Achal Dhariwal

Streptococcus pneumoniae is a leading pathogen in terms of deaths attributable to or associated with antimicrobial resistance globally. Thus, monitoring antibiotic resistance determinants constitutes a key aspect of surveillance efforts for this microbe. Leveraging publicly available whole-genome sequencing (WGS) data, we aimed to investigate the presence and distribution patterns of antibiotic resistance determinants in S. pneumoniae with a focus on multidrug resistance (MDR) and serotype distribution. Metadata and genomes were obtained from the National Center for Biotechnology Information Pathogen Detection database. Curation and harmonization were performed in R and SPSS. Data on resistance patterns were defined according to AMRFinderPlus, and a combination of prediction tools was employed for in silico serotyping. Analyses involved 75,161 genomes totalling 122,673 gene/allele counts from 14 antibiotic classes. MDR was observed in 16.7% of isolates, with the highest increasing rates in Asia and South America. Within antibiotic classes, an increase in macrolide resistance genes was highlighted, particularly in the proportion of genomes presenting mef(A)/msr(D). Over a third of isolates with serotypes 19F, 23F, 15A, 6B and 19A showed MDR. We further observed the highest significant increases in the presence of resistance in 33F, 22F, 10A and 23A. Serotype 13, not included in any vaccine formulation, presented high MDR rates with a strong increasing trend. The findings of this study highlight variations in resistance determinants globally and across serotypes over time. Collectively, these data underscore the added value of utilizing public WGS data to investigate the effectiveness and repercussions of treatment and vaccination strategies on managing antibiotic resistance.

Natural products are ubiquitously produced by many species that we encounter during our daily lives. One genus, Streptococcus , can produce a wide array of quorum sensing linked natural products known as RaS-RiPPs (ribosomally synthesized and post-translationally modified peptides). Their production is triggered by the induction of an Rgg-SHP quorum sensing system, which senses the presence of SHPs (short hydrophobic peptides) and induces the gene expression of these operons. Previous work has found that streptococcal RaS-RiPPs modulate the growth of different streptococci and might play a role in antibiotic tolerance. This is of particular importance to the oral microbiome, where streptococci are a predominant genus. This study examines the impact of the RaS-RiPP Tryglysin A on ex-vivo oral systems and provides the groundwork for determining how producer streptococci might impact the composition and function of oral microbiome communities. We additionally explore important factors to consider when culturing ex-vivo oral systems.

Background: Streptococcus pneumoniae is a leading pathogen in terms of deaths attributable to or associated with antimicrobial resistance globally. Thus, monitoring antibiotic resistance determinants constitutes a key aspect of surveillance efforts. Aim: Leveraging on publicly available whole-genome sequencing data, we aimed to investigate the presence and distribution patterns of antibiotic resistance in S. pneumoniae with a focus on multi-drug resistance and serotype distribution. Methods: Metadata and genomes were obtained from the National Center for Biotechnology Information Pathogen Detection database. Curation and harmonization were performed in R and SPSS. Data on resistance patterns was defined according to AMRFinderPlus and a combination of prediction tools were employed for in silico serotyping. Results: Analyses involved 75,161 genomes totaling 122,673 gene/allele counts from 14 antibiotic classes. Multi-drug resistance was observed in 16.7% of isolates with the highest increasing rates in Asia and South America. Within antibiotic classes, increase in macrolide resistance genes was highlighted, particularly in the proportion of genomes presenting mefA / msrD . Over a third of isolates with serotypes 19F, 23F, 15A, 6B, and 19A showed multi-drug resistance. We further observed the highest significant increases in the presence of resistance in 33F, 22F, 10A, and 23A. Serotype 13, not included in any vaccine formulation, presented high multi-drug resistance rates with a strong increasing trend. Conclusions: The findings of this study highlight variations in resistance determinants globally and across serotypes over time. Collectively these data underscore the added value of utilizing public whole-genome sequencing data to investigate the effectiveness and repercussions of treatment and vaccination strategies on managing antibiotic resistance.

