Biology subjects

Sala, D.

Publications and source records attributed to Sala, D..

9 recordsLinked to original sources

bioRxiv2026

Tumor-derived SAA1-TLR4 signaling drives tumor-to-muscle communication in pancreatic cancer cachexia

Cancer cachexia limits treatment tolerance and survival in pancreatic ductal adenocarcinoma (PDAC), yet the tumor-derived signals driving tissue dysfunction remain poorly understood. Here, we identify serum amyloid A1 (SAA1) as a mediator of tumor-to-host communication acting through Toll-like receptor 4 (TLR4). Tumor-derived SAA1 was elevated in human PDAC and in a mouse PDAC model and disrupted both myofiber and muscle stem cell (MuSC) homeostasis. Genetic reduction of tumor-derived SAA1 uncoupled tumor progression from host wasting, preserving muscle mass and function and prolonging survival without affecting primary tumor growth. Mechanistically, SAA1-TLR4 signaling drove multicellular remodeling of the skeletal muscle microenvironment. Therapeutic TLR4 inhibition after cachexia onset restored muscle mass, function and MuSC abundance and prolonged survival independently of tumor growth. Conservation of SAA1-TLR4 signaling in human skeletal muscle identifies a therapeutically actionable tumor-host pathway and demonstrates that host deterioration can be targeted independently of tumor progression.

cancer biology↗

bioRxiv2026

Prevalent Gut Phages Encode Modular Adhesins Mediating Epithelial Binding and Endoplasmic Reticulum Trafficking

Bacteriophages are crucial components of the human microbiome and hold promise as therapeutic agents. Yet, their physical interactions with mammalian cells remain poorly understood. Here, we developed a high-throughput platform to identify phages that adhere to epithelial layers and the proteins that mediate this interaction. The identified phages encode immunoglobulin (Ig)-like domain-containing proteins that, when displayed on a non-adherent phage, confer epithelial binding and internalization in vitro, and increased phage retention in the mouse gut in vivo. Phages encoding these adhesins are among the most abundant and prevalent human gut phages, including crAss-like phages and a distinct cluster of uncharacterized myophages. Domain sequence variation alters epithelial interaction profiles, and internalized phages traffic to the endoplasmic reticulum through the Golgi apparatus, suggesting access to non-degradative internalization pathways. These findings reveal widespread phage-human interactions in the human virome, with potential impacts on health and implications for next-generation phage therapeutics.

microbiology↗

bioRxiv2022

Biasing AlphaFold2 to predict GPCRs and Kinases with user-defined functional or structural properties

Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While experimental methods combined with integrative structural biology has been the most effective way to get high accuracy structures and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high accuracy single chain modeling. Since then, different customizations expanded the number of conformational states accessible through AF2. Here, we further extended AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and Kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in automatic fashion.

biophysics↗

bioRxiv2022

Modeling of protein conformational changes with Rosetta guided by limited experimental data

Conformational changes are an essential component of functional cycles of many proteins such as transporters or receptors. Despite their importance, the characterization of functionally-relevant structural intermediates is often a hard task for both experimental and computational methods. This limitation can be at least partially relieved by integrating experimental data within computational approaches. Such integrative structural biology strategies can yield an ensemble of models consistent with experimental data. While the macromolecular modeling suite Rosetta has been extensively used to predict protein structures de novo using various types of experimental data, an all-purpose conformational change modeling approach has been absent from this suite. Here, we introduce and benchmark ConfChangeMover (CCM), a new Rosetta Mover tailored to model conformational changes in proteins using sparse experimental data. CCM is able to rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with sparse experimental double electron-electron resonance (DEER) restraints and simulated C - C distance restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. Thus, our method will contribute to the biophysical characterization of protein dynamics.

biophysics↗

bioRxiv2021

Therapeutic advantages of combined gene/cell therapy strategies in a murine model of GM2 gangliosidosis

