Biology subjects

Nouri, N.

Publications and source records attributed to Nouri, N..

10 recordsLinked to original sources

bioRxiv2025

An Agentic AI Framework for Ingestion and Standardization of Single-Cell RNA-seq Data Analysis

The proliferation of publicly available single-cell RNA sequencing (scRNA-seq) data has created significant opportunities in biomedical research. However, the reuse of these resources is constrained by a series of preparatory steps, including metadata extraction from primary literature, retrieval of datasets from corresponding repositories, and the subsequent manual execution of standardized downstream analysis. These tasks often require manual scripting and rely on fragmented workflows, limiting accessibility and increasing turnaround time. To address these challenges, we designed a two-component system consisting of an artificial intelligence (AI) agent coordinating an automated analysis pipeline. CellAtria (Agentic Triage of Regulated single-cell data Ingestion and Analysis) is an agentic AI framework that enables dialogue-driven, document-to-analysis automation through a chatbot interface. Built on a graph-based, multi-actor architecture, CellAtria integrates a large language model (LLM) with tool-execution capabilities to orchestrate the full lifecycle of data reuse. To support downstream analysis, CellAtria incorporates CellExpress, a co-developed pipeline that applies state-of-the-art scRNA-seq processing steps to transform raw count matrices into analysis-ready single-cell profiles. Thus, CellAtria provides computational skill-agnostic and time-efficient access to standardized single-cell data ingestion and analysis.

bioinformatics↗

bioRxiv2024

Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis

Immune-mediated diseases are characterized by aberrant immune responses, posing significant challenges to global health. In both inflammatory and autoimmune diseases, dysregulated immune reactions mediated by tissue-residing immune and non-immune cells precipitate chronic inflammation and tissue damage that is amplified by peripheral immune cell extravasation into the tissue. Chemokine receptors are pivotal in orchestrating immune cell migration, yet deciphering the signaling code across cell types, diseases and tissues remains an open challenge. To delineate disease-specific cell-cell communications involved in immune cell migration, we conducted a meta-analysis of publicly available single-cell RNA sequencing (scRNA-seq) data across diverse immune diseases and tissues. Our comprehensive analysis spanned multiple immune disorders affecting major organs: atopic dermatitis and psoriasis (skin), chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis (lung), ulcerative colitis (colon), IgA nephropathy and lupus nephritis (kidney). By interrogating ligand-receptor (L-R) interactions, alterations in cell proportions, and differential gene expression, we unveiled intricate disease- specific and common immune cell chemoattraction and extravasation patterns. Our findings delineate disease-specific L-R networks and shed light on shared immune responses across tissues and diseases. Insights gleaned from this analysis hold promise for the development of targeted therapeutics aimed at modulating immune cell migration to mitigate inflammation and tissue damage. This nuanced understanding of immune cell dynamics at the single-cell resolution opens avenues for precision medicine in immune disease management.

immunology↗

bioRxiv2024

scGFT: single-cell RNA-seq data augmentation using generative Fourier transformer

Integrating single-cell RNA sequencing (scRNA-seq) with artificial intelligence (AI) ushers in a new frontier for advanced therapeutic discoveries. However, for this synergy to achieve its full potential, extensive datasets are required to effectively train the AI component. This demand is particularly challenging when delving into rare diseases and uncommon cell types. Generative models designed to address data scarcity often face similar limitations due to their reliance on pre-training, inadvertently perpetuating a cycle of data inadequacy. To overcome this obstacle, we introduce scGFT (single-cell Generative Fourier Transformer), a train-free, cell-centric generative model adept at synthesizing single cells that exhibit natural gene expression profiles present within authentic datasets. Using both simulated and experimental data, we demonstrate the mathematical rigor of scGFT and validate its ability to synthesize cells that preserve the intrinsic characteristics delineated in scRNA-seq data. By streamlining single-cell data augmentation, scGFT offers a scalable solution to overcome data scarcity and holds the potential to advance AI-driven precision medicine.

