Biology subjects

Shehu, A.

Publications and source records attributed to Shehu, A..

4 recordsLinked to original sources

bioRxiv2024

Melanopsin ganglion cells in the mouse retina independently evoke pupillary light reflex

PurposeThe pupillary light reflex (PLR) is crucial for protecting the retina from bright light. The intrinsic photosensitive ganglion cells (ipRGCs) in the retina mediate the PLR, which directly sense light and receive inputs from rod/cone photoreceptors. Previous work used genetic knockout mice to reveal that rod/cone photoreceptors drive transient constriction, and ipRGCs drive the sustained component. We acutely ablated photoreceptors by a chemical injection to examine the role of rod and cone photoreceptors in PLR. MethodsPLR and the multiple electrode array (MEA) recording were conducted with C57BL6/J (wildtype: WT) and Cnga3-/-; Gnat1-/- (rod/cone dysfunctional) mice. n-Nitroso-n-methylurea (MNU) was applied to C57 mice by intraperitoneal injection, and PLR was conducted after 5-7 days of injection. Three different light levels (mesopic, low photopic, and high photopic) were tested. Immunohistochemistry was conducted using the anti-Gnat1 and anti-melanopsin antibodies with DAPI. ResultsPLR was induced by all light levels we tested, and the level of constriction increased as the light level increased. After the MNU injection, PLR was not induced at mesopic light stimulus, but was fully induced by high light. The level of PLR was identical between WT and MNU mice, suggesting that ipRGCs fully contributed to the PLR at this light level. Immunohistochemistry revealed that photoreceptors were ablated by the MNU injection, but ipRGCs were preserved. The MEA recording revealed that a population of ipRGCs generated fast and robust spikes in MNU-injected retinal tissues in ex vivo. ConclusionsContrary to previous observations, our results demonstrate that ipRGCs are the major contributor to the PLR induced by high light.

neuroscience↗

bioRxiv2024

Advancing Transcription Factor Binding Site Prediction Using DNA Breathing Dynamics and Sequence Transformers via Cross Attention

Understanding the impact of genomic variants on transcription factor binding and gene regulation remains a key area of research, with implications for unraveling the complex mechanisms underlying various functional effects. Our study delves into the role of DNAs biophysical properties, including thermodynamic stability, shape, and flexibility in transcription factor (TF) binding. We developed a multi-modal deep learning model integrating these properties with DNA sequence data. Trained on ChIP-Seq (chromatin immunoprecipitation sequencing) data in vivo involving 690 TF-DNA binding events in human genome, our model significantly improves prediction performance in over 660 binding events, with up to 9.6% increase in AUROC metric compared to the baseline model when using no DNA biophysical properties explicitly. Further, we expanded our analysis to in vitro high-throughput Systematic Evolution of Ligands by Exponential enrichment (SELEX) and Protein Binding Microarray (PBM) datasets, comparing our model with established frameworks. The inclusion of DNA breathing features consistently improved TF binding predictions across different cell lines in these datasets. Notably, for complex ChIP-Seq datasets, integrating DNABERT2 with a cross-attention mechanism provided greater predictive capabilities and insights into the mechanisms of disease-related non-coding variants found in genome-wide association studies. This work highlights the importance of DNA biophysical characteristics in TF binding and the effectiveness of multi-modal deep learning models in gene regulation studies.

bioinformatics↗

bioRxiv2023

Examining DNA Breathing with pyDNA-EPBD

MotivationThe two strands of the DNA double helix locally and spontaneously separate and recombine in living cells due to the inherent thermal DNA motion.This dynamics results in transient openings in the double helix and is referred to as "DNA breathing" or "DNA bubbles." The propensity to form local transient openings is important in a wide range of biological processes, such as transcription, replication, and transcription factors binding. However, the modeling and computer simulation of these phenomena, have remained a challenge due to the complex interplay of numerous factors, such as, temperature, salt content, DNA sequence, hydrogen bonding, base stacking, and others. ResultsWe present pyDNA-EPBD, a parallel software implementation of the Extended Peyrard-Bishop-Dauxois (EPBD) nonlinear DNA model that allows us to describe some features of DNA dynamics in detail. The pyDNA-EPBD generates genomic scale profiles of average base-pair openings, base flipping probability,DNA bubble probability, and calculations of the characteristically dynamic length indicating the number of base pairs statistically significantly affected by a single point mutation using the Markov Chain Monte Carlo (MCMC) algorithm.

genomics↗

bioRxiv2022

GOProFormer: A Multi-modal Transformer Method for Gene Ontology Protein Function Prediction

Protein Language Models (PLMs) are shown capable of learning sequence representations useful for various prediction tasks, from subcellular localization, evolutionary relationships, family membership, and more. They have yet to be demonstrated useful for protein function prediction. In particular, the problem of automatic annotation of proteins under the Gene Ontology (GO) framework remains open. This paper makes two key contributions. It debuts a novel method that leverages the transformer architecture in two ways. A sequence transformer encodes protein sequences in a task-agnostic feature space. A graph transformer learns a representation of GO terms while respecting their hierarchical relationships. The learned sequence and GO terms representations are combined and utilized for multi-label classification, with the labels corresponding to GO terms. The method is shown superior over recent representative GO prediction methods. The second major contribution in this paper is a deep investigation of different ways of constructing training and testing datasets. The paper shows that existing approaches under- or over-estimate the generalization power of a model. A novel approach is proposed to address these issues, resulting a new benchmark dataset to rigorously evaluate and compare methods and advance the state-of-the-art.

bioinformatics↗