Periodic Reporting for period 1 - OUTSMART-infection (Microbiome centered prediction and prevention of recurrent infections)
Reporting period: 2023-01-01 to 2025-06-30
Antibiotic resistance is growing as a major public health concern. Yet, infections are often treated “empirically”, in absence of susceptibility measurements, leading to mismatches between prescription and infection susceptibility. We and others have done extensive work, including the use of Machine Learning (ML) tools, for algorithmically matching the prescribed antibiotic to the resistance profile of the infecting pathogen. However, antibiotics are a double-edged sword: while they help clear the current infection, they also select for future resistant pathogens that are harder to treat. Tailoring antibiotics for a given infection at the single patient level should therefore focus not just on maximizing clearance of the focal infection (“greedy” algorithm), but also on reducing the risk of future resistant infections (“look-ahead” algorithm). Urinary Tract Infections (UTIs) often persist and recur, necessitating a look-ahead treatment strategy. The majority of women will be affected by a UTI over their lifetime and, importantly, for many women infections persist and recur over months and years. This recurrent nature of UTIs and the common practice of empirical treatments necessitates thinking about treatment not as individual steps but as a long-term patient-specific strategy.
UTIs are often self-seeded from the gut microbiome, highlighting the microbiome strain-level composition as a predictive and even causal factor for infections. We thereby hypothesize that profiling the microbiome can help predict the resistance profile of infection and risk of recurrences, thereby guiding treatment that optimizes current and future efficacy. Furthermore, we hypothesize that it may be possible to manipulate the microbiome to be less prone to seeding infections, or to minimize the risk of resistance in future infections, should they occur. By understanding and exploiting the three-way interaction between a patient's microbiome, antibiotic treatment, and UTI risk, we aim to develop methods to predict and manipulate recurrent infections, leading to a personalized patient-specific strategy. We will: (1) use microbiome sequencing and phenotyping to predict the strain and resistance profile of infection; (2) use microbiome-informed reinforcement learning to optimize multi-step antibiotic treatments; (3) use in vitro selection of the microbiome to predict the potential of antibiotic treatments to manipulate the microbiome composition to reduce virulence and recurrence.
UTIs are often self-seeded from the gut microbiome, highlighting the microbiome strain-level composition as a predictive and even causal factor for infections. We thereby hypothesize that profiling the microbiome can help predict the resistance profile of infection and risk of recurrences, thereby guiding treatment that optimizes current and future efficacy. Furthermore, we hypothesize that it may be possible to manipulate the microbiome to be less prone to seeding infections, or to minimize the risk of resistance in future infections, should they occur. By understanding and exploiting the three-way interaction between a patient's microbiome, antibiotic treatment, and UTI risk, we aim to develop methods to predict and manipulate recurrent infections, leading to a personalized patient-specific strategy. We will: (1) use microbiome sequencing and phenotyping to predict the strain and resistance profile of infection; (2) use microbiome-informed reinforcement learning to optimize multi-step antibiotic treatments; (3) use in vitro selection of the microbiome to predict the potential of antibiotic treatments to manipulate the microbiome composition to reduce virulence and recurrence.
In collaboration with an Israeli HMO, we assembled a unique and large collection of faecal and UTI samples, including a cohort of patients with concurring faecal and urine samples (550) and a cohort of patients for which we have multiple faecal samples over two years (>600). We extracted pathogenic strains from the first cohort as isolates and as pools, phenotyped, and sequenced them. We have designed methods to assess within and between patient variability and shown that gut-residing pathogen reservoirs are highly personal and diverse in their genetic content and resistance profiles. Most importantly, we have established a predictive relationship between faecal and UTI antibiotic resistance. Specifically, we can report these main results:
We have shown that faecal Enterobacteriaceae communities are diverse, with approximately 70% of samples displaying strain-level (MLST) or species-level diversity.
We have shown that gut-residing pathogens are patient-specific: despite the presence of some pandemic strains, including ST131, more than 60% of the time (from a selection of 150 patients), the dominant E. coli strain we identify is not the dominant strain in any other sample.
We have established between and within patient variability also at the level of antibiotic resistance, with high-throughput isolate and community phenotyping (Aim 1.6.) and metagenomic sequencing.
We have discovered a predictive relationship between faecal and UTI antibiotic resistance at the genotypic level. We show that the coverage of a resistance gene linearly predicts the number of UTIs resistant to that particular antibiotic in the future (modification of Aim 1.4. and Aim 1.6.).
We have discovered a predictive relationship also between faecal and UTI antibiotic resistance at the phenotypic level. Surprisingly, just a simple plating of the raw faecal sample on antibiotic agar, with a coarse growth/no growth assessment, is predictive of a future resistant UTI for that patient (Aim 1.6).
We have shown that faecal Enterobacteriaceae communities are diverse, with approximately 70% of samples displaying strain-level (MLST) or species-level diversity.
We have shown that gut-residing pathogens are patient-specific: despite the presence of some pandemic strains, including ST131, more than 60% of the time (from a selection of 150 patients), the dominant E. coli strain we identify is not the dominant strain in any other sample.
We have established between and within patient variability also at the level of antibiotic resistance, with high-throughput isolate and community phenotyping (Aim 1.6.) and metagenomic sequencing.
We have discovered a predictive relationship between faecal and UTI antibiotic resistance at the genotypic level. We show that the coverage of a resistance gene linearly predicts the number of UTIs resistant to that particular antibiotic in the future (modification of Aim 1.4. and Aim 1.6.).
We have discovered a predictive relationship also between faecal and UTI antibiotic resistance at the phenotypic level. Surprisingly, just a simple plating of the raw faecal sample on antibiotic agar, with a coarse growth/no growth assessment, is predictive of a future resistant UTI for that patient (Aim 1.6).
Currently, the personalization of antibiotic treatment is informed either by direct culture-based methods of resistance detection of only the current infection or by using algorithmic predictions based on electronic patient records. Here, we show a new method of predicting the antibiotic resistance of future infections based on the microbiome. We show that genotypic and phenotypic profiling of gut microbiomes is highly correlated with infection profiles of the same patients. These exciting results, in combination with electronic health records, will allow a new paradigm of prediction of resistance and personalization of treatment.