MYT1L haploinsufficiency in human neurons and mice causes autism-associated phenotypes that can be reversed by genetic and pharmacologic intervention.

MYT1L is an autism spectrum disorder (ASD)-associated transcription factor that is expressed in virtually all neurons throughout life. How MYT1L mutations cause neurological phenotypes and whether they can be targeted remains enigmatic. Here, we examine the effects of MYT1L deficiency in human neurons and mice. Mutant mice exhibit neurodevelopmental delays with thinner cortices, behavioural phenotypes, and gene expression changes that resemble those of ASD patients. MYT1L target genes, including WNT and NOTCH, are activated upon MYT1L depletion and their chemical inhibition can rescue delayed neurogenesis in vitro. MYT1L deficiency also causes upregulation of the main cardiac sodium channel, SCN5A, and neuronal hyperactivity, which could be restored by shRNA-mediated knockdown of SCN5A or MYT1L overexpression in postmitotic neurons. Acute application of the sodium channel blocker, lamotrigine, also rescued electrophysiological defects in vitro and behaviour phenotypes in vivo. Hence, MYT1L mutation causes both developmental and postmitotic neurological defects. However, acute intervention can normalise resulting electrophysiological and behavioural phenotypes in adulthood.

ExplaiNN: interpretable and transparent neural networks for genomics.

Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear models. ExplaiNN can predict TF binding, chromatin accessibility, and de novo motifs, achieving performance comparable to state-of-the-art methods. Its predictions are transparent, providing global (cell state level) as well as local (individual sequence level) biological insights into the data. ExplaiNN can serve as a plug-and-play platform for pretrained models and annotated position weight matrices. ExplaiNN aims to accelerate the adoption of deep learning in genomic sequence analysis by domain experts.

Changes in health service use due to alcohol during the COVID-19 pandemic among individuals with and individuals without pre-existing alcohol-related medical diagnoses.

OBJECTIVE: To compare changes in outpatient and acute care visits due to alcohol during the COVID-19 pandemic between individuals with and those without a history of alcohol-related health service use (AHSU). METHODS: We conducted a cross-sectional analysis of health administrative data in Ontario, Canada. The Ontario population was stratified into those with and those without 1+ health service encounter(s) due to alcohol in the past 2 years. We compared age- and sex-standardized rates of alcohol-related outpatient visits, emergency department (ED) visits, and hospitalizations during the first 15 months of the pandemic (March 2020-May 2021) to those during the same 15-month period prior to the pandemic (March 2018-May 2019). RESULTS: Of 13,450,750 eligible Ontarians on March 11, 2022, 129,434 (1.0%) had AHSU in the previous 2 years. Overall, rates of alcohol-related outpatient visits and hospitalizations increased, while rates of alcohol-related ED visits decreased during the pandemic. There was a similar relative increase in rates of alcohol-related outpatient visits and hospitalizations between those with and those without prior AHSU. However, the absolute increase in rates of alcohol-related outpatient visits and hospitalizations was higher among those with prior AHSU (outpatient rate difference (RD) per 10,000 population: 852.3, 95% confidence interval (CI): 792.7, 911.9; inpatient RD: 26.0, 95% CI: -2.3, 54.2) than among those without (outpatient RD: 6.5, 95% CI: 6.0, 6.9; inpatient RD: 0.4, 95% CI: 0.2, 0.7). CONCLUSION: Rates of alcohol-related outpatient and inpatient care increased during the COVID-19 pandemic, and high rate of recurrent harm among individuals with pre-pandemic AHSU was an important contributor to this trend.

RéSUMé: OBJECTIF: Comparer les changements dans consultations externes et les consultations en soins actifs liées à l'alcool pendant la pandémie de COVID-19 chez les personnes avec et chez celles sans antécédents d'utilisation des services de santé liée à l'alcool (USSLA). MéTHODE: Nous avons effectué une analyse transversale des données administratives sur la santé de l'Ontario, au Canada. Nous avons stratifié la population ontarienne selon la présence (1+) ou l'absence de contacts avec les services de santé pour des raisons liées à l'alcool au cours des deux années antérieures. Nous avons comparé les taux de consultations externes, de consultations à l'urgence et d'hospitalisations liées à l'alcool, standardisés pour l'âge et le sexe, au cours des 15 premiers mois de la pandémie (mars 2020­mai 2021) aux taux correspondants pour la même période de 15 mois avant la pandémie (mars 2018­mai 2019). RéSULTATS: Sur les 13 450 750 Ontariens et Ontariennes admissibles le 11 mars 2022, 129 434 (1,0 %) avaient utilisé les services de santé pour des raisons liées à l'alcool au cours des deux années antérieures. Dans l'ensemble, les taux de consultations externes et d'hospitalisations liées à l'alcool ont augmenté, tandis que les taux de consultations à l'urgence liées à l'alcool ont diminué pendant la pandémie. Il y a eu une augmentation relative semblable des taux de consultations externes et d'hospitalisations liées à l'alcool entre les personnes avec et sans antécédents d'USSLA. Par contre, l'augmentation absolue des taux de consultations externes et d'hospitalisations liées à l'alcool a été plus élevée chez les personnes ayant des antécédents d'USSLA (différence de taux [DT] de consultations externes pour 10 000 habitants : 852,3; intervalle de confiance de 95 % [IC] : 792,7, 911,9; DT d'hospitalisations : 26,0; IC de 95 % : -2,3, 54,2) que chez les personnes sans antécédents d'USSLA (DT de consultations externes : 6,5; IC de 95 % : 6,0, 6,9; DT d'hospitalisations : 0,4; IC de 95 % : 0,2, 0,7). CONCLUSION: Les taux de consultations externes et d'hospitalisations liées à l'alcool ont augmenté pendant la pandémie de COVID-19, et les taux élevés de méfaits récurrents chez les personnes ayant utilisé les services de santé pour des raisons liées à l'alcool avant la pandémie ont beaucoup contribué à cette tendance.

