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Single-cell omics is advancing our understanding of selective neuronal vulnerability in Alzheimer's disease (AD), revealing specific subtypes that are either susceptible or resilient to neurodegeneration. Using single-nucleus and spatial transcriptomics to compare neocortical regions affected early (prefrontal cortex and precuneus) or late (primary visual cortex) in AD, we identified a resilient excitatory population in layer 4 of the primary visual cortex expressing RORB , CUX2 , and EYA4 . Layer 4 neurons in association neocortex also remained relatively preserved as AD progressed and shared overlapping molecular signatures of resilience. Early in the disease, resilient neurons upregulated genes associated with synapse maintenance, synaptic plasticity, calcium homeostasis, and neuroprotective factors, including GRIN2A, RORA, NRXN1, NLGN1, NCAM2, FGF14, NRG3, NEGR1 , and CSMD1 . We also identified KCNIP4 , which encodes a voltage-gated potassium (Kv) channel-interacting protein that interacts with Kv4.2 channels and presenilins, as a key factor linked to resilience. KCNIP4 was consistently upregulated in the early stages of pathology. Furthermore, AAV-mediated overexpression of Kcnip4 in a humanized AD mouse model reduced the expression of the activity-dependent genes Arc and c-Fos , suggesting compensatory mechanisms against neuronal hyperexcitability. Our dataset provides a valuable resource for investigating mechanisms underlying resilience to neurodegeneration., Competing Interests: Ethics declarations Competing interests The authors declare no competing interests.

Background/objectives: Amyotrophic Lateral Sclerosis is a progressive neurodegenerative disorder characterized by the loss of upper and lower motor neurons. Key factors contributing to neuronal death include mitochondrial energy damage, oxidative stress, and excitotoxicity. The frontal cortex is crucial for action initiation, planning, and voluntary movements whereas the spinal cord facilitates communication with the brain, walking, and reflexes. By investigating transcriptome data from the frontal cortex and spinal cord, we aim to elucidate common pathological mechanisms and pathways involved in ALS for understanding the disease progression and identifying potential therapeutic targets., Methods: In this study, we quantified gene and transcript expression patterns, predicted variants, and assessed their functional effects using computational tools. It also includes predicting variant-associated regulatory effects, constructing functional interaction networks, and performing a gene enrichment analysis., Results: We found novel genes for the upregulation of immune response, and the downregulation of metabolic-related and defective degradation processes in both the spinal cord and frontal cortex. Additionally, we observed the dysregulation of histone regulation and blood pressure-related genes specifically in the frontal cortex., Conclusions: These results highlight the distinct and shared molecular disruptions in ALS, emphasizing the critical roles of immune response and metabolic dysfunction in neuronal degeneration. Targeting these pathways may provide new therapeutic avenues to combat neurodegeneration and preserve neuronal health.

Huntington's disease (HD) is a progressive neurodegenerative disorder caused due to a CAG repeat expansion in the huntingtin ( HTT ) gene. The primary symptoms of HD include motor dysfunction such as chorea, dystonia, and involuntary movements. The primary motor cortex (BA4) is the key brain region responsible for executing motor/movement activities. Investigating patient and control samples from the BA4 region will provide a deeper understanding of the genes responsible for neuron degeneration and help to identify potential markers. Previous studies have focused on overall differential gene expression and associated biological functions. In this study, we illustrate the relationship between variants and differentially expressed genes/transcripts. We identified variants and their associated genes along with the quantification of genes and transcripts. We also predicted the effect of variants on various regulatory activities and found that many variants are regulating gene expression. Variants affecting miRNA and its targets are also highlighted in our study. Co-expression network studies revealed the role of novel genes. Function interaction network analysis unveiled the importance of genes involved in vesicle-mediated transport. From this unified approach, we propose that genes expressed in immune cells are crucial for reducing neuron death in HD.

