Dataset Information

Similarities and dissimilarities between psychiatric cluster disorders.

ABSTRACT: The common molecular mechanisms underlying psychiatric disorders are not well understood. Prior attempts to assess the pathological mechanisms responsible for psychiatric disorders have been limited by biased selection of comparable disorders, datasets/cohort availability, and challenges with data normalization. Here, using DisGeNET, a gene-disease associations database, we sought to expand such investigations in terms of number and types of diseases. In a top-down manner, we analyzed an unbiased cluster of 36 psychiatric disorders and comorbid conditions at biological pathway, cell-type, drug-target, and chromosome levels and deployed density index, a novel metric to quantify similarities (close to 1) and dissimilarities (close to 0) between these disorders at each level. At pathway level, we show that cognition and neurotransmission drive the similarity and are involved across all disorders, whereas immune-system and signal-response coupling (cell surface receptors, signal transduction, gene expression, and metabolic process) drives the dissimilarity and are involved with specific disorders. The analysis at the drug-target level supports the involvement of neurotransmission-related changes across these disorders. At cell-type level, dendrite-targeting interneurons, across all layers, are most involved. Finally, by matching the clustering pattern at each level of analysis, we showed that the similarity between the disorders is influenced most at the chromosomal level and to some extent at the cellular level. Together, these findings provide first insights into distinct cellular and molecular pathologies, druggable mechanisms associated with several psychiatric disorders and comorbid conditions and demonstrate that similarities between these disorders originate at the chromosome level and disperse in a bottom-up manner at cellular and pathway levels.

SUBMITTER: Smail MA

PROVIDER: S-EPMC8313609 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundWith the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data.ResultsInspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets.ConclusionsIn summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

Project description:Nitrogen levels in grape-juices are of major importance in winemaking ensuring adequate yeast growth and fermentation performance. Here we used a comparative transcriptome analysis to uncover wine yeasts responses to nitrogen availability during fermentation. Gene expression was assessed in three genetically and phenotypically divergent commercial wine strains (CEG, VL1 and QA23), under low (67 mg/L) and high nitrogen (670 mg/L) regimes, at three time points during fermentation (12 h, 24 h and 96 h). Two-way ANOVA analysis of each fermentation condition led to the identification of genes whose expression was dependent on strain, fermentation stage and on the interaction of both factors. The high fermenter yeast strain QA23 was more clearly distinct from the other two strains, by differential expression of genes involved in flocculation, mitochondrial functions, energy generation and protein folding and stabilization. For all strains, higher transcriptional variability due to fermentation stage was seen in the high nitrogen fermentations. A positive correlation between maximum fermentation rate and the expression of genes involved in stress response was observed. The finding of common genes correlated with both fermentation activity and nitrogen up-take underlies the role of nitrogen on yeast fermentative fitness. The comparative analysis of genes differentially expressed between both fermentation conditions at 12 h, where the main difference was the level of nitrogen available, showed the highest variability amongst strains revealing strain-specific responses. Nevertheless, we were able to identify a small set of genes whose expression profiles can quantitatively assess the common response of the yeast strains to varying nitrogen conditions. The use of three contrasting yeast strains in gene expression analysis prompts the identification of more reliable, accurate and reproducible biomarkers that will facilitate the diagnosis of deficiency of this nutrient in the grape-musts and the development of strategies to optimize yeast performance in industrial fermentations.

Project description:BackgroundIn vertebrates, the primordium of the brain is subdivided by the expression of Otx genes (forebrain/anterior midbrain), Hox genes (posterior hindbrain), and the genes Pax2, Pax5 and Pax8 (intervening region). The latter includes the midbrain/hindbrain boundary (MHB), which acts as a key organizer during brain patterning. Recent studies in Drosophila revealed that orthologous sets of genes are expressed in a similar tripartite pattern in the late embryonic brain, which suggested correspondence between the Drosophila deutocerebral/tritocerebral boundary region and the vertebrate MHB. To gain more insight into the evolution of brain regions, and particularly the MHB, I examined the expression of a comprehensive array of MHB-specific gene orthologs in the procephalic neuroectoderm and in individually identified neuroblasts during early embryonic stages 8-11, at which the segmental organization of the brain is most clearly displayed.Results and conclusionI show that the early embryonic brain exhibits an anterior Otx/otd domain and a posterior Hox1/lab domain, but that Pax2/5/8 orthologs are not expressed in the neuroectoderm and neuroblasts of the intervening territory. Furthermore, the expression domains of Otx/otd and Gbx/unpg exhibit a small common interface within the anterior deutocerebrum. In contrast to vertebrates, Fgf8-related genes are not expressed posterior to the otd/unpg interface. However, at the otd/unpg interface the early expression of other MHB-specific genes (including btd, wg, en), and of dorsoventral patterning genes, closely resembles the situation at the vertebrate MHB. Altogether, these results suggest the existence of an ancestral territory within the primordium of the deutocerebrum and adjacent protocerebrum, which might be the evolutionary equivalent of the region of the vertebrate MHB. However, lack of expression of Pax2/5/8 and Fgf8-related genes, and significant differences in the expression onset of other key regulators at the otd/unpg interface, imply that genetic interactions crucial for the vertebrate organizer activity are absent in the early embryonic brain of Drosophila.

Dataset Information

Similarities and dissimilarities between psychiatric cluster disorders.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets