Risk factors of lower limb cellulitis in a level-two healthcare facility in Cameroon: a case-control study.

BACKGROUND: Cellulitis is a common infection of the skin and subcutaneous tissues. It is associated with significant morbidity from necrosectomies and amputations especially in sub-Saharan Africa. We aimed at identifying the risk factors and burden of lower limb cellulitis to inform preventive strategies in Cameroon. METHODS: This was a hospital-based case-control study carried out in the Bamenda Regional Hospital (BRH) between September 2015 and August 2016. Cases were defined as consenting adults admitted to the surgical unit who presented with a localised area of lower limb erythema, warmth, oedema and pain, associated with fever (temperature ? 38 °C) and/or chills of sudden onset. Controls were adults hospitalised for diseases other than cellulitis, necrotising fasciitis, myositis, abscess or other variants of dermo-hypodermitis. Cases and controls were matched (1:2) for age and sex. RESULTS: Of the 183 participants (61 cases of cellulitis and 122 controls) included in the study, the median age was 52 years [Interquartile range (IQR): 32.5-74.5]. After controlling for potential confounders, obesity [adjusted odds ratio (AOR) = 4.7, 95% CI (1.5-14.7); p = 0.009], history of skin disruption [AOR = 12.4 (3.9-39.1); p < 0.001], and presence of toe-web intertrigo [AOR = 51.4 (11.7-225.6); p < 0.001] were significantly associated with cellulitis. Median hospital stay was longer (14 days [IQR: 6-28]) in cases compared to the controls (3 days [IQR: 2-7]). Among the cases, Streptococci species were the most frequent (n = 50, 82%) isolated germ followed by staphylococci species (n = 9, 15%). Patients with cellulitis were more likely to undergo necrosectomy (OR: 21.2; 95% CI: 7.6-59.2). Toe-web intertrigo had the highest (48.9%) population attributable risk for cellulitis, followed by history of disruption of skin barrier (37.8%) and obesity (20.6%). CONCLUSION: This study showed a high disease burden among patients with cellulitis. While risk factors identified are similar to prior literature, this study provides a contextual evidence-base for clinicians in this region to be more aggressive in management of these risk factors to prevent disease progression and development of cellulitis

The skin microbiome in health and atopic dermatitis

The skin, as the body’s outermost layer of cells, plays a crucial dual role in protecting against foreign pathogens while providing a habitat for commensal microbes. Despite the skin’s harsh conditions, which include desiccation, acidity, and scarce nutrients, the skin hosts a diverse community of bacteria, fungi, and viruses. Prior work associating fluctuations in the skin microbiome with health and disease has been limited by our limited understanding of the skin microbiome composition and functions. One way to characterise skin microbial diversity is through metagenomics. A previous investigation of the skin microbiome found that more than half of the sequenced skin metagenomic reads did not align to reference genomes, complicating the analysis of skin metagenomic datasets. To address this issue, we combined bacterial cultivation and metagenomic sequencing to create the Skin Microbial Genome Collection (SMGC), the most comprehensive catalogue of prokaryotic, eukaryotic, and viral genomes from the skin. The SMGC allows for the classification of a median of 85% of skin metagenomic sequencing reads, providing a comprehensive view of skin microbial diversity. Using the SMGC, we investigated the skin microbiome in atopic dermatitis, a prevalent inflammatory skin condition characterized by recurring episodes of red, itchy, and swollen skin. Atopic dermatitis flares have been associated with the proliferation of various staphylococcal species, with only S. aureus strains cultured from atopic dermatitis inducing inflammation in a mouse model. Our extensive genomic survey of the skin microbiome in atopic dermatitis, supported by cultured isolates from the same samples, identified Staphylococcus strains and genomic loci associated with higher disease severity. Our work also showed that the Staphylococcus strains found in AD are influenced by factors such as geography and strain sharing within households. Additionally, our examination of the mobilome of multiple Staphylococcus species colonising the same individuals revealed widespread inter-species transfer of genetic material, highlighting the fluid nature of staphylococcal genetic composition. In conclusion, our work shows how novel genomic approaches and the integration of sequencing data can be used to characterise the skin microbiome at an unparalleled resolution, allowing for new insights into how skin microbes vary in health and disease

Additional file 2 of Making life difficult for Clostridium difficile: augmenting the pathogenâ s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

GR of biomass production in icdf834 to internal and external perturbations. (PDF 656 kb

Additional file 3 of Making life difficult for Clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

Gene essentiality analysis of icdf834 and iMLTC806cdf. (PDF 49 kb

Additional file 3 of Making life difficult for Clostridium difficile: augmenting the pathogenâ s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

Gene essentiality analysis of icdf834 and iMLTC806cdf. (PDF 49 kb

Making life difficult for Clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

Abstract Background Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship. Results We present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium’s metabolism, such as changes in the bacterium’s growth in response to different environmental conditions. Conclusions After an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens

Making life difficult for Clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

Abstract Background Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship. Results We present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium’s metabolism, such as changes in the bacterium’s growth in response to different environmental conditions. Conclusions After an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens