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Non-academic partners can be vital in successful public engagement activities on antimicrobial resistance. With collaboration between academic and non-academic partners, we developed and launched an open-access web-based application, the ‘antibiotic footprint calculator’, in both Thai and English. The application focused on a good user experience, addressing antibiotic overuse and its impact, and encouraging immediate action. The application was unveiled in joint public engagement activities. From 1 Nov 2021 to 31 July 2022 (9 month period), 2554 players estimated their personal antibiotic footprint by using the application.

Aim Pseudomonas aeruginosa, a leading opportunistic pathogen causing hospital-acquired infections, is also commonly found in agricultural settings. However, there are minimal attempts to examine the molecular and functional attributes shared by agricultural and clinical strains of P. aeruginosa. This study investigates the presence of P. aeruginosa in edible vegetable plants (including salad vegetables) and analyses the evolutionary and metabolic relatedness of the agricultural and clinical strains. Methods and Results Eighteen rhizospheric and endophytic P. aeruginosa strains were isolated from cucumber, tomato, eggplant, and chili directly from the farms. The identity of these strains was confirmed using biochemical and molecular assays. The genetic and metabolic traits of these plant-associated P. aeruginosa isolates were compared with clinical strains. DNA fingerprinting and 16S rDNA-based phylogenetic analyses revealed that the plant- and human-associated strains are evolutionarily related. Both agricultural and clinical isolates possessed plant-beneficial properties, including mineral solubilization to release essential nutrients (phosphorous, potassium, and zinc), ammonification, and the ability to release extracellular pyocyanin, siderophore, and indole-3 acetic acid. Conclusion These findings suggest that rhizospheric and endophytic P. aeruginosa strains are genetically and functionally analogous to the clinical isolates. In addition, the genotypic and phenotypic traits do not correlate with plant sources or ecosystems. Significance and Impact of the Study This study reconfirms that edible plants are the potential source for human and animal transmission of P. aeruginosa.

Microbial biofilms are composed of surface-adhered microorganisms enclosed in extracellular polymeric substances. The biofilm lifestyle is the intrinsic drug resistance imparted to bacterial cells protected by the matrix. So far, conventional drug susceptibility tests for biofilm are reagent and time-consuming, and most of them are in static conditions. Rapid and easy-to-use methods for biofilm formation and antibiotic activity testing need to be developed to accelerate the discovery of new antibiofilm strategies. Herein, a Lab-On-Chip (LOC) device is presented that provides optimal microenvironmental conditions closely mimicking real-life clinical biofilm status. This new device allows homogeneous attachment and immobilization of Pseudomonas aeruginosa PA01-EGFP cells, and the biofilms grown can be monitored by fluorescence microscopy. P. aeruginosa is an opportunistic pathogen known as a model for drug screening biofilm studies. The influence of flow rates on biofilms growth was analyzed by flow simulations using COMSOL® 5.2. Significant cell adhesion to the substrate and biofilm formation inside the microchannels were observed at higher flow rates 100 µL/h. After biofilm formation, the effectiveness of silver nanoparticles (SNP), chitosan nanoparticles (CNP), and a complex of chitosan-coated silver nanoparticles (CSNP) to eradicate the biofilm under a continuous flow was explored. The most significant loss of biofilm was seen with CSNP with a 65.5% decrease in average live/dead cell signal in biofilm compared to the negative controls. Our results demonstrate that this system is a user-friendly tool for antibiofilm drug screening that could be simply applied in clinical laboratories.Key Points• A continuous-flow microreactor that mimics real-life clinical biofilm infections was developed.• The antibiofilm activity of three nano drugs was evaluated in dynamic conditions.• The highest biofilm reduction was observed with chitosan-silver nanoparticles.

The lung microbiome impacts on lung function, making any smoking-induced changes in the lung microbiome potentially significant. The complex co-occurrence and co-avoidance patterns between the bacterial taxa in the lower respiratory tract (LRT) microbiome were explored for a cohort of active (AS), former (FS) and never (NS) smokers. Bronchoalveolar lavages (BALs) were collected from 55 volunteer subjects (9 NS, 24 FS and 22 AS). The LRT microbiome composition was assessed using 16S rRNA amplicon sequencing. Identification of differentially abundant taxa and co-occurrence patterns, discriminant analysis and biomarker inferences were performed. The data show that smoking results in a loss in the diversity of the LRT microbiome, change in the co-occurrence patterns and a weakening of the tight community structure present in healthy microbiomes. The increased abundance of the genus Ralstonia in the lung microbiomes of both former and active smokers is significant. Partial least square discriminant and DESeq2 analyses suggested a compositional difference between the cohorts in the LRT microbiome. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS. The linear discriminant analysis effect size (LEfSe) analyses identified several bacterial taxa as potential biomarkers of smoking status. Network-based clustering analysis highlighted different co-occurring and co-avoiding microbial taxa in the three groups. The analysis found a cluster of bacterial taxa that co-occur in smokers and non-smokers alike. The clusters exhibited tighter and more significant associations in NS compared to FS and AS. Higher degree of rivalry between clusters was observed in the AS. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS.

The use of whole metagenomic data to infer the relative abundance of all its microbes is well established. The same data can be used to determine the replication rate of all eubacterial taxa with circular chromosomes. Despite their availability, the replication rate profiles (metareplicome) have not been fully exploited in microbiome analyses. Another relatively new approach is the application of causal inferencing to analyse microbiome data that goes beyond correlational studies. A novel scalable pipeline called MeRRCI (Metagenome, metaResistome, and metaReplicome for Causal Inferencing) was developed. MeRRCI combines efficient computation of the metagenome (bacterial relative abundance), metaresistome (antimicrobial gene abundance) and metareplicome (replication rates), and integrates environmental variables (metadata) for causality analysis using Bayesian networks. MeRRCI was applied to an infant gut microbiome data set to investigate the microbial community’s response to antibiotics. Our analysis suggests that the current treatment stratagem contributes to preterm infant gut dysbiosis, allowing a proliferation of pathobionts. The study highlights the specific antibacterial resistance genes that may contribute to exponential cell division in the presence of antibiotics for various pathogens, namely Klebsiella pneumoniae, Citrobacter freundii, Staphylococcus epidermidis, Veilonella parvula and Clostridium perfringens . These organisms often contribute to the harmful long-term sequelae seen in these young infants.

Background Chronic infection with high-risk human papillomavirus is a necessary cause for cervical carcinogenesis. This study examined prevalence of nonavalent vaccine preventable HPV types over four months among sexually active women in the United States. Methods This sub-study obtained meta-data for 80 of the 1,365 women (18–25 years), enrolled in the BRAVO study, a randomized, open-label trial of home screening and treatment of asymptomatic bacterial vaginosis at high-risk for sexually transmitted infections conducted between 2008 and 2013. Participants were randomized to treatment or standard-of-care, and followed every 2-months for 12 months. Stored vaginal swabs from the first three visits were tested for the nine vaccine preventable HPV types using quantitative PCR. Prevalence and associated 95% confidence intervals for the HPV types were assessed using R (version 3.6.1). Results The average age of the participants was 21.5 (SD ± 2.11) years, with 60% having ever been pregnant and all were African-American. Majority (71%) reported ≥ two sex partners in the prior year with 89% having unprotected vaginal sex and 45% having a new sex partner in the prior year. About 30% had ≥ one of the nine nonavalent vaccine HPV types at all three time points over a period of four months, 15% at two of any three visits, 19% at one of the three visits and 36% were negative for all nine vaccine HPV types at all time points. The most frequently detected HPV vaccine types were 52, 58, 16, and 18. The prevalence of any vaccine HPV types, and high-risk HPV types was 63.8% and 58.8%, respectively. Conclusions Our findings suggest that HPV vaccination which is currently recommended for all unvaccinated persons through age 26 years, is likely to be more beneficial than previously thought as nonavalent HPV vaccine was not available during the time these data were collected.

Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is a complex ecosystem of interdependent taxa with an exponentially complex web of interactions involving these taxa, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and composition, and have proposed several biomarkers. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture.We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset from 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We conduct our study in two parts. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential, biomarker, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p < 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We conclude by summarizing our results, and provide conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

Introduction. Pseudomonas aeruginosa causes fatal infections in immunocompromised individuals and patients with pulmonary disorders. Gap Statement. Agricultural ecosystems are the vast reservoirs of this dreaded pathogen. However, there are limited attempts to analyse the pathogenicity of P. aeruginosa strains associated with edible plants. Aim. This study aims to (i) elucidate the virulence attributes of P. aeruginosa strains isolated from the rhizosphere and endophytic niches of cucumber, tomato, eggplant and chili;and (ii) compare these phenotypes with that of previously characterized clinical isolates. Methodology. Crystal-violet microtitre assay, swarm plate experiment, gravimetric quantification and sheep blood lysis were performed to estimate the biofilm formation, swarming motility, rhamnolipid production and haemolytic activity, respectively, of P. aeruginosa strains. In addition, their pathogenicity was also assessed based on their ability to antagonize plant pathogens (Xanthomonas oryzae, Pythium aphanidermatum, Rhizoctonia solani and Fusarium oxysporum) and kill a select nematode (Caenorhabditis elegans). Results. Nearly 80 % of the plant-associated strains produced rhamnolipid and exhibited at least one type of lytic activity (haemolysis, proteolysis and lipolysis). Almost 50 % of these strains formed significant levels of biofilm and exhibited swarming motility. The agricultural strains showed significantly higher and lower virulence against the bacterial and fungal pathogens, respectively, compared to the clinical strains. In C. elegans, a maximum of 40 and 100% mortality were induced by the agricultural and clinical strains, respectively. Conclusion. This investigation shows that P. aeruginosa in edible plants isolated directly from the farm express virulence and pathogenicity. Furthermore, clinical and agricultural P. aeruginosa strains antagonized the tested fungal phytopathogens, Pythium aphanidermatum, Rhizoctonia solani and Fusarium oxysporum. Thus, we recommend using these fungi as simple eukaryotic model systems to test P. aeruginosa pathogenicity.

Pseudomonas aeruginosais an urgent threat pathogen due to its evolving resistance to multiple antibiotics. Agricultural soil and plants are the vast reservoirs of this much-dreaded opportunistic bacterium. A few human isolates of P. aeruginosa are known to infect plants and insects. However, there is no report on the occurrence of multi-drug resistant P. aeruginosa in edible vegetable crops. This study compared 18 P. aeruginosa isolates from the rhizosphere and endophytic niches of four different vegetable crops (cucumber, tomato, eggplant, and chili) with three known clinical strains. All the isolates were tested for virulence traits such as resistance to various antibiotics classes, motility, biofilm, and production of virulence factors (rhamnolipid, pyocyanin, hemolysin, proteases, and lipases). Hierarchical clustering based on Ward minimum variance with Manhattan distance matrix grouped the strains into three clusters based on their phenotypic traits. Strains were exhibiting the highest virulence co-clustered with the human pathogenic isolates. These strains were resistant to cephalosporins, aminoglycosides, macrolides, nitrofurans, tetracyclines, and sulfonamides. These extensively drug-resistant (XDR) strains were only susceptible to polymyxin (colistin) and quinolone (cephalosporin). This study shows that the virulence traits are shared between plant- and human-isolates of P. aeruginosa. More importantly, the occurrence of XDR strains in vegetable crops is a serious global threat.

Upon infection, foreign antigenic proteins stimulate the host’s immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antibody called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the immune epitope database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in Extended or Coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.

Molecular mimicry between viral antigens and host proteins can produce cross-reacting antibodies leading to autoimmunity. Previous studies have shown that antibodies that cross-react with thrombopoietin, a regulator of platelet production, can induce thrombocytopenia which is characterized by low platelet count. Thrombocytopenia is associated with increased mortality in COVID-19 patients and is a rare side effect following vaccination against SARS-CoV-2. Here we show molecular mimicry between the Spike protein from SARS-CoV-2, the causative agent of COVID-19, and human thrombopoietin. Our computational analyses identify a shared five-amino acid long sequence motif (TQLPP) with similar structure and antibody-binding properties for these proteins, strongly indicative of molecular mimicry with potential for cross-reactivity. Our results are supported by reports of the presence of antibodies against epitopes containing TQLPP in both COVID-19 and pre-pandemic patient samples, indicating antibody cross-reactivity between Spike and a human protein. Since the Spike protein is the antigenic component of the SARS-CoV-2 vaccines, altering the motif may reduce the risk for thrombocytopenia and improve long-term protection against evolving variants. Our findings highlight the importance of considering peptide-level molecular mimicry when developing therapeutic interventions and designing vaccines to avoid potential autoimmune reactions.

