any targeting ligands and/or biomolecules. We further demonstrate the strong photoacoustic signal generation of these nJAAZ in vitro and in vivo, highlighting their potential as a modular high-performance contrast agent platform for PAI. This work establishes a scalable and tunable platform for engineering functional JAs, opening new avenues for targeted molecular imaging and theranostic applications.

bioengineering↗

bioRxiv2023

Information- and Communication-Centric Approach in Cell Metabolism for Analyzing Behavior of Microbial Communities

Microorganisms naturally form community ecosystems to improve fitness in diverse environments and conduct otherwise intractable processes. Microbial communities are therefore central to biogeochemical cycling, human health, agricultural productivity, and technologies as nuanced as nanotechnology-enabled devices; however, the combinatorial scaling of exchanges with the environment that predicate community functions are experimentally untenable. Several computational tools have been presented to capture these exchanges, yet, no attempt has been made to understand the total information flow to a community from its environment. We therefore adapted a recently developed model for singular organisms, which blends molecular communication and the Shannon Information theory to quantify information flow, to communities and exemplify this expanded model on idealized communities: one of Escherichia coli (E. coli) and Pseudomonas fluorescens to emulate an ecological community and the other of Bacteroides thetaiotaomicron (B. theta) and Kleb Ciella to emulate a human microbiome interaction. Each of these sample communities exhibit critical syntrophy in certain environmental conditions, which should be evident through our community mutual information model. We further explored alternative frameworks for constructing community genome-scale metabolic models (GEMs) - mixed-bag and compartmentalized. Our study revealed that information flow is greater through communities than isolated models, and that the mixed-bag framework conducts greater information flow than the compartmentalized framework for community GEMs, presumably because the latter is encumbered with transport reactions that are absent in the former. This community Mutual Information model is furthermore wrapped as a KBase Application (Run Flux Mutual Information Analysis, RFMIA) for optimum accessibility to biological investigators. We anticipate that this unique quantitative approach to consider information flow through metabolic systems will accelerate both basic and applied discovery in diverse biological fields. Author SummaryMicroorganisms frequently communicate information via information-bearing molecules, which must be fundamentally understood to engineer biological cells that properly engage with their environments, such as the envisioned Internet of Bio-NanoThings. The study of these molecular communications has employed information and communication theory to analyze the exchanged information via chemical reactions and molecular transport. We introduce an information- and communication-centric computational approach to estimate the information flow in biological cells and its impacts on the behavior of single organisms and communities. This study complements our previous work of cell metabolism by developing an end-to-end perspective of molecular communication based on enzyme-regulated reactions. We explore the mutual information using Shannon information theory, measured in bits, between influential nutrients and cellular growth rate. The developed RFMIA computational tool is deployed in the U.S. Department of Energys Systems Biology Knowledgebase, where it quantitatively estimates information flow in both organism and community metabolic networks and extends recent developments in computer communications to explore and explain a new biology for the open-source community.

bioinformatics↗

bioRxiv2023

A Molecular Communication model for cellular metabolism

Understanding cellular engagement with its environment is essential to control and monitor metabolism. Molecular Communication theory (MC) offers a computational means to identify environmental perturbations that direct or signify cellular behaviors by quantifying the information about a molecular environment that is transmitted through a metabolic system. We developed an model that integrates conventional flux balance analysis metabolic modeling (FBA) and MC to mechanistically expand the scope of MC, and thereby uniquely blends mechanistic biology and information theory to understand how substrate consumption is captured reaction activity, metabolite excretion, and biomass growth. This is enabled by defining several channels through which environmental information transmits in a metabolic network. The information flow in bits that is calculated through this workflow further determines the maximal metabolic effect of environmental perturbations on cellular metabolism and behaviors, since FBA simulates maximal efficiency of the metabolic system. We exemplify this method on two intestinal symbionts - Bacteroides thetaiotaomicron and Methanobrevibacter smithii - and visually consolidated the results into constellation diagrams that facilitate interpretation of information flow from given environments and thereby cultivate the design of controllable biological systems. The unique confluence of metabolic modeling and information theory in this model advances basic understanding of cellular metabolism and has applied value for the Internet of Bio-Nano Things, synthetic biology, microbial ecology, and autonomous laboratories.

