Biology subjects

Abdi, A.

Publications and source records attributed to Abdi, A..

9 recordsLinked to original sources

bioRxiv2026

PER2- and state-dependent transcriptional programs gate neural stem cell proliferation with niche-specific circadian autonomy

Adult neural stem cells (NSCs) in the mouse brain are predominantly quiescent, with activation tightly regulated to balance neurogenesis and stem cell maintenance. Circadian clocks temporally organize core cellular processes, potentially gating NSC activation. We examined adult NSC temporal dynamics across the day and observed rhythmic expression of core circadian clock components BMAL1 and PER2 in both mammalian niches, the subgranular zone (SGZ) of the dentate gyrus and the subventricular zone (SVZ). While cell cultures derived from both niches show BMAL1 and PER2 protein, only SGZ-derived NSCs exhibit synchronized self-sustained core clock oscillations across the population. Comparative analyses of WT and Per2 knockout NSCs revealed state-specific, circadian clock-dependent oscillatory transcriptional programs. This identified ASCL1 and CCND1 as candidate regulators of cell-cycle coordination, with daytime accumulation preceding a nighttime S-phase peak. The temporal control of S-phase entry in NSCs was abolished in Per2 knockout mice. Together, our findings reveal state-specific, PER2-dependent circadian regulation of adult NSCs in both neurogenic niches, and a specific dependence on external stimuli for synchronization in the SVZ.

cell biology↗

bioRxiv2026

In vitro dormancy models improve ability to predict treatment response in severe marmoset tuberculosis lesions

Tuberculosis lesions are structurally and physiologically heterogeneous, creating microenvironments that restrict antibiotic penetration and alter bacterial susceptibility. This heterogeneity remains a major impediment to shortening tuberculosis treatment, as it is difficult to predict which regimens will sterilize bacteria in these hard-to-treat lesion niches. Many in vitro systems and commonly used animal models fail to recapitulate the necrotic, caseous lesion pathology associated with treatment failure and relapse. Here, we show that resource-efficient in vitro assays engineered to mimic key lesion microenvironments, including those with lipid-rich, caseum-like matrices with controlled oxygenation and pH, generate drug-response metrics that predict lesion-level treatment responses in a non-human primate model. We stratified individual marmoset lesions by baseline 2-deoxy-2-[18F] fluoro-D-glucose ([18F] FDG) positron emission tomography/computed tomography (PET/CT) features, then quantified drug potency and interactions across three lipid-induced dormancy (LIDs) conditions under both equipotent and lesion pharmacokinetic-informed dosing schemes. LIDs-based and pharmacokinetic-informed measurements aligned more closely with treatment responses in severe lesions than conventional in vitro data and recapitulated interaction patterns consistent with known regimen performance. Integrating imaging-derived and in vitro features in multivariate, sequential computational models improved predictive accuracy over baseline imaging alone and identified a subset of informative in vitro metrics. Together, these results establish a scalable framework linking lesion-mimicking assays to lesion-specific outcomes, enabling earlier, more cost-effective prioritization of pre-clinical regimens aimed at sterilizing hard-to-treat tuberculosis lesions and shortening therapy.

molecular biology↗

bioRxiv2025

A Neyman-Pearson Framework for Modeling Cellular Decision Making Using Single-Cell TNF-NF-κB Signaling Data

Cells make hard calls under noise. When signaling is abnormal, those calls can go wrong and drive pathological conditions and diseases. In this research, we develop a Neyman-Pearson (NP) detection-theory framework that maximizes probability of detection (PD) for a chosen false alarm probability (PFA), without requiring prior probabilities, using experimental single-cell measurements of NF-{kappa}B responses to tumor necrosis factor (TNF), a critical pathway involved in cell survival, apoptosis, immune signaling, and stress response, in wild-type and A20-deficient fibroblasts. We model log-responses as (multi)variate Gaussian and compute optimal thresholds, PD-PFA trade-offs, and ROC curves at 30 minutes and 4 hours. The NP framework captures expected biology: PD increases with TNF dose; wild-type cells outperform A20-/- at matched conditions; and combining two time points (bivariate analysis) improves detection (e.g., for 0.0052 vs. 0.2 ng/mL, PD rises from 0.71 (30 minutes) and 0.42 (4 hours) to 0.80 at PFA = 0.1). The analysis recovers expected biology (higher TNF causes higher detectability; negative feedback lowers late responses) and flags cases where decision quality degrades (e.g., perturbations that blunt separation between conditions). The same recipe extends to multivariate readouts without changing the logic. Overall, the NP detection framework provides a compact, quantitative score of pathway performance and failure. It turns noisy single-cell readouts into actionable decision metrics that compare doses, time points, and perturbations, and ultimately, can help explain when and how cellular decisions drift toward pathology.

systems biology↗

bioRxiv2024

Identification of novel PfEMP1 variants containing domain cassettes 11, 15 and 8 that mediate the Plasmodium falciparum virulence-associated rosetting phenotype

