Biology subjects

Nemati Fard, L. A.

Publications and source records attributed to Nemati Fard, L. A..

5 recordsLinked to original sources

bioRxiv2026

GABAergic Circuit Activation Induces a Therapeutically Responsive State for anti-PD-L1 Immunotherapy in Glioblastoma

Glioblastoma (GBM) disrupts cortical excitatory-inhibitory balance and establishes an immunosuppressive microenvironment that limits therapeutic efficacy. Whether restoring inhibitory signaling can restrain glioma progression and improve responsiveness to immune checkpoint blockade remains unknown. Peritumoral parvalbumin-positive (PV+) interneurons were bidirectionally manipulated by chemogenetics in orthotopic GL261 gliomas to assess tumor growth and neurological function. GABAB signaling was pharmacologically activated with baclofen in GL261 and CT-2A models and combined with anti-PD-L1 blockade in GL261. Therapeutic response, survival, tumor rechallenge, and early myeloid remodeling were evaluated. Human GBM single-cell transcriptomic data were analyzed to examine the relationship between GABAergic and PD-L1 intercellular signaling. PV activation transiently restrained glioma growth, reduced tumor proliferation and preserved cortical function, whereas PV+ silencing increased seizure susceptibility and neurological impairment without accelerating tumor growth. Baclofen monotherapy did not affect survival, whereas its combination with anti-PD-L1 immunotherapy induced complete tumor eradication in 66% of GL261-bearing mice, prolonged survival, and conferred durable protection against tumor rechallenge. Combination therapy also altered the proportions of Arg1+ and CD11c+ cells within the intratumoral F4/80+ compartment. Human single-cell analysis revealed a shared myeloid-centered communication axis linking GABAB and PD-L1 signaling. These findings identify GABAergic signaling as a modulator of GBM progression and demonstrate that combining baclofen with anti-PD-L1 induces durable tumor regression, and prolongs survival in the GL261 model, supporting a neuro-immune framework for combining GABAergic modulation with immunotherapy.

cancer biology↗

bioRxiv2026

The mutational landscape of innervation axes in cancer

The mutational landscape of neural innervation axes in cancer has garnered increasing attention due to their significant influence on tumorigenesis, metastasis, and treatment resistance. This study provides an overview of the alterations of neural signaling pathways based on multi-omics data analysis. In particular, we conducted a comprehensive analysis of mutations in key genes associated with neurotrophin signaling, norepinephrine and cholinergic pathways, alongside paracrine and synaptogenic factors. Our findings reveal that 65% of patients in a pancancer cohort exhibit at least one somatic alteration in these pathways, with neurotrophin genes being the most frequently altered. Notably, alterations in these genes (e.g., NGF) correlate with poor patient survival across various cancer types, suggesting their oncogenic potential. We also identified significant co-occurrence of mutations and differential expression patterns from both bulk and single-cell RNAseq data, indicating complex interactions among these pathways that contribute to tumor innervation and neuroplasticity. This study underscores the necessity of understanding the crosstalk between neural signaling and cancer biology to improve patient stratification based on mutational profiles and identify potential therapeutic targets. Further research utilizing advanced genomic techniques is essential to elucidate the mechanistic roles of these pathways in tumor innervation.

