Dataset Information

16S rDNA based skin microbiome data of healthy individuals and leprosy patients from India.

ABSTRACT: Leprosy is an infectious disease that has predilection in skin and peripheral nerves. Skin has its own microbiome, however it is not extensively studied in Indian leprosy patients. Here, by using next-generation 16S rDNA sequencing, we have attempted to assess the skin associated microbial diversity pertaining to affected and unaffected skin of Indian leprosy patients. A total of 90 skin swab samples were collected from 60 individuals (30 healthy controls, 30 patients) residing in Hyderabad and Miraj, two distinct geographical locations in India to assess the homo/heterogeneity of skin microbial signatures. While a large increase in genus Methylobacterium and Pseudomonas was seen in patients from Miraj and Hyderabad respectively, a considerable decrease in genus Staphylococcus in the leprosy patients (as compared to controls) from both geographical locations was also observed. We expect that, these datasets can not-only provide further interesting insights, but will also help to observe dynamics of microbiome in the diseased state and generate hypotheses to test for skin microbiome transplantation studies in leprosy.

SUBMITTER: Bayal N

PROVIDER: S-EPMC6805899 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

16S rDNA based skin microbiome data of healthy individuals and leprosy patients from India.

Bayal Nitin N Nagpal Sunil S Haque Mohammed Monzoorul MM Patole Milind S MS Valluri Vijayalakshmi V Suryavanshi Rohini R Mande Sharmila S SS Mande Shekhar C SC

Scientific data 20191022 1

Leprosy is an infectious disease that has predilection in skin and peripheral nerves. Skin has its own microbiome, however it is not extensively studied in Indian leprosy patients. Here, by using next-generation 16S rDNA sequencing, we have attempted to assess the skin associated microbial diversity pertaining to affected and unaffected skin of Indian leprosy patients. A total of 90 skin swab samples were collected from 60 individuals (30 healthy controls, 30 patients) residing in Hyderabad and ...[more]

PMID: 31641132

Similar Datasets

Project description:Microbiome research and improvements in high throughput sequencing technologies revolutionize our current scientific viewpoint. The human associated microbiome is a prominent focus of clinical research. Large cohort studies are often required to investigate the human microbiome composition and its changes in a multitude of human diseases. Reproducible analyses of large cohort samples require standardized protocols in study design, sampling, storage, processing, and data analysis. In particular, the effect of sample storage on actual results is critical for reproducibility. So far, the effect of storage conditions on the results of microbial analysis has been examined for only a few human biological materials (e.g., stool samples). There is a lack of data and information on appropriate storage conditions on other human derived samples, such as skin. Here, we analyzed skin swab samples collected from three different body locations (forearm, V of the chest and back) of eight healthy volunteers. The skin swabs were soaked in sterile buffer and total DNA was isolated after freezing at -80°C for 24 h, 90 or 365 days. Hypervariable regions V1-2 were amplified from total DNA and libraries were sequenced on an Illumina MiSeq desktop sequencer in paired end mode. Data were analyzed using Qiime 1.9.1. Summarizing all body locations per time point, we found no significant differences in alpha diversity and multivariate community analysis among the three time points. Considering body locations separately significant differences in the richness of forearm samples were found between d0 vs. d90 and d90 vs. d365. Significant differences in the relative abundance of major skin genera (Propionibacterium, Streptococcus, Bacteroides, Corynebacterium, and Staphylococcus) were detected in our samples in Bacteroides only among all time points in forearm samples and between d0 vs. d90 and d90 vs. d365 in V of the chest and back samples. Accordingly, significant differences were detected in the ratios of the main phyla Actinobacteria, Firmicutes, and Bacteroidetes: Actinobacteria vs. Bacteroidetes at d0 vs. d90 (p-value = 0.0234), at d0 vs. d365 (p-value = 0.0234) and d90 vs. d365 (p-value = 0.0234) in forearm samples and at d90 vs. d365 in V of the chest (p-value = 0.0234) and back samples (p-value = 0.0234). The ratios of Firmicutes vs. Bacteroidetes showed no significant changes in any of the body locations as well as the ratios of Actinobacteria vs. Firmicutes at any time point. Studies with larger sample sizes are required to verify our results and determine long term storage effects with regard to specific biological questions.

Project description:BackgroundClinical dogma is that healthy urine is sterile and the presence of bacteria with an inflammatory response is indicative of urinary tract infection (UTI). Asymptomatic bacteriuria (ABU) represents the state in which bacteria are present but the inflammatory response is negligible. Differentiating ABU from UTI is diagnostically challenging, but critical because overtreatment of ABU can perpetuate antimicrobial resistance while undertreatment of UTI can result in increased morbidity and mortality. In this study, we describe key characteristics of the healthy and ABU urine microbiomes utilizing 16S rRNA gene (16S rDNA) sequencing and metaproteomics, with the future goal of utilizing this information to personalize the treatment of UTI based on key individual characteristics.MethodsA cross-sectional study of 26 healthy controls and 27 healthy subjects at risk for ABU due to spinal cord injury-related neuropathic bladder (NB) was conducted. Of the 27 subjects with NB, 8 voided normally, 8 utilized intermittent catheterization, and 11 utilized indwelling Foley urethral catheterization for bladder drainage. Urine was obtained by clean catch in voiders, or directly from the catheter in subjects utilizing catheters. Urinalysis, urine culture and 16S rDNA sequencing were performed on all samples, with metaproteomic analysis performed on a subsample.ResultsA total of 589454 quality-filtered 16S rDNA sequence reads were processed through a NextGen 16S rDNA analysis pipeline. Urine microbiomes differ by normal bladder function vs. NB, gender, type of bladder catheter utilized, and duration of NB. The top ten bacterial taxa showing the most relative abundance and change among samples were Lactobacillales, Enterobacteriales, Actinomycetales, Bacillales, Clostridiales, Bacteroidales, Burkholderiales, Pseudomonadales, Bifidobacteriales and Coriobacteriales. Metaproteomics confirmed the 16S rDNA results, and functional human protein-pathogen interactions were noted in subjects where host defenses were initiated.ConclusionsCounter to clinical belief, healthy urine is not sterile. The healthy urine microbiome is characterized by a preponderance of Lactobacillales in women and Corynebacterium in men. The presence and duration of NB and method of urinary catheterization alter the healthy urine microbiome. An integrated approach of 16S rDNA sequencing with metaproteomics improves our understanding of healthy urine and facilitates a more personalized approach to prevention and treatment of infection.

Dataset Information

16S rDNA based skin microbiome data of healthy individuals and leprosy patients from India.

Publications

16S rDNA based skin microbiome data of healthy individuals and leprosy patients from India.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets