Search for: All records

Abstract Skin disorders are one of the most common complications of type II diabetes (T2DM). Long-term effects of high blood glucose leave individuals with T2DM more susceptible to cutaneous diseases, but its underlying molecular mechanisms are unclear. Network-based methods consider the complex interactions between genes which can complement the analysis of single genes in previous research. Here, we use network analysis and topological properties to systematically investigate dysregulated gene co-expression patterns in type II diabetic skin with skin samples from the Genotype-Tissue Expression database. Our final network consisted of 8812 genes from 73 subjects with T2DM and 147 non-T2DM subjects matched for age, sex, and race. Two gene modules significantly related to T2DM were functionally enriched in the pathway lipid metabolism, activated by PPARA and SREBF ( SREBP ). Transcription factors KLF10 , KLF4 , SP1 , and microRNA-21 were predicted to be important regulators of gene expression in these modules. Intramodular analysis and betweenness centrality identified NCOA6 as the hub gene while KHSRP and SIN3B are key coordinators that influence molecular activities differently between T2DM and non-T2DM populations. We built a TF-miRNA-mRNA regulatory network to reveal the novel mechanism ( miR-21-PPARA-NCOA6 ) of dysregulated keratinocyte proliferation, differentiation, and migration in diabetic skin, which may provide new insights into the susceptibility of skin disorders in T2DM patients. Hub genes and key coordinators may serve as therapeutic targets to improve diabetic skincare. 

Abstract Lichen thalli are formed through the symbiotic association of a filamentous fungus and photosynthetic green alga and/or cyanobacterium. Recent studies have revealed lichens also host highly diverse communities of secondary fungal and bacterial symbionts, yet few studies have examined the viral component within these complex symbioses. Here, we describe viral biodiversity and functions in cyanolichens collected from across North America and Europe. As current machine-learning viral-detection tools are not trained on complex eukaryotic metagenomes, we first developed efficient methods to remove eukaryotic reads prior to viral detection and a custom pipeline to validate viral contigs predicted with three machine-learning methods. Our resulting high-quality viral data illustrate that every cyanolichen thallus contains diverse viruses that are distinct from viruses in other terrestrial ecosystems. In addition to cyanobacteria, predicted viral hosts include other lichen-associated bacterial lineages and algae, although a large fraction of viral contigs had no host prediction. Functional annotation of cyanolichen viral sequences predicts numerous viral-encoded auxiliary metabolic genes (AMGs) involved in amino acid, nucleotide, and carbohydrate metabolism, including AMGs for secondary metabolism (antibiotics and antimicrobials) and fatty acid biosynthesis. Overall, the diversity of cyanolichen AMGs suggests that viruses may alter microbial interactions within these complex symbiotic assemblages. 

Abstract Background Diabetic foot ulcers (DFUs) account for the majority of all limb amputations and hospitalizations due to diabetes complications. With 30 million cases of diabetes in the USA and 500,000 new diagnoses each year, DFUs are a growing health problem. Diabetes patients with limb amputations have high postoperative mortality, a high rate of secondary amputation, prolonged inpatient hospital stays, and a high incidence of re-hospitalization. DFU-associated amputations constitute a significant burden on healthcare resources that cost more than 10 billion dollars per year. Currently, there is no way to identify wounds that will heal versus those that will become severely infected and require amputation. Main body Accurate identification of causative pathogens in diabetic foot ulcers is a critical component of effective treatment. Compared to traditional culture-based methods, advanced sequencing technologies provide more comprehensive and unbiased profiling on wound microbiome with a higher taxonomic resolution, as well as functional annotation such as virulence and antibiotic resistance. In this review, we summarize the latest developments in defining the microbiology of diabetic foot ulcers that have been unveiled by sequencing technologies and discuss both the future promises and current limitations of these approaches. In particular, we highlight the temporal patterns and system dynamics in the diabetic foot microbiome monitored and measured during wound progression and medical intervention, and explore the feasibility of molecular diagnostics in clinics. Conclusion Molecular tests conducted during weekly office visits to clean and examine DFUs would allow clinicians to offer personalized treatment and antibiotic therapy. Personalized wound management could reduce healthcare costs, improve quality of life for patients, and recoup lost productivity that is important not only to the patient, but also to healthcare payers and providers. These efforts could also improve antibiotic stewardship and control the rise of “superbugs” vital to global health. 

Abstract In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each ‘omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/. 

The CSSI 2019 workshop was held on October 28-29, 2019, in Austin, Texas. The main objectives of this workshop were to (1) understand the impact of the CSSI program on the community over the last 9 years, (2) engage workshop participants in identifying gaps and opportunities in the current CSSI landscape, (3) gather ideas on the cyberinfrastructure needs and expectations of the community with respect to the CSSI program, and (4) prepare a report summarizing the feedback gathered from the community that can inform the future solicitations of the CSSI program. The workshop participants included a diverse mix of researchers and practitioners from academia, industry, and national laboratories. The participants belonged to diverse domains such as quantum physics, computational biology, High Performance Computing (HPC), and library science. Almost 50% participants were from computer science domain and roughly 50% were from non-computer science domains. As per the self-reported statistics, roughly 27% of the participants were from the different underrepresented groups as defined by the National Science Foundation (NSF). The workshop brought together different stakeholders interested in provisioning sustainable cyberinfrastructure that can power discoveries impacting the various fields of science and technology and maintaining the nation's competitiveness in the areas such as scientific software, HPC, networking, cybersecurity, and data/information science. The workshop served as a venue for gathering the community-feedback on the current state of the CSSI program and its future directions. Before they arrived at the workshop, the participants were encouraged to take an online survey on the challenges that they face in using the current cyberinfrastructure and the importance of the CSSI program in enabling cutting-edge research. The workshop included 16 brain-storming sessions of one hour each. Additionally, the workshop program included 16 lightning talks and an extempore session. The information collected from the survey, brainstorming sessions, lightning talks, and the extempore session are summarized in this report and can potentially be useful for the NSF in formulating the future CSSI solicitations. The workshop fostered an environment in which the participants were encouraged to identify gaps and opportunities in the current cyberinfrastructure landscape, and develop thoughts for proposing new projects. 

Big-data analytics platforms, such as Hadoop, are appealing for scientific computation because they are ubiquitous, well-supported, and well-understood. Unfortunately, load-balancing is a common challenge of implementing large-scale scientific computing applications on these platforms. In this paper we present the design and implementation of Libra, a Hadoop-based tool for comparative metagenomics (comparing samples of genetic material collected from the environment). We describe the computation that Libra performs and how that computation is implemented using Hadoop tasks, including the techniques used by Libra to ensure that the task workloads are balanced despite nonuniform sample sizes and skewed distributions of genetic material in the samples. On a 10-machine Hadoop cluster Libra can analyze the entire Tara Ocean Viromes of ~4.2 billion reads in fewer than 20 hours. 

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical.