Biology subjects

Babaei, A.

Publications and source records attributed to Babaei, A..

3 recordsLinked to original sources

bioRxiv2026

Pan-pediatric brain tumor microRNA networks define immune microenvironment states and responses to CAR T-cell therapy

Pediatric central nervous system (CNS) tumors remain a leading cause of cancer-related mortality in children, yet molecular programs that shape their immune microenvironments and response to immunotherapy remain poorly defined. Because microRNAs (miRNAs) coordinately regulate gene expression across multiple pathways, we hypothesized that miRNA programs may capture broader immune states than individual immune-regulatory molecules. We integrated small RNA and bulk transcriptomic profiles from diffuse midline glioma, medulloblastoma, ependymoma, and low-grade glioma to identify miRNA programs associated with tumor immune states. Across tumor histologies, dysregulated miRNAs were associated with T-cell, natural killer cell, antigen-presentation, and myeloid programs. Diffuse midline glioma exhibited a distinct miRNA program associated with reduced lymphocyte signatures and increased suppressive myeloid features. Longitudinal profiling of immunocompetent glioma models during B7-H3 CAR T-cell therapy revealed dynamic remodeling of miRNA and immune transcriptional programs. Human tumor-associated miRNAs were significantly enriched among miRNAs associated with CAR T response in vivo. Functional interrogation of miR-149 demonstrated enhanced CAR T-cell expansion and sustained antitumor activity during repeated antigen stimulation. Together, these data define miRNA programs associated with immune states across pediatric CNS tumors and identify miR-149 as a candidate regulator of CAR T-cell function, providing a genomic framework for developing miRNA-directed strategies to improve CAR T-cell therapy.

cancer biology↗

bioRxiv2026

Ecological Network Inference Reveals 737 Cross-Kingdom Associations Structuring Human Microbiomes

The human microbiome is a complex, multikingdom ecosystem where bacteria and fungi cohabit and interact. Despite their ecological and clinical significance, cross-kingdom dynamics remain poorly characterized due to dominant single-kingdom research approaches. To understand the principles structuring multi-kingdom microbial communities, we applied the sparse inference method SpiecEasi to 45 publicly available samples from the gastrointestinal tract, skin, and oral cavity. Bacterial (16S rRNA) and fungal (ITS) sequencing data were processed using QIIME2, managed in phyloseq, and co-occurrence networks were inferred via SpiecEasi with Meinshausen- Buhlmann estimation. To validate robustness, we employed SparCC as a secondary inference method and performed 100 bootstrap iterations. Body site stratification controlled for environmental confounders. Our analysis revealed a microbial network of 5,023 taxa (5,020 bacterial, 3 fungal) connected by 30,478 significant associations. Crucially, we identified 737 robust bacterial-fungal interkingdom interactions (689 positive, 48 negative) confirmed by both inference methods. The network exhibited sparse connectivity (density = 0.0024) and modular structure (modularity = 0.45). Hub analysis identified 15 keystone taxa, including Bacteroides uniformis and Faecalibacterium prausnitzii. Interaction patterns were body-site-specific (P < 0.001), with the gastrointestinal tract showing the highest interkingdom connectivity (385 edges). This study provides systematic evidence that bacterial-fungal interactions are abundant and integral to human microbiome architecture. The discovery of 737 cross-kingdom associations challenges the prevailing single-kingdom paradigm and advocates for an integrated multikingdom perspective. These interactions, particularly those mediated by keystone hubs, represent novel targets for microbiome-based therapeutics and diagnostics. ImportanceThis study challenges the prevailing single-kingdom paradigm in microbiome research by demonstrating that bacterial-fungal interactions are abundant and integral to human microbiome architecture. The discovery of 737 cross-kingdom associations across three body sites provides a foundational resource for understanding multikingdom microbial ecology. The identification of keystone bacterial hubs--particularly Bacteroides uniformis and Faecalibacterium prausnitzii--as central connectors in interkingdom networks opens new avenues for microbiome-based therapeutics and diagnostics. Our integrated analytical framework, combining SpiecEasi and SparCC with body site stratification, offers a robust methodological template for future cross-kingdom studies.

microbiology↗

bioRxiv2026

Large-Scale Assessment of NF1 Single Amino Acid Variants as HLA Class I Neoantigens

Neoantigens are cancer-specific antigens arising from genomic alterations. Single Amino Acid Variants (SAAVs) represent a primary class of these neoantigens. To evaluate the therapeutic potential of Neurofibromin 1 (NF1)-derived SAAVs - given that NF1 is frequently mutated in malignant brain tumors - we prioritized the 40 NF1 SAAVs determined to be HLA-A*02:01 binders using computational prediction coupled with experimental validation. To validate these predicted neoepitopes, we employed a two-tiered experimental approach in HLA-A*02:01 homozygous U87-MG cells. We first synthesized minigene constructs encoding the predicted neoepitopes, introduced them via lentiviral transfection and confirmed their expression by mass spectrometry (MS). Subsequently, we performed endogenous validation using pan-HLA immunoprecipitation mass spectrometry (IP-MS), confirming 4 (10 neoepitopes) of the 40 candidate SAAVs. We observed a discrepancy between in silico predictions and the observed sequences. Our endogenous peptidomics further revealed conserved peptide motifs and demonstrated that peptide selection for HLA presentation is transient. While our study substantiates the therapeutic feasibility of T-cell immunotherapies targeting NF1 mutations, these results underscore a limitation in current computational prediction. Our study highlights the necessity of experimental validation to refine neoantigen prioritization strategies.

immunology↗