bioinformatics↗

bioRxiv2022

Mediator Kinase Inhibition Impedes Transcriptional Plasticity and Prevents Resistance to ERK/MAPK-Targeted Therapy in KRAS-Mutant Cancers

Acquired resistance remains a major challenge for therapies targeting oncogene activated pathways. KRAS is the most frequently mutated oncogene in human cancers, yet strategies targeting its downstream signaling kinases have failed to produce durable treatment responses. Here, we developed multiple models of acquired resistance to dual-mechanism ERK/MAPK inhibitors across KRAS-mutant pancreatic, colorectal, and lung cancers, and then probed the long-term events enabling survival against this novel class of drugs. These studies revealed that resistance emerges secondary to large-scale transcriptional adaptations that are diverse and tumor-specific. Transcriptional reprogramming extends beyond the well-established early response, and instead represents a dynamic, evolved population-level process that is refined to attain a stably resistant phenotype. Mechanistic and translational studies reveal that resistance to dual-mechanism ERK/MAPK inhibition is broadly susceptible to manipulation of the epigenetic machinery, and that Mediator kinase, in particular, can be co-targeted at a bottleneck point to prevent diverse, tumor-specific resistance programs.

cancer biology↗

bioRxiv2021

Defining the KRAS-regulated kinome in KRAS-mutant pancreatic cancer

Oncogenic KRAS drives cancer growth by activating diverse signaling networks, not all of which have been fully delineated. We set out to establish a system-wide profile of the KRAS-regulated kinase signaling network (kinome) in KRAS-mutant pancreatic ductal adenocarcinoma (PDAC). We knocked down KRAS expression in a panel of six cell lines, and then applied Multiplexed Inhibitor Bead/Mass Spectrometry (MIB/MS) chemical proteomics to monitor changes in kinase activity and/or expression. We hypothesized that depletion of KRAS would result in downregulation of kinases required for KRAS-mediated transforming activities, and in upregulation of other kinases that could potentially compensate for the deleterious consequences of the loss of KRAS. We identified 15 upregulated and 13 downregulated kinases in common across the panel. In agreement with our hypothesis, all 15 of the upregulated kinases have established roles as cancer drivers (e.g., SRC, TGFBR1, ILK), and pharmacologic inhibition of the upregulated kinase, DDR1, suppressed PDAC growth. Interestingly, 11 of the 13 downregulated kinases have established driver roles in cell cycle progression, particularly in mitosis (e.g., WEE1, Aurora A, PLK1). Consistent with a crucial role for the downregulated kinases in promoting KRAS-driven proliferation, we found that pharmacologic inhibition of WEE1 also suppressed PDAC growth. The unexpected paradoxical activation of ERK upon WEE1 inhibition led us to inhibit both WEE1 and ERK concurrently, which caused further potent growth suppression and enhanced apoptotic death than WEE1 inhibition alone. We conclude that system-wide delineation of the KRAS-regulated kinome can identify potential therapeutic targets for KRAS-mutant pancreatic cancer.

cancer biology↗

bioRxiv2020

Convergent evolution of p38/MAPK activation in hormone resistant prostate cancer mediates pro-survival, immune evasive, and metastatic phenotypes

Adaptation of cancer cells to targeted therapy follows ecological paradigms observed in natural populations that encounter resource depletion and changing environments, including activation of pro-survival mechanisms, migration to new locations, and escape of predation. We identified the p38 MAPK pathway as a common molecular driver of these three responses during the adaptation to hormone therapy resistance in prostate cancer. The p38 pathway is activated in therapy-resistant cells and mechanistically drives these three convergent responses through sustained AR activity, enhanced invasion and metastasis, and immune evasion. Targeting p38 signaling may represent a new therapeutic strategy to treat men with metastatic, hormone therapy-resistant prostate cancer.

cancer biology↗