ABSTRACT. Plasma immunoglobulin concentrations of premature infants of birth weight less than 1500 g were measured longitudinally from birth to 10 months chronological age. Infants were divided into two groups based on gestational age (group I: 25-28 wk; group 11: 29-32 wk). In the 1st wk of life, plasma IgG levels correlated with gestational age ( r = 0.5, p < 0.001). At 3 months chronological age, the geometric mean plasma IgG levels were 60 mg/dl in group I and 104 mg/dl in group I1 infants. Most infants remained hypogammaglobulinemic at 6 months with seven of 11 infants in group I and 13 of 21 infants in group I1 having plasma IgG levels below 200 mgldl. In the 1st wk of life, plasma IgM concentrations were 7.6 and 9.1 mgldl in groups I and 11, respectively. They rose to 41.8 and 34.7 by 8 to 10 months of life. Plasma IgA concentrations were comparable for groups I and I1 in the 1st wk of life (1.2 and 0.6 mg/dl, respectively), but at 1 month of age group I infants had a transient increase in IgA which was not seen in the group I1 infants (4.5 versus 1.9 mgldl, respectively, p < 0.02). This transient elevation in IgA did not correlate with type or route of feeding or amounts of transfused blood. Group I and group I1 infants had comparable rates of infections prior to discharge from the nursery ( p = 0.27). After discharge, the 43 preterm infants followed until 10 months chronological age had a significantly higher incidence of infections than 41 term infants ( p = 0.04). In addition, the preterm infants had more lower respiratory infections after discharge than term infants (14 versus 2, p = 0.003), and were rehospitalized more often for infection (8 versus 0, p = 0.01). Despite severe prolonged hypogammaglobulinemia, none of the preterm infants had bacteremic illness after discharge from the nursery. This study provides normal age-group immunoglobulin values for the first 10 months of life in very small premature infants. The lack of invasive bacterial infections in this small group of preterm infants after discharge from the nursery suggests that further studies will be necessary to determine whether the hypogammaglobulinemia places these very low birth weight infants at risk for serious infection. (Pediatr Res 20:899-904, 1986) Abbreviations VLBW, very low birth weight NICU, Neonatal Intensive Care Unit

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Antibody to the capsular polysaccharide (CP) of Haemophilus influenzae b (Hib) is bactericidal, opsonic, and protective. Minimum protective levels of primarily IgG antibody to Hib CP, calculated from passive immunization studies, have been found to be approximately .06-.15 microgram/ml of serum. The human response to antigenic challenge with the Hib capsule, however, includes production of antibody to Hib CP of different isotypes whose function against Hib is unclear. In order to characterize the function of antibody to Hib CP of different isotypes, we purified human IgG, IgM, and IgA from the serum of an adult donor who had been previously immunized with purified Hib CP vaccine. The globulin preparations were greater than 99% isotypically pure, contained large quantities of anticapsular antibody, and differed in function against Hib. IgG antibody to Hib CP was bactericidal and opsonic for human polymorphonuclear leukocytes (PMNLs) in the presence of complement and protective in infant rats. IgM, although more bactericidal than IgG (P less than .01) and equally protective in rats, opsonized Hib poorly for PMNLs. IgA was not bactericidal or opsonic and did not prevent bacteremia and meningitis in rats challenged with Hib. We conclude that antibody directed against the capsule of Hib differs in antibacterial function depending on class. These data may be important to acurately estimate minimum protective levels of anticapsular antibody after vaccination or natural infection and may have implications for the manner in which the host clears Hib from the circulation.

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ATAC-seq is widely used to measure chromatin accessibility and identify open chromatin regions (OCRs). OCRs usually indicate active regulatory elements in the genome and are directly associated with the gene regulatory network. The identification of differential accessibility regions (DARs) between different biological conditions is critical in determining the differential activity of regulatory elements. Differential analysis of ATAC-seq shares many similarities with differential expression analysis of RNAseq data. However, the distribution of ATAC-seq signal intensity is different from that of RNA-seq data, and higher sensitivity is required for DARs identification. Many different tools can be used to perform differential analysis of ATAC-seq data, but a comprehensive comparison and benchmarking of these methods is still lacking. Here, we used simulated datasets to systematically measure the sensitivity and specificity of six different methods. We further discussed the statistical and signal density cutoffs in the differential analysis of ATAC-seq by applying them to real data. Batch effects are very common in high-throughput sequencing experiments. We illustrated that batch-effect correction can dramatically improve sensitivity in the differential analysis of ATAC-seq data. Finally, we developed a user-friendly package, BeCorrect, to perform batch effect correction and visualization of corrected ATAC-seq signals in a genome browser. Gene regulation in the mammalian genome involves different types of regulatory elements, such as promoters, enhancers, and insulators. It was estimated that there are over two million regulatory elements in the human and mouse genomes 1,2 , and these regulatory elements recruit different epigenetic modifications to regulate the expression of genes in cell type-specific and developmental stage-specific manners 3-5. Active regulatory elements must remain in an accessible state to allow the binding of different transcription factors to activate or silence target genes. ATAC-seq (assay for transposase-accessible chromatin followed by sequencing) is a recently developed technique to measure genome-wide chromatin accessibility (or open chromatin) 6,7. Compared with other techniques, such as DNase-seq, Mnase-seq, and FAIRE-seq, ATAC-seq experiments are relatively easier to perform across different tissues and cell types. Furthermore, ATAC-seq experiments allow ultra-low input cell numbers, even down to the single-cell level 8. These advantages propelled ATAC-seq to be the most widely used technology to define open chromatin by many large genomics consortiums, including ENCODE 9 , TCGA 10 , PsychENCODE 11 , IHEC 12 , and TaRGET II 13. The peak-calling analysis used to identify open chromatin regions (OCRs) by using ATAC-seq is generally adapted from ChIP-seq data analysis. However, there are fundamental differences between ATAC-seq and ChIP-seq-most notably that ATAC-seq is performed without control or input samples. Nonetheless, peak callers, such as macs2 14 , can identify OCRs by evalua...