Huntington's disease (HD) is a neurodegenerative disorder with autosomal dominant inheritance caused by glutamine expansion in the Huntingtin gene (HTT). Striatal projection neurons (SPNs) in HD are more vulnerable to cell death. The executive striatal population is directly connected with the Brodmann Area (BA9), which is mainly involved in motor functions. Analyzing the disease samples from BA9 from the SRA database provides insights related to neuron degeneration, which helps to identify a promising therapeutic strategy. Most gene expression studies examine the changes in expression and associated biological functions. In this study, we elucidate the relationship between variants and their effect on gene/downstream transcript expression. We computed gene and transcript abundance and identified variants from RNA-seq data using various pipelines. We predicted the effect of genome-wide association studies (GWAS)/novel variants on regulatory functions. We found that many variants affect the histone acetylation pattern in HD, thereby perturbing the transcription factor networks. Interestingly, some variants affect miRNA binding as well as their downstream gene expression. Tissue-specific network analysis showed that mitochondrial, neuroinflammation, vasculature, and angiogenesis-related genes are disrupted in HD. From this integrative omics analysis, we propose that abnormal neuroinflammation acts as a two-edged sword that indirectly affects the vasculature and associated energy metabolism. Rehabilitation of blood-brain barrier functionality and energy metabolism may secure the neuron from cell death.

Glioblastoma multiforme (GBM) is the most common infiltrating lethal tumor of the brain. Tumor heterogeneity and the precise characterization of GBM remain challenging, and the disease-specific and effective biomarkers are not available at present. To understand GBM heterogeneity and the disease prognosis mechanism, we carried out a single-cell transcriptome data analysis of 3389 cells from four primary IDH-WT (isocitrate dehydrogenase wild type) glioblastoma patients and compared the characteristic features of the tumor and periphery cells. We observed that the marker gene expression profiles of different cell types and the copy number variations (CNVs) are heterogeneous in the GBM samples. Further, we have identified 94 differentially expressed genes (DEGs) between tumor and periphery cells. We constructed a tissue-specific co-expression network and protein-protein interaction network for the DEGs and identified several hub genes, including CX3CR1, GAPDH, FN1, PDGFRA, HTRA1, ANXA2 THBS1, GFAP, PTN, TNC , and VIM . The DEGs were significantly enriched with proliferation and migration pathways related to glioblastoma. Additionally, we were able to identify the differentiation state of microglia and changes in the transcriptome in the presence of glioblastoma that might support tumor growth. This study provides insights into GBM heterogeneity and suggests novel potential disease-specific biomarkers which could help to identify the therapeutic targets in GBM.

Alzheimer's disease (AD) and Parkinson's disease (PD) are well-known neuronal degenerative disorders that share common pathological events. Approved medications alleviate symptoms but do not address the root cause of the disease. Energy dysfunction in the neuronal population leads to various pathological events and ultimately results in neuronal death. Identifying common therapeutic targets for these disorders may help in the drug discovery process. The Brodmann area 9 (BA9) region is affected in both the disease conditions and plays an essential role in cognitive, motor, and memory-related functions. Analyzing transcriptome data of BA9 provides deep insights related to common pathological pathways involved in AD and PD. In this work, we map the preprocessed BA9 fastq files generated by RNA-seq for disease and control samples with reference hg38 genomic assembly and identify common variants and differentially expressed genes (DEG). These variants are predominantly located in the 3' UTR (non-promoter) region, affecting the conserved transcription factor (TF) binding motifs involved in the methylation and acetylation process. We have constructed BA9-specific functional interaction networks, which show the relationship between TFs and DEGs. Based on expression signature analysis, we propose that MAPK1, VEGFR1/FLT1, and FGFR1 are promising drug targets to restore blood-brain barrier functionality by reducing neuroinflammation and may save neurons., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dharshini, Jemimah, Taguchi and Gromiha.)

