Biology subjects

Salathe, M.

Publications and source records attributed to Salathe, M..

6 recordsLinked to original sources

bioRxiv2021

The SARS-CoV-2 transcriptome and the dynamics of the S gene furin cleavage site in primary human airway epithelia

The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) caused the devastating ongoing coronavirus disease-2019 (COVID-19) pandemic which poses a great threat to global public health. The spike (S) polypeptide of SARS-CoV-2 consists of the S1 and S2 subunits and is processed by cellular proteases at the S1/S2 boundary. The inclusion of the 4 amino acids (PRRA) at the S1/S2 boundary forms a furin cleavage site (FCS), 682RRAR{downarrow}S686, distinguishing SARS-CoV-2 from its closest relative, the SARS-CoV. Various deletions surrounding the FCS have been identified in patients. When SARS-CoV-2 propagated in Vero cells, the virus acquired various deletions surrounding the FCS. In the present study, we studied the viral transcriptome in SARS-CoV-2 infected primary human airway epithelia (HAE) cultured at an air-liquid interface (ALI) with an emphasis on the viral genome stability at the S1/S2 boundary using RNA-seq. While we found overall the viral transcriptome is similar to that generated from infected Vero cells, we identified a high percentage of mutated viral genome and transcripts in HAE-ALI. Two highly frequent deletions were found at the S1/S2 boundary of the S gene: one is a deletion of 12 amino acids, 678TNSPRRAR{downarrow}SVAS689, which contains the FCS, another is a deletion of 5 amino acids, 675QTQTN679, which is two amino acids upstream of the FCS. Further studies on the dynamics of the FCS deletions in apically released virions revealed that the selective pressure for the FCS maintains the S gene stability in HAE-ALI but with exceptions, in which the FCS deletions are remained at a high rate. Thus, our study presents evidence for the role of unique properties of human airway epithelia in the dynamics of the FCS region during infection of human airways, which is donor-dependent.

microbiology↗

bioRxiv2020

Phenotype prediction from genome-wide genotyping data: a crowdsourcing experiment

BackgroundThe increasing statistical power of genome-wide association studies is fostering the development of precision medicine through genomic predictions of complex traits. Nevertheless, it has been shown that the results remain relatively modest. A reason might be the nature of the methods typically used to construct genomic predictions. Recent machine learning techniques have properties that could help to capture the architecture of complex traits better and improve genomic prediction accuracy. MethodsWe relied on crowd-sourcing to efficiently compare multiple genomic prediction methods. This represents an innovative approach in the genomic field because of the privacy concerns linked to human genetic data. There are two crowd-sourcing elements building our study. First, we constructed a dataset from openSNP (opensnp.org), an open repository where people voluntarily share their genotyping data and phenotypic information in an effort to participate in open science. To leverage this resource we release the openSNP Cohort Maker, a tool that builds a homogeneous and up-to-date cohort based on the data available on opensnp.org. Second, we organized an open online challenge on the CrowdAI platform (crowdai.org) aiming at predicting height from genome-wide genotyping data. ResultsThe openSNP Height Prediction challenge lasted for three months. A total of 138 challengers contributed to 1275 submissions. The winner computed a polygenic risk score using the publicly available summary statistics of the GIANT study to achieve the best result (r2 = 0.53 versus r2 = 0.49 for the second-best). ConclusionWe report here the first crowd-sourced challenge on publicly available genome-wide genotyping data. We also deliver the openSNP Cohort Maker that will allow people to make use of the data available on opensnp.org.

genomics↗

bioRxiv2020

Breaking Apart Contact Networks with Vaccination

Infectious diseases can cause large disease outbreaks due to their transmission potential from one individual to the next. Vaccination is an effective way of cutting off possible chains of transmission, thereby mitigating the outbreak potential of a disease in a population. From a contact network perspective, vaccination effectively removes nodes from the network, thereby breaking apart the contact network into a much smaller network of susceptible individuals on which the disease can spread. Here, we look at the continuum of small world networks to random networks, and find that vaccination breaks apart networks in ways that can dramatically influence the maximum outbreak size. In particular, after the removal of a constant number of nodes (representing vaccination coverage), the more clustered small world networks more readily fall apart into many disjoint and small susceptible sub-networks, thus preventing large outbreaks, while more random networks remain largely connected even after node removal through vaccination. We further develop a model of social mixing that moves small world networks closer to the random regime, thereby facilitating larger disease outbreaks after vaccination. Our results show that even when vaccination is entirely random, social mixing can lead to contact network structures that strongly influence outbreak sizes. We find the largest effects to be in the regime of relatively high vaccination coverages of around 80%, where despite vaccination being random, outbreak sizes can vary by a factor of 20.

