Biology subjects

Hasan, M. A.

Publications and source records attributed to Hasan, M. A..

6 recordsLinked to original sources

bioRxiv2023

Feeding-state dependent modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila

Animals feeding state changes behavioral priorities and thus influences even non-feeding related decisions. How is the feeding state information transmitted to non-feeding related circuits and what are the circuit mechanisms involved in biasing non-feeding related decisions remains an open question. By combining calcium imaging, neuronal manipulations, behavioral analysis and computational modeling, we determined that the competition between different aversive responses to mechanical cues is biased by feeding state changes. We found that this is achieved by differential modulation of two different types of reciprocally connected inhibitory neurons promoting opposing actions. This modulation results in a more frequent active type of response and less frequently a protective type of response if larvae are fed sugar compared to when they are fed a balanced diet. The information about the internal state is conveyed to the inhibitory neurons through homologues of the vertebrate neuropeptide Y known to be involved in regulating feeding behavior.

neuroscience↗

bioRxiv2023

Neural circuits underlying context-dependent competition between defensive actions in Drosophila larva

To ensure their survival, animals must be able to respond adaptively to threats within their environment. However, the precise neural circuit mechanisms that underlie such flexible defensive behaviors remain poorly understood. Using neuronal manipulations, machine-learning-based behavioral detection, Electron Microscopy (EM) connectomics and calcium imaging in Drosophila larva, we have mapped the second-order interneurons differentially involved in the competition between different defensive actions and the main pathways to the motor side putatively involved in inhibiting startle-type behaviors and promoting escape behaviors in a context dependent manner. We found that mechanosensory stimulation modulates the nociceptive escape sequences and inhibits C-shape bends and Rolls in favor of startle-like behaviors. This suggests a competition between mechanosensory-induced startle responses and escape behaviors. Structural and functional connectivity revealed that the second order interneurons receive their main input from projection neurons that integrate mechanosensory and nociceptive stimuli. The analysis of their postsynaptic connectivity in EM revealed that they make indirect connections to the pre-motor and motor neurons. Finally, we identify a pair of descending neurons that could promote modulate the escape sequence and promote startle behaviors. Altogether, these results characterize the pathways involved in the Startle and Escape competition, modulated by the sensory context.

neuroscience↗

bioRxiv2023

Investigating the association of resting-state brain effective connectivity with basic negative emotions

The neurotic personality has an impact on the regulation of basic negative emotions such as anger, fear, and sadness. There has been extensive research in search of functional connectivity biomarkers of neuroticism and basic negative emotions but there is a lack of research based on effective connectivity. In the current research, we intended to determine the significance of causal interaction of three large-scale resting-state networks - default mode, salience, and executive networks - to predict neuroticism and basic negative emotions. In this study, a large-scale human connectome project dataset comprising functional MRI scans and self-reported scores of neuroticism and negative emotions of 1079 subjects, was utilized. Spectral dynamic causal modelling and parametric empirical Bayes was used to estimate the subject-level effective connectivity parameters and their group-level associations with the neuroticism and emotional scores. Leave-one-out cross-validation using parametric empirical Bayes was employed for prediction analysis. Our results for heightened emotions showed that the self-connection of right hippocampus can predict individuals with high fear, self-connections of dorsal anterior cingulate cortex, posterior cingulate cortex and left dorsolateral prefrontal cortex can predict individuals with high sadness. High anger, low sadness, and neuroticism scores of any emotion category except low fear, could not be predicted using triple network effective connectivity. Our findings revealed that the causal (directed) connections of the resting-state triple network can potentially serve as a connectomic signature for people with high and low fear, high sadness, low anger, and neuroticism with low fear.

neuroscience↗

bioRxiv2020

Multi-omic analysis of lung tumors defines pathways activated in neuroendocrine transformation

Lineage plasticity, a capacity to reprogram cell phenotypic identity under evolutionary pressure, is implicated in treatment resistance and metastasis in multiple cancers. In lung adenocarcinomas (LUADs) amenable to treatment with targeted inhibitors, transformation to an aggressive neuroendocrine (NE) carcinoma resembling small cell lung cancer (SCLC) is a recognized mechanism of acquired resistance. Defining molecular mechanisms of NE transformation in lung cancer has been limited by a paucity of well annotated pre- and post-transformation clinical samples. We hypothesized that mixed histology LUAD/SCLC tumors may capture cancer cells proximal to, and on either side of, histologic transformation. We performed detailed genomic, epigenomic, transcriptomic and proteomic characterization of combined LUAD/SCLC tumors as well as pre- and post-transformation clinical samples. Our data support that NE transformation is primarily driven by transcriptional reprogramming rather than mutational events. We identify genomic contexts in which NE transformation is favored, including frequent loss of the 3p chromosome arm in pre-transformation LUADs. Consistent shifts in gene expression programs in NE transformation include induction of several stem/progenitor cell regulatory pathways, including upregulation of PRC2 and WNT signaling, and suppression of Notch pathway activity. We observe induction of PI3K/AKT and an immunosuppressive phenotype in NE transformation. Taken together our findings define a novel landscape of potential drivers and therapeutic vulnerabilities of NE transformation in lung cancer.

cancer biology↗

bioRxiv2020

In situ classification of cell types in human kidney tissue using 3D nuclear staining

To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.

cell biology↗

bioRxiv2019

DeeplyEssential: A Deep Neural Network for Predicting Essential Genes in Microbes

Essential genes are genes that critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies. Here we propose a deep neural network (DNN) for predicting essential genes in microbes. Our DNN-based architecture called DO_SCPLOWEEPLYC_SCPLOWEO_SCPLOWSSENTIALC_SCPLOW makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction, thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. Our extensive experimental results show that DO_SCPLOWEEPLYC_SCPLOWEO_SCPLOWSSENTIALC_SCPLOW outperforms existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes. We also expose and study a hidden performance bias that affected previous classifiers.\n\nThe code of DO_SCPLOWEEPLYC_SCPLOWEO_SCPLOWSSENTIALC_SCPLOW is freely available at https://github.com/ucrbioinfo/DeeplyEssential

bioinformatics↗