Biology subjects

Afzal, A.

Publications and source records attributed to Afzal, A..

7 recordsLinked to original sources

bioRxiv2026

PBF/PTTG1IP coordinates focal adhesion formation, polarity and cell motility

Directional cell migration requires cells to sense extracellular cues and coordinate adhesion, cytoskeletal remodelling and polarity, yet the molecular regulators that integrate these events remain incompletely defined. PTTG1-binding factor (PBF/PTTG1IP) is a transmembrane glycoprotein extensively characterised in pathological overexpression and cancer models, where elevated expression promotes tumourigenic cellular phenotypes. However, its endogenous physiological function remains poorly understood. Here, separate enrichment analyses of transcriptomic and phosphoproteomic profiles from PBF-overexpressing cells collectively highlighted cell adhesion, extracellular matrix organisation, cytoskeletal regulation and cell motility as major PBF-associated programmes. Using a novel Pbf knockout mouse model, we show that primary Pbf-KO mouse embryonic fibroblasts exhibit impaired migration and invasion. PBF loss reduced fibronectin adhesion, disrupted focal adhesion number, size and distribution, attenuated FAK Tyr397 phosphorylation and delayed early adhesion formation. Live-cell imaging and Golgi orientation assays further revealed altered actin organisation and impaired front-rear polarity in Pbf-KO cells. Key phenotypes were conserved in CRISPR-Cas9 PBF-KO human thyroid cancer cells, while targeted PBF re-expression restored migration in Pbf-KO MEFs. We thus identify PBF as an endogenous regulator of cell adhesion, polarity and directional motility, providing a framework for understanding how pathological PBF overexpression may promote invasive behaviour in cancer.

cell biology↗

bioRxiv2026

Biological Network Organization, Not Generic Graph Topology, Drives Graph-Based Gene Essentiality Prediction

Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but it remains unclear whether their performance gains arise from biologically meaningful connectivity or generic graph structure. Here, we systematically evaluate the role of network information in gene essentiality prediction using 2,741 genes across three tissues. We compare GNNs to feature-only baselines, including multilayer perceptron (MLP) and random forest (RF) methods, under a strict gene-level 5-fold cross-validation scheme to prevent information leakage. To isolate the role of network information, we assess models on the STRING protein-protein interaction network, a degree-preserving shuffled network, and a fully randomized network, with and without network-derived features. GNNs outperform feature-only models, reducing mean squared error and improving Matthews correlation coefficient across all tissues. However, these gains depend critically on biologically structured connectivity: performance degrades substantially under randomized topology and is not preserved by degree-constrained rewiring. Network features are largely redundant when using biologically meaningful graphs, as their information is recovered through message passing, but become important when topology is uninformative. Per-gene analyses reveal uniformly low correlations across models, highlighting intrinsic limits imposed by data variability. Graph Transformer models incorporating global attention do not outperform standard GNNs, indicating that predictive signals are predominantly local. Together, these results show that predictive gains arise from biologically structured connectivity rather than generic graph topology.

systems biology↗

bioRxiv2024

Phytochemical Analysis and Cytotoxic Effects of Phyllanthus polygonoides Leaf Extracts on Hepatocellular Carcinoma: A Combined In-Vitro and In-Silico Approach

The authors have withdrawn their manuscript owing to the Paper was mistakenly uploaded by the students and the paper was having a lots of mistake and fake data. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2024

ARYANA-BS: Context-Aware Alignment of Bisulfite-Sequencing Reads

MotivationDNA methylation plays a crucial role in biological processes, including imprinting, development, inflammation, and several disorders, such as cancer. Bisulfite sequencing (BS) is the gold standard for single-base resolution in measuring DNA methylation. This process involves treating genomic DNA with sodium bisulfite, followed by polymerase chain reaction (PCR), converting unmethylated cytosines to thymines (C to T) and guanines to adenines (G to A). However, aligning reads obtained through next-generation sequencing (NGS) of the converted DNA is challenging due to the high number of mismatches caused by these conversions. Various BS aligners aim to simplify BS read alignment to conventional DNA alignment by modifying the reference genome and/or reads. Methods include three-letter alignment and wild-card alignment, each with its limitations. This work introduces a novel BS aligner, ARYANA-BS, which departs from conventional DNA aligners by considering base alterations in BS reads within its alignment engine. Leveraging well-established DNA methylation patterns in different genomic contexts, ARYANA-BS generates five indexes from the reference, aligns each read to all indexes, and selects the hit with the minimum penalty. To further enhance alignment accuracy, an optional EM step has been developed, incorporating methylation probability information in the decision-making process for the index with the minimum penalty for each read. The presented approach seeks to improve the accuracy of BS read alignment by accounting for the intricacies of DNA methylation patterns in diverse genomic contexts. ResultsOur experimental results, based on both simulated and real data, demonstrate that ARYANA-BS achieves state-of-the-art accuracy while maintaining competitive speed and memory usage. AvailabilityThe source code of ARYANA and ARYANA-BS, the read simulator for both normal and bisulfite-treated reads, SAM file analyzer which is used for post processing of the alignment penalties, and test procedures for benchmarking different aligners using simulated and real data, are publicly available in https://github.com/hnikaein/aryana. Contactsharifi@sharif.edu Supplementary informationSupplementary data are available at Journal Name online.

