Genomic characterization of antibiotic-resistant Staphylococcus epidermidis with observed shifts in optimal temperature.

AIMS: Antibiotic resistance genes (ARGs) in the environment pose significant public health concerns and are influenced by conditions like temperature changes. We previously observed that resistance evolution to gentamicin and colistin affects optimal growth temperatures in Staphylococcus epidermidis isolates. Despite significant phenotype observations, the genetic basis remains unclear. We aim to identify the genetic changes linked to antibiotic resistance evolution that alter optimal growth temperature. METHODS AND RESULTS: Using whole-genome sequencing, we sequenced the genomes of gentamicin-resistant (GEN-1, GEN-2) and colistin-resistant (COL-4, COL-6) S. epidermidis isolates. Variant analysis with the BV-BRC bioinformatics tool identified genes involved in antibiotic resistance and temperature response. We found 12 genetic variants, including two unique to GEN-2 and one in COL-4. One shared mutation was observed in GEN-1 and GEN-2, and another in COL-4 and COL-6. Five mutations were shared among all isolates related to mobile gene elements, including a transposase IS4 family, two putative transposases, and two transposase-like insertion elements. CONCLUSIONS: Our findings indicate that the same genes involved in gentamicin and colistin resistance, especially those related to mobile genetic elements, may also play a crucial role in temperature response.

Unique Metabolic Contexts Sensitize Cancer Cells and Discriminate between Glycolytic Tumor Types.

Cancer cells utilize variable metabolic programs in order to maintain homeostasis in response to environmental challenges. To interrogate cancer cell reliance on glycolytic programs under different nutrient availabilities, we analyzed a gene panel containing all glycolytic genes as well as pathways associated with glycolysis. Using this gene panel, we analyzed the impact of an siRNA library on cellular viability in cells containing only glucose or only pyruvate as the major bioenergetic nutrient source. From these panels, we aimed to identify genes that elicited conserved and glycolysis-dependent changes in cellular bioenergetics across glycolysis-promoting and OXPHOS-promoting conditions. To further characterize gene sets within this panel and identify similarities and differences amongst glycolytic tumor RNA-seq profiles across a pan-cancer cohort, we then used unsupervised statistical classification of RNA-seq profiles for glycolytic cancers and non-glycolytic cancer types. Here, Kidney renal clear cell carcinoma (KIRC); Head and Neck squamous cell carcinoma (HNSC); and Lung squamous cell carcinoma (LUSC) defined the glycolytic cancer group, while Prostate adenocarcinoma (PRAD), Thyroid carcinoma (THCA), and Thymoma (THYM) defined the non-glycolytic cancer group. These groups were defined based on glycolysis scoring from previous studies, where KIRC, HNSC, and LUSC had the highest glycolysis scores, meanwhile, PRAD, THCA, and THYM had the lowest. Collectively, these results aimed to identify multi-omic profiles across cancer types with demonstrated variably glycolytic rates. Our analyses provide further support for strategies aiming to classify tumors by metabolic phenotypes in order to therapeutically target tumor-specific vulnerabilities.

Lung Adenocarcinoma Transcriptomic Analysis Predicts Adenylate Kinase Signatures Contributing to Tumor Progression and Negative Patient Prognosis.

The ability to detect and respond to hypoxia within a developing tumor appears to be a common feature amongst most cancers. This hypoxic response has many molecular drivers, but none as widely studied as Hypoxia-Inducible Factor 1 (HIF-1). Recent evidence suggests that HIF-1 biology within lung adenocarcinoma (LUAD) may be associated with expression levels of adenylate kinases (AKs). Using LUAD patient transcriptome data, we sought to characterize AK gene signatures related to lung cancer hallmarks, such as hypoxia and metabolic reprogramming, to identify conserved biological themes across LUAD tumor progression. Transcriptomic analysis revealed perturbation of HIF-1 targets to correlate with altered expression of most AKs, with AK4 having the strongest correlation. Enrichment analysis of LUAD tumor AK4 gene signatures predicts signatures involved in pyrimidine, and by extension, nucleotide metabolism across all LUAD tumor stages. To further discriminate potential drivers of LUAD tumor progression within AK4 gene signatures, partial least squares discriminant analysis was used at LUAD stage-stage interfaces, identifying candidate genes that may promote LUAD tumor growth or regression. Collectively, these results characterize regulatory gene networks associated with the expression of all nine human AKs that may contribute to underlying metabolic perturbations within LUAD and reveal potential mechanistic insight into the complementary role of AK4 in LUAD tumor development.