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In many omics data, including microbiome sequencing data, we are only able to measure relative information. Various computational or statistical methods have been proposed to extract absolute (or biologically relevant) information from this relative information; however, these methods are under rather strong assumptions that may not be suitable for multigroup (more than two groups) and/or longitudinal outcome data. In this article, we first introduce the minimal assumption required to extract absolute from relative information. This assumption is less stringent than those imposed in existing methods, thus being applicable to multigroup and/or longitudinal outcome data. We then propose the first normalization method that works under this minimal assumption. The optimality and validity of the proposed method and its beneficial effects on downstream analysis are demonstrated in extensive simulation studies, where existing methods fail to produce consistent performance under the minimal assumption. We also demonstrate its application to real microbiome datasets to determine biologically relevant microbes to a specific disease/condition.

Staphylococcus aureus is a major cause of ocular infections, often resulting in devastating vision loss. Despite the significant morbidity associated with these infections, little is yet known regarding the specific strain types that may have a predilection for ocular tissues nor the set of virulence factors that drive its pathogenicity in this specific biological niche. Whole genome sequencing (WGS) can provide valuable insight in this regard by providing a prospective, comprehensive assessment of the strain types and virulence factors driving disease among specific subsets of clinical isolates. As such, a set of 163-member S. aureus ocular clinical strains were sequenced and assessed for both common strain types (multilocus sequence type (MLST), spa, agr) associated with ocular infections as well as the presence/absence of 235 known virulence factors in a high throughput manner. This ocular strain set was then directly compared to a fully sequenced 116-member non-ocular S. aureus strain set curated from NCBI in order to identify key differences between ocular and non-ocular S. aureus isolates. The most common sequence types found among ocular S. aureus isolates were ST5, ST8 and ST30, generally reflecting circulating non-ocular pathogenic S. aureus strains. However, importantly, ocular isolates were found to be significantly enriched for a set of enterotoxins, suggesting a potential role for this class of virulence factors in promoting ocular disease. Further genomic analysis revealed that these enterotoxins are located on mobile pathogenicity islands, thus horizontal gene transfer may promote the acquisition of enterotoxins, potentially amplifying S. aureus virulence in ocular tissues.

Endobronchial stents are increasingly used to treat airway complications in multiple conditions including lung transplantation but little is known about the biofilms that form on these devices. We applied deep sequencing to profile luminal biofilms of 46 endobronchial stents removed from 20 subjects primarily with lung transplantation-associated airway compromise. Microbial communities were analyzed by bacterial 16S rRNA and fungal ITS marker gene sequencing. Corynebacterium was the most common bacterial taxa across biofilm communities. Clustering analysis revealed three bacterial biofilm types: one low diversity and dominated by Corynebacterium; another was polymicrobial and characterized by Staphylococcus; and the third was polymicrobial and associated with Pseudomonas, Streptococcus, and Prevotella. Biofilm type was significantly correlated with stent material: covered metal with the Staphylococcus-type biofilm, silicone with the Corynebacterium-dominated biofilm, and uncovered metal with the polymicrobial biofilm. Subjects with sequential stents had frequent transitions between community types. Fungal analysis found Candida was most prevalent, Aspergillus was common and highly enriched in two of three stents associated with airway anastomotic dehiscence, and fungal taxa not typically considered pathogens were highly enriched in some stents. Thus, molecular analysis revealed a complex and dynamic endobronchial stent biofilm with three bacterial types that associate with stent material, a central role for Corynebacterium, and that both expected and unexpected fungi inhabit this unique niche. The current work provides a foundation for studies to investigate the relationship between stent biofilm composition and clinical outcomes, mechanisms of biofilm establishment, and strategies for improved stent technology and use in airway compromise.

