Predicting hosts and cross-species transmission of Streptococcus agalactiae by interpretable machine learning

Ren, Yunxiao; Li, Carmen; Nanayakkara Sapugahawatte, Dulmini; Zhu, Chendi; Spänig, Sebastian; Jamrozy, Dorota; Rothen, Julian; Daubenberger, Claudia A.; Bentley, Stephen D.; Ip, Margaret; Heider, Dominik

doi:10.1016/j.compbiomed.2024.108185

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…This decrease was statistically significant when juxtaposed against the outcomes observed in the NC and BC groups (p < 0.01), underscoring the pivotal role of AGPAT4 in modulating these critical cellular functions. of extensive genetic and transcriptomic datasets, facilitating the discernment of intricate patterns and correlations within the vast expanse of bioinformatics data (Li et al 2023;Ren et al 2024). This confluence of Mendelian Randomization, eQTL analytics, and advanced computational algorithms has significantly augmented our capacity to decode the genetic and molecular frameworks of endometriosis, thereby propelling our understanding of its pathophysiology to new horizons (de Leeuw et al 2022;Li et al 2023).…”

Section: Suppression Of Agpat4 Attenuates Migration and Invasion Capa...mentioning

confidence: 99%

Unraveling the significance of AGPAT4 for the pathogenesis of endometriosis via a multi-omics approach

Chen,

Shen,

et al. 2024

Hum. Genet.

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Endometriosis is characterized by the ectopic proliferation of endometrial cells, posing considerable diagnostic and therapeutic challenges. Our study investigates AGPAT4’s involvement in endometriosis pathogenesis, aiming to unveil new therapeutic targets. Our investigation by analyzing eQTL data from GWAS for preliminary screening. Subsequently, within the GEO dataset, we utilized four machine learning algorithms to precisely identify risk-associated genes. Gene validity was confirmed through five Mendelian Randomization methods. AGPAT4 expression was measured by Single-Cell Analysis, ELISA and immunohistochemistry. We investigated AGPAT4’s effect on endometrial stromal cells using RNA interference, assessing cell proliferation, invasion, and migration with CCK8, wound-healing, and transwell assays. Protein expression was analyzed by western blot, and AGPAT4 interactions were explored using AutoDock. Our investigation identified 11 genes associated with endometriosis risk, with AGPAT4 and COMT emerging as pivotal biomarkers through machine learning analysis. AGPAT4 exhibited significant upregulation in both ectopic tissues and serum samples from patients with endometriosis. Reduced expression of AGPAT4 was observed to detrimentally impact the proliferation, invasion, and migration capabilities of endometrial stromal cells, concomitant with diminished expression of key signaling molecules such as Wnt3a, β-Catenin, MMP-9, and SNAI2. Molecular docking analyses further underscored a substantive interaction between AGPAT4 and Wnt3a.Our study highlights AGPAT4’s key role in endometriosis, influencing endometrial stromal cell behavior, and identifies AGPAT4 pathways as promising therapeutic targets for this condition.

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Section: Suppression Of Agpat4 Attenuates Migration and Invasion Capa...mentioning

confidence: 99%

Unraveling the significance of AGPAT4 for the pathogenesis of endometriosis via a multi-omics approach

Chen,

Shen,

et al. 2024

Hum. Genet.

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Metagenomic next-generation sequencing assistance in identifying Mycobacterium iranicum pulmonary infection: A case report

Xu,

Wang,

Song

et al. 2024

Diagnostic Microbiology and Infectious Disease

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XAI Unveiled: Revealing the Potential of Explainable AI in Medicine - A Systematic Review

Scarpato,

Ferroni,

Guadagni

2024

IEEE Access

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Predicting hosts and cross-species transmission of Streptococcus agalactiae by interpretable machine learning

Cited by 3 publications

References 35 publications

Unraveling the significance of AGPAT4 for the pathogenesis of endometriosis via a multi-omics approach

Unraveling the significance of AGPAT4 for the pathogenesis of endometriosis via a multi-omics approach

Metagenomic next-generation sequencing assistance in identifying Mycobacterium iranicum pulmonary infection: A case report

XAI Unveiled: Revealing the Potential of Explainable AI in Medicine - A Systematic Review

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