ecology↗

bioRxiv2019

Combining Crowdsourcing and Deep Learning to Assess Public Opinion on CRISPR-Cas9

The discovery of the CRISPR-Cas9-based gene editing method has opened un-precedented new potential for biological and medical engineering, sparking a growing public debate on both the potential and dangers of CRISPR applications. Given the speed of technology development, and the almost instantaneous global spread of news, its important to follow evolving debates without much delay and in sufficient detail, as certain events may have a major long-term impact on public opinion and later influence policy decisions. Social media networks such as Twitter have shown to be major drivers of news dissemination and public discourse. They provide a vast amount of semi-structured data in almost real-time and give direct access to the content of the conversations. Such data can now be mined and analyzed quickly because of recent developments in machine learning and natural language processing. Here, we used BERT, an attention-based transformer model, in combination with statistical methods to analyse the entirety of all tweets ever published on CRISPR since the publication of the first gene editing application in 2013. We show that the mean sentiment of tweets was initially very positive, but began to decrease over time, and that this decline was driven by rare peaks of strong negative sentiments. Due to the high temporal resolution of the data, we were able to associate these peaks with specific events, and to observe how trending topics changed over time. Overall, this type of analysis can provide valuable and complementary insights into ongoing public debates, extending the traditional empirical bioethics toolset.

scientific communication and education↗

bioRxiv2018

Assessment of Menstrual Health Status and Evolution through Mobile Apps for Fertility Awareness

For most women of reproductive age, assessing menstrual health and fertility typically involves regular visits to a gynecologist or another clinician. While these evaluations provide critical information on an individuals reproductive health status, they typically rely on memory-based self-reports, and the results are rarely, if ever, assessed at the population level. In recent years, mobile apps for menstrual tracking have become very popular, allowing us to evaluate the reliability and tracking frequency of millions of self-observations, thereby providing an unparalleled view, both in detail and scale, on menstrual health and its evolution for large populations. In particular, we were interested in exploring the tracking behavior of the app users and their overall observation patterns in an effort to understand if they were consistent with previous small-scale medical studies. We also investigated whether their precision allowed the detection and estimation of ovulation timing, which is critical for reproductive and menstrual health. Retrospective self-observation data were acquired from two mobile apps dedicated to the application of the sympto-thermal fertility awareness method, resulting in a dataset of more than 30 million days of observations from over 2.7 million cycles. The analysis of the data showed that up to 40% of the cycles in which users were seeking pregnancy had recordings every single day. With a modeling approach using Hidden Markov Models to describe the collected data and estimate ovulation timing, it was found that follicular phases average duration and range were larger than previously reported, with only 24% of ovulations occurring at days 14 to 15, while the luteal phase duration and range were in line with previous reports, although short luteal phases (10 days or less) were more frequently observed (in up to 20% of cycles). The digital epidemiology approach presented here can help to lead to a better understanding of menstrual health and its connection to womens health overall, which has historically been severely understudied.

epidemiology↗

bioRxiv2017

Assessing The Dynamics And Control Of Droplet- and Aerosol-Transmitted Influenza Using An Indoor Positioning System

There is increasing evidence that aerosol transmission is a major contributor to the spread of influenza. Despite this, virtually all studies assessing the dynamics and control of influenza assume that it is transmitted solely through direct contact and large droplets, requiring close physical proximity. Here, we use wireless sensors to measure simultaneously both the location and close proximity contacts in the population of a US high school. This dataset, highly resolved in space and time, allows us to model both droplet and aerosol transmission either in isolation or in combination. In particular, it allows us to computationally assess the effectiveness of overlooked mitigation strategies such as improved ventilation that are available in the case of aerosol transmission. While the effects of the type of transmission on disease outbreak dynamics appear to be weak, we find that good ventilation could be as effective in mitigating outbreaks as vaccinating the majority of the population. In simulations using empirical transmission levels observed in households, we find that bringing ventilation to recommended levels has the same mitigating effect as a vaccination coverage of 50% to 60%. Our results therefore suggest that improvements of ventilation in public spaces could be an important and easy-to-implement strategy supplementing vaccination efforts for effective control of influenza spread.

epidemiology↗