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Abstract Background Ischemic stroke (IS) is a principal contributor to long-term disability in adults. A new cell death mediated by iron is ferroptosis, characterized by lethal aggregation of lipid peroxidation. However, a paucity of ferroptosis-related biomarkers early identify IS until now. This study investigated potential ferroptosis-related gene pair biomarkers in IS and explored their roles in immune infiltration. Results In total, we identified 6 differentially expressed ferroptosis-related genes (DEFRGs) in the metadata cohort. Of these genes, 4 DEFRGs were incorporated into the competitive endogenous RNA (ceRNA) network, including 78 lncRNA-miRNA and 16 miRNA-mRNA interactions. Based on relative expression values of DEFRGs, we constructed gene pairs. An integrated scheme consisting of machine learning algorithms, ceRNA network, and gene pair was proposed to screen the key DEFRG biomarkers. The receiver operating characteristic (ROC) curve witnessed that the diagnostic performance of DEFRG pair CDKN1A/JUN was superior to that of single gene. Moreover, the CIBERSORT algorithm exhibited immune infiltration landscapes: plasma cells, resting NK cells, and resting mast cells infiltrated less in IS samples than controls. Spearman correlation analysis confirmed a significant correlation between plasma cells and CDKN1A/JUN (CDKN1A: r = − 0.503, P

Abstract Background Fast, complete, and ultimate removal of inhibitory compounds derived from lignocellulose pretreatment is the prerequisite for efficient production of cellulosic ethanol and biochemicals. Biodetoxification is the most promising method for inhibitor removal by its unique advantages. The biodetoxification mechanisms of a unique diploid fungus responsible for highly efficient biodetoxification in solid-state culture was extensively investigated in the aspects of cellular structure, genome sequencing, transcriptome analysis, and practical biodetoxification. Results The inborn heterozygous diploid structure of A. resinae ZN1 uniquely contributed to the enhancement of inhibitor tolerance and conversion. The co-expression of gene pairs contributed to the enhancement of the degradation of lignocellulose-derived model inhibitors. The ultimate inhibitors degradation pathways and sugar conservation were elucidated by microbial degradation experimentation as well as the genomic and transcriptomic sequencing analysis. Conclusions The finding of the heterozygous diploid structure in A. resinae ZN1 on biodetoxification took the first insight into the global overview of biodetoxification mechanism of lignocellulose-derived inhibitors. This study provided a unique and practical biodetoxification biocatalyst of inhibitor compounds for lignocellulose biorefinery processing, as well as the synthetic biology tools on biodetoxification of biorefinery fermenting strains.

Abstract Background This article concerns the identification of gene pairs or combinations of gene pairs associated with biological phenotype or clinical outcome, allowing for building predictive models that are not only robust to normalization but also easily validated and measured by qPCR techniques. However, given a small number of biological samples yet a large number of genes, this problem suffers from the difficulty of high computational complexity and imposes challenges to the accuracy of identification statistically. Results In this paper, we propose a parsimonious model representation and develop efficient algorithms for identification. Particularly, we derive an equivalent model subject to a sum-to-zero constraint in penalized linear regression, where the correspondence between nonzero coefficients in these models is established. Most importantly, it reduces the model complexity of the traditional approach from the quadratic order to the linear order in the number of candidate genes, while overcoming the difficulty of model nonidentifiablity. Computationally, we develop an algorithm using the alternating direction method of multipliers (ADMM) to deal with the constraint. Numerically, we demonstrate that the proposed method outperforms the traditional method in terms of the statistical accuracy. Moreover, we demonstrate that our ADMM algorithm is more computationally efficient than a coordinate descent algorithm with a local search. Finally, we illustrate the proposed method on a prostate cancer dataset to identify gene pairs that are associated with pre-operative prostate-specific antigen. Conclusion Our findings demonstrate the feasibility and utility of using gene pairs as biomarkers.