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α-Diversity analysis of hepatic transcriptome reveals distinct pathways in alcohol-associated hepatitis
Sudrishti Chaudhary, Jia-Jun Liu, Silvia Liu, Marissa Di, Juliane I. Beier, Ramon Bataller, Josepmaria Argemi, Panayiotis V. Benos, Gavin E. Arteel
Sudrishti Chaudhary, Jia-Jun Liu, Silvia Liu, Marissa Di, Juliane I. Beier, Ramon Bataller, Josepmaria Argemi, Panayiotis V. Benos, Gavin E. Arteel
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Research Article Hepatology Inflammation

α-Diversity analysis of hepatic transcriptome reveals distinct pathways in alcohol-associated hepatitis

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Abstract

Next-generation sequencing can identify previously uncharacterized gene expression patterns in disease. Beyond differentially expressed gene (DEG) analysis, we investigated the ability of within-population diversity (α-diversity) of the transcriptome to reveal additional biological information in alcohol-associated liver disease (ALD), comparing differential Shannon diversity (DSD) to transcriptome heterogeneity changes. RNA sequencing data from normal livers and patients with early ALD and severe AH were analyzed. α-Diversity indices and percentage Shannon diversity of a gene, which refers to this gene’s contribution to total Shannon entropy, were calculated. Ingenuity pathway analysis identified canonical pathways determined by DEG and DSD approaches. ALD significantly decreased hepatic transcriptome α-diversity, correlating with increased relative contribution of select genes. These changes were driven by lower-abundance gene expression loss. DEG and DSD analyses showed overlapping genes and canonical pathways, but DSD also identified additional genes and pathways not highlighted by DEGs, including fatty acid oxidation, extracellular matrix degradation, and cholesterol metabolism pathways that may represent additional therapeutic targets. Importantly, DSD more effectively identified differences between ASH and AH. Overall, α-diversity analysis revealed that ALD progressively reduces transcriptome heterogeneity, and that DSD provides complementary insights into disease mechanisms missed by standard approaches.

Authors

Sudrishti Chaudhary, Jia-Jun Liu, Silvia Liu, Marissa Di, Juliane I. Beier, Ramon Bataller, Josepmaria Argemi, Panayiotis V. Benos, Gavin E. Arteel

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Figure 9

Liver cell type distribution.

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Liver cell type distribution.
(A) Radar plot displaying liver cell type ...
(A) Radar plot displaying liver cell type distribution in healthy controls (n = 10) on log10 scale. Data points show mean values positioned on radial axes, with error bars representing ±1 SEM. Dashed lines indicate upper and lower confidence boundaries (mean ± SEM). Vector length is proportional to log10(cell count), effectively visualizing the 643-fold dynamic range from 0.1 (monocytes) to 64.3 (hepatocytes) cells per field. Gold circle around monocytes indicates high measurement uncertainty (±100% CV). (B) Heatmap showing percentage change from healthy controls across disease conditions with integrated statistical significance indicators. ASH (n = 12) and AH (n = 18) columns display changes for each cell type. Complete cell loss represented as –100% change in dark blue. White indicates minimal change (~0%). Statistical significance displayed below each percentage value: ***P < 0.001, **P < 0.01, *P < 0.05 by Welch’s t test versus healthy controls. NS, not significant.

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