SummaryPseudomonas aeruginosa is a leading opportunistic pathogen capable of causing fatal infections in immunocompromised individuals and patients with degenerative lung diseases. Agricultural soil and plants are the vast reservoirs of this dreaded pathogen. However, there have been limited attempts to analyze the pathogenicity of P. aeruginosa strains associated with edible vegetable plants. This study aims to elucidate the virulence attributes of P. aeruginosa strains isolated from the rhizosphere and endophytic niches of cucumber, tomato, eggplant, and chili collected from agricultural fields. Virulence of the agricultural strains was compared to three previously characterized clinical isolates. Our results showed that 50% of the plant-associated strains formed significant levels of biofilm and exhibited swarming motility. Nearly 80% of these strains produced considerable levels of rhamnolipid and exhibited at least one type of lytic activity (hemolysis, proteolysis, and lipolysis). Their virulence was also assessed based on their ability to suppress the growth of plant pathogens (Xanthomonas oryzae, Pythium aphanidermatum, Rhizoctonia solani, and Fusarium oxysporum) and kill a select nematode (Caenorhabditis elegans). The plant-associated strains showed significantly higher virulence against the bacterial phytopathogen whereas the clinical strains had significantly higher antagonism against the fungal pathogens. In C. elegans slow-killing assay, the clinical strains caused 50-100% death while a maximum of 40% mortality was induced by the agricultural strains. This study demonstrates that some of the P. aeruginosa strains associated with edible plants harbor multiple virulence traits. Upon infection of humans or animals, these strains may evolve to be more pathogenic and pose a significant health hazard.

The Journal of Medical Microbiology has a global presence with an international Editorial Board. Asian countries such as PR China, India and Iran are prolific in the submission of manuscripts. Overall, the acceptance rate has been highest for European countries, the USA, Canada and Australia, and lowest for African, Asian and Latin American (LATAM) countries. The creation of regional Editors to assist the authors from these countries would serve the scientific community.

A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions. IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is the cause of an infection known as coronavirus infectious disease 2019 (COVID-19). COVID-19 has become a global source of morbidity, mortality and social disruption since its emergence in East Asia in late 2019 and subsequent pandemic spread. Typical symptoms include cough, sore throat, fever, and sudden loss of taste and smell. Persistent, post-infection sequelae have been noted in a minority of cases. Severe complications and deaths occur mostly in older adults. Laboratory confirmation can be performed by viral RNA and antigen detection in nasal swabs or by detecting specific neutralizing antibodies. There is no effective and approved antiviral treatment, but several vaccines with favourable safety and efficacy profiles are being used in mass vaccination programmes. Vaccine-based COVID control should be seen as an addition to existing hygiene measures such as physical distancing, increased hand hygiene, cough etiquette, and barrier protection with personal protective equipment for frontline healthcare workers and other high-risk professions.

Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or “disease” variable, and then computing the causal network, referred to as a “disease network”, with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is the cause of an infection known as coronavirus infectious disease 2019 (COVID-19). COVID-19 has become a global source of morbidity, mortality and social disruption since its emergence in East Asia in late 2019 and subsequent pandemic spread. Typical symptoms include cough, sore throat, fever, and sudden loss of taste and smell. Persistent, post-infection sequelae have been noted in a minority of cases. Severe complications and deaths occur mostly in older adults. Laboratory confirmation can be performed by viral RNA and antigen detection in nasal swabs or by detecting specific neutralizing antibodies. There is no effective and approved antiviral treatment, but several vaccines with favourable safety and efficacy profiles are being used in mass vaccination programmes. Vaccine-based COVID control should be seen as an addition to existing hygiene measures such as physical distancing, increased hand hygiene, cough etiquette, and barrier protection with personal protective equipment for frontline healthcare workers and other high-risk professions.

