Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia

Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding and occurs when the metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic inter-dependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function and stability of microbial communities. To evaluate how ppGpp-mediated resource allocation drives the population-level profile of interaction, here we postulate a multi-scale mathematical model that incorporates dynamics of proteome partition into a population dynamics model. We compare our computational results with experimental data obtained from co-cultures of auxotrophic Escherichia coli K12 strains under a range of amino acid concentrations and population structures. We conclude by arguing that the stringent response promotes cooperation by inhibiting the growth of fast-growing strains and promoting the synthesis of metabolites essential for other community members.

P–244 ERICA’s (Embryo Ranking Intelligent Classification Assistant) ranking, based on ploidy prediction, is strongly correlated with pregnancy outcomes

Study question

How does ERICA perform when ranking the most suitable embryos for transfer in terms of clinical pregnancy, and the presence of a fetal heartbeat (FHB)?

Summary answer

ERICA’s Artificial Intelligence ranking system was positively correlated with outcomes defined as implantation and presence of FHB. Best-ranking embryos outperformed lower-ranking embryos by statistical significance.

What is known already

ERICA, the Embryo Ranking Intelligent Classification Assistant, is a deep learning AI system trained to rank embryos based on their ploidy status, which is highly correlated with successful treatments.

ERICA ranks the embryos according to their prognosis predictions and labels them into four quality categories: optimal, good, fair, and poor. ERICA’s performance in the clinic remains to be tested.

Study design, size, duration

Retrospective analysis on ERICA’s performance over 4 consecutive months after quality assurance and fine-tuning processes. We compared both the ranking and prognosis of the AI algorithm against clinical outcomes in IVF cycles and subsequent embryo transfers. For this study, all cycles where ERICA was used to assist embryologists during the embryo selection process were included. Double embryo transfers with a single FHB where excluded.

Participants/materials, setting, methods

Total 77 cycles with 81 transfers of 98 embryos (17 cases underwent a double embryo transfer) from two IVF clinics. Evaluated clinical outcomes included biochemical pregnancy test (defined as beta human chorionic gonadotropin >20 mUI/ml), and presence/absence of FHB. We compared the ERICA rankings and predictions against outcome and a sub-analysis was performed on transferred embryos with known ploidy status (14 embryos).

Main results and the role of chance

The distribution of embryos within the ERICA categories are 42% for optimal, 38% for good, 19% for fair, and 6% for poor. The observed biochemical pregnancy rate was 51%, 25%, 47% and 33% respectively, and 39%, 22%, 42%, 17% for FHB. We found statistical significance (Z = 1.78; p = 0.0378) for the proportion of biochemical pregnancy between transfers labelled by ERICA as optimal (51%) and all lower rankings (33%). The proportion of transfers with presence of FHB within the optimal group was 39%, compared with 29% for the rest of the embryos. This did not show statistical significance (Z = 1.141; p = 0.127). Additionally, we observed that the proportion of biochemical pregnancy and presence of FHB in the group of transfers with known ploidy (n = 14) was 50% and 36% respectively, and the transfers with unknown ploidy and labelled as optimal by ERICA (n = 35) was 54% and 43% respectively.

Limitations, reasons for caution

This is the first report on ERICA’s performance on real clinical data, and despite being a relatively small dataset, we observed statistical significance of the embryos labelled by ERICA as having optimal quality. Further studies should be conducted with larger datasets and more clinics included to strengthen the evidence.

Wider implications of the findings: This is the first report on ERICA’s performance on real clinical data, and despite being a relatively small dataset, we observed statistical significance of the embryos labelled by ERICA as having optimal quality. Further studies should be conducted with larger datasets and more clinics included to strengthen the evidence.

Trial registration number

Not applicable