Decoding Antimicrobial Resistance Transmission in Mother-Infant Dyads

Mother-infant health is an indicator of a country’s healthcare condition. The maternal microbiome is a precursor for the infant’s gut microbial colonization. The gut microbiome carries antibiotic-resistance genes and often leads to untreatable drug-resistant infections. Maternal-infant AMR transmission dynamics will cater to designing prophylactic measures.

Prof. Asad U. Khan

Lead PI

Antibiotic Resistance Laboratory

Interdisciplinary Biotechnology Unit

Aligarh Muslim University, India

COVID-19 led to 3 million deaths in 2020, overshadowing antimicrobial resistance (AMR) which caused 5 million deaths in 2019. Despite its global spread, AMR didn't spark significant public outcry. However, it poses a grave economic threat, potentially pushing 24 million into extreme poverty and reducing GDP by $3.4 trillion annually by 2030.

Mother-neonate health is a major global concern. Globally, there are approximately 1.3 million annual cases of neonatal sepsis and other infections. In LMICs, resource unavailability and prejudiced antimicrobial usage further exacerbate the problem. Maternal gut microbiota influences bacterial colonization in neonates. Antibiotic resistance genes of neonates are remarkably affected by prenatal and postnatal antibiotic exposure.

Decoding AMR Transmission from mother to infant is the first step toward curating prophylactic measures. In this study, we will use Next-generation sequencing to explore the same in-depth. Real-time surveillance is crucial yet challenging due to financial constraints in LMICs. We will cover at least five hospitals from India to understand mother-infant AMR transmission and its compounding factors such as socio-economic condition, hygiene, regular check-up affordability, and antibiotic consumption pattern. To explore the difference, we will create two cohorts, one with normal deliveries and a second with adverse pregnancy outcomes.

The target audience is the general public, especially in economically constrained countries with a huge gap in AMR awareness. The practicing health workers can design a more effective treatment regimen using this data. Our multi-pronged approach with a comprehensive report of socio-economic conditions, hygiene, regular check-up affordability, and antibiotic consumption patterns will also aid in collecting these updated data in Indian settings.

Novel ARGs can also be identified using this huge amount of data, and the surveillance approach can also be further developed for tracking ARG sources and dissemination in neonates.

The global scientific fraternity can use the publicly submitted metagenome for other in-depth exploration and comparative studies. We will be designing an AI/ML-based tool for AMR prediction in neonates from the mothers’ gut microbiome in the third trimester. This will help the physician take precautions and personalize the antibiotic regimen.

Our proposal aims to benefit the general public, healthcare professionals, and the global scientific community by addressing antimicrobial resistance (AMR) transmission in mother-neonate pairs. Through comprehensive AMR surveillance using metagenomic sequencing, we intend to develop tailored treatment regimens for the subjects involved in our study, thus improving clinical outcomes and reducing the burden of AMR-related infections. Furthermore, by generating large-scale publicly available metagenomic data on AMR transmission in mother-neonate dyads, our initiative will be a valuable resource for researchers worldwide. This data repository will enable the scientific community to gain deeper insights into the mechanisms of AMR dissemination and inform the development of targeted interventions. 

Moreover, leveraging the collected data, we plan to design an innovative AI/ML-based tool for predicting AMR in neonates based on analyzing their respective mothers' gut microbiomes during the third trimester of pregnancy. This predictive model will empower healthcare providers to proactively identify neonates at high risk of AMR acquisition, enabling timely interventions to prevent infections and improve patient outcomes.

Overall, our study holds the potential to significantly reduce mortality and morbidity rates in both mothers and newborns by advancing our understanding of AMR transmission dynamics and facilitating the implementation of personalized interventions.

The tangible impact of employing metagenomic sequencing in maternal-neonatal AMR dissemination extends beyond surveillance and intervention strategies. By reducing mortality and morbidity rates in pregnant women and neonates, metagenomic sequencing plays a pivotal role in safeguarding maternal and child health. Furthermore, by elucidating the factors associated with AMR transmission, such as plasmids and mobile genetic elements in bacteria, metagenomic analysis facilitates the development of targeted interventions to prevent the spread of resistant pathogens. Additionally, identifying novel antimicrobial resistance genes (ARGs) through metagenomic sequencing opens avenues for discovering new therapeutic targets and creating personalized antibiotic treatment regimens. Overall, integrating metagenomic sequencing into maternal and neonatal healthcare holds immense promise for combating AMR and improving health outcomes for vulnerable populations.

We will focus on scaling our impact by expanding the reach of our AMR surveillance program in mother-neonate pairs. This will involve collaborating with additional healthcare facilities and research institutions to increase the number of participants in our study. By broadening our sample size, we can enhance the robustness and generalizability of our findings. Furthermore, we will prioritize disseminating our research findings through academic publications, conference presentations, and public engagement initiatives. By sharing our insights with the global scientific community and raising awareness among the general public, we aim to foster collaboration and knowledge exchange, ultimately catalyzing efforts to combat AMR on a larger scale. Based on maternal gut microbiome analyses, we will develop advanced AI/ML algorithms to refine our predictive models for AMR in neonates. Additionally, we will explore integrating other omics data, such as metatranscriptomics and metabolomics, to gain deeper insights into host-microbiome interactions and AMR mechanisms.

Furthermore, we will seek opportunities for strategic partnerships with government agencies, non-profit organizations, and industry stakeholders to implement our findings into clinical practice and public health policy. 

Our long-term vision is to establish a sustainable AMR surveillance and intervention framework that can be replicated and adapted in diverse healthcare settings worldwide. 

To measure the success of our impact goals, we'll implement a range of assessment methods focused on quantifiable data and qualitative insights. 

1. Participant Enrollment: We will track the number of mother-neonate pairs joining our study. Increased enrollment signals the expanding reach and influence of our AMR surveillance initiative.

2. Data Quality: A rigorous assessment of the quality and completeness of our metagenomic sequencing data will be conducted. 

3. AMR Detection Rates: A reduction in Antibiotic resistance rates indicates the effectiveness of our interventions in mitigating AMR transmission.

4. Clinical Outcomes: We'll evaluate the impact of our tailored treatment regimens on clinical outcomes such as mortality and morbidity rates in mothers and newborns. 

5. Predictive Model Performance: We'll assess the accuracy and reliability of our AI/ML-based predictive model for AMR in neonates. This will help to identify high-risk neonates and guide clinical decisions.

6. Stakeholder Engagement: We'll seek feedback from healthcare professionals, policymakers, and other maternal and neonatal healthcare stakeholders. Engaging stakeholders ensures that our initiatives remain relevant, sustainable, and scalable.

The major barrier that we face right now is financial constraint. Limited funding may hinder our capacity to scale our AMR surveillance program, conduct metagenomic sequencing, and develop predictive models.

The major challenge we face is financial constraints for the sample collection, metagenomic sequencing, and further downstream processing. As NGS is expensive, it requires manpower and financial support to carry out a surveillance project of 1500 samples. 

We want to collaborate with people working in Antibiotic resistance.