Project description:Metagenome data from soil samples were collected at 0 to 10cm deep from 2 avocado orchards in Channybearup, Western Australia, in 2024. Amplicon sequence variant (ASV) tables were constructed based on the DADA2 pipeline with default parameters.
Project description:The prevailing theory of autoimmune disease, that the body creates autoantibodies that attack “self,” was developed during an era when culture-based methods vastly underestimated the number of microbes capable of persisting in and on Homo sapiens. Thanks to the advent of culture-independent tools, the human body is now known to harbor billions of microbes whose collective genomes work in concert with the human genome. Thus, the human genome can no longer be studied in isolation. Some of these microbes persist by slowing the activity of the vitamin D receptor nuclear receptor, affecting the expression of endogenous antimicrobials and other key components of the innate immune system. It seems that bacteria that cause autoimmune disease accumulate over a lifetime, with individuals picking up pathogens with greater ease over time, as the immune response becomes increasingly compromised. Any one autoimmune disease is likely due to many different microbes within the metagenomic microbiota. This helps explain the high levels of comorbidity observed among patients with autoimmune conditions. What are commonly believed to be autoantibodies may instead be created in response to this metagenomic microbiota when the adaptive immune system is forced to deal with disintegration of infected cells. Similarly, haplotypes associated with autoimmune conditions vary widely among individuals and populations. They are more suggestive of a regional infectious model rather than a model in which an illness is caused by inherited variation of HLA haplotypes
Project description:A common technique used for sensitive and specific diagnostic virus detection in clinical samples is PCR. However, an unbiased diagnostic microarray containing probes for all human pathogens could replace hundreds of individual PCR-reactions and remove the need for a clear clinical hypothesis regarding a suspected pathogen. We have established such a diagnostic platform for unbiased random amplification and subsequent microarray identification of viral pathogens in clinical samples. We show that Phi29 polymerase-amplification of a diverse set of clinical samples generates enough viral material for successful identification by the Microbial Detection Array developed at the Lawrence Livermore National Laboratory, California, USA, demonstrating the potential of the microarray technique for broad-spectrum pathogen detection. We conclude that this method detects both DNA and RNA virus, present in the same sample, as well as differentiates between different virus subtypes. We propose this assay for unbiased diagnostic analysis of all viruses in clinical samples.
Project description:The initial project of the data of origin is described in Ouwendijk, et al. (2020). Analysis of Virus and Host Proteomes During Productive HSV-1 and VZV Infection in Human Epithelial Cells. Frontiers in Microbiology, 11, 1179. https://doi.org/10.3389/fmicb.2020.01179. To increase the number of virus species that can be detected we developed a shotgun proteomics based approach, which was applied to viral samples, after which the identified peptides were searched for in a database equipped with proteomic data of 46 viruses, known to be infectious to humans, using a web application (‘proteome2virus.com’). To validate proteome2virus application (proteome2virus.com) data of cultured and clinical samples was generated. The method has been successfully tested against cultured viruses and 8 clinical fecales samples of 10 different viral species from 7 different virus families, including SARS-CoV-2, Betacoronavirus OC43, human coronavirus 229E, human orthopneumovirus (RSV A and RSV B), human metapneumovirus, Influenza A (H1N1 and H3N2), mamastrovirus 1, Norwalk virus, Rotavirus A and human mastadenovirus F, representing 7 different virus families. The samples were prepared with two different sample preparation methods and were measured on two different mass spectrometers. Results demonstrated that the developed data analysis pipeline is applicable to different MS data sets generated on 2 different instruments and that it this approach identifies a high variety of clinically relevant viral species. This emphasizes the potential and feasibility for the diagnosis of a wide range of viruses in clinical samples with a single shotgun proteomics analysis.