The Park Grass experiment (PGE) in the UK has been ongoing since 1856. Its purpose is to study the response of biological communities to the long-term treatments and associated changes in soil parameters, particularly soil pH. In this study, soil samples were collected across pH gradient (pH 3.6-7) and a range of fertilizers (nitrogen as ammonium sulfate, nitrogen as sodium nitrate, phosphorous) to evaluate the effects nutrients have on soil parameters and microbial community structure. Illumina 16S ribosomal RNA (rRNA) amplicon sequencing was used to determine the relative abundances and diversity of bacterial and archaeal taxa. Relationships between treatments, measured soil parameters, and microbial communities were evaluated. Clostridium, Bacteroides, Bradyrhizobium, Mycobacterium, Ruminococcus, Paenibacillus, and Rhodoplanes were the most abundant genera found at the PGE. The main soil parameter that determined microbial composition, diversity, and biomass in the PGE soil was pH. The most probable mechanism of the pH impact on microbial community may include mediation of nutrient availability in the soil. Addition of nitrogen to the PGE plots as ammonium sulfate decreases soil pH through increased nitrification, which causes buildup of soil carbon, and hence increases C/N ratio. Plant species richness and plant productivity did not reveal significant relationships with microbial diversity; however, plant species richness was positively correlated with soil microbial biomass. Plants responded to the nitrogen treatments with an increase in productivity and a decrease in the species richness.

No abstract

The incidence of the autoimmune disease, type 1 diabetes (T1D), has increased dramatically over the last half century in many developed countries and is particularly high in Finland and other Nordic countries. Along with genetic predisposition, environmental factors are thought to play a critical role in this increase. As with other autoimmune diseases, the gut microbiome is thought to play a potential role in controlling progression to T1D in children with high genetic risk, but we know little about how the gut microbiome develops in children with high genetic risk for T1D. In this study, the early development of the gut microbiomes of 76 children at high genetic risk for T1D was determined using high-throughput 16S rRNA gene sequencing. Stool samples from children born in the same hospital in Turku, Finland were collected at monthly intervals beginning at 4–6 months after birth until 2.2 years of age. Of those 76 children, 29 seroconverted to T1D-related autoimmunity (cases) including 22 who later developed T1D, the remaining 47 subjects remained healthy (controls). While several significant compositional differences in low abundant species prior to seroconversion were found, one highly abundant group composed of two closely related species, Bacteroides dorei and Bacteroides vulgatus, was significantly higher in cases compared to controls prior to seroconversion. Metagenomic sequencing of samples high in the abundance of the B. dorei/vulgatus group before seroconversion, as well as longer 16S rRNA sequencing identified this group as Bacteroides dorei. The abundance of B. dorei peaked at 7.6 months in cases, over 8 months prior to the appearance of the first islet autoantibody, suggesting that early changes in the microbiome may be useful for predicting T1D autoimmunity in genetically susceptible infants. The cause of increased B. dorei abundance in cases is not known but its timing appears to coincide with the introduction of solid food.

Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

The two-component signal transduction system BarA-UvrY of Escherichia coli and its orthologs globally regulate metabolism, motility, biofilm formation, stress resistance, virulence of pathogens and quorum sensing by activating the transcription of genes for regulatory sRNAs, e.g. CsrB and CsrC in E. coli. These sRNAs act by sequestering the RNA binding protein CsrA (RsmA) away from lower affinity mRNA targets. In this study, we used ChIP-exo to identify, at single nucleotide resolution, genomic sites for UvrY (SirA) binding in E. coli and Salmonella enterica. The csrB and csrC genes were the strongest targets of crosslinking, which required UvrY phosphorylation by the BarA sensor kinase. Crosslinking occurred at two sites, an inverted repeat sequence far upstream of the promoter and a site near the -35 sequence. DNAse I footprinting revealed specific binding of UvrY in vitro only to the upstream site, indicative of additional binding requirements and/or indirect binding to the downstream site. Additional genes, including cspA, encoding the cold-shock RNA-binding protein CspA, showed weaker crosslinking and modest or negligible regulation by UvrY. We conclude that the global effects of UvrY/SirA on gene expression are primarily mediated by activating csrB and csrC transcription. We also used in vivo crosslinking and other experimental approaches to reveal new features of csrB/csrC regulation by the DeaD and SrmB RNA helicases, IHF, ppGpp and DksA. Finally, the phylogenetic distribution of BarA-UvrY was analyzed and found to be uniquely characteristic of γ-Proteobacteria and strongly anti-correlated with fliW, which encodes a protein that binds to CsrA and antagonizes its activity in Bacillus subtilis. We propose that BarA-UvrY and orthologous TCS transcribe sRNA antagonists of CsrA throughout the γ-Proteobacteria, but rarely or never perform this function in other species.