DNA methylation signatures of youth-onset type 2 diabetes and exposure to maternal diabetes.

OBJECTIVE: Youth-onset type 2 diabetes (T2D) is physiologically distinct from adult-onset, but it is not clear how the two diseases differ at a molecular level. In utero exposure to maternal type 2 diabetes (T2D) is known to be a specific risk factor for youth-onset T2D. DNA methylation (DNAm) changes associated with T2D but which differ between youth- and adult-onset might delineate the impacts of T2D development at different ages and could also determine the contribution of exposure to in utero diabetes. METHODS: We performed an epigenome-wide analysis of DNAm on whole blood from 218 youth with T2D and 77 normoglycemic controls from the iCARE (improving renal Complications in Adolescents with type 2 diabetes through REsearch) cohort. Associations were tested using multiple linear regression models while adjusting for maternal diabetes, sex, age, BMI, smoking status, second-hand smoking exposure, cell-type proportions and genetic ancestry. RESULTS: We identified 3830 differentially methylated sites associated with youth T2D onset, of which 3794 were moderately (adjusted p-value < 0.05 and effect size estimate > 0.01) associated and 36 were strongly (adjusted p-value < 0.05 and effect size estimate > 0.05) associated. A total of 3725 of these sites were not previously reported in the EWAS Atlas as associated with T2D, adult obesity or youth obesity. Moreover, three CpGs associated with youth-onset T2D in the PFKFB3 gene were also associated with maternal T2D exposure (FDR < 0.05 and effect size > 0.01). This is the first study to link PFKFB3 and T2D in youth. CONCLUSION: Our findings support that T2D in youth has different impacts on DNAm than adult-onset, and suggests that changes in DNAm could provide an important link between in utero exposure to maternal diabetes and the onset of T2D.

Constraint on boric acid resistance and tolerance evolvability in Candida albicans.

Boric acid is a broad-spectrum antimicrobial used to treat vulvovaginal candidiasis when patients relapse on the primary azole drug fluconazole. Candida albicans is the most common cause of vulvovaginal candidiasis, colloquially referred to as a "vaginal yeast infection". Little is known about the propensity of C. albicans to develop BA resistance or tolerance (the ability of a subpopulation to grow slowly in high levels of drug). We evolved 96 replicates from eight diverse C. albicans strains to increasing BA concentrations to test the evolvability of BA resistance and tolerance. Replicate growth was individually assessed daily, with replicates passaged when they had reached an optical density consistent with exponential growth. Many replicates went extinct quickly. Although some replicates could grow in much higher levels of BA than the ancestral strains, evolved populations isolated from the highest terminal BA levels (after 11 weeks of passages) surprisingly showed only modest growth improvements and only at low levels of BA. No large increases in resistance or tolerance were observed in the evolved replicates. Overall, our findings illustrate that there may be evolutionary constraints limiting the emergence of BA resistance and tolerance, which could explain why it remains an effective treatment for recurrent yeast infections.

Differential Response of Candida Species Morphologies and Isolates to Fluconazole and Boric Acid.

Candida albicans is the most prevalent cause of vulvovaginal candidiasis ("yeast infection" or VVC) and recurrent vulvovaginal candidiasis (RVVC), although the incidence of non-albicans yeast species is increasing. The azole fluconazole is the primary antifungal drug used to treat RVVC, yet isolates from some species have intrinsic resistance to fluconazole, and recurrent infection can occur even with fluconazole-susceptible populations. The second-line broad-spectrum antimicrobial drug, boric acid, is an alternative treatment that has been found to successfully treat complicated VVC infections. Far less is known about how boric acid inhibits growth of yeast isolates in different morphologies compared to fluconazole. We found significant differences in drug resistance and drug tolerance (the ability of a subpopulation to grow slowly in high levels of drug) between C. albicans, Candida glabrata, and Candida parapsilosis isolates, with the specific relationships dependent on both drug and phenotype. Population-level variation for both susceptibility and tolerance was broader for fluconazole than boric acid in all species. Unlike fluconazole, which neither prevented hyphal formation nor disrupted mature biofilms, boric acid inhibited C. albicans hyphal formation and reduced mature biofilm biomass and metabolic activity in all isolates in a dose-dependent manner. Variation in planktonic response did not generally predict biofilm phenotypes for either drug. Overall, our findings illustrate that boric acid is broadly effective at inhibiting growth across many isolates and morphologies, which could explain why it is an effective treatment for RVVC.

High-Throughput Computational Analysis of Biofilm Formation from Time-Lapse Microscopy.

Candida albicans biofilm formation in the presence of drugs can be examined through time-lapse microscopy. In many cases, the images are used qualitatively, which limits their utility for hypothesis testing. We employed a machine-learning algorithm implemented in the Orbit Image Analysis program to detect the percent area covered by cells from each image. This is combined with custom R scripts to determine the growth rate, growth asymptote, and time to reach the asymptote as quantitative proxies for biofilm formation. We describe step-by-step protocols that go from sample preparation for time-lapse microscopy through image analysis parameterization and visualization of the model fit. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Sample preparation Basic Protocol 2: Time-lapse microscopy: Evos protocol Basic Protocol 3: Batch file renaming Basic Protocol 4: Machine learning analysis of Evos images with Orbit Basic Protocol 5: Parametrization of Orbit output in R Basic Protocol 6: Visualization of logistic fits in R.