Respiratory pathogens, commonly colonizing nasopharynx, are among the leading causes of death due to antimicrobial resistance. Yet, antibiotic resistance determinants within nasopharyngeal microbial communities remain poorly understood. In this prospective cohort study, we investigate the nasopharynx resistome development in preterm infants, assess early antibiotic impact on its trajectory, and explore its association with clinical covariates using shotgun metagenomics. Our findings reveal widespread nasopharyngeal carriage of antibiotic resistance genes (ARGs) with resistomes undergoing transient changes, including increased ARG diversity, abundance, and composition alterations due to early antibiotic exposure. ARGs associated with the critical nosocomial pathogen Serratia marcescens persist up to 8–10 months of age, representing a long-lasting hospitalization signature. The nasopharyngeal resistome strongly correlates with microbiome composition, with inter-individual differences and postnatal age explaining most of the variation. Our report on the collateral effects of antibiotics and prolonged hospitalization underscores the urgency of further studies focused on this relatively unexplored reservoir of pathogens and ARGs.

Prolonged exposure to antibiotics at low concentration can promote processes associated with bacterial biofilm formation, virulence and antibiotic resistance. This can be of high relevance in microbial communities like the oral microbiome, where commensals and pathogens share a common habitat and where the total abundance of antibiotic resistance genes surpasses the abundance in the gut. Here, we used an ex vivo model of human oral biofilms to investigate the impact of ampicillin on biofilm viability. The ecological impact on the microbiome and resistome was investigated using shotgun metagenomics. The results showed that low concentrations promoted significant shifts in microbial taxonomic profile and could enhance biofilm viability by up to 1 to 2-log. For the resistome, low concentrations had no significant impact on antibiotic resistance gene (ARG) diversity, while ARG abundance decreased by up to 84%. A positive correlation was observed between reduced microbial diversity and reduced ARG abundance. The WHO priority pathogens Streptococcus pneumoniae and Staphylococcus aureus were identified in some of the samples, but their abundance was not significantly altered by ampicillin. Most of the antibiotic resistance genes that increased in abundance in the ampicillin group were associated with streptococci, including Streptococcus mitis, a well-known potential donor of ARGs to S. pneumoniae. Overall, the results highlight the potential of using the model to further our understanding of ecological and evolutionary forces driving antimicrobial resistance in oral microbiomes.

Challenges from infections caused by biofilms and antimicrobial resistance highlight the need for novel antimicrobials that work synergistically with antibiotics and minimize resistance risk. In this study we investigated the potential synergistic effect of HAMLET (human alpha-lactalbumin made lethal to tumor cells), a human milk protein-lipid complex and amoxicillin on microbial ecology using an ex-vivo oral biofilm model. HAMLET was chosen due to its multi-targeted antimicrobial mechanism, together with its synergistic effect with antibiotics on single species pathogens, and low risk of resistance development. The combination of HAMLET and amoxicillin significantly reduced biofilm viability, while each of them alone had little or no impact. Using a whole metagenomics approach, we found that the combination group promoted a most remarkable shift in overall microbial composition compared to the untreated samples. Up to 90% of the bacterial species in the combined treatment were Lactobacillus crispatus , a species with probiotic effects, whereas it was detected in minor fraction in untreated samples. Resistome analysis indicated no major shifts on alpha-diversity, while beta-diversity revealed distinct clustering patterns for each treatment group, signifying that each treatment group harbors a unique resistome. TEM beta-lactamase genes were detected in low proportions in all treated samples but absent in untreated samples. Our study highlights the potential of HAMLET to synergize with amoxicillin in an ex-vivo model of the oral microbiome and modulate the proportion of probiotic bacteria. The findings extend the knowledge on the synergistic effects of HAMLET and antibiotics from single-species studies to polymicrobial biofilms of human origin. Importance: Polymicrobial infections are challenging to treat and prevent, requiring the use of antibiotics that exhibit reduced efficacy due to biofilm formation. HAMLET has recently emerged as an antimicrobial agent that can synergize with antibiotics while limiting microbial resistance. We investigated the effects of HAMLET, alone and combined with low concentrations of amoxicillin, on ex vivo oral biofilms to simulate complex microbial interactions observed in the oral cavity. The combination of HAMLET and amoxicillin effectively targeted polymicrobial biofilms and led to an increase in Lactobacillus crispatus . The potency of this combination appears to be due to the synergistic effect of HAMLET and amoxicillin. These findings underscore the potential of combining antimicrobials with different modes of action for the development of more effective strategies for preventing and treating polymicrobial infections.

