Investigating Neuron Degeneration in Huntington's Disease Using RNA-Seq Based Transcriptome Study.

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.

Transgenic sugarcane with higher levels of BRK1 showed improved drought tolerance.

KEY MESSAGE: Transgenic sugarcane overexpressing BRK1 showed improved tolerance to drought stress through modulation of actin polymerization and formation of interlocking marginal lobes in epidermal leaf cells, a typical feature associated with BRK1 expression under drought stress. BRICK1 (BRK1) genes promote leaf epidermal cell morphogenesis and division in plants that involves local actin polymerization. Although the changes in actin filament organization during drought have been reported, the role of BRK in stress tolerance remains unknown. In our previous work, the drought-tolerant Erianthus arundinaceus exhibited high levels of the BRK gene expression under drought stress. Therefore, in the present study, the drought-responsive gene, BRK1 from Saccharum spontaneum, was transformed into sugarcane to test if it conferred drought tolerance in the commercial sugarcane cultivar Co 86032. The transgenic lines were subjected to drought stress, and analyzed using physiological parameters for drought stress. The drought-induced BRK1-overexpressing lines of sugarcane exhibited significantly higher transgene expression compared with the wild-type control and also showed improved physiological parameters. In addition, the formation of interlocking marginal lobes in the epidermal leaf cells, a typical feature associated with BRK1 expression, was observed in all transgenic BRK1 lines during drought stress. This is the first report to suggest that BRK1 plays a role in sugarcane acclimation to drought stress and may prove to be a potential candidate in genetic engineering of plants for enhanced biomass production under drought stress conditions.

Alz-Disc: A Tool to Discriminate Disease-causing and Neutral Mutations in Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is the most common neurodegenerative disorder that affects the neuronal system and leads to memory loss. Many coding gene variants are associated with this disease and it is important to characterize their annotations. METHODS: We collected the Alzheimer's disease-causing and neutral mutations from different databases. For each mutation, we computed the different features from protein sequence. Further, these features were used to build a Bayes network-based machine-learning algorithm to discriminate between the disease-causing and neutral mutations in AD. RESULTS: We have constructed a comprehensive dataset of 314 Alzheimer's disease-causing and 370 neutral mutations and explored their characteristic features such as conservation scores, positionspecific scoring matrix (PSSM) profile, and the change in hydrophobicity, different amino acid residue substitution matrices and neighboring residue information for identifying the disease-causing mutations. Utilizing these features, we have developed a disease-specific tool named Alz-disc, for discriminating the disease-causing and neutral mutations using sequence information alone. The performance of the present method showed an accuracy of 89% for independent test set, which is 13% higher than available generic methods. This method is freely available as a web server at https://web.iitm.ac.in/bioinfo2/alzdisc/. CONCLUSIONS: This study is useful to annotate the effect of new variants and develop mutation specific drug design strategies for Alzheimer's disease.

Integrative Meta-Analysis of Huntington's Disease Transcriptome Landscape.

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.

Exploring Plausible Therapeutic Targets for Alzheimer's Disease using Multi-omics Approach, Machine Learning and Docking.

The progressive deterioration of neurons leads to Alzheimer's disease (AD), and developing a drug for this disorder is challenging. Substantial gene/transcriptome variability from multiple cell types leads to downstream pathophysiologic consequences that represent the heterogeneity of this disease. Identifying potential biomarkers for promising therapeutics is strenuous due to the fact that the transcriptome, epigenetic, or proteome changes detected in patients are not clear whether they are the cause or consequence of the disease, which eventually makes the drug discovery efforts intricate. The advancement in scRNA-sequencing technologies helps to identify cell type-specific biomarkers that may guide the selection of the pathways and related targets specific to different stages of the disease progression. This review is focussed on the analysis of multi-omics data from various perspectives (genomic and transcriptomic variants, and single-cell expression), which provide insights to identify plausible molecular targets to combat this complex disease. Further, we briefly outlined the developments in machine learning techniques to prioritize the risk-associated genes, predict probable mutations and identify promising drug candidates from natural products.

Tumor Heterogeneity and Molecular Characteristics of Glioblastoma Revealed by Single-Cell RNA-Seq Data Analysis.

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.

Exploring Common Therapeutic Targets for Neurodegenerative Disorders Using Transcriptome Study.

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.

Comparative de novo transcriptome analysis identifies salinity stress responsive genes and metabolic pathways in sugarcane and its wild relative Erianthus arundinaceus [Retzius] Jeswiet.

Erianthus arundinaceus [Retzius] Jeswiet, a wild relative of sugarcane has a high biomass production potential and a reservoir of many genes for superior agronomic traits and tolerance to biotic and abiotic stresses. A comparative physiological, anatomical and root transcriptome analysis were carried out to identify the salt-responsive genes and metabolic pathways associated with salt-tolerant E. arundinaceus genotype IND99-907 and salinity-sensitive sugarcane genotype Co 97010. IND99-907 recorded growth of young leaves, higher proline content, higher relative water content, intact root anatomical structures and lower Na+/K+, Ca2+/K+ and Mg2+/K+ ratio as compared to the sugarcane genotype Co 97010. We have generated four de novo transcriptome assemblies between stressed and control root samples of IND99-907 and Co 97010. A total of 649 and 501 differentially expressed genes (FDR<0.01) were identified from the stressed and control libraries of IND99-907 and Co 97010 respectively. Genes and pathways related to early stress-responsive signal transduction, hormone signalling, cytoskeleton organization, cellular membrane stabilization, plasma membrane-bound calcium and proton transport, sodium extrusion, secondary metabolite biosynthesis, cellular transporters related to plasma membrane-bound trafficking, nucleobase transporter, clathrin-mediated endocytosis were highly enriched in IND99-907. Whereas in Co 97010, genes related to late stress-responsive signal transduction, electron transport system, senescence, protein degradation and programmed cell death, transport-related genes associated with cellular respiration and mitochondrial respiratory chain, vesicular trafficking, nitrate transporter and fewer secondary metabolite biosynthetic genes were highly enriched. A total of 27 pathways, 24 biological processes, three molecular functions and one cellular component were significantly enriched (FDR≤ 0.05) in IND99-907 as compared to 20 pathways, two biological processes without any significant molecular function and cellular components in Co 97010, indicates the unique and distinct expression pattern of genes and metabolic pathways in both genotypes. The genomic resources developed from this study is useful for sugarcane crop improvement through development of genic SSR markers and genetic engineering approaches.

VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

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.

Neurodegenerative Diseases - Is Metabolic Deficiency the Root Cause?

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.

Identifying suitable tools for variant detection and differential gene expression using RNA-seq data.

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.

Investigating the energy crisis in Alzheimer disease using transcriptome study.

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.

Isolation and characterization of water-deficit stress-responsive α-expansin 1 (EXPA1) gene from Saccharum complex.

In this study, full-length (1282-1330 bp) α-expansin 1 (EXPA1) gene from three different accessions belonging to Saccharum complex (Saccharum officinarum-SoEXPA1, Erianthus arundinaceus-EaEXPA1, and Saccharum spp. hybrid-ShEXPA1) was isolated using RAGE technique and characterized. The intronic and coding regions of isolated expansin genes ranged between 526-568 and 756-762 bp, respectively. An open reading frame encoding a polypeptide of 252 amino acids was obtained from S. officinarum and commercial sugarcane hybrid, whereas 254 amino acids were obtained in E. arundinaceus, a wild relative of Saccharum. Bioinformatics analysis of deduced protein revealed the presence of specific signature sequences and conserved amino acid residues crucial for the functioning of the protein. The predicted physicochemical characterization showed that the protein is stable in nature with instability index (II) value less than 40 and also clearly shown the dominance of random coil in the protein structure. Phylogenetic analysis revealed high conservation of EXPA1 among Saccharum complex and related crop species, Sorghum bicolor and Zea mays. The docking study of EXPA1 protein showed the interaction with xylose, which is present in xyloglucan of plant cell wall, elucidated the role of the expansin proteins in plant cell wall modification. This was further supported by the subcellular localization experiment in which it is clearly seen that the expansin protein localizes in the cell wall. Relative expression analysis of EXPA1 gene in Saccharum complex during drought stress showed high expression of the EaEXPA1 in comparison with SoEXPA1 and ShEXPA1 indicating possible role of EaEXPA1 in increased water-deficit stress tolerance in E. arundinaceus. These results suggest the potential use of EXPA1 for increasing the water-deficient stress tolerance levels in crop plants.

Exploring the selective vulnerability in Alzheimer disease using tissue specific variant analysis.

The selective vulnerability of distinct regions of the brain is a critical factor in neurodegenerative disorders. In Alzheimer's disease (AD), neurons in hippocampus situated in medial temporal lobe are immensely damaged. Identifying tissue-specific variants is essential in order to perceive the selective vulnerability in AD. In current work, we aligned mRNA-seq data with HG19/HG38 genomic assembly and identified specific variations present in temporal, frontal and other lobes of the AD using sequence alignment map tools. We compared the results with the genome-wide association and gene expression quantitative trait loci studies of the various neurological disorders. We also distinguished variants and epitranscriptomic modifications through the RNA-modification database and evaluated the variant effect in the coding/UTR regions. In addition, we developed genetic and functional interaction networks to understand the relationship between predicted vulnerable variations and differentially expressed genes. We found that genes involved in gliogenesis, intermediate filament organization are altered in the temporal lobe. Oxidative phosphorylation, and calcium ion homeostasis are modified in the frontal lobe, and protein degradation, apoptotic signaling are altered in other lobes. From this study, we propose that disruption of glial cell structural integrity, defective gliogenesis, and failure in glia-neuron communication are the primary factors for selective vulnerability.

De novo sequencing and transcriptome analysis of a low temperature tolerant Saccharum spontaneum clone IND 00-1037.

Saccharum spontaneum L., a wild relative of sugarcane, is known for its adaptability to environmental stresses, particularly cold stress. In the present study, an attempt was made for transcriptome profiling of the low temperature (10°C) tolerant S. spontaneum clone IND 00-1037 collected from high altitude regions of Arunachal Pradesh, North Eastern India. The Illumina Nextseq500 platform yielded a total of 47.63 and 48.18 million reads corresponding to 4.7 and 4.8 gigabase pairs (Gb) of processed reads for control and cold stressed (10°C for 24h) samples, respectively. These reads were de novo assembled into 214,611 unigenes with an average length of 801bp. Further, all unigenes were aligned to GO, KEGG and COG databases in order to identify novel genes and pathways responsive upon low temperature conditions. The differential gene expression analysis revealed that about 2583 genes were upregulated and 3302 genes were down regulated during the stress. This is perhaps the comprehensive transcriptome data of a low temperature tolerant clone of S. spontaneum. This study would aid in identifying novel genes and also in future genomic studies pertaining to sugarcane and its wild relatives.