Biology subjects

Ibrahim, B.

Publications and source records attributed to Ibrahim, B..

5 recordsLinked to original sources

bioRxiv2026

Reduced MOV10 reveals novel functional cortical connections in an increased fear response

The RNA helicase MOV10 is highly expressed in developing brain, is present in synapses and is required for embryonic viability. A murine brain-specific knockout of MOV10 (Mov10 Deletion) has a thickened cortex, abnormal dendritic arborization and enhanced fear memory. In human studies, MOV10 is among the loci that is correlated with enhanced cortical brain volumes and is also significantly associated with substance dependence by epigenetic profiling. Here we demonstrate that Mov10 Deletion mice show enhanced fear learning that is aligned with impaired structural connectivity of canonical fear circuits revealed by Diffusion Tensor Imaging. We propose a model where MOV10 loss leads to increased GABRA2 expression in the hippocampus and reduced anatomical connectivity that drives augmented fear learning. Memory reactivation is observable during fear memory retrieval as an overall increase in fMRI functional activity in cortical regions. Taken together, this framework identifies that enhanced fear in the MOV10 model is driven via a "corticalized" fear response during re-exposure to the training context that is not driven by the canonical fear circuit. These findings support a molecular basis for non-traditional enhanced learning mechanisms activated by fearful events that shed light on the intractability of fear memories with the potential to inform PTSD and substance dependence disorders.

neuroscience↗

bioRxiv2020

V-NeuroStack: 3D Time Stacks for Identifying Patterns in Calcium Imaging Data

BackgroundUnderstanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of cortical neurons in a mouse brain slice. New MethodV-NeuroStack creates 3D time stacks by stacking 2D time frames for a period of 600 seconds. It provides a web interface that enables exploration and analysis of data using a combination of 3D and 2D visualization techniques. Comparison with existing MethodsPrevious attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack can scale data sets with at least a few thousand temporal snapshots. ResultsV-NeuroStacks 3D view is used to explore patterns in the dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of a single neuron or a functional cluster of neurons. ConclusionsV-NeuroStack enables easy exploration and analysis of large spatio-temporal datasets using two visualization paradigms: (a) Space-Time cube (b)Two-dimensional networks, via web interface. It will support future advancements in in vitro and in vivo data capturing techniques and can bring forth novel hypotheses by permitting unambiguous visualization of large-scale patterns in the neuronal activity data.

neuroscience↗

bioRxiv2019

Differential equation based minimal model describing metabolic oscillations in Bacillus subtilis biofilms

Biofilms offer an excellent example of ecological interaction among bacteria. Temporal and spatial oscillations in biofilms are an emerging topic. In this paper we describe the metabolic oscillations in Bacillus subtilis biofilms by applying the smallest theoretical chemical reaction system showing Hopf bifurcation proposed by Wilhelm and Heinrich in 1995. The system involves three differential equations and a single bilinear term. We perform computer simulations and a detailed analysis of the system including bifurcation analysis and quasi-steady-state approximation. We also discuss the feedback structure of the system and the correspondence of the simulations to biological observations. We also specifically select parameters that are more suitable for the biological scenario of biofilm oscillations. Our theoretical work suggests potential scenarios about the oscillatory behaviour of biofilms and also serves as an application of a previously described chemical oscillator to a biological system.

systems biology↗

bioRxiv2018

SilentMutations (SIM): a tool for analyzing long-range RNA-RNA interactions in viral genomes and structural RNAs

BackgroundA single nucleotide change in the coding region can alter the amino acid sequence of a protein. In consequence, natural or artificial sequence changes in viral RNAs may have various effects not only on protein stability, function and structure but also on viral replication.\n\nIn recent decades, several tools have been developed to predict the effect of mutations in structured RNAs such as viral genomes or non-coding RNAs. Some tools use multiple point mutations and also take coding regions into account. However, none of these tools was designed to specifically simulate the effect of mutations on viral long-range interactions.\n\nResultsHere, we developed SilentMutations (SIM), an easy-to-use tool to analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs. The tool can simulate disruptive and compensatory mutants of two interacting single-stranded RNAs. This allows a fast and accurate assessment of key regions potentially involved in functional long-range RNA-RNA interactions and will eventually help virologists and RNA-experts to design appropriate experiments.\n\nSIM only requires two interacting single-stranded RNA regions as input. The output is a plain text file containing the most promising mutants and a graphical representation of all interactions.\n\nConclusionWe applied our tool on two experimentally validated influenza A virus and hepatitis C virus interactions and we were able to predict potential double mutants for in vitro validation experiments.\n\nAvailabilityThe source code and documentation of SIM are freely available at github.com/desiro/silentMutations.

bioinformatics↗

bioRxiv2018

Identification of potential microRNAs associated with Herpesvirus family based on bioinformatic analysis

MicroRNAs (miRNAs) are known key regulators of gene expression on posttranscriptional level in many organisms encoded in mammals, plants and also several viral families. To date, no homologous gene of a virus-originated miRNA is known in other organisms. To date, only a few homologous miRNA between two different viruses are known, however, no gene of a virus-originated miRNA is known in any organism of other kingdoms. This can be attributed to the fact that classical miRNA detection approaches such as homology-based predictions fail at viruses due to their highly diverse genomes and their high mutation rate.\n\nHere, we applied the virus-derived precursor miRNA (pre-miRNA) prediction pipeline ViMiFi, which combines information about sequence conservation and machine learning-based approaches, on Human Herpesvirus 7 (HHV7) and Epstein-Barr virus (EBV). ViMiFi was able to predict 61 candidates in EBV, which has 25 known pre-miRNAs. From these 25, ViMiFi identified 20. It was further able to predict 18 candidates in the HHV7 genome, in which no miRNA had been described yet. We also studied the undescribed candidates of both viruses for potential functions and found similarities with human snRNAs and miRNAs from mammals and plants.

bioinformatics↗