genomics↗

bioRxiv2026

Computed atlas of the human GPCR-G protein signaling complexes

Experimental mapping of G protein-coupled receptors (GPCR)-G protein signaling coupling has illuminated hundreds of receptors, yet the coupling specificity of a large fraction of this large receptor family remains unknown, thereby preventing the development of new GPCR-targeting therapies. Here, we used AlphaFold3 (AF3) to predict the 3D structures of the human GPCRome in complex with heterotrimeric G proteins. We used experimental GPCR-G protein binding data to show that AF3 predictions significantly discriminate between positive and negative binders, and used 3D structural features to train a machine learning (ML) algorithm to predict coupling potency. Interpretation of the ML model helped discriminate universal features governing the strength of G protein coupling from those determining binding specificity. We computationally illuminated the coupling preferences of 180 non-olfactory GPCRs (non-OR) with previously unreported transduction mechanisms and experimentally validate the predicted couplings for multiple previously uncharacterized GPCRs, including QRFPR, GPR50, GPR37, GPR37L1 and GPRC5A. Our predictions established that Gi/o is the most prevalent coupling among non-OR GPCRs, which is often co-occurring with Gq/11 and, to a lesser extent, G12/13 signaling. Gs coupling is less common and restricted to specific clusters within the non-OR GPCRome phylogeny, likely due to stricter structural requirements for its binding. We also computed G protein complexes for over 400 ORs, establishing Gs as the most prevalent coupling. ORs are predicted to bind to Gs with a simpler interface compared to non-ORs, ultimately leading to energetically less stable complexes. Additionally, we predict recurrent bindings to Gq/11 and Gi/o proteins for ORs, suggesting potentially novel ORs signaling mechanisms. We exploited the GPCRome coupling atlas to interpret healthy and cancer expression data, revealing the coupling of most GPCR-G protein co-expressed pairs. This analysis highlights a richer coupling repertoire in healthy tissues compared to cancer, likely reflecting the high signaling requirements of specialized normal cell functions, which are lost in most cancer cells due to their de-differentiated state or under cancer selection processes. In summary, this study provides the first computational 3D atlas of the human GPCR-G protein transductome, thereby illuminating the signaling mechanisms of neglected GPCR classes and providing the basis for interpreting omics datasets from a myriad of pathological conditions, thus enabling the development of novel precision therapeutics.

bioinformatics↗

bioRxiv2025

Communication breakdown and evolution of the cancer cell

1We studied cell-cell interactions (CCIs) in large-scale transcriptomic datasets, which showed higher co-expression in cancer compared to healthy tissues. CCIs are more co-expressed than any other type of intracellular interaction and, likewise, they are the protein-protein interaction (PPI) class that is most co-evolved in sequenced genomes. Similar trends of stricter regulation and evolutionary pressure are observed when comparing extracellular versus intracellular interactions mediated by G protein Coupled Receptors (GPCRs), whose ligand interactions are also characterized by a higher mutational burden in later tumor stages when considering somatic mutations associated with tumor clonal evolution. CCIs undergo the most extensive rewiring of their tumor co-expression networks relative to healthy tissues, more so than any other PPI type, with a set of CCI hubs highly conserved across multiple tumor tissues, and a higher diversity on healthy ones. Cancer rewiring is also associated with the formation of recurrent circuits of co-expressed CCI pairs, represented by enriched network motifs such as triad or tetrad cliques. These act as integrative hotspots to facilitate the crosstalk of distinct processes and the interaction of the cancer cell with its tumor microenvironment (TME). Remarkably, many CCI circuits are significantly associated with patient survival and are predictive of patient response to immunotherapy. CCI circuits mapping to allograft rejection and inflammatory response inform immunotherapy response prediction, while those related to epithelial-mesenchymal transition are associated with poorer prognosis. Overall, we show that CCIs expression signatures could be effectively exploited to stratify patients and, at the same time, they highlight new combination therapeutic opportunities in personalized medicine settings.

bioinformatics↗

bioRxiv2024

Prediction and discovery of protein-protein direct interactions and stable complexes based on gene co-expression and co-evolution

In this study we employed a data-driven approach to explore the evolutionary and genetic determinants of protein direct interactions and stable complex formation in the human proteome. We found that simple co-evolutionary and co-expression metrics are highly informative of direct interactions and stable complexes. We used this information to train supervised binary classifiers to predict interactions either directly involved in the formation of a complex (as annotated in IntAct) or forming stable complexes (from Complex Portal). In the former task, our model was able to discriminate direct interactions with an AUROC=0.813, while in the latter it discriminated interaction forming stable complexes with an AUROC=0.964. In both cases, our approach outperformed String, that we employed as a baseline. Feature importance analysis revealed different contributions to the prediction of these distinct interaction types. Co-evolutionary features, in particular those referred to protein domains involved in interaction interfaces, are more important to discriminate direct interactions. On the other hand, co-expression features contributed more to the prediction of stable complexes. From these pairwise predictions we generated a proteome-wide network that we clustered to assess the recovery of known complexes from Complex Portal within network communities. We were able to recover known complexes at a higher accuracy compared to other approaches. In conclusion, we propose a new method able to discriminate direct interactions as well as forming stable complexes. This method can be used to stratify molecular interaction networks, as well as to perform discovery of new functional complexes at a proteome-wide scale.

bioinformatics↗