Conservation of C4BP-binding sequence patterns in Streptococcus pyogenes M and Enn proteins.

Antigenically sequence variable M proteins of the major bacterial pathogen Streptococcus pyogenes (Strep A) are responsible for recruiting human C4b-binding protein (C4BP) to the bacterial surface, which enables Strep A to evade destruction by the immune system. The most sequence divergent portion of M proteins, the hypervariable region (HVR), is responsible for binding C4BP. Structural evidence points to the conservation of two C4BP-binding sequence patterns (M2 and M22) in the HVR of numerous M proteins, with this conservation applicable to vaccine immunogen design. These two patterns, however, only partially explain C4BP binding by Strep A. Here, we identified several M proteins that lack these patterns but still bind C4BP and determined the structures of two, M68 and M87 HVRs, in complex with a C4BP fragment. Mutagenesis of these M proteins led to the identification of amino acids that are crucial for C4BP binding, enabling formulation of new C4BP-binding patterns. Mutagenesis was also carried out on M2 and M22 proteins to refine or generate experimentally grounded C4BP-binding patterns. The M22 pattern was the most prevalent among M proteins, followed by the M87 and M2 patterns, while the M68 pattern was rare. These patterns, except for M68, were also evident in numerous M-like Enn proteins. Binding of C4BP via these patterns to Enn proteins was verified. We conclude that C4BP-binding patterns occur frequently in Strep A strains of differing M types, being present in their M or Enn proteins, or frequently both, providing further impetus for their use as vaccine immunogens.

Conservation of C4BP-binding Sequence Patterns in Streptococcus pyogenes M and Enn Proteins.

Antigenically sequence variable M proteins of the major bacterial pathogen Streptococcus pyogenes (Strep A) are responsible for recruiting human C4b-binding protein (C4BP) to the bacterial surface, which enables Strep A to evade destruction by the immune system. The most sequence divergent portion of M proteins, the hypervariable region (HVR), is responsible for binding C4BP. Structural evidence points to the conservation of two C4BP-binding sequence patterns (M2 and M22) in the HVR of numerous M proteins, with this conservation applicable to vaccine immunogen design. These two patterns, however, only partially explain C4BP-binding by Strep A. Here, we identified several M proteins that lack these patterns but still bind C4BP, and determined the structures of two, M68 and M87 HVRs, in complex with a C4BP fragment. Mutagenesis of these M proteins led to identification of amino acids that are crucial for C4BP-binding, enabling formulation of new C4BP-binding patterns. Mutagenesis was also carried out on M2 and M22 proteins to refine or generate experimentally grounded C4BP-binding patterns. The M22 pattern was the most populated among M proteins, followed by the M87 and M2 patterns, while the M68 pattern was rare. These patterns, except for M68, were also evident in numerous M-like Enn proteins. Binding of C4BP via these patterns to Enn proteins was verified. We conclude that C4BP-binding patterns occur frequently in Strep A strains of differing M types, being present in their M or Enn proteins, or frequently both, providing further impetus for their use as vaccine immunogens.

Addressing noise in co-expression network construction.

Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The 'one-size-fits-all' approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow.

Chromosomal characteristics of salt stress heritable gene expression in the rice genome.

BACKGROUND: Gene expression is potentially an important heritable quantitative trait that mediates between genetic variation and higher-level complex phenotypes through time and condition-dependent regulatory interactions. Therefore, we sought to explore both the genomic and condition-specific characteristics of gene expression heritability within the context of chromosomal structure. RESULTS: Heritability was estimated for biological gene expression using a diverse, 84-line, Oryza sativa (rice) population under optimal and salt-stressed conditions. Overall, 5936 genes were found to have heritable expression regardless of condition and 1377 genes were found to have heritable expression only during salt stress. These genes with salt-specific heritable expression are enriched for functional terms associated with response to stimulus and transcription factor activity. Additionally, we discovered that highly and lowly expressed genes, and genes with heritable expression are distributed differently along the chromosomes in patterns that follow previously identified high-throughput chromosomal conformation capture (Hi-C) A/B chromatin compartments. Furthermore, multiple genomic hot-spots enriched for genes with salt-specific heritability were identified on chromosomes 1, 4, 6, and 8. These hotspots were found to contain genes functionally enriched for transcriptional regulation and overlaps with a previously identified major QTL for salt-tolerance in rice. CONCLUSIONS: Investigating the heritability of traits, and in-particular gene expression traits, is important towards developing a basic understanding of how regulatory networks behave across a population. This work provides insights into spatial patterns of heritable gene expression at the chromosomal level.

Effects of ceftiofur and chlortetracycline treatment strategies on antimicrobial susceptibility and on tet(A), tet(B), and bla CMY-2 resistance genes among E. coli isolated from the feces of feedlot cattle.

A randomized controlled field trial was conducted to evaluate the effects of two sets of treatment strategies on ceftiofur and tetracycline resistance in feedlot cattle. The strategies consisted of ceftiofur crystalline-free acid (CCFA) administered to either one or all of the steers within a pen, followed by feeding or not feeding a therapeutic dose of chlortetracycline (CTC). Eighty-eight steers were randomly allocated to eight pens of 11 steers each. Both treatment regimens were randomly assigned to the pens in a two-way full factorial design. Non-type-specific (NTS) E. coli (n = 1,050) were isolated from fecal samples gathered on Days 0, 4, 12, and 26. Antimicrobial susceptibility profiles were determined using a microbroth dilution technique. PCR was used to detect tet(A), tet(B), and bla CMY-2 genes within each isolate. Chlortetracycline administration greatly exacerbated the already increased levels of both phenotypic and genotypic ceftiofur resistance conferred by prior CCFA treatment (P<0.05). The four treatment regimens also influenced the phenotypic multidrug resistance count of NTS E. coli populations. Chlortetracycline treatment alone was associated with an increased probability of selecting isolates that harbored tet(B) versus tet(A) (P<0.05); meanwhile, there was an inverse association between finding tet(A) versus tet(B) genes for any given regimen (P<0.05). The presence of a tet(A) gene was associated with an isolate exhibiting reduced phenotypic susceptibility to a higher median number of antimicrobials (n = 289, median = 6; 95% CI = 4-8) compared with the tet(B) gene (n = 208, median = 3; 95% CI = 3-4). Results indicate that CTC can exacerbate ceftiofur resistance following CCFA therapy and therefore should be avoided, especially when considering their use in sequence. Further studies are required to establish the animal-level effects of co-housing antimicrobial-treated and non-treated animals together.