Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing

Sagulkoo, Pakorn; Suratanee, Apichat; Plaimas, Kitiporn

doi:10.3390/biom12050690

Cited by 5 publications

(2 citation statements)

References 238 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Markov clustering algorithm (MCL) was used to identify the gene sub-networks ( 55 ). The inflation parameter of MCL was set to 1.5 ( 56 ). The functional enrichment analysis of each module was also performed in the STRING v11.5 database using the GO terms and REACTOME pathway.…”

Section: Methodsmentioning

confidence: 99%

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences

Cuppens,

Kaur,

Kumar

et al. 2023

Front. Pediatr.

View full text Add to dashboard Cite

ObjectiveIndividuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering.MethodsUsing the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach.ResultsHAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3–12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC.ConclusionOur study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.

show abstract

Section: Methodsmentioning

confidence: 99%

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences

Cuppens,

Kaur,

Kumar

et al. 2023

Front. Pediatr.

View full text Add to dashboard Cite

show abstract

“…The Markov Clustering Algorithm (MCL) was used to identify gene subnetworks ( 45 ). The inflation parameter of MCL was set to 1.5 ( 46 ). Functional enrichment analysis of each module was also performed in the STRING v11.5 database using the GO terms, REACTOME pathways, and KEGG pathways.…”

Section: Methodsmentioning

confidence: 99%

Sex difference contributes to phenotypic diversity in individuals with neurodevelopmental disorders

Cuppens,

Shatto,

Mangnier

et al. 2023

Front. Pediatr.

View full text Add to dashboard Cite

ObjectiveGain a better understanding of sex-specific differences in individuals with global developmental delay (GDD), with a focus on phenotypes and genotypes.MethodsUsing the Deciphering Developmental Disorders (DDD) dataset, we extracted phenotypic information from 6,588 individuals with GDD and then identified statistically significant variations in phenotypes and genotypes based on sex. We compared genes with pathogenic variants between sex and then performed gene network and molecular function enrichment analysis and gene expression profiling between sex. Finally, we contrasted individuals with autism as an associated condition.ResultsWe identified significantly differentially expressed phenotypes in males vs. females individuals with GDD. Autism and macrocephaly were significantly more common in males whereas microcephaly and stereotypies were more common in females. Importantly, 66% of GDD genes with pathogenic variants overlapped between both sexes. In the cohort, males presented with only slightly increased X-linked genes (9% vs. 8%, respectively). Individuals from both sexes harbored a similar number of pathogenic variants overall (3) but females presented with a significantly higher load for GDD genes with high intolerance to loss of function. Sex difference in gene expression correlated with genes identified in a sex specific manner. While we identified sex-specific GDD gene mutations, their pathways overlapped. Interestingly, individuals with GDD but also co-morbid autism phenotypes, we observed distinct mutation load, pathways and phenotypic presentation.ConclusionOur study shows for the first time that males and females with GDD present with significantly different phenotypes. Moreover, while most GDD genes overlapped, some genes were found uniquely in each sex. Surprisingly they shared similar molecular functions. Sorting genes by predicted tolerance to loss of function (pLI) led to identifying an increased mutation load in females with GDD, suggesting potentially a tolerance to GDD genes of higher pLI compared to overall GDD genes. Finally, we show that considering associated conditions (for instance autism) may influence the genomic underpinning found in individuals with GDD and highlight the importance of comprehensive phenotyping.

show abstract

Reconstruction of genomic and proteomic scale network structures and functions

Choudhari,

Gupta,

Bhatt

et al. 2025

Systems Biology and in-Depth Applications for Unlocking Diseases

View full text Add to dashboard Cite

Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing

Cited by 5 publications

References 238 publications

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences

Sex difference contributes to phenotypic diversity in individuals with neurodevelopmental disorders

Reconstruction of genomic and proteomic scale network structures and functions

Contact Info

Product

Resources

About