Biology subjects

Fatima, Z.

Publications and source records attributed to Fatima, Z..

3 recordsLinked to original sources

bioRxiv2026

UPLC-ESI-MS based lipidomics revealed novel biomarkers in insulin receptor knockdown induced type 2 diabetes model of Drosophila

Type 2 diabetes (T2D) is a prevalent metabolic disorder affecting millions worldwide, characterized by insulin resistance and impaired glucose homeostasis. While mammalian models are widely used, Drosophila melanogaster provides a powerful alternative due to its conserved insulin signaling pathways, genetic tractability, and suitability for high throughput studies. In addition to glucose dysregulation, lipid metabolism plays a crucial role in T2D pathophysiology, as alterations in lipid composition contribute to insulin resistance and metabolic dysfunction. Lipidomic studies have emerged as an essential approach to identify metabolic signatures and potential biomarkers for disease progression and therapeutic targeting. In this study, T2D like model was established by inducing insulin resistance through knockdown of the insulin receptor in brain insulin-producing cells using the dilp2-Gal4>UAS-InRRNAi system. This genetic manipulation resulted in significant metabolic dysregulation, including elevated glucose, trehalose, and triacylglyceride levels, along with increased oxidative stress indicators. Additionally, mRNA expression analysis of key insulin signaling components, including insulin receptor substrate 1, dilp2, dilp3, dilp5, and phosphorylated Akt, further validated the model. To further investigate metabolic alterations, Lipid profiling was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in non targeted LC-MS-based metabolomics approach to identify lipid biomarkers associated with T2D. Multivariate statistical analyses, including PCA and PLS-DA, revealed distinct lipid signatures between wild-type and T2D flies. Notably, specific phosphatidylglycerol species PG 34:0, PG 34:4, PA 38:3, PIP 38:1, PIP2 38:6, and LPS 24:0 demonstrated an area under the curve (AUC) of 1, indicating their strong reliability as lipid biomarkers for T2D diagnosis.

biochemistry↗

bioRxiv2025

Plasmodium falciparum aquaglyceroporin (PfAQP) orchestrates glycerol-dependent lipid metabolism, membrane biogenesis, metabolomic flux, parasite growth and stress adaptation

Aquaglyceroporins (AQPs) are integral membrane channel proteins that facilitate transport of water and other solutes such as glycerol, thereby playing vital roles in cellular homeostasis. Here, we functionally characterized Plasmodium falciparum aquaglyceroporin (PfAQP) through localization, inducible knockdown, and comprehensive metabolomic analyses revealing its key role in lipid homeostasis and metabolomic flux. PfAQP localizes to the parasite plasma membrane and food vacuole membrane, at critical interfaces for nutrient and metabolite exchange. A glmS-ribozyme mediated inducible knock-down of PfAQP severely impairs parasite development from trophozoite to schizont, leading to marked suppression in parasite growth. Ultra-Expansion Microscopy (UExM) analyses show profound defects in membrane biogenesis, along with disruption in cytokinesis under PfAQP knock-down conditions. Detailed lipidomic analyses suggests that PfAQP disruption cause altered lipid homeostasis, characterized by reduced membrane phospholipid biosynthesis, impaired lipid scavenging, and accumulation of storage lipids. Metabolomic analyses further indicate metabolic dysregulation characterized by reduction in amino acid levels and dysregulated mitochondrial metabolism, including impaired tricarboxylic acid (TCA) cycle. Strikingly, PfAQP expression gets transiently upregulated under nutrient starvation condition, and its ablation drastically compromise parasite recovery, highlighting its role in adaptive responses. Overall, these results establish PfAQP as a critical regulator of glycerol-dependent metabolic pathways, orchestrating membrane biogenesis, energy metabolism, and stress resilience--functions indispensable for parasite growth and survival.

microbiology↗

bioRxiv2017

Defining subtypes of autism spectrum disorder using static and dynamic functional connectivity

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder, characterized by impairments in social communication and restricted, repetitive behaviours. Neuroimaging studies have shown complex patterns of functional connectivity (FC) in ASD, with no clear consensus on brain-behaviour relationships or shared patterns of FC with typically developing controls. Here, we used k-means clustering and multivariate statistical analyses to characterize distinct FC patterns and FC-behaviour relationships in participants with and without ASD. Two FC subtypes were identified by the clustering analysis. One subtype was defined by increased FC within resting-state networks and decreased FC across networks compared to the other subtype. A separate FC pattern distinguished ASD from controls, particularly within default mode, cingulo-opercular, sensorimotor, and occipital networks. There was no significant interaction between subtypes and diagnostic groups. Finally, analysis of FC patterns with behavioural measures of IQ, social responsiveness and ASD severity showed unique brain-behaviour relations in each subtype, and a continuum of brain-behavior relations from ASD to controls within one subtype. These results demonstrate that distinct clusters of FC patterns exist in both ASD and controls, and that FC subtypes can reveal unique information about brain-behaviour relationships.\n\nAuthor SummaryAutism spectrum disorder (ASD) is a neurodevelopmental disorder, with high variation in the types of severity of impairments in social communication and restricted, repetitive behaviours. Neuroimaging studies have shown complex patterns of communication between brain regions, or functional connectivity (FC), in ASD. Here, we defined two distinct FC patterns and relationships between FC and behaviour in participants with and without ASD. One subtype was defined by increased FC within distinct networks of brain regions, and decreased FC between networks compared to the other subtype. A separate FC pattern distinguished ASD from controls. The interaction between subtypes and diagnostic groups was not significant. Analysis of FC patterns with behavioural measures revealed unique information about brain-behaviour relations in each subtype.

neuroscience↗