The GM2 gangliosidoses Tay-Sachs disease and Sandhoff disease (SD) are respectively caused by mutations in the HEXA and HEXB genes encoding the and {beta} subunits of {beta}-N-acetylhexosaminidase (Hex). The consequential accumulation of ganglioside in the brain leads to severe and progressive neurological impairment. There are currently no approved therapies to counteract or reverse the effects of GM2 gangliosidosis. Adeno-associated vector (AAV)-based investigational gene therapy (GT) products have raised expectations but come with safety and efficacy issues that need to be addressed. Thus, there is an urgent need to develop novel therapies targeting the CNS and other affected tissues that are appropriately timed to ensure pervasive metabolic correction and counteract disease progression. In this report, we show that the sequential administration of lentiviral vector (LV)-mediated intracerebral (IC) GT and bone marrow transplantation (BMT) in pre-symptomatic SD mice provide a timely and long-lasting source of the Hex enzyme in the central and peripheral nervous systems and peripheral tissues, leading to global rescue of the disease phenotype. Combined therapy showed a clear therapeutic advantage compared to individual treatments in terms of lifespan extension and normalization of the neuroinflammatory and neurodegenerative phenotypes of the SD mice. These benefits correlated with a time-dependent increase in Hex activity and a remarkable reduction in GM2 storage in the brain tissues that single treatments failed to achieve. Our results highlight the complementary and synergic mode of action of LV-mediated IC GT and BMT, clarify the relative contribution of treatments to the therapeutic outcome, and inform on the realistic threshold of enzymatic activity that is required to achieve a significant therapeutic benefit, with important implications for the monitoring and interpretation of ongoing experimental therapies, and for the design of more effective treatment strategies for GM2 gangliosidosis.

genetics↗

bioRxiv2021

Sampling the conformational landscapes of transporters and receptors with AlphaFold2

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward-facing (IF) and outward-facing (OF) states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein coupled receptors (GPCRs) that are absent from the AF2 training set. Whereas models generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments (MSAs) by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling (TM)-score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

biophysics↗

bioRxiv2020

Competition NMR for Detection of Hit/Lead Inhibitors of Protein-Protein Interactions

Screening for small-molecule fragments that can lead to potent inhibitors of protein-protein interactions (PPIs) is often a laborious step as the fragments cannot dissociate the targeted PPI due to their low M-mM affinities. Here, we describe an NMR competition assay - called w-AIDA-NMR (weak-Antagonist Induced Dissociation Assay-NMR) - that is sensitive to weak M-mM ligand-protein interactions and which can be used in initial fragment screening campaigns. By introducing point mutations in the complexs protein that is not targeted by the inhibitor, we lower the effective affinity of the complex allowing for short fragments to dissociate the complex. We illustrate the method with the compounds that block the Mdm2/X-p53 and PD-1/PD-L1 oncogenic interactions. Targeting the PD-/PD-L1 PPI has profoundly advanced the treatment of different types of cancers.

biophysics↗

bioRxiv2019

A protocol to automatically calculate homo-oligomeric protein structures through the integration of evolutionary constraints and ambiguous contacts derived from solid- or solution-state NMR

Protein assemblies are involved in many important biological processes. Solid-state NMR (SSNMR) spectroscopy is a technique suitable for the structural characterization of samples with high molecular weight and thus can be applied to such assemblies. A significant bottleneck in terms of both effort and time required is the manual identification of unambiguous intermolecular contacts. This is particularly challenging for homo-oligomeric complexes, where simple uniform labeling may not be effective. We tackled this challenge by exploiting coevolution analysis to extract information on homo-oligomeric interfaces from NMR-derived ambiguous contacts. After removing the evolutionary couplings (ECs) that are already satisfied by the 3D structure of the monomer, the predicted ECs are matched with the automatically generated list of experimental contacts. This approach provides a selection of potential interface residues that is used directly in monomer-monomer docking calculations. We validated the protocol on tetrameric L-asparaginase II and dimeric Sod1.

biophysics↗

bioRxiv2019

CA-170 a potent small-molecule PD-L1 inhibitor or not?

CA-170 is currently the only small-molecule modulator in clinical trials targeting PD-L1 and VISTA proteins - important negative checkpoint regulators of immune activation. The reported therapeutic results to some extent mimic those of FDA-approved monoclonal antibodies overcoming the limitations of the high production costs and adverse effects of the latter. However, no conclusive biophysical evidence proving the binding to hPD-L1 has ever been presented. Using well-known in vitro methods: NMR binding assay, HTRF and cell-based activation assays, we clearly show that there is no direct binding between CA-170 and PD-L1. To strengthen our reasoning, we performed control experiments on AUNP-12 - a 29-mer peptide, which is a precursor of CA-170. Positive controls consisted of the well-documented small-molecule PD-L1 inhibitors: BMS-1166 and peptide p57.

cancer biology↗