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) is a diverse family of variant surface antigens, encoded by var genes, that mediates binding of infected erythrocytes to human cells and plays a key role in parasite immune evasion and malaria pathology. The increased availability of parasite genome sequence data has revolutionised the ability to study PfEMP1 diversity across multiple P. falciparum isolates. However, making functional sense of genomic data relies on the ability to infer binding phenotype from var gene sequence. For P. falciparum rosetting, the binding of infected erythrocytes to uninfected erythrocytes, the analysis of var gene/PfEMP1 sequences encoding the phenotype is limited, with only eight rosette-mediating PfEMP1 variants described to date. These known rosetting PfEMP1 variants fall into two types, characterised by N- terminal domains known as "domain cassette" 11 (DC11) and DC16. Here we test the hypothesis that DC11 and DC16 are the only PfEMP1 types in the P. falciparum genome that mediate rosetting, by examining a set of thirteen recent culture-adapted Kenyan parasite lines. We first analysed the var gene/PfEMP1 repertoires of the Kenyan lines and identified an average of three DC11 or DC16 PfEMP1 variants per genotype. In vitro rosette selection of the parasite lines yielded four with a high rosette frequency, and analyses of their var gene transcription, infected erythrocyte PfEMP1 surface expression, rosette disruption and erythrocyte binding function identified four novel rosette- mediating PfEMP1 variants. Two of these were of the predicted DC11 type (one showing the dual rosetting/IgM-Fc-binding phenotype), whereas two contained DC15 (DBL1.2-CIDR1.5b) a PfEMP1 type not previously associated with rosetting. We also showed that a Thai parasite line expressing a DC8-like PfEMP1 binds to erythrocytes to form rosettes. Hence, these data expand current knowledge of rosetting mechanisms and emphasize that the PfEMP1 types mediating rosetting are more diverse than previously recognised. Author summary Malaria is an infectious tropical disease caused by the parasite Plasmodium falciparum that kills more than half a million people every year, mostly young children in sub-Saharan Africa. Life- threatening episodes of malaria are characterised by huge numbers of parasitised red blood cells in the infected host, many of which bind to blood vessel walls and block blood flow, causing tissue damage and organ failure. Sometimes parasitised red cells also bind to uninfected red cells to form clusters of cells called rosettes, which make the blockage of blood flow in vital organs even worse. Previous research has begun to decipher how parasitised red cells bind to uninfected red cells to form rosettes, but little is yet known about the process. Here we identify some new versions of the "sticky proteins" (adhesion molecules) that are made by malaria parasites and displayed on the surface of infected red cells to bring about rosette formation. The rosette-mediating adhesion molecules are members of a large family, and we identify here a few characteristic types within this family that mediate rosetting. This work is an important step towards the goal of understanding how malaria parasite rosettes form rosettes in order to develop preventions or treatments to reverse rosetting and reduce the number of people dying from severe malaria.

microbiology↗

bioRxiv2023

Single Cell Measurements and Modeling and Computation of Decision Making Errors in a Molecular Signaling System with Two Output Molecules

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, the cell may incorrectly perceive the signals. It may mistakenly behave as if there is a signal - although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system that has two outputs, and introduce and develop methods to model and compute key cell decision making parameters based on the two outputs, and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor {kappa}B (NF{kappa}B) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single cell measured concentration levels of NF{kappa}B and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of a signaling system, one can learn more about complex cellular decision making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.

systems biology↗

bioRxiv2022

Consuming Royal Jelly Causes Mosquitoes to Shift Into and Out of Their Overwintering Dormancy

Females of the Northern house mosquito, Culex pipiens, enter an overwintering dormancy, or diapause, in response to short day lengths and low environmental temperatures. Diapausing female mosquitoes feed exclusively on sugar-rich products rather than human or animal blood, thereby reducing disease transmission. During diapause, Major Royal Jelly Protein 1 (MRJP1) is upregulated in females of Cx. pipiens. This protein is highly abundant in royal jelly, a substance produced by honey bees (Apis mellifera), that is fed to future queens throughout larval development and stimulates longevity and fecundity. However, the role of MRJP1 in Cx. pipiens is unknown. We investigated how supplementing the diets of both diapausing and nondiapausing females of Cx. pipiens with royal jelly affects gene expression, egg follicle length, fat content, protein content, longevity, and metabolic profile. We found that feeding royal jelly to long day-reared females significantly reduced the egg follicle lengths of females and switched their metabolic profiles to be similar to diapausing females. In contrast, feeding royal jelly to short day-reared females significantly reduced lifespan and switched their metabolic profile to be similar nondiapausing mosquitoes. Moreover, RNAi directed against MRJPI significantly increased egg follicle length of short day-reared females, suggesting that these females averted diapause, although RNAi against MRJP1 also extended the lifespan of short day-reared females. Taken together, our data show that consuming royal jelly reverses the seasonal responses of Cx. pipiens and that these responses are likely mediated in part by MRJP1. Summary StatementConsuming royal jelly reversed seasonal differences in physiological states, lifespan and metabolic profiles in females of the Northern house mosquito, a major vector of West Nile virus.