Early exposure to antibiotics and prolonged hospitalization in preterm infants may perturb microbiome development and contribute to adverse health outcomes. Although nasopharyngeal microbiomes are linked to respiratory infections, their early development is underexplored and often assessed with 16S rRNA sequencing, which lacks species resolution. Here, we investigated nasopharyngeal microbiota dynamics in 66 preterm infants by performing deep shotgun metagenomics on 369 nasopharyngeal aspirates collected from birth until 6 months corrected age (∼7-10 months chronological age). The nasopharyngeal microbiota evolved dynamically, exhibiting age-structured and individualized patterns shaped by postnatal antibiotic exposure and hospitalization. Early-life antibiotic exposure (ampicillin + gentamicin) had transient but significant effects on microbial diversity, composition, stability, and community dynamics. Conversely, an unexpected NICU outbreak left a lasting signature, with persistent S. marcescens carriage observed at 6 months corrected age, which our machine learning algorithm accurately predicted based on microbiome composition in the first days of life. A S. marcescens -dominated community type was present in 45% of total samples and exhibited remarkable stability over time, with minimal transitions to other types regardless of antibiotic exposure. These findings underscore the need for deeper insight into how antibiotics and potential outbreaks can lead to alterations in microbiome trajectories, with possible long-term health implications.

Respiratory pathogens, commonly colonizing nasopharynx, are among the leading causes of death due to antimicrobial resistance. Yet, antibiotic resistance determinants within nasopharyngeal microbial communities remain poorly understood. Utilizing shotgun metagenomics, we investigated the nasopharynx resistome development in preterm infants, assessed early antibiotic impact on its trajectory, and explored its association with clinical covariates. Our findings revealed widespread nasopharyngeal carriage of antibiotic resistance genes (ARGs) with resistomes undergoing transient changes, including increased ARG diversity, abundance, and composition alterations due to early antibiotic exposure. ARGs associated with the critical nosocomial pathogen Serratia marcescens persisted up to 8-10 months of age, representing a long-lasting hospitalization signature. The nasopharyngeal resistome strongly correlated with microbiome composition, with inter-individual differences and postnatal age explaining most of the variation. Our report on the collateral effects of antibiotics and prolonged hospitalization underscores the urgency of further studies focused on this relatively unexplored reservoir of pathogens and ARGs.

Background: Braak’s hypothesis states that sporadic Parkinson’s disease (PD) follows a specific progression of pathology from the peripheral to the central nervous system, and this progression can be monitored by detecting the accumulation of alpha-Synuclein (α-Syn) protein. Consequently, there is growing interest in understanding how the gut (commensal) microbiome can regulate α-Syn accumulation, as this could potentially lead to PD. Methods: We used 16S rRNA and shotgun sequencing to characterise microbial diversity. ¹H-NMR was employed to understand the metabolite production and intestinal inflammation estimated using ELISA and RNA-sequencing from feces and the intestinal epithelial layer respectively. The Na⁺ channel current and gut permeability were measured using an Ussing chamber. Immunohistochemistry and immunofluorescence imaging were applied to detect the α-Syn protein. LC-MS/MS was used for characterization of proteins from metabolite treated neuronal cells. Finally, Metascape and Ingenuity Pathway Analysis (IPA) bioinformatics tools were used for identification of dysregulated pathways. Results: We studied a transgenic (TG) rat model overexpressing the human SNCA gene and found that a progressive gut microbial composition alteration characterized by the reduction of Firmicutes to Bacteroidetes ratio could be detected in the young TG rats. Interestingly, this ratio then increased with ageing. The dynamics of Lactobacillus and Alistipes were monitored and reduced Lactobacillus and increased Alistipes abundance was discerned in ageing TG rats. Additionally, the SNCA gene overexpression resulted in gut α-Syn protein expression and increased with advanced age. Further, older TG animals had increased intestinal inflammation, decreased Na⁺ current and a robust alteration in metabolite production characterized by the increase of succinate levels in feces and serum. Manipulation of the gut bacteria by short-term antibiotic cocktail treatment revealed a complete loss of short-chain fatty acids and a reduction in succinate levels. Although antibiotic cocktail treatment did not change α-Syn expression in the enteric nervous system of the colon, however, reduced α-Syn expression was detected in the olfactory bulbs (forebrain) of the TG rats. Conclusion: Our data emphasize that the gut microbiome dysbiosis synchronous with ageing leads to a specific alteration of gut metabolites and can be modulated by antibiotics which may affect PD pathology.

The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules—the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no.