Tolley, N. | bioRxiv Science

bioRxiv2024

Heterozygous Eif4nif1 Stop Gain Mice Replicate the Primary Ovarian Insufficiency Phenotype in Women

We created the c.1286C>G stop-gain mutation found in a family with primary ovarian insufficiency (POI) at age 30 years. The Eif4enif1 C57/Bl6 transgenic mouse model contained a floxed exon 10-19 cassette with a conditional knock-in cassette containing the c.1286C>G stop-gain mutation in exon 10. The hybrid offspring of CMV-Cre mice with Eif4enif1WT/flx mice were designated Eif4enif1WT/{Delta} for simplicity. A subset of female heterozygotes (Eif4enif1WT/{Delta}) had no litters. In those with litters, the final litter was earlier (5.4{+/-}2.6 vs. 10.5{+/-}0.7 months; p=0.02). Heterozygous breeding pair (Eif4enif1WT/{Delta} x Eif4enif1WT/{Delta}) litter size was 60% of WT litter size (3.9{+/-}2.0 vs. 6.5{+/-}3.0 pups/litter; p<0.001). The genotypes were 35% Eif4enif1WT/flx and 65% Eif4enif1WT/{Delta}, with no homozygotes. Homozygote embryos did not develop beyond the 4-8 cell stage. The number of follicles in ovaries from Eif4enif1WT/{Delta} mice was lower starting at the primordial (499{+/-}290 vs. 1445{+/-}381) and primary follicle stage (1069{+/-}346 vs. 1450{+/-}193) on day 10 (p<0.05). The preantral follicle number was lower starting on day 21 (213{+/-}86 vs. 522{+/-}227; p<0.01). Examination of ribosome protected mRNAs (RPR) demonstrated altered mRNA expression. The Eif4enif1 stop-gain mice replicate the POI phenotype in women. The unique mouse model provides a platform to study regulation of protein translation across oocyte and embryo development in mammals.

developmental biology↗

bioRxiv2023

Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference

Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics. Author summaryA central problem in computational neural modeling is estimating model parameters that can account for observed activity patterns. While several techniques exist to perform parameter inference in special classes of abstract neural models, there are comparatively few approaches for large scale biophysically detailed neural models. In this work, we describe challenges and solutions in applying a deep learning based statistical framework to estimate parameters in a biophysically detailed large scale neural model, and emphasize the particular difficulties in estimating parameters for time series data. Our example uses a multi-scale model designed to connect human MEG/EEG recordings to the underlying cell and circuit level generators. Our approach allows for crucial insight into how cell-level properties interact to produce measured neural activity, and provides guidelines for diagnosing the quality of the estimate and uniqueness of predictions for different MEG/EEG biomarkers.

neuroscience↗