Interrupting peptidoglycan deacetylation during Bdellovibrio predator-prey interaction prevents ultimate destruction of prey wall, liberating bacterial-ghosts

The peptidoglycan wall, located in the periplasm between the inner and outer membranes of the cell envelope in Gram-negative bacteria, maintains cell shape and endows osmotic robustness. Predatory Bdellovibrio bacteria invade the periplasm of other bacterial prey cells, usually crossing the peptidoglycan layer, forming transient structures called bdelloplasts within which the predators replicate. Prey peptidoglycan remains intact for several hours, but is modified and then degraded by predators escaping. Here we show predation is altered by deleting two Bdellovibrio N-acetylglucosamine (GlcNAc) deacetylases, one of which we show to have a unique two domain structure with a novel regulatory-”plug”. Deleting the deacetylases limits peptidoglycan degradation and rounded prey cell “ghosts” persist after mutant-predator exit. Mutant predators can replicate unusually in the periplasmic region between the peptidoglycan wall and the outer membrane rather than between wall and inner-membrane, yet still obtain nutrients from the prey cytoplasm. Deleting two further genes encoding DacB/PBP4 family proteins, known to decrosslink and round prey peptidoglycan, results in a quadruple mutant Bdellovibrio which leaves prey-shaped ghosts upon predation. The resultant bacterial ghosts contain cytoplasmic membrane within bacteria-shaped peptidoglycan surrounded by outer membrane material which could have promise as “bacterial skeletons” for housing artificial chromosomes

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

Reduction in Structural Disorder and Functional Complexity in the Thermal Adaptation of Prokaryotes

Genomic correlates of evolutionary adaptation to very low or very high optimal growth temperature (OGT) values have been the subject of many studies. Whereas these provided a protein-structural rationale of the activity and stability of globular proteins/enzymes, the point has been neglected that adaptation to extreme temperatures could also have resulted from an increased use of intrinsically disordered proteins (IDPs), which are resistant to these conditions in vitro. Contrary to these expectations, we found a conspicuously low level of structural disorder in bacteria of very high (and very low) OGT values. This paucity of disorder does not reflect phylogenetic relatedness, i.e. it is a result of genuine adaptation to extreme conditions. Because intrinsic disorder correlates with important regulatory functions, we asked how these bacteria could exist without IDPs by studying transcription factors, known to harbor a lot of function-related intrinsic disorder. Hyperthermophiles have much less transcription factors, which have reduced disorder compared to their mesophilic counterparts. On the other hand, we found by systematic categorization of proteins with long disordered regions that there are certain functions, such as translation and ribosome biogenesis that depend on structural disorder even in hyperthermophiles. In all, our observations suggest that adaptation to extreme conditions is achieved by a significant functional simplification, apparent at both the level of the genome and individual genes/proteins

Genome-wide analyses identify common variants associated with macular telangiectasia type 2

Idiopathic juxtafoveal retinal telangiectasis type 2 (macular telangiectasia type 2; MacTel) is a rare neurovascular degenerative retinal disease. To identify genetic susceptibility loci for MacTel, we performed a genome-wide association study (GWAS) with 476 cases and 1,733 controls of European ancestry. Genome-wide significant associations (P < 5 × 10−8) were identified at three independent loci (rs73171800 at 5q14.3, P = 7.74 × 10−17; rs715 at 2q34, P = 9.97 × 10−14; rs477992 at 1p12, P = 2.60 × 10−12) and then replicated (P < 0.01) in an independent cohort of 172 cases and 1,134 controls. The 5q14.3 locus is known to associate with variation in retinal vascular diameter, and the 2q34 and 1p12 loci have been implicated in the glycine/serine metabolic pathway. We subsequently found significant differences in blood serum levels of glycine (P = 4.04 × 10−6) and serine (P = 2.48 × 10−4) between MacTel cases and controls

Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P<1.09 × 10¯⁹) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N=1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research

Quantitative analysis of γ‐glutamylisoleucine, γ‐glutamylthreonine, and γ‐glutamylvaline in HeLa cells using UHPLC‐MS/MS

γ‐Glutamylpeptides have been identified as potential biomarkers for a number of diseases including cancer, diabetes, and liver disease. In this study, we developed and validated a novel quantitative analytical strategy for measuring γ‐glutamylisoleucine, γ‐glutamylthreonine, and γ‐glutamylvaline, all of which have been previously reported as potential biomarkers for prostate cancer in HeLa cells using ultra‐high‐performance liquid chromatography‐tandem mass spectrometry. A BEH C18 column was used as the stationary phase. Mobile phase A was 99:1 water:formic acid and mobile phase B was acetonitrile. Chemical isotope labeling using benzoyl chloride was used as the internal standardization strategy. Sample preparation consisted of the addition of water to a frozen cell pellet, sonication, derivatization, centrifugation, and subsequent addition of an internal standard solution. The method was validated for selectivity, accuracy, precision, linearity, and stability. The determined concentrations of γ‐glutamylisoleucine, γ‐glutamylthreonine, and γ‐glutamylvaline in HeLa cells were 1.92 ± 0.06, 10.8 ± 0.4, and 1.96 ± 0.04 pmol/mg protein, respectively. In addition, the qualitative analysis of these analytes in human serum was achieved using a modified sample preparation strategy. To the best of our knowledge, this is the first report of the use of benzoyl chloride for chemical isotope labeling for metabolite quantitation in cells.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/169280/1/jssc7308.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169280/2/jssc7308_am.pd