Introduction: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments., Objective: The primary purpose of this study is to develop a classification model using machine learning for predicting the deleterious effect of variants with respect to AD., Methods: We have constructed a set of 20,401 deleterious and 37,452 control variants from Genome-Wide Association Study (GWAS) and Genotype-Tissue Expression (GTEx) portals, respectively. Recursive feature elimination using cross-validation (RFECV) followed by a forward feature selection method was utilized to select the important features and a random forest classifier was used for distinguishing between deleterious and neutral variants., Results: Our method showed an accuracy of 81.21% on 10-fold cross-validation and 70.63% on a test set of 5785 variants. The same test set was used to compare the performance of CADD and FATHMM and their accuracies are in the range of 54%-62%., Conclusion: Our model is freely available as the Variant Effect Predictor for Alzheimer's Disease (VEPAD) at http://web.iitm.ac.in/bioinfo2/vepad/. VEPAD can be used to predict the effect of new variants associated with AD., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Neurodegenerative diseases are the most predominate brain disorders around the globe and the affected populations are rapidly increasing. Recently, these diseases have been addressed using the data obtained from RNA-sequencing technology to reveal the changes in gene/transcript expression, effect of variants, and pathways involved in disease mechanisms. However, the observations mainly depend on the aligners/tools and the performance of existing RNA-seq tools on hg38 genome assembly has not yet been documented. In this study, we performed a systematic analysis of various spliced aligners, transcript assembling and variant calling tools based on both genomic assemblies (hg19/hg38) from hippocampus brain tissue. This helps to identify the best possible combination tools for hg38 annotation. In order to evaluate the identified variants from various pipelines, we compared them with expression Quantitative Trait Loci (eQTL) and Genome-Wide Association Study (GWAS). In addition, the identified differentially expressed genes (DG) were compared with microarray studies. From our analysis of variant calling, the combination of GATK (Genome Analysis Tool-kit) and STAR (Spliced Transcripts Alignment to a Reference) protocol yields a larger number of GWAS/eQTL variants compared to SAMtools (Sequence Alignment Map). We also identified a higher number of non-coding variants in hg38 compared to hg19 due to enhanced annotation. In the case of various DG pipelines, we found that the Salmon-based hg38 transcriptomic quantification yields a higher number of reported DG compared to other genome-based quantification methods. This study revealed that higher number of reads maps to multiple location of the genome with hg38 compared to hg19, and these spurious multi-mapped reads may affect the gene quantification techniques. We suggest that it is necessary to develop efficient algorithms, which can handle the multi-mapped reads and improve the performance of genome-based alignment quantification., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Neurodegenerative diseases, including Alzheimer, Parkinson, Huntington, and amyotrophic lateral sclerosis, are a prominent class of neurological diseases currently without a cure. They are characterized by an inexorable loss of a specific type of neurons. The selective vulnerability of specific neuronal clusters (typically a subcortical cluster) in the early stages, followed by the spread of the disease to higher cortical areas, is a typical pattern of disease progression. Neurodegenerative diseases share a range of molecular and cellular pathologies, including protein aggregation, mitochondrial dysfunction, glutamate toxicity, calcium load, proteolytic stress, oxidative stress, neuroinflammation, and aging, which contribute to neuronal death. Efforts to treat these diseases are often limited by the fact that they tend to address any one of the above pathological changes while ignoring others. Lack of clarity regarding a possible root cause that underlies all the above pathologies poses a significant challenge. In search of an integrative theory for neurodegenerative pathology, we hypothesize that metabolic deficiency in certain vulnerable neuronal clusters is the common underlying thread that links many dimensions of the disease. The current review aims to present an outline of such an integrative theory. We present a new perspective of neurodegenerative diseases as metabolic disorders at molecular, cellular, and systems levels. This helps to understand a common underlying mechanism of the many facets of the disease and may lead to more promising disease-modifying therapeutic interventions. Here, we briefly discuss the selective metabolic vulnerability of specific neuronal clusters and also the involvement of glia and vascular dysfunctions. Any failure in satisfaction of the metabolic demand by the neurons triggers a chain of events that precipitate various manifestations of neurodegenerative pathology., (Copyright © 2020 Muddapu, Dharshini, Chakravarthy and Gromiha.)

Alzheimer disease (AD) is a devastating neurological disorder, which initiates from hippocampus and proliferates to cortical regions. The neurons of hippocampus require higher energy to preserve the firing pattern. In AD, aberrant energy metabolism is the critical factor for neurodegeneration. However, the reason for the energy crisis in hippocampus neurons is still unresolved. Transcriptome analysis enables us in understanding the underlying mechanism of energy crisis. In this study, we identified variants/differential gene/transcript expression profiles from hippocampus RNA-seq data. We predicted the effect of variants in transcription factor (TF) binding using in silico tools. Further, a hippocampus-specific co-expression and functional interaction network were designed to decipher the relationships between TF and differentially expressed genes (DG). Identified variants predominantly influence TF binding, which subsequently regulates the DG. From the results, we hypothesize that the loss of vascular integrity is the fundamental attribute for the energy crisis, which leads to neurodegeneration.