bioinformatics↗

bioRxiv2024

Evidence for NR2F2/COUP-TFII involvement in human testis development

NR2F2 encodes COUP-TFII, an orphan nuclear receptor involved in mammalian gonadogenesis. COUP-TFII is expressed in the interstitial/stromal compartment of both fetal testes and ovaries, where it is required for developing steroidogenic lineages. Pathogenic variants in human NR2F2 are linked to testis formation in 46,XX individuals (46,XX disorders of sex development, 46,XX DSD). Such findings propose a regulatory role of COUP-TFII in the developing ovary, whereas its function in testis remains unknown. We evaluate the effect of a de novo heterozygous, predicted damaging, missense variant in NR2F2 (p.Arg246His) in a 46,XY under-masculinized boy. In-vitro assays show that the mutant protein significantly loses the inhibitory effect on NR5A1-mediated activation of both the LHB and INSL3 promoters. The data support the pathogenicity of the p.Arg246His variant in 46,XY DSD and a role for NR2F2 in human testis formation. In addition to NR5A1 and WT1, NR2F2 variants are thus associated with both 46,XX and 46,XY DSD. This expands the list of genes that function in both male and female sex development, which is originally thought to be regulated by two entirely different sets of genes.

molecular biology↗

bioRxiv2023

Comparative Analysis of Association Networks Using Single-Cell RNA Sequencing Data Reveals Perturbation-Relevant Gene Signatures

Single-cell RNA sequencing (scRNA-seq) data has elevated our understanding of systemic perturbations to organismal physiology at the individual cell level. However, despite the rich information content of scRNA-seq data, the relevance of genes to a perturbation is still commonly assessed through differential expression analysis. This approach provides a one-dimensional perspective of the transcriptomic landscape, risking the oversight of tightly controlled genes characterized by modest changes in expression but with profound downstream effects. We present GENIX (Gene Expression Network Importance eXamination), a novel platform for constructing gene association networks, equipped with an innovative network-based comparative model to uncover condition-relevant genes. To demonstrate the effectiveness of GENIX, we analyze influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) collected from recovered COVID-19 patients, shedding light on the mechanistic underpinnings of gender differences. Our methodology offers a promising avenue to identify genes relevant to perturbation responses in biological systems, expanding the scope of response signature discovery beyond differential gene expression analysis. HIGHLIGHTSO_LIConventional methods used to identify perturbation-relevant genes in scRNA-seq data rely on differential expression analysis, susceptible to overlooking essential genes. C_LIO_LIGENIX leverages cell-type-specific inferred gene association networks to identify condition-relevant genes and gene programs, irrespective of their specific expression alterations. C_LIO_LIGENIX provides insight into the gene-regulatory response to the influenza vaccine in naive and recovered COVID-19 patients, expanding on previously observed gender-specific differences. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/556872v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1837d3eorg.highwire.dtl.DTLVardef@1937860org.highwire.dtl.DTLVardef@c40114org.highwire.dtl.DTLVardef@22d3b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Apical polarity and actomyosin dynamics regulate Hippo signaling by controlling Kibra subcellular localization

Cell polarity and actomyosin networks are key features that organize cells of epithelial tissues and are known to regulate tissue growth. However, the mechanisms by which polarity cues and actomyosin cytoskeleton influence intracellular signaling cascades that control growth remain poorly understood. The Hippo pathway is an evolutionarily conserved regulator of tissue growth and is known to integrate inputs from both polarity and actomyosin components. An upstream activator of the Hippo pathway, Kibra, localizes into distinct pools at the junctional and medial regions of the apical cortex in epithelial cells, and medial accumulation was shown to promote Kibra activity. Here, we demonstrate that cortical Kibra distribution is controlled by a tug of war between apical polarity and actomyosin dynamics. We show that while the apical polarity network, in part via aPKC, tethers Kibra at the junctional cortex to silence its activity, medial actomyosin flows promote Kibra-mediated Hippo complex formation at the medial cortex, thereby activating the Hippo pathway. This study provides a mechanistic understanding of the relationship between the Hippo pathway, polarity, and actomyosin cytoskeleton and offers novel insights into how fundamental features of epithelial tissue architecture can serve as inputs into signaling cascades that control tissue growth, patterning, and morphogenesis.

cell biology↗

bioRxiv2022

CipA mediates complement resistance of Acinetobacter baumannii by formation of a Factor I-dependent quadripartite assemblage

