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See detailSingle-cell transcriptional profiling and gene regulatory network modeling in Tg2576 mice reveal gender-dependent molecular features preceding Alzheimer-like pathologies
Ali, Muhammad UL; Huarte, Oihane; Heurtaux, Tony UL et al

in Molecular Neurobiology (2022), in press (doi:10.1007/s12035-022-02985-2)(in press),

Alzheimer’s disease (AD) onset and progression is influenced by a complex interplay of several environmental and genetic factors, one of them gender. Pronounced gender differences have been observed both ... [more ▼]

Alzheimer’s disease (AD) onset and progression is influenced by a complex interplay of several environmental and genetic factors, one of them gender. Pronounced gender differences have been observed both in the relative risk of developing AD and in clinical disease manifestations. A molecular level understanding of these gender disparities is still missing, but could provide important clues on cellular mechanisms modulating the disease and reveal new targets for gender-oriented disease-modifying precision therapies. We therefore present here a comprehensive single-cell analysis of disease-associated molecular gender differences in transcriptomics data from the neocortex, one of the brain regions most susceptible to AD, in one of the most widely used AD mouse models, the Tg2576 model. Cortical areas are also most commonly used in studies of post-mortem AD brains. To identify disease-linked molecular processes that occur before the onset of detectable neuropathology, we focused our analyses on an age with no detectable plaques and microgliosis. Cell-type specific alterations were investigated at the level of individual genes, pathways, and gene regulatory networks. The number of differentially expressed genes (DEGs) was not large enough to build context-specific gene regulatory networks for each individual cell type, and thus, we focused on the study of cell types with dominant changes and included analyses of changes across the combination of cell types. We observed significant disease-associated gender differences in cellular processes related to synapse organization and axonogenesis, and identified a limited set of transcription factors, including Egr1 and Klf6, as key regulators of many of the disease-associated and gender-dependent gene expression changes in the model. Overall, our analyses revealed significant celltype-specific gene expression changes in individual genes, pathways and subnetworks, including gender-specific and gender-dimorphic changes in both upstream transcription factors and their downstream targets, in the Tg2576 AD model before the onset of overt disease. This opens a window into molecular events that could determine gender-susceptibility to AD, and uncovers tractable target candidates for potential gender-specific precision medicine for AD. [less ▲]

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See detailEmotion Recognition Based on Facial Expressions Using Convolutional Neural Network (CNN)
Begaj, S.; Topal, Ali Osman UL; Ali, Muhammad UL et al

in Proceedings - 2020 International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2020 (2020)

Over the last few years, there has been an increasing number of studies about facial emotion recognition because of the importance and the impact that it has in the interaction of humans with computers ... [more ▼]

Over the last few years, there has been an increasing number of studies about facial emotion recognition because of the importance and the impact that it has in the interaction of humans with computers. With the growing number of challenging datasets, the application of deep learning techniques have all become necessary. In this paper, we study the challenges of Emotion Recognition Datasets and we also try different parameters and architectures of the Conventional Neural Networks (CNNs) in order to detect the seven emotions in human faces, such as: anger, fear, disgust, contempt, happiness, sadness and surprise. We have chosen iCV MEFED (Multi-Emotion Facial Expression Dataset) as the main dataset for our study, which is relatively new, interesting and very challenging. © 2020 IEEE. [less ▲]

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See detailSynapse alterations precede neuronal damage and storage pathology in a human cerebral organoid model of CLN3-juvenile neuronal ceroid lipofuscinosis
Gomez Giro, Gemma UL; Arias-Fuenzalida, Jonathan; Jarazo, Javier UL et al

in Acta Neuropathologica Communications (2020)

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See detailAlzheimer’s disease-associated (hydroxy)methylomic changes in the brain and blood
Ali, Muhammad UL

in Clinical Epigenetics (2019)

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See detailIntegrative Network-Based Approaches For Modeling Human Disease
Ali, Muhammad UL

Doctoral thesis (2019)

The large-scale development of high-throughput sequencing technologies has allowed the generation of reliable omics data related to various regulatory levels. Moreover, integrative computational modeling ... [more ▼]

The large-scale development of high-throughput sequencing technologies has allowed the generation of reliable omics data related to various regulatory levels. Moreover, integrative computational modeling has enabled the disentangling of a complex interplay between these interconnected levels of regulation by interpreting concomitant large quantities of biomedical information (‘big data’) in a systematic way. In the context of human disorders, network modeling of complex gene-gene interactions has been successfully used for understanding disease-related dysregulation and for predicting novel drug targets to revert the diseased phenotype. Recent evidence suggests that changes at multiple levels of genomic regulation are responsible for the development and course of multifactorial diseases. Although existing computational approaches have been able to explain cell-type-specific and disease-associated transcriptional regulation, they so far have been unable to utilize available epigenetic data for systematically dissecting underlying disease mechanisms. In this thesis, we first provided an overview of recent advances in the field of computational modeling of cellular systems, its major strengths and limitations. Next, we highlighted various computational approaches that integrate information from different regulatory levels to understand mechanisms behind the onset and progression of multifactorial disorders. For example, we presented INTREGNET, a computational method for systematically identifying minimal sets of transcription factors (TFs) that can induce desired cellular transitions with increased efficiency. As such, INTREGNET can guide experimental attempts for achieving effective in vivo cellular transitions by overcoming epigenetic barriers restricting the cellular differentiation potential. Furthermore, we introduced an integrative network-based approach for ranking Alzheimer’s disease (AD)-associated functional genetic and epigenetic variation. The proposed approach explains how genetic and epigenetic variation can induce expression changes via gene-gene interactions, thus allowing for a systematic dissection of mechanisms underlying the onset and progression of multifactorial diseases like AD at a multi-omics level. We also showed that particular pathways, such as sphingolipids (SL) function, are significantly dysregulated in AD. In-depth integrative analysis of these SL-related genes reveals their potential as biomarkers and for SL-targeted drug development for AD. Similarly, in order to understand the functional consequences of CLN3 gene mutation in Batten disease (BD), we conducted a differential gene regulatory network (GRN)-based analysis of transcriptomic data obtained from an in vitro BD model and revealed key regulators maintaining the disease phenotype. We believe that the work conducted in this thesis provides the scientific community with a valuable resource to understand the underlying mechanism of multifactorial diseases from an integrative point of view, helping in their early diagnosis as well as in designing potential therapeutic treatments. [less ▲]

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See detailModeling of Cellular Systems: Application in Stem Cell Research and Computational Disease Modeling
Ali, Muhammad UL; del Sol Mesa, Antonio UL

Book published by Springer International Publishing (2018)

The large-scale development of high-throughput sequencing technologies has allowed the generation of reliable omics data at different regulatory levels. Integrative computational models enable the ... [more ▼]

The large-scale development of high-throughput sequencing technologies has allowed the generation of reliable omics data at different regulatory levels. Integrative computational models enable the disentangling of a complex interplay between these interconnected levels of regulation by interpreting these large quantities of biomedical information in a systematic way. In the context of human diseases, network modeling of complex gene-gene interactions has been successfully used for understanding disease-related dysregulations and for predicting novel drug targets to revert the diseased phenotype. Furthermore, these computational network models have emerged as a promising tool to dissect the mechanisms of developmental processes such as cellular differentiation, transdifferentiation, and reprogramming. In this chapter, we provide an overview of recent advances in the field of computational modeling of cellular systems and known limitations. A particular attention is paid to highlight the impact of computational modeling on our understanding of stem cell biology and the complex multifactorial nature of human diseases and their treatment. [less ▲]

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