bioinformatics↗

bioRxiv2023

pH-responsive upconversion mesoporous silica nanospheres for combined multimodal diagnostic imaging and targeted photodynamic and photothermal cancer therapy

Photodynamic therapy (PDT) and photothermal therapy (PTT) have garnered considerable interest as non-invasive cancer treatment modalities. However, these approaches remain limited by low solubility, poor stability and inefficient targeting of many common photosensitizers (PSs) and photothermal agents (PTAs). To overcome these limitations, we have designed biocompatible and biodegradable tumor-targeted upconversion nanospheres with imaging capabilities. The multifunctional nanospheres consist of a sodium yttrium fluoride core doped with lanthanides (ytterbium, erbium and gadolinium) and bismuth selenide (NaYF4:Yb/Er/Gd,Bi2Se3) within a mesoporous silica shell that encapsulates a PS, Chlorin e6 (Ce6), in its pores. NaYF4:Yb/Er converts deeply penetrating near-infrared (NIR) light to visible light, which excites the Ce6 to generate cytotoxic reactive oxygen species (ROS), while the PTA Bi2Se3 efficiently converts absorbed NIR light to heat. Additionally, Gd enables magnetic resonance imaging (MRI) of the nanospheres. The mesoporous silica shell is coated with lipid/polyethylene glycol (DPPC/cholesterol/DSPE-PEG) to ensure retention of the encapsulated Ce6 and minimize interactions with serum proteins and macrophages that impede tumor targeting. Finally, the coat is functionalized with the acidity-triggered rational membrane (ATRAM) peptide, which promotes specific and efficient internalization into cancer cells within the mildly acidic tumor microenvironment. Following uptake by cancer cells in vitro, NIR laser irradiation of the nanospheres caused substantial cytotoxicity due to ROS production and hyperthermia. The nanospheres facilitated tumor MRI and thermal imaging, and exhibited potent NIR laser light-induced antitumor effects in vivo via combined PDT and PTT, with no observable toxicity to healthy tissue, thereby substantially prolonging survival. Our results demonstrate that the ATRAM-functionalized, lipid/PEG-coated upconversion mesoporous silica nanospheres (ALUMSNs) offer multimodal diagnostic imaging and targeted combinatorial cancer therapy.

bioengineering↗

bioRxiv2021

Gamma Entrainment Improves Synchronization Deficits Caused by Dementia

Non-invasive gamma entrainment has shown promising results in alleviating cognitive symptoms of Alzheimers disease in mice and humans. In this study, we examine improvements in the synchronization characteristics of the brains oscillations induced by 40Hz auditory stimulation based on electroencephalography data recorded from a group of dementia patients. We observed that when the quality of entrainment surpasses a certain level, several indicators of brain synchronization significantly improve. Specifically, the entrained oscillatory activity maintains temporal phase stability in the frontal, parietal, and occipital regions, and persistent spatial phase coupling between them. In addition, notable theta-gamma phase-amplitude coupling is observed in these areas. Interestingly, a high theta power at rest predicts the quality of entrainment. We identify differentiating attributes of temporal/spatial synchronization and cross-frequency coupling in the data of two groups with entrained and non-entrained responses which point to enhanced network synchronization caused by entrainment and can explain its potential therapeutic effects.

neuroscience↗

bioRxiv2019

Bidirectional modulation of human emotional conflict resolution using intracranial stimulation

The ability to regulate emotions in the service of meeting ongoing goals and task demands is a key aspect of adaptive human behavior in our volatile social world. Consequently, difficulties in processing and responding to emotional stimuli underlie many psychiatric diseases ranging from depression to anxiety, the common thread being effects on behavior. Behavior, which is made up of shifting, difficult to measure hidden states such as attention and emotion reactivity, is a product of integrating external input and latent mental processes. Directly measuring, and differentiating, separable hidden cognitive, emotional, and attentional states contributing to emotion conflict resolution, however, is challenging, particularly when only using task-relevant behavioral measures such as reaction time. State-space representations are a powerful method for investigating hidden states underlying complex systems. Using state-space modeling of behavior, we identified relevant hidden cognitive states and predicted behavior in a standardized emotion regulation task. After identifying and validating models which best fit the behavior and narrowing our focus to one model, we used targeted intracranial stimulation of the emotion regulation-relevant neurocircuitry, including prefrontal structures and the amygdala, to causally modulate separable states. Finally, we focused on this one validated state-space model to perform real-time, bidirectional closed-loop adaptive stimulation in a subset of participants. These approaches enable an improved understanding of how to sample and understand emotional processing in a way which could be leveraged in neuromodulatory therapy for disorders of emotional regulation.

neuroscience↗