Multidrug-resistant Acinetobacter baumannii is known to be one of the leading pathogens that cause severe nosocomial infections. To overcome eradication by the innate immune system during infection, A. baumannii developed a number of immune evasion strategies. Previously, we identified CipA as a plasminogen-binding and complement-inhibitory protein. Here we show that CipA strongly inhibits all three complement activation pathways and interacts with key complement components C3, C3b, C4b, C5, Factor B, Factor D, and in particular Factor I. CipA also targets function of the C5 convertase as cleavage of C5 was impaired. Systematic screening of CipA variants identified two separate binding sites for C3b and a Factor I-interacting domain located at the C-terminus. Structure predictions using AlphaFold2 and binding analyses employing CipA variants lacking Factor I-binding capability confirmed that the orientation of the C-terminal domain is essential for the interaction with Factor I. Hence, our analyses point to a novel, Factor I-dependent mechanisms of complement inactivation mediated by CipA of A. baumannii. Recrutiment of Factor I by CipA initiates the assembly of a quadripartite complex following binding of either Factor H or C4b-binding protein to degrade C3b and C4b, respectively. Loss of Factor I binding in a CipA-deficient strain, or a strain producing a CipA variant lacking Factor I-binding capability, correlated with a higher susceptibility to human serum, indicating that recruitment of Factor I enables A. baumannii to resist complement-mediated killing. Significance StatementThe Gram-negative bacterium Acinetobacter baumannii causes severe nosocomial infections and has developed various immune evasion strategies to overcome complement. The mechanisms how Acinetobacter baumannii successfully circumvents complement-mediated bacteriolysis are still poorly understood. Here, we show that the plasminogen-binding and complement inhibitory protein CipA terminates all three complement activation pathways. We describe a novel mechanism by which CipA cleaves the key complement components C3b and C4b in the presence of Factor H and C4b-binding protein by formation of a novel Factor I-dependent quadripartite complex. CipA, which has recently been successfully used for vaccination approaches, might represent an attractive target for the development of novel therapeutic interventions to block disorders with excessive hyperinflammatory complement activation.

microbiology↗

bioRxiv2020

Alignment free identification of clones in B cell receptor repertoires

Following pathogenic challenge, activated B cells rapidly expand and undergo somatic hypermutation, yielding groups of clonally related B-cells with diversified immunoglobulin receptors. Inference of clonal relationships based on the receptor sequence is an essential step in many adaptive immune receptor repertoire sequencing studies. These relationships are typically identified by a multi-step process that involves: (1) grouping sequences based on shared V and J gene assignments, and junction lengths, and (2) clustering these sequences using a junction-based distance. However, this approach is sensitive to the initial V(D)J gene assignments, which are error-prone, and fails to identify clonal relatives whose junction length has changed through accumulation of indels. Through defining a translation-invariant feature space in which we cluster the sequences, we develop an alignment-free clonal identification method that does not require gene assignments and is not restricted to a fixed junction length. This alignment-free approach has higher sensitivity compared to a typical junction-based distance method without loss of specificity and PPV. While the alignment-free procedure identifies clones that are broadly consistent with the junction-based distance method, it also identifies clones with characteristics (multiple V or J gene assignments or junction lengths) that are not detectable with the junction based distance method.

immunology↗

bioRxiv2019

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

MotivationAdaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell receptore (BCR) sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse junction region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. AvailabilitySource code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or later) in the Immcantation framework: www.immcantation.org under the CC BY-SA 4.0 license. Contactsteven.kleinstein@yale.edu

immunology↗

bioRxiv2017

Performance-optimized partitioning of clonotypes from high-throughput immunoglobulin repertoire sequencing data

MotivationDuring adaptive immune responses, activated B cells expand and undergo somatic hypermutation of their immunoglobulin (Ig) receptor, forming a clone of diversified cells that can be related back to a common ancestor. Identification of B cell clonotypes from high-throughput Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data relies on computational analysis. Recently, we proposed an automate method to partition sequences into clonal groups based on single-linkage clustering of the Ig receptor junction region with length-normalized hamming distance metric. This method could identify clonally-related sequences with high confidence on several benchmark experimental and simulated data sets. However, this approach was computationally expensive, and unable to provide estimates of accuracy for new data. Here, a new method is presented that address this computational bottleneck and also provides a study-specific estimation of performance, including sensitivity and specificity. The method uses a finite mixture modeling fitting procedure for learning the parameters of two univariate curves which fit the bimodal distributions of the distance vector between pairs of sequences. These distribution are used to estimate the performance of different threshold choices for partitioning sequences into clonotypes. These performance estimates are validated using simulated and experimental datasets. With this method, clonotypes can be identified from AIRR-seq data with sensitivity and specificity profiles that are user-defined based on the overall goals of the study.\n\nAvailabilitySource code is freely available at the Immcantation Portal: www.immcantation.com under the CC BY-SA 4.0 license.\n\nContactsteven.kleinstein@yale.edu

immunology↗