BackgroundMicrobe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by aBayesian Network(BN) about a microbial community. Unlike the previously proposedCo-occurrence Networks(CoNs), BNs are based on conditional dependencies and can help in revealing complex associations.ResultsIn this paper, we propose a way of combining a BN and a CoN to construct asigned Bayesian Network(sBN). We report a surprising association between directed edges in signed BNs and knowncolonization orders.ConclusionsBNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.

Vaginal dysbiosis-induced by an overgrowth of anaerobic bacteria is referred to as bacterial vaginosis (BV). The dysbiosis is associated with an increased risk for acquisition of sexually transmitted infections. Women with symptomatic BV are treated with oral metronidazole (MET), but its effectiveness remains to be elucidated. This study used whole-genome sequencing (WGS) to determine the changes in the microbiota among women treated with MET. WGS was conducted on DNA obtained from 20 vaginal swabs collected at four time points over 12 months from five randomly selected African American (AA) women. The baseline visit included all women who were diagnosed with asymptomatic BV and were untreated. All subjects were tested subsequently once every 2 months and received a course of MET for each BV episode during the 12 months. The BV status was classified according to Nugent scores (NSs) of vaginal smears. The microbial and resistome profiles were analysed along with the sociodemographic metadata. Despite treatment, none of the five participants reverted to normal vaginal flora — two were consistently positive for BV, and the rest experienced episodic cases of BV. WGS analyses showed Gardnerella spp. as the most abundant organism. After treatment with MET, there was an observed decline of Lactobacillus and Prevotella species. One participant had a healthy vaginal microbiota based on NS at one follow-up time point. Resistance genes including tetM and lscA were detected. Though limited in subjects, this study shows specific microbiota changes with treatment, presence of many resistant genes in their microbiota, and recurrence and persistence of BV despite MET treatment. Thus, MET may not be an effective treatment option for asymptomatic BV, and whole metagenome sequence would better inform the choice of antibiotics.

SUMMARYRecently emerged beta-coronavirus, SARS-CoV-2 has resulted in the current pandemic designated COVID-19. COVID-19 manifests as severe illness exhibiting systemic inflammatory response syndrome, acute respiratory distress syndrome (ARDS), thrombotic events, and shock, exacerbated further by co-morbidities and age1–3. Recent clinical reports suggested that the pulmonary failure seen in COVID-19 may not be solely driven by acute ARDS, but also microvascular thrombotic events, likely driven by complement activation4,5. However, it is not fully understood how the SARS-CoV-2 infection mechanisms mediate thrombotic events, and whether such mechanisms and responses are unique to SARS-CoV-2 infection, compared to other respiratory infections. We address these questions here, in the context of normal lung epithelia, in vitro and in vivo, using publicly available data. Our results indicate that plasmin is a crucial mediator which primes interactions between complement and platelet-activating systems in lung epithelia upon SARS-CoV-2 infection, with a potential for therapeutic intervention.

Motivation: Metagenomics sequencing data can be used to compute not just the relative abundance profile, but also the replication rates of every taxon in the microbiome sample. We investigate how the dynamics implied by the replication rates can be used to understand the antibiotic response in microbiomes, given the significant variation in the types of antibiotics and the types of response by different taxa. The analysis is further expanded by factoring in the resistome of the microbiomes, which can be readily profiled from the metagenomic sequence data. The fact that some antibiotics such as β -lactams target replicating cells makes it even more critical to use replication rates to analyze the antibiotic response. Results: We introduce a novel approach for metagenomic analysis that integrates microbial community profiling, replication rate calculation, and causal structural learning to analyze the antibiotic response. First, we developed PeTRi, which involves efficient cluster computation of bacterial replication rates from metagenomic sequence data. Second, we integrate the abundance profile, replication profile, resistome profile, and environmental variables to perform causality analysis. Finally, we applied the integrated analysis to the data from an infant gut microbiome study. Conclusions from our analysis are as follows: (i) Microbes tend to lower their replication rates in response to β -lactams; (ii) The presence of antibiotic resistance genes combined with the causality analysis strongly suggest that genes fosA5, oqxA, kpnF, arnA, and acrA provides resistance for the taxon K. pneumoniae, allowing it to replicate and dominate the microbiome after the drug ticarcillin-clavulanate was administered; and (iii) Human and donor milk strongly influence the resistome of the infant gut microbiome.