physiology↗

bioRxiv2021

Learning Feedback Molecular Network Models Using Integer Linear Programming

Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding the effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network models constructed using literature and pathway databases may not accurately predict and reproduce experimental network data. This can be due to the incompleteness of literature on molecular pathways, the resources used to construct the networks, or some conflicting information in the resources. In this paper, we propose a network learning approach via an integer linear programming formulation that can efficiently incorporate biological dynamics and regulatory mechanisms of molecular networks in the learning process. Moreover, we present a method to properly take into account the feedback paths, while learning the network from data. Examples are also provided to show how one can apply the proposed learning approach to a network of interest. Overall, the proposed methods are useful for reducing the gap between the curated networks and experimental network data, and result in calibrated networks that are more reliable for making biologically meaningful predictions.

systems biology↗

bioRxiv2021

Exploring Extreme Signaling Failures in Intracellular Molecular Networks

Developing novel methods for the analysis of intracellular signaling networks is essential for understanding interconnected biological processes that underlie complex human disorders. A fundamental goal of this research is to quantify the vulnerability of a signaling network to the dysfunction of one or multiple molecules, when the dysfunction is defined as an incorrect response to the input signals. In this study, we propose an efficient algorithm to identify the extreme signaling failures that can induce the most detrimental impact on the physiological function of a molecular network. The algorithm basically finds the molecules, or groups of molecules, with the maximum vulnerability, i.e., the highest probability of causing the network failure, when they are dysfunctional. We propose another algorithm that efficiently accounts for signaling feedbacks in this analysis. The algorithms are tested on two experimentally verified ERBB and T cell signaling networks. Surprisingly, results reveal that as the number of concurrently dysfunctional molecules increases, the maximum vulnerability values quickly reach to a plateau following an initial increase. This suggests the specificity of vulnerable molecule (s) involved, as a specific number of faulty molecules cause the most detrimental damage to the function of the network. Increasing a random number of simultaneously faulty molecules does not further deteriorate the function of the network. Such a group of specific molecules whose dysfunction causes the extreme signaling failures can better elucidate the molecular mechanisms underlying the pathogenesis of complex trait disorders, and can offer new insights for the development of novel therapeutics.

systems biology↗

bioRxiv2019

A Primer on Modeling and Measurement of Signaling Outcomes Affecting Decision Making in Cells: Methods for Determining Optimal and Incorrect Outcomes in Noisy Biochemical Dynamics

Characterization of decision makings in a cell in response to received signals is of high importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic and statistical signal processing methods and metrics to model the precision of signaling decisions, given uncertainty, using single cell data. First, we introduce erroneous decisions that may result from signaling processes, and identify false alarm and miss event that are associated with such decisions. Then, we present an optimal decision strategy which minimizes the total decision error probability. The optimal decision threshold or boundary is determined using the maximum likelihood principle that chooses the hypothesis under which the data are most probable. Additionally, we demonstrate how graphing receiver operating characteristic curve conveniently reveals the trade-off between false alarm and miss probabilities associated with different cell responses. Furthermore, we extend the introduced signaling outcome modeling framework to incorporate the dynamics of biochemical processes and reactions in a cell, using multi-time point measurements and multi-dimensional outcome analysis and decision making algorithms. The introduced multivariate signaling outcome modeling framework can be used to analyze several molecular species measured at the same or different time instants. We also show how the developed binary outcome analysis and decision making approach can be extended to include more than two possible outcomes. To show how the overall set of introduced models and methods can be used in practice and as an example, we apply them to single cell data of an intracellular regulatory molecule called Phosphatase and Tensin homolog (PTEN) in a p53 system, in wild-type and abnormal, e.g., mutant cells. These molecules are involved in tumor suppression, cell cycle regulation and apoptosis. The unified signaling outcome modeling framework presented here can be applied to various organisms ranging from simple ones such as viruses, bacteria, yeast, and lower metazoans, to more complex organisms such as mammalian cells. Ultimately, this signaling outcome modeling approach can be useful for better understanding of transition from physiological to pathological conditions such as inflammation, various cancers and autoimmune diseases. Brief SummaryCells are supposed to make correct decisions, i.e., respond properly to various signals and initiate certain cellular functions, based on the signals they receive from the surrounding environment. Due to signal transduction noise, signaling malfunctions or other factors, cells may respond differently to the same input signals, which may result in incorrect cell decisions. Modeling and quantification of decision making processes and signaling outcomes in cells have emerged as important research areas in recent years. Here we present univariate and multivariate data-driven statistical models and methods for analyzing dynamic decision making processes and signaling outcomes. Furthermore, we exemplify the methods using single cell data generated by a p53 system, in wild-type and abnormal cells.

systems biology↗