Biology subjects

Ahmadian, M.

Publications and source records attributed to Ahmadian, M..

8 recordsLinked to original sources

bioRxiv2026

Identification of Kinome Biomarkers and Therapeutic Rescue in cGMP-Linked Retinal Degeneration

Inherited retinal degenerations, such as Retinitis Pigmentosa (RP) and Leber's congenital amaurosis (LCA), are major causes of progressive and irreversible vision loss. The large number of genes associated with these disorders makes their clinical and molecular characterization difficult, while systemic biomarkers for disease progression or treatment response remain limited. These challenges were addressed by targeting cGMP-associated mechanisms shared by RP and LCA and integrating systemic biomarker profiling with retinal outcomes. Kinase activity analysis (kinome analysis) in peripheral blood mononuclear cells distinguished healthy and diseased states and captured therapy-associated signatures in rd10 mice treated with LP-CN03, a liposomal formulation of the cGMP analog CN03. This first systemic analysis in a therapeutically responsive animal model highlights kinome analysis as a potential translational readout. Given shared cGMP signaling in RP and AIPL1-associated LCA, LP-CN03's therapeutic potential was further evaluated in a new CRISPR-Cas9-generated Aipl1-/- mouse model. Treatment with LP-CN03 preserved retinal structure and function, as shown by electroretinography, longitudinal optical coherence tomography, fundus imaging, optomotor testing, and photoreceptor-specific gene expression. Together, these findings provide independent in vivo evidence for the neuroprotective effect of LP-CN03 in both cGMP-related RP and LCA, also highlighting the translational value of kinome profiling.

neuroscience↗

bioRxiv2026

Large-scale automated detection reveals pervasive sex imbalance in biomedical research

Sex is a critical biological variable that impacts disease risk, progression, and treatment response across virtually every organ system. However, decades of biomedical research have relied primarily on male study subjects, leaving large gaps in our understanding of female-specific disease biology. Quantifying the extent of this imbalance across thousands of disease areas and millions of publicly available biological samples has remained computationally intractable. Here, we present a multimodal computational framework that infers the biological sex of [~]230,000 publicly available human transcriptome samples and links inferred sex labels to disease terms extracted from [~]9,000 associated study records and [~]5,000 publication abstracts to quantify sex imbalance at scale. Applying this approach revealed that the majority of disease terms with the largest research-derived sex imbalance are skewed toward male representation, including areas with no known biological justification for that imbalance. After adjusting for global sex-specific disease prevalence to isolate biologically unjustified imbalance, up to 58% of all disease terms showed male-leaning association. Diseases including glioblastoma, cirrhosis, idiopathic pulmonary fibrosis, and schizophrenia emerged as critically understudied in females despite affecting both sexes comparably. These findings provide a principled, data-driven basis for prioritizing compensatory research efforts and offer a reusable framework for ongoing monitoring of sex representation in the biomedical literature. HighlightsO_LISkewed male and female study subject representation in biomedical research is the result of decades of studies conducted without adequate female representation. C_LIO_LIWe developed an automated, multimodal framework to estimate the sex imbalance across thousands of disease terms using metadata from [~]230,000 transcriptomics samples and their associated [~]9,000 studies and [~]5,000 publications. C_LIO_LIOur approach identifies non-sex-specific disease research areas that have been studied using an unbalanced sex demographic. These areas need compensatory and balanced studies to understand sex differences. C_LI

genomics↗

bioRxiv2024

Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata

Reusing massive collections of publicly available biomedical data can significantly impact knowledge discovery. However, these public samples and studies are typically described using unstructured plain text, hindering the findability and further reuse of the data. To combat this problem, we propose txt2onto 2.0, a general-purpose method based on natural language processing and machine learning for annotating biomedical unstructured metadata to controlled vocabularies of diseases and tissues. Compared to the previous version (txt2onto 1.0), which uses numerical embeddings as features, this new version uses words as features, resulting in improved interpretability and performance, especially when few positive training instances are available. Txt2onto 2.0 uses embeddings from a large language model during prediction to deal with unseen-yet-relevant words related to each disease and tissue term being predicted from the input text, thereby explaining the basis of every annotation. We demonstrate the generalizability of txt2onto 2.0 by accurately predicting disease annotations for studies from independent datasets, using proteomics and clinical trials as examples. Overall, our approach can annotate biomedical text regardless of experimental types or sources. Code, data, and trained models are available at https://github.com/krishnanlab/txt2onto2.0. Key pointsO_LIWe developed txt2onto 2.0, a computational method that combines language models and machine learning to annotate public samples and studies with standardized tissue and disease terms, with a focus on interpretability and explainability. C_LIO_LITxt2onto 2.0 uses word/phrase occurrence statistics to represent sample/study metadata, train machine learning models, and predict terms in controlled vocabularies to annotate each sample and study. This approach allows the model to keep track of predictive words related to model decisions and easily separate informative from uninformative words. C_LIO_LITxt2onto 2.0 outperforms its predecessor, txt2onto 1.0, in tissue and disease annotation, especially when training data is limited. C_LIO_LIThe predictive features learned by txt2onto 2.0 are highly interpretable. These features not only include explicit mentions of the actual disease or tissue terms but also related biomedical concepts, including words that are unseen by the model during training. C_LIO_LIAlthough trained on metadata of transcriptomes, txt2onto 2.0 is capable of annotating disease and tissue for any kind of biomedical metadata, making it a versatile tool for sample and study annotation. C_LI

bioinformatics↗

bioRxiv2024

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors -- one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled -- and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

cancer biology↗

bioRxiv2022

A Platform-Independent Framework for Phenotyping of Multiplex Tissue Imaging Data

Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-accurate representations of the input data, but exhibit signifi-cantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

bioinformatics↗

bioRxiv2022

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular architectures by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering for the spatial contribution. As such, none of the existing approaches has fully accounted for the heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage the spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular architectures. Then, functional principal component analysis (FPCA) targeting sparse data is applied to estimate FPC scores which are then predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, holding other clinical variables constant. Using an ovarian cancer dataset (n = 114) as a case study, we found that the spatial heterogeneity in the TME immune compositions of CD19+ B cells, CD4+ T cells, CD8+ T cells, and CD68+ macrophages, had a significant non-zero effect on the overall survival (p = 0.027). In the simulations studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

bioinformatics↗

bioRxiv2019

Effects of cLFchimera, a recombinant antimicrobial peptide, on intestinal morphology, microbiota, and gene expression of immune cells and tight junctions in broiler chickens challenged with C. perfringens

The current study was conducted to investigate the effects of cLFchimera, a recombinant antimicrobial peptide (AMP), on various productive performance and gut health attributes of broilers experimentally challenged with Clostridium perfringens (Cp). Three hundred and sixty 1-day-old chickens were randomly allocated to 4 treatments of 6 replicates as follows: T1) unchallenged group fed with corn-soybean meal (CSM) without Cp challenge and additives; T2) challenge group fed with CSM and challenged with Cp without any additives; T3) peptide group challenged with NE supplemented with 20 mg cLF36/kg diet (AMP); T4) antibiotic group challenged with NE and supplemented with 45 mg antibiotic (bacitracin methylene disalicylate)/kg diet (antibiotic). Birds had free access to feed and water, sampling for villi morphology and ileal microbiota were performed on days 10 and 22, while jejunal section was sampled for gene expression of cytokines, tight junctions proteins, and mucin only on day 22. Results showed that AMP ameliorated NE-related lesion in the jejunum and ileum and reduced mortality in challenged birds compared to challenge group with Cp without any additives. Also, supplementing challenged birds with AMP improved growth performance and reconstructed villi morphology. While antibiotic non-selectively reduced the count of bacteria, AMP positively restored ileal microflora in favor of good bacteria (i.e. Bifidobacteria spp. and Lactobacillus spp.). AMP beneficially regulated the expression of cytokines, junctional proteins, and mucin in the jejunum of challenged birds with Cp. Since cLFchimera ameliorated NE lesion score, reduced mortality, improved productive performance and gut health attributes in chickens compared to challenged group and also were mostly similar with those of antibiotics and therefore, it could be concluded that this chimeric peptide can be a worthy candidate to substitute growth promoter antibiotics, while more research is required to unveil the exact mode of action of this synthetic peptide. Author summaryNecrotic enteritis (NE) is a detrimental enteric disease in the poultry industry worldwide. The etiological factor of this disease is Clostridium perfringens, which is gram-positive anaerobic bacterium. This bacterium is common inhabitant of the intestine in lower counts (105), but it becomes pathogenic in higher counts and can secrete NetB toxin, which is the main cause of inducing NE in broilers. Due to the emergence of antibiotic-resistant bacteria, new generation of antimicrobial additives such as antimicrobial peptides (AMPs) has been introduced to the poultry industry. AMPs are small molecules with 12-50 amino acids having antibacterial activity. Recently, we extracted new AMP from camel milk, expressed in E. coli, refined and lyophilized to produce purified peptides. The current study investigated the effects of this peptide on prevention of NE in broilers. Results showed that AMP ameliorated lesion scores in the intestine and reduced mortality in challenged birds. AMP improved growth performance and reconstructed villi morphology in NE-challenged broilers. While antibiotic non-selectively reduced the count of bacteria, AMP positively restored ileal microflora. AMP beneficially regulated the expression of cytokines, junctional proteins, and mucin in the jejunum of NE-challenged birds.

pathology↗

bioRxiv2019

A Hybrid Stochastic Model of the Budding Yeast Cell Cycle

The growth and division of eukaryotic cells are regulated by complex, multi-scale networks. In this process, the mechanism controlling cell cycle progression has to be robust against inherent noise in the system. In this paper, a hybrid stochastic model is developed to study the effects of noise on the control mechanism of the budding yeast cell cycle. The modeling approach leverages, in a single multi-scale model, the advantages of two regimes: 1) the computational efficiency of a deterministic approach, and 2) the accuracy of stochastic simulations. Our results show that this hybrid stochastic model achieves high computational efficiency while generating simulation results that match very well with published experimental measurements.

systems biology↗