Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock

Hu, Yingchun; Liu, Cheng; Zhong, Wu; Chen, Muhu; Zhang, Qian

doi:10.12659/msm.918491

Cited by 16 publications

(15 citation statements)

References 48 publications

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“…Existing asthma-related targets were obtained from the following five resources: (1) Omicshare tools (http://www.omicshare.com/tools) 34 to better understand their roles.…”

Section: Collection Of the Known Asthma-related Targetsmentioning

confidence: 99%

Molecular mechanisms of An-Chuan Granule for the treatment of asthma based on a network pharmacology approach and experimental validation

et al. 2021

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An-Chuan Granule (ACG), a traditional Chinese medicine (TCM) formula, is an effective treatment for asthma but its pharmacological mechanism remains poorly understood. In this study, network pharmacology was applied to explore the potential mechanism of ACG in the treatment of asthma. The tumor necrosis factor (TNF), toll-like receptor (TLR), and Th17 cell differentiation-related, nucleotide-binding oligomerization domain (NOD)-like receptor, and NF-kappaB pathways were identified as the most significant signaling pathways involved in the therapeutic effect of ACG on asthma. A mouse asthma model was established using ovalbumin (OVA) to verify the effect of ACG and the underlying mechanism. The results showed that ACG treatment not only attenuated the clinical symptoms, but also reduced inflammatory cell infiltration, mucus secretion and MUC5AC production in lung tissue of asthmatic mice. In addition, ACG treatment notably decreased the inflammatory cell numbers in bronchoalveolar lavage fluid (BALF) and the levels of pro-inflammatory cytokines (including IL-6, IL-17, IL-23, TNF-alpha, IL-1beta and TGF-beta) in lung tissue of asthmatic mice. In addition, ACG treatment remarkably down-regulated the expression of TLR4, p-P65, NLRP3, Caspase-1 and adenosquamous carcinoma (ASC) in lung tissue. Further, ACG treatment decreased the expression of receptor-related orphan receptor (RORγt) in lung tissue but increased that of Forkhead box (Foxp3). In conclusion, the above results demonstrate that ACG alleviates the severity of asthma in a “multi-compound and multi-target” manner, which provides a basis for better understanding of the application of ACG in the treatment of asthma.

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“…Existing asthma-related targets were obtained from the following five resources: (1) Omicshare tools (http://www.omicshare.com/tools) 34 to better understand their roles.…”

Section: Collection Of the Known Asthma-related Targetsmentioning

confidence: 99%

Molecular mechanisms of An-Chuan Granule for the treatment of asthma based on a network pharmacology approach and experimental validation

et al. 2021

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“…Finally, we explored the possibility to use ensemble gene noise in the prediction of clinical outcomes. Previously some promising biomarkers and gene expression endotypes associated with septic shock and mortality have been identified based on DGE analysis [8,9]. However, as already mentioned, ensemble gene noise looks at gene expression from a different, yet complementary, angle, thus enabling the identification of novel pathways and biomarkers for sepsis and other diseases.…”

Section: Because Viral/bacterial Infections and Sepsis Results In Overmentioning

confidence: 99%

“…Thus, despite recent progress in identification of molecular biomarkers for sepsis [8][9][10][11][12][13][14][15], treatment remains mainly non-curative and clinical outcomes are mostly inferred from clinical signs [5].…”

Section: Introductionmentioning

confidence: 99%

Discovery of pharmaceutically-targetable pathways and prediction of survivorship for pneumonia and sepsis patients from the view point of ensemble gene noise

Guryev

Moshkin

2020

Preprint

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Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is rather challenging. This stems from the heterogenous response of individuals to disease which is also reflected in the inter-individual variability of gene expression responses. This in turn obscures differential gene expression analysis (DGE). In the midst of the COVID-19 pandemic, we wondered whether an alternative to DGE approaches could be applied to dissect the molecular nature of a host-response to infection exemplified here by an analysis of H1N1 influenza, community/hospital acquired pneumonia (CAP) and sepsis. To this end, we turned to the analysis of ensemble gene noise. Ensemble gene noise, as we defined it here, represents a variance within an individual for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes. From the law of total variance, ensemble gene noise depends on the stoichiometry of the ensemble genes' expression and on their average noise (variance). Thus, rather than focusing on specific genes, ensemble gene noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. Comparing H1N1, CAP and sepsis patients we spotted common disturbances in a number of pathways/protein complexes relevant to the sepsis pathology which lead to an increase in the ensemble gene noise. Among others, these include mitochondrial respiratory chain complex I and peroxisomes which could be readily targeted for adjuvant treatment by methylene blue and 4-phenylbutyrate respectively. Finally, we showed that ensemble gene noise could be successfully applied for the prediction of clinical outcome, namely mortality, of CAP and sepsis patients. Thus, we conclude that ensemble gene noise represents a promising approach for the investigation of molecular mechanisms of a pathology through a prism of alterations in coherent expression of gene circuits.

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“…Finally, we explored the possibility to use gene ensemble noise in the prediction of clinical outcomes. Previously some promising biomarkers and gene expression endotypes associated with septic shock and mortality have been identified based on DGE analysis 8 , 15 . However, as already mentioned, gene ensemble noise looks at gene expression from a different, yet complementary, angle, thus enabling the identification of novel pathways and biomarkers for sepsis and other diseases.…”

Section: Discussionmentioning

confidence: 99%

Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients

Jong

Guryev

Moshkin

2021

Sci Rep

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Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene expression analysis. Here, we developed an alternative approach that could be applied to dissect the disease-associated molecular changes. We define gene ensemble noise as a measure that represents a variance for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes and calculated within an individual. The gene ensemble noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. By comparing gene expression data from COVID-19, H1N1, and sepsis patients we identified common disturbances in a number of pathways and protein complexes relevant to the sepsis pathology. Among others, these include the mitochondrial respiratory chain complex I and peroxisomes. This suggests a Warburg effect and oxidative stress as common hallmarks of the immune host–pathogen response. Finally, we showed that gene ensemble noise could successfully be applied for the prediction of clinical outcome namely, the mortality of patients. Thus, we conclude that gene ensemble noise represents a promising approach for the investigation of molecular mechanisms of pathology through a prism of alterations in the coherent expression of gene circuits.

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Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock

Cited by 16 publications

References 48 publications

Molecular mechanisms of An-Chuan Granule for the treatment of asthma based on a network pharmacology approach and experimental validation

Molecular mechanisms of An-Chuan Granule for the treatment of asthma based on a network pharmacology approach and experimental validation

Discovery of pharmaceutically-targetable pathways and prediction of survivorship for pneumonia and sepsis patients from the view point of ensemble gene noise

Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients

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