Human pathologies such as Alzheimer’s disease, type 2 diabetes-induced insulin resistance, cancer, and cardiovascular diseases have altered lipid homeostasis. Among these imbalanced lipids, the bioactive sphingolipids ceramide and sphingosine-1 phosphate (S1P) are pivotal in the pathophysiology of these diseases. Several enzymes within the sphingolipid pathway contribute to the homeostasis of ceramide and S1P. Ceramidase is key in the degradation of ceramide into sphingosine and free fatty acids. In humans, five different ceramidases are known—acid ceramidase, neutral ceramidase, and alkaline ceramidase 1, 2, and 3—which are encoded by five different genes (ASAH1, ASAH2, ACER1, ACER2, and ACER3, respectively). Notably, the neutral ceramidase N-acylsphingosine amidohydrolase 2 (ASAH2) shows considerable differences between humans and animals in terms of tissue expression levels. Besides, the subcellular localization of ASAH2 remains controversial. In this review, we sum up the results obtained for identifying gene divergence, structure, subcellular localization, and manipulating factors and address the role of ASAH2 along with other ceramidases in human diseases.

Motivation MIB2 attempts to overcome the limitation of structure-based prediction approaches, with many proteins lacking a solved structure. MIB2 also offers more accurate prediction performance and more metal ion types. Results MIB2 utilizes both the (PS)2 method and the AlphaFold Protein Structure Database to acquire predicted structures to perform metal ion docking and predict binding residues. MIB2 offers marked improvements over MIB by collecting more metal ion-binding residue templates and using the metal ion type-specific scoring function. It offers a total of 18 types of metal ions for binding site predictions. Availability Freely available on the web at http://bioinfo.cmu.edu.tw/MIB2/. Supplementary information Supplementary data are available at Bioinformatics online.

Microbial diversity has always presented taxonomic challenges. With the popularity of next-generation sequencing technology, more unculturable bacteria have been sequenced, facilitating the discovery of additional new species and complicated current microbial classification. The major challenge is to assign appropriate taxonomic names. Hence, assessing the consistency between taxonomy and genomic relatedness is critical. We proposed and applied a genome comparison approach to a large-scale survey to investigate the distribution of genomic differences among microorganisms. The approach applies a genome-wide criterion, homologous coverage ratio (HCR), for describing the homology between species. The survey included 7861 microbial genomes that excluded plasmids, and 1220 pairs of genera exhibited ambiguous classification. In this study, we also compared the performance of HCR and average nucleotide identity (ANI). The results indicated that HCR and ANI analyses yield comparable results, but a few examples suggested that HCR has a superior clustering effect. In addition, we used the Genome Taxonomy Database (GTDB), the gold standard for taxonomy, to validate our analysis. The GTDB offers 120 ubiquitous single-copy proteins as marker genes for species classification. We determined that the analysis of the GTDB still results in classification boundary blur between some genera and that the marker gene-based approach has limitations. Although the choice of marker genes has been quite rigorous, the bias of marker gene selection remains unavoidable. Therefore, methods based on genomic alignment should be considered for use for species classification in order to avoid the bias of marker gene selection. On the basis of our observations of microbial diversity, microbial classification should be re-examined using genome-wide comparisons.

Antimicrobial resistance (AMR) in pathogenic microorganisms with multidrug resistance (MDR) constitutes a severe threat to human health. A major causative mechanism of AMR is mediated through the multidrug efflux pump (MEP). The resistance-nodulation-division superfamily (RND family) of Gram-negative bacteria is usually the major cause of MDR in clinical studies. In Salmonella enterica, the RND pump is translated from the acrAB gene, which is regulated by the activator RamA. Many MEP-caused AMR strains have high ramA gene expression due to mutations in RamR, which has a homodimeric structure comprising the dimerization domain and DNA-binding domain (DBD). Three mutations on the dimerization domain, namely Y59H, M84I, and E160D, are far from the DBD; the molecular mechanism through which they influence RamR’s binding affinity to the ramA gene promoter and consequently disrupt RamA remains unclear. The present study conducted molecular dynamics simulations, binding free energy calculations, and normal mode analysis to investigate the mechanism through which Y59H, M84I, and E160D mutations on the dimerization domain influence the binding affinity of RamR to the ramA promoter. The present results suggest that the three mutations alter the RamR structure, resulting in decreased DNA-binding affinity.

Lactobacillus acidophilus is one of the most commonly used industrial products worldwide. Since its probiotic efficacy is strain-specific, the identification of probiotics at both the species and strain levels is necessary. However, neither phenotypic nor conventional genotypic methods have enabled the effective differentiation of L. acidophilus strains. In this study, a whole-genome sequence-based analysis was carried out to establish high-resolution strain typing of 41 L. acidophilus strains (including commercial isolates and reference strains) using the cano-wgMLST_BacCompare analytics platform; consequently, a strain-specific discrimination method for the probiotic strain LA1063 was developed. Using a core-genome multilocus sequence-typing (cgMLST) scheme based on 1390 highly conserved genes, 41 strains could be assigned to 34 sequence types. Subsequently, we screened a set of 92 loci with a discriminatory power equal to that of the 1390 loci cgMLST scheme. A strain-specific polymerase chain reaction combined with a multiplex minisequencing method was developed based on four (phoU, secY, tilS, and uvrA_1) out of 21 loci, which could be discriminated between LA1063 and other L. acidophilus strains using the cgMLST data. We confirmed that the strain-specific single-nucleotide polymorphisms method could be used to quickly and accurately identify the L. acidophilus probiotic strain LA1063 in commercial products.