Patients with antibody deficiency face frequent respiratory infections, but the antibiotics intended to protect them may also help select widespread resistance. This study traces that resistance and tests a possible way to guide treatment.

Antibody-deficient patients are at high risk of respiratory tract infections. Many therefore receive antibiotic prophylaxis and have access to antibiotics for self-administration when breakthrough infections occur.

That combination may increase antimicrobial resistance, or AMR, creating a tension between preventing infection and preserving antibiotic effectiveness. The study investigated phenotypic and molecular antibiotic resistance within a cohort of antibody-deficient patients.

It also sought possible transmission of genetic elements encoding antibiotic resistance and tested whether changes in sputum neutrophil elastase level associate with bacterial infection. Patients needed primary or secondary antibody deficiency requiring immunoglobulin replacement therapy and the ability to spontaneously produce sputum.

The cross-sectional cohort provided a single sputum sample at each visit, while the prospective cohort provided samples every two weeks for twelve weeks and when additional antibiotics began for presumed breakthrough infection. The samples supported neutrophil elastase measurement, microbiome analysis, and collection of clinical, demographic, and weekly symptom data in the prospective cohort.

The cross-sectional cohort included seventy samples from twenty-nine patients, with between one and six samples per patient and a median of two. CVID was the most common diagnosis, most patients had known bronchiectasis, and nineteen of twenty-nine patients received long-term antibiotic prophylaxis.

The prospective cohort comprised fourteen patients; twelve had known bronchiectasis and eight received antibiotic prophylaxis. Figure one begins with seventy sputum samples, yielding three hundred forty-three visible colonies identified to species level using MALDI-TOF.

The bar chart shows the percentage of isolates for each organism, with species present in at least two percent of samples marked for further phenotypic antibiotic-resistance analysis. The Venn diagram then shows organisms shared across repeated samples, supporting the authors’ description of a largely stable microbial community, particularly among commensal streptococci.

Phenotypic antibiotic sensitivity was assessed in species making up at least two percent of isolates, with summary data for two hundred fifty-five Streptococcus species and Haemophilus influenzae isolates. Azithromycin resistance was particularly prevalent, even among patients not receiving macrolide prophylaxis.

Overall, eighty-one point seven percent of isolates resisted azithromycin, thirty-five percent resisted tetracyclines, twenty-three point nine percent resisted ampicillin, and nineteen point three percent resisted cefotaxime. Figure two examines phenotypic antibiotic resistance among two hundred fifty-five Streptococcus or H.

influenzae isolates, using disc diffusion. Panel a separates isolates by patients’ prophylaxis, while panel b summarizes susceptibility across all isolates. The authors report particularly prevalent azithromycin resistance, including among patients without macrolide prophylaxis; overall, eighty-one point seven percent of isolates were resistant to azithromycin, thirty-five percent to tetracyclines, and twenty-three point nine percent to ampicillin.

Twenty-two of twenty-eight patients had a positive PCR for erm(B) or mef(A), the macrolide resistance genes, in at least one sample. Sequencing localized these genes, along with genes conferring resistance to tetracyclines, fluoroquinolones, and other antibiotics, predominantly to commensal streptococcal species.

These species may act as a reservoir for horizontal transmission to pathogenic species. Figure Three examines macrolide-resistance genes in sputum, separated by macrolide prophylaxis, non-macrolide prophylaxis, or no prophylaxis.

Panels a and b show individual erm(B) and mef(A) results per sample as counts and percentages, while panel c asks the patient-level question: whether any sample contained either gene. The authors report that over forty percent of samples carried erm(B) and/or mef(A), regardless of prophylaxis type, highlighting a widespread resistance reservoir in this antibody-deficient cohort.

Phylogenetic analysis of extracted plasmid sequences showed that conjugative plasmids such as repUS43 and repUS38 clustered across different streptococcal isolates. These plasmids may carry erm(B) or tet(M), and the clustering included isolates from the same sample, serial samples from one patient, and even different patients.

That pattern may suggest cross-transmission. Figure six is a phylogenetic clustering analysis of plasmid-derived sequences, with colored sidebars identifying plasmids and linked erm(B) or tet(M) resistance genes. The pink clades contain isolates from different patients, the yellow clade contains two different streptococci from serial sputum samples of one patient, and the blue clades pair different streptococci from the same sputum samples.

The authors interpret these shared plasmid clusters, including repUS43 and repUS38, as evidence that cross-transmission may have occurred. Figure four combines clustering, resistance genes, and disc-diffusion results for eight H. influenzae and six P.

aeruginosa isolates. The tree shows two close pairs in each species, including serial P. aeruginosa samples from one patient, while the heat maps identify gene variants such as PBP3, OXA, and PDC and the antibiograms mark phenotypic resistance or intermediate susceptibility.

This matters because it links genetic resistance determinants with observed antibiotic responses and patient prophylaxis status. Figure five combines a core-genome k-mer clustering tree with a resistome heat map and an antibiogram for fifty-one sequenced streptococcal isolates.

The branches identify clades corresponding to Streptococcus gordonii, S. parasanguinis, S. oralis, S. mitis, plus a mixed group, while coloured squares show prophylaxis status and detected resistance genes, including their links to plasmids, insertion sequences, or mobile genetic elements.

Circles report phenotypic resistance or intermediate susceptibility to the listed antibiotics, connecting genetic findings with observed drug responses. The high prevalence of antibiotic resistance demands strategies to minimize antibiotic usage.

Earlier research suggested that respiratory exacerbations in patients with antibody deficiency are more often positive for pathogenic viruses than bacteria. Patients self-treated presumed bacterial infection even with predominantly upper respiratory tract symptoms, while response to antibiotics was predicted by sputum purulence.

The study therefore investigated the relationship between neutrophil elastase and bacterial populations in sputum as a rapid tool to inform antibiotic use. Fourteen patients provided serial sputum samples for measurement of elastase and the sixteen S rRNA microbiome.

There was a significant negative relationship between neutrophil elastase concentration and markers of alpha-diversity, including the Shannon entropy alpha-diversity index. Nine patients took eleven courses of antibiotics during the study period.

Based on elastase and microbiome analysis, five courses were classified as appropriate, while four were classified as inappropriate because there was no change in The study finds high resistance in respiratory microbiota, especially to macrolides, alongside evidence suggesting genetic transmission between streptococci.

Neutrophil elastase is investigated as a rapid tool to inform antibiotic use.