Risk Factors and Mortality Rates Associated With Invasive Group B Streptococcus Infections Among Patients in the US Veterans Health Administration.

Importance: The incidence of invasive infections caused by group B Streptococcus (GBS) continues to increase in the United States. Although diabetes is a key risk factor for invasive GBS, the influence of long-term glycemic control is not well characterized; other risk factors and mortality rates associated with specific types of invasive GBS infections are unknown. Objective: To investigate risk factors and mortality rates associated with specific invasive GBS infectious syndromes. Design, Setting, and Participants: This cohort study used US Veterans Health Administration data to assess active users of the Veterans Affairs health care system between January 1, 2008, and December 31, 2017. Data analysis was conducted from April 2018 to August 2019. Exposures: Invasive GBS infections. Main Outcomes and Measures: The specific types of infectious syndromes and comorbid conditions among patients with an invasive GBS infection were evaluated. Results: Between 2008 and 2017, 5175 patients in the Veterans Affairs health care system experienced 5497 invasive GBS infections (5027 [97.1%] men, 3737 [72.2%] white, and 4545 [87.8%] non-Latino); all-cause 30-day mortality was 8.7% (451 of 5175). The most frequently observed infections were osteomyelitis (1171 [21.3%]), bacteremia without focus (1009 [18.4%]), skin or soft-tissue infections (919 [16.7%]), and pneumonia or empyema (694 [12.6%]). All-cause 30-day mortality following the index infection for each patient was highest among patients with peritonitis (38 of 138 [27.5%]) and pneumonia or empyema (116 of 664 [17.5%]) and lowest among those with osteomyelitis (15 of 1075 [1.4%]) or joint infection (17 of 501 [3.4%]). The most common comorbid conditions among patients with invasive GBS infections were diabetes (3364 [65.0%]), obesity (2669 [51.6%]), and chronic heart conditions (1633 [31.6%]). From 2008 to 2017, the incidence of invasive GBS infections increased from 9.23 to 11.67 cases per 100 000 person-years (P = .049). Stratification by body mass index showed the highest incidence of infections among patients at the extremes of body mass index (body mass index <18.5, 25.1 cases per 100 000 person-years; body mass index ≥40, 31.0 cases per 100 000 person-years). The incidence among patients with diabetes and poor long-term glycemic control (ie, hemoglobin A1c ≥9.5%) was 4-fold greater than among patients with diabetes and good glycemic control (ie, hemoglobin A1c <7.5%) (78.3 cases vs 19.0 cases per 100 000 person-years; unadjusted incident rate ratio, 4.1; 95% CI, 3.7-4.4; P < .001). Conclusions and Relevance: In this cohort study, comorbid conditions and 30-day mortality varied among types of invasive GBS infections. Obesity and poor long-term glycemic control were associated with invasive GBS infections. Efforts to reverse the trend of an increased incidence of invasive GBS infections should continue to support reducing obesity and focus on improving glycemic control.

A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis.

We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach.

A new approach for distinguishing cathepsin E and D activity in antigen-processing organelles.

Cathepsin E (CatE) and D (CatD) are the major aspartic proteinases in the endolysosomal pathway. They have similar specificity and therefore it is difficult to distinguish between them, as known substrates are not exclusively specific for one or the other. In this paper we present a substrate-based assay, which is highly relevant for immunological investigations because it detects both CatE and CatD in antigen-processing organelles. Therefore it could be used to study the involvement of these proteinases in protein degradation and the processing of invariant chain. An assay combining a new monospecific CatE antibody and the substrate, MOCAc-Gly-Lys-Pro-Ile-Leu-Phe-Phe-Arg-Leu-Lys(Dnp)-D-Arg-NH2[where MOCAc is (7-methoxycoumarin-4-yl)acetyl and Dnp is dinitrophenyl], is presented. This substrate is digested by both proteinases and therefore can be used to detect total aspartic proteinase activity in biological samples. After depletion of CatE by immunoprecipitation, the remaining activity is due to CatD, and the decrease in activity can be assigned to CatE. The activity of CatE and CatD in cytosolic, endosomal and lysosomal fractions of B cells, dendritic cells and human keratinocytes was determined. The data clearly indicate that CatE activity is mainly located in endosomal compartments, and that of CatD in lysosomal compartments. Hence this assay can also be used to characterize subcellular fractions using CatE as an endosomal marker, whereas CatD is a well-known lysosomal marker. The highest total aspartic proteinase activity was detected in dendritic cells, and the lowest in B cells. The assay presented exhibits a lower detection limit than common antibody-based methods without lacking the specificity.

Interferon-gamma regulates cathepsin G activity in microglia-derived lysosomes and controls the proteolytic processing of myelin basic protein in vitro.

The serine protease cathepsin (Cat) G dominates the proteolytic processing of the multiple sclerosis (MS)-associated autoantigen myelin basic protein (MBP) in lysosomes from primary human B cells and dendritic cells. This is in contrast to B-lymphoblastoid cell lines, where the asparagine endopeptidase (AEP) is responsible for this task. We have analysed microglia-derived lysosomal proteases for their ability to process MBP in vitro. In lysosomes derived from primary murine microglia, CatD, CatS, AEP and CatG were involved in the processing of MBP. Interestingly, when microglia were treated with interferon-gamma to mimic a T helper type 1-biased cytokine milieu in MS, CatG was drastically down-regulated, in contrast to CatS, CatB, CatL, CatD or AEP. This resulted in significantly increased stability of MBP and a selective lack of CatG-derived proteolytic fragments; however, it did not affect the gross pattern of MBP processing. Inhibition of serine proteases eliminated the processing differences between lysosomal extracts from resting microglia compared to interferon-stimulated microglia. Thus, the cytokine environment modulates lysosomal proteases in microglia by a selective down-regulation of CatG, leading to decreased MBP-processing by microglia-derived lysosomal proteases in vitro.

Cathepsin D is present in human eccrine sweat and involved in the postsecretory processing of the antimicrobial peptide DCD-1L.

The protein pattern of healthy human eccrine sweat was investigated and 10 major proteins were detected from which apolipoprotein D, lipophilin B, and cathepsin D (CatD) were identified for the first time in human eccrine sweat. We focused our studies on the function of the aspartate protease CatD in sweat. In vitro digestion experiments using a specific fluorescent CatD substrate showed that CatD is enzymatically active in human sweat. To identify potential substrates of CatD in human eccrine sweat LL-37 and DCD-1L, two antimicrobial peptides present in sweat, were digested in vitro with purified CatD. LL-37 was not significantly digested by CatD, whereas DCD-1L was cleaved between Leu(44) and Asp(45) and between Leu(29) and Glu(30) almost completely. The DCD-1L-derived peptides generated in vitro by CatD were also found in vivo in human sweat as determined by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Furthermore, besides the CatD-processed peptides we identified additionally DCD-1L-derived peptides that are generated upon cleavage with a 1,10-phenanthroline-sensitive carboxypeptidase and an endoprotease. Taken together, proteolytic processing generates 12 DCD-1L-derived peptides. To elucidate the functional significance of postsecretory processing the antimicrobial activity of three CatD-processed DCD-1L peptides was tested. Whereas two of these peptides showed no activity against Gram-positive and Gram-negative bacteria, one DCD-1L-derived peptide showed an even higher activity against Escherichia coli than DCD-1L. Functional analysis indicated that proteolytic processing of DCD-1L by CatD in human sweat modulates the innate immune defense of human skin.

An artificial neural network for estimating haplotype frequencies.

The problem of estimating haplotype frequencies from population data has been considered by numerous investigators, resulting in a wide variety of possible algorithmic and statistical solutions. We propose a relatively unique approach that employs an artificial neural network (ANN) to predict the most likely haplotype frequencies from a sample of population genotype data. Through an innovative ANN design for mapping genotype patterns to diplotypes, we have produced a prototype that demonstrates the feasibility of this approach, with provisional results that correlate well with estimates produced by the expectation maximization algorithm for haplotype frequency estimation. Given the computational demands of estimating haplotype frequencies for 20 or more single-nucleotide polymorphisms, the ANN approach is promising because its design fits well with parallel computing architectures.