Abstract Background Older adults with myeloid malignancies are susceptible to treatment-related toxicities. Accelerated DNAm age, or the difference between DNA methylation (DNAm) age and chronological age, may be used as a biomarker of biological age to predict individuals at risk. In addition, cancer treatment can also lead to accelerated DNAm age. Exercise is a promising intervention to reduce or prevent functional, psychological, and cognitive impairments in older patients with myeloid malignancies, yet there is little evidence of the effects of exercise on DNAm age. We explored (1) the associations of accelerated DNAm age with physical, psychological, and cognitive functions at baseline; (2) changes in DNAm age from baseline to post-intervention; and (3) the associations of changes in accelerated DNAm age with changes in functions from baseline to post-intervention. Methods We enrolled older patients with myeloid malignancies to a single-arm pilot study testing a mobile health (mHealth) exercise intervention that combines an exercise program (EXCAP©®) with a mobile application over 2 cycles of chemotherapy (8–12 weeks). Patients completed measures of physical, psychological, and cognitive functions and provided blood samples for analyses of DNAm age at baseline and post-intervention. Paired t-tests or Wilcoxon signed rank tests assessed changes in DNAm ages, and Spearman’s correlation assessed the relationships between accelerated ages and functions. Results We included 20 patients (mean age: 72 years, range 62–80). Accelerated GrimAge, accelerated PhenoAge, and DunedinPACE were stable from baseline to post-intervention. At baseline, DunedinPACE was correlated with worse grip strength (r = -0.41, p = 0.08). From baseline to post-intervention, decreases in accelerated GrimAge (r = -0.50, p = 0.02), accelerated PhenoAge (r = − 0.39, p = 0.09), and DunedinPace (r = − 0.43, p = 0.06) were correlated with increases in distance walked on 6-min walk test. Decreases in accelerated GrimAge (r = − 0.49, p = 0.03), accelerated PhenoAge (r = − 0.40, p = 0.08), and DunedinPace (r = − 0.41, p = 0.07) were correlated with increases in in grip strength. Conclusions Among older adults with myeloid malignancies receiving chemotherapy, GrimAge and PhenoAge on average are stable after a mHealth exercise intervention. Decreases in accelerated GrimAge, accelerated PhenoAge, and DunedinPACE over 8–12 weeks of exercise were correlated with increased physical performance. Future trials assessing the effects of exercise on treatment-related toxicities should evaluate DNAm age. Trial registration Clinicaltrials.gov identifier: NCT04981821.

Background: Elevated markers of inflammation, such as interleukin-6 (IL-6), are associated with aging, cancer, and functional decline. We assessed the association of pre-diagnosis IL-6 levels with post-diagnosis functional trajectories among older adults with cancer. Black and White participants experience different social structures, therefore we sought to understand whether these associations differ between Black and White participants. Methods: We conducted secondary analysis of the Health Aging, Body, and Composition (ABC) prospective longitudinal cohort study. Participants were recruited from 4/1997 to 6/1998. We included 179 participants with a new cancer diagnosis and IL-6 level measured within 2 years before diagnosis. Primary endpoint was functional measures (self-reported ability to walk 1/4, 20-meter gait speed). Nonparametric longitudinal models were used to cluster the trajectories; multinomial and logistic regressions to model associations. Findings: Mean age was 74 (SD 2.9); 36 % identified as Black. For self-reported functional status, we identified 3 clusters: high stable, decline, low stable. For gait speed, we identified 2 clusters: resilient, decline. The relationship between cluster trajectory and IL-6 was different between Black and White participants (p for interaction

BackgroundInfectious keratitis (IK) is a blinding disease and an important cause of ocular morbidity. Understanding regional trends in IK are important to understand the epidemiology and clinical outcomes of this disease.MethodsIn this 10-year retrospective review, patient characteristics including sociodemographic factors, medical history, and ocular history were collected as well as the clinical course and outcomes. This study particularly focused on these characteristics as it relates to treatment failure in IK, as defined as requiring more than 2 weeks to heal or surgical intervention, likelihood of having microbiology cultures collected, surgical intervention, and presenting disease severity.Results935 cases of IK were identified at the University of Rochester. Age (p=0.004), history of prior corneal transplant (p=0.009), severe vision loss on presentation (p

Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated (Prevotella histicola, Streptococcus mutans, and Rothia muciloginosa) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors.