21. Metabolomic Profiling Reveals Distinct Breed-Specific Metabolite Signatures in Buck Sperm.

Abstract The objectives of this study were to characterize and compare the metabolomic profiles of sperm samples from bucks using untargeted LC-MS, with a focus on identifying group-specific metabolic features and discriminating biomarkers. Untargeted LC-MS profiling was conducted on biological samples from three groups: SPN (n = 4), ALP (n = 8), and BR (n = 8). Data were acquired in both positive (n = 196 features) and negative (n = 121 features) ionization modes, yielding 317 annotated metabolites following quality filtering. Metabolite annotation was performed using accurate mass, retention time, MS/MS fragmentation scoring, and database cross-referencing against HMDB, KEGG, and ChemSpider. Normalized data were log2-transformed and autoscaled prior to multivariate analysis. Principal component analysis (PCA) was applied to assess overall group structure and analytical reproducibility, with quality control (QC) samples included to monitor instrument stability. Differential abundance between groups was assessed using adjusted p-values (Benjamini–Hochberg correction, padj < 0.05) and log2 fold change (LFC).The 317 annotated metabolites spanned a broad chemical space, with lipids and lipid-like molecules constituting the dominant super-class (n = 178, 56%). The major chemical classes were fatty acyls (n = 72), glycerophospholipids (n = 51), steroids and steroid derivatives (n = 21), organooxygen compounds (n = 21), glycerolipids (n = 13), and sphingolipids (n = 8), reflecting the lipid-rich character of the biological matrices examined. PCA of the 317-feature dataset demonstrated clear group structure, with PC1 (37.6%) and PC2 (15.8%) together explaining 53.3% of total variance. The comparison between SPN and BR yielded 154 significantly altered metabolites (padj < 0.05), representing 49% of all annotated features. Of these, 55 were elevated and 59 were reduced in SPN relative to BR. The most discriminating metabolites by VIP score were PUFA-enriched lysophosphatidylethanolamine (LysoPE) species — specifically LysoPE(20:4), LysoPE(0:0/20:4), and LysoPE(DHA/22:6) — a long-chain acylcarnitine (tetracosahexaenoylcarnitine, LFC = −2.83), LysoPC(20:4) (LFC = −2.79), arachidonic acid (LFC = −3.17), and docosahexaenoic acid (DHA, LFC = −2.06), all significantly reduced in SPN compared to BR. Glycerophospholipids and fatty acyls accounted for the majority of significantly altered features (n = 32 and n = 44, respectively). Metabolites elevated in SPN included undecanedioic acid (LFC = +1.66), prostaglandin F3α (LFC = +3.90), PE(22:2/P-16:0) (LFC = +3.18), PC(15:0/14:0) (LFC = +3.09), and 8-iso-15-keto-PGF2α (LFC = +3.01). These results provide a reproducible metabolomic reference framework for sperm biology and reproduction, and nominate PUFA-enriched lysophospholipids as candidate biomarkers warranting further functional validation.

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Journal
Journal of Animal Science
Published
2026-09-29
DOI
https://doi.org/10.1093/jas/skag272.301
Primary Topic
Sperm and Testicular Function
Type
article
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article

21. Metabolomic Profiling Reveals Distinct Breed-Specific Metabolite Signatures in Buck Sperm.

Mustafa Bodu, Mustafa Hitit, Erdoğan Memili, Seher Şimşek et al.
Journal of Animal Science
Sperm and Testicular Function
article

21. Metabolomic Profiling Reveals Distinct Breed-Specific Metabolite Signatures in Buck Sperm.

Mustafa Bodu, Mustafa Hitit, Erdoğan Memili, Seher Şimşek, Kamari D King, Trinity Wilson, Andres Alfredo Pech Cervantes, Raheem D Murray, Keenan Hardy, Amari Jackson
article en

Abstract

Abstract The objectives of this study were to characterize and compare the metabolomic profiles of sperm samples from bucks using untargeted LC-MS, with a focus on identifying group-specific metabolic features and discriminating biomarkers. Untargeted LC-MS profiling was conducted on biological samples from three groups: SPN (n = 4), ALP (n = 8), and BR (n = 8). Data were acquired in both positive (n = 196 features) and negative (n = 121 features) ionization modes, yielding 317 annotated metabolites following quality filtering. Metabolite annotation was performed using accurate mass, retention time, MS/MS fragmentation scoring, and database cross-referencing against HMDB, KEGG, and ChemSpider. Normalized data were log2-transformed and autoscaled prior to multivariate analysis. Principal component analysis (PCA) was applied to assess overall group structure and analytical reproducibility, with quality control (QC) samples included to monitor instrument stability. Differential abundance between groups was assessed using adjusted p-values (Benjamini–Hochberg correction, padj < 0.05) and log2 fold change (LFC).The 317 annotated metabolites spanned a broad chemical space, with lipids and lipid-like molecules constituting the dominant super-class (n = 178, 56%). The major chemical classes were fatty acyls (n = 72), glycerophospholipids (n = 51), steroids and steroid derivatives (n = 21), organooxygen compounds (n = 21), glycerolipids (n = 13), and sphingolipids (n = 8), reflecting the lipid-rich character of the biological matrices examined. PCA of the 317-feature dataset demonstrated clear group structure, with PC1 (37.6%) and PC2 (15.8%) together explaining 53.3% of total variance. The comparison between SPN and BR yielded 154 significantly altered metabolites (padj < 0.05), representing 49% of all annotated features. Of these, 55 were elevated and 59 were reduced in SPN relative to BR. The most discriminating metabolites by VIP score were PUFA-enriched lysophosphatidylethanolamine (LysoPE) species — specifically LysoPE(20:4), LysoPE(0:0/20:4), and LysoPE(DHA/22:6) — a long-chain acylcarnitine (tetracosahexaenoylcarnitine, LFC = −2.83), LysoPC(20:4) (LFC = −2.79), arachidonic acid (LFC = −3.17), and docosahexaenoic acid (DHA, LFC = −2.06), all significantly reduced in SPN compared to BR. Glycerophospholipids and fatty acyls accounted for the majority of significantly altered features (n = 32 and n = 44, respectively). Metabolites elevated in SPN included undecanedioic acid (LFC = +1.66), prostaglandin F3α (LFC = +3.90), PE(22:2/P-16:0) (LFC = +3.18), PC(15:0/14:0) (LFC = +3.09), and 8-iso-15-keto-PGF2α (LFC = +3.01). These results provide a reproducible metabolomic reference framework for sperm biology and reproduction, and nominate PUFA-enriched lysophospholipids as candidate biomarkers warranting further functional validation.

Journal of Animal ScienceVol. 104(Supplement_5)
Ministry of Agriculture and Forestry (LA), Tarımsal Araştırmalar ve Politikalar Genel Müdürlüğü (TR)
Reduced inequalities
Openalex Percentile: Top 9%
Sperm and Testicular Function
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