Biologically relevant transfer learning improves transcription factor binding prediction.

BACKGROUND: Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall model performance, compared to training a separate model for each new task. RESULTS: We assess a transfer learning strategy for TF binding prediction consisting of a pre-training step, wherein we train a multi-task model with multiple TFs, and a fine-tuning step, wherein we initialize single-task models for individual TFs with the weights learned by the multi-task model, after which the single-task models are trained at a lower learning rate. We corroborate that transfer learning improves model performance, especially if in the pre-training step the multi-task model is trained with biologically relevant TFs. We show the effectiveness of transfer learning for TFs with ~ 500 ChIP-seq peak regions. Using model interpretation techniques, we demonstrate that the features learned in the pre-training step are refined in the fine-tuning step to resemble the binding motif of the target TF (i.e., the recipient of transfer learning in the fine-tuning step). Moreover, pre-training with biologically relevant TFs allows single-task models in the fine-tuning step to learn useful features other than the motif of the target TF. CONCLUSIONS: Our results confirm that transfer learning is a powerful technique for TF binding prediction.

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network.

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.

Evidence for transmission of COVID-19 prior to symptom onset.

We collated contact tracing data from COVID-19 clusters in Singapore and Tianjin, China and estimated the extent of pre-symptomatic transmission by estimating incubation periods and serial intervals. The mean incubation periods accounting for intermediate cases were 4.91 days (95%CI 4.35, 5.69) and 7.54 (95%CI 6.76, 8.56) days for Singapore and Tianjin, respectively. The mean serial interval was 4.17 (95%CI 2.44, 5.89) and 4.31 (95%CI 2.91, 5.72) days (Singapore, Tianjin). The serial intervals are shorter than incubation periods, suggesting that pre-symptomatic transmission may occur in a large proportion of transmission events (0.4-0.5 in Singapore and 0.6-0.8 in Tianjin, in our analysis with intermediate cases, and more without intermediates). Given the evidence for pre-symptomatic transmission, it is vital that even individuals who appear healthy abide by public health measures to control COVID-19.

The first cases of COVID-19 were identified in Wuhan, a city in Central China, in December 2019. The virus quickly spread within the country and then across the globe. By the third week in January, the first cases were confirmed in Tianjin, a city in Northern China, and in Singapore, a city country in Southeast Asia. By late February, Tianjin had 135 cases and Singapore had 93 cases. In both cities, public health officials immediately began identifying and quarantining the contacts of infected people. The information collected in Tianjin and Singapore about COVID-19 is very useful for scientists. It makes it possible to determine the disease's incubation period, which is how long it takes to develop symptoms after virus exposure. It can also show how many days pass between an infected person developing symptoms and a person they infect developing symptoms. This period is called the serial interval. Scientists use this information to determine whether individuals infect others before showing symptoms themselves and how often this occurs. Using data from Tianjin and Singapore, Tindale, Stockdale et al. now estimate the incubation period for COVID-19 is between five and eight days and the serial interval is about four days. About 40% to 80% of the novel coronavirus transmission occurs two to four days before an infected person has symptoms. This transmission from apparently healthy individuals means that staying home when symptomatic is not enough to control the spread of COVID-19. Instead, broad-scale social distancing measures are necessary. Understanding how COVID-19 spreads can help public health officials determine how to best contain the virus and stop the outbreak. The new data suggest that public health measures aimed at preventing asymptomatic transmission are essential. This means that even people who appear healthy need to comply with preventive measures like mask use and social distancing.

Functional, impulse-based quantification of plantar pressure patterns in typical adult gait.

BACKGROUND: Dynamic pedobarography is used to measure the change in plantar pressure distribution during gait. Clinical methods of pedobarographic analysis lack, however, a standardized, functional segmentation or require costly motion capture technology and expertise. Furthermore, while commonly used pedobarographic measures are mostly based on peak pressures, progressive foot deformities also depend on the duration the pressure is applied, which can be quantified via impulse measures. RESEARCH QUESTION: Our objectives were to: (1) develop a standardized method for functionally segmenting pedobarographic data during gait without the need for motion capture; (2) compute pedobarographic measures that are based on each segment's vertical impulse; and (3) obtain a normative set of such pedobarographic measures for non-disabled gait. METHODS: Pedobarographic data was collected during gait from sixty adults with normal feet. Using the maximum pressure map for each trial, an expert and novice rater independently identified the hallux, heel, medial forefoot, and lateral forefoot and computed nine normalized vertical impulse measures. RESULTS: From the computed impulse measures, the Heel-to-Forefoot Balance was 33.3 ± 5.5%, the Medial-Lateral Forefoot Balance (with hallux) 59.2 ± 8.0%, the Medial-Lateral Forefoot Balance (without hallux) 53.5 ± 7.7%, and the Hallux-to-Medial Forefoot Balance 21.0 ± 8.9% (mean ± standard deviation). The intra- and inter-rater reliability ranged between 0.93 and 1.00 and between 0.89 and 0.99, respectively (ICC(2,1)). SIGNIFICANCE: We developed a simple, stand-alone method for pedobarographic segmentation that is mechanistically linked to relevant anatomical regions of the foot. The normative impulse measures exhibited excellent reliability. This normative dataset is currently used in the clinical assessment of different foot deformities and gait impairments, and in the evaluation of treatment outcomes.