scholarly journals Rapid Assessment of the Temporal Function and Phenotypic Reversibility of Neurodevelopmental Disorder Risk Genes in C. elegans

2021 ◽  
Author(s):  
Lexis D Kepler ◽  
Troy A McDiarmid ◽  
Catharine H Rankin

Hundreds of genes have been implicated in neurodevelopmental disorders. Previous studies have indicated that some phenotypes caused by decreased developmental function of select risk genes can be reversed by restoring gene function in adulthood. However, very few risk genes have been assessed for adult reversibility. We developed a strategy to rapidly assess the temporal requirements and phenotypic reversibility of neurodevelopmental disorder risk gene orthologs using a conditional protein degradation system and machine vision phenotypic profiling in Caenorhabditis elegans. Using this approach, we measured the effects of degrading and re-expressing orthologs of 3 neurodevelopmental risk genes EBF3, BRN3A, and DYNC1H1 across 30 morphological, locomotor, sensory, and learning phenotypes at multiple timepoints throughout development. We found some degree of phenotypic reversibility was possible for each gene studied. However, the temporal requirements of gene function and degree of phenotypic reversibility varied by gene and phenotype. The data reflects the dynamic nature of gene function and the importance of using multiple time windows of degradation and re-expression to understand the many roles a gene can play over developmental time. This work also demonstrates a strategy of using a high-throughput model system to investigate temporal requirements of gene function across a large number of phenotypes to rapidly prioritize neurodevelopmental disorder genes for re-expression studies in other organisms.

2017 ◽  
Vol 49 (4) ◽  
pp. 515-526 ◽  
Author(s):  
Holly A F Stessman ◽  
Bo Xiong ◽  
Bradley P Coe ◽  
Tianyun Wang ◽  
Kendra Hoekzema ◽  
...  

Author(s):  
NA LI ◽  
MARTIN CRANE ◽  
HEATHER J. RUSKIN

SenseCam is an effective memory-aid device that can automatically record images and other data from the wearer's whole day. The main issue is that, while SenseCam produces a sizeable collection of images over the time period, the vast quantity of captured data contains a large percentage of routine events, which are of little interest to review. In this article, the aim is to detect "Significant Events" for the wearers. We use several time series analysis methods such as Detrended Fluctuation Analysis (DFA), Eigenvalue dynamics and Wavelet Correlations to analyse the multiple time series generated by the SenseCam. We show that Detrended Fluctuation Analysis exposes a strong long-range correlation relationship in SenseCam collections. Maximum Overlap Discrete Wavelet Transform (MODWT) was used to calculate equal-time Correlation Matrices over different time scales and then explore the granularity of the largest eigenvalue and changes of the ratio of the sub-dominant eigenvalue spectrum dynamics over sliding time windows. By examination of the eigenspectrum, we show that these approaches enable detection of major events in the time SenseCam recording, with MODWT also providing useful insight on details of major events. We suggest that some wavelet scales (e.g., 8 minutes–16 minutes) have the potential to identify distinct events or activities.


2017 ◽  
Vol 13 (5) ◽  
pp. 1-27
Author(s):  
Nurhadi Siswanto ◽  
◽  
Stefanus Eko Wiratno ◽  
Ahmad Rusdiansyah ◽  
Ruhul Sarker ◽  
...  

2021 ◽  
Author(s):  
Xueya Zhou ◽  
Pamela Feliciano ◽  
Tianyun Wang ◽  
Irina Astrovskaya ◽  
Chang Shu ◽  
...  

AbstractDespite the known heritable nature of autism spectrum disorder (ASD), studies have primarily identified risk genes with de novo variants (DNVs). To capture the full spectrum of ASD genetic risk, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 ASD cases, including 35,130 new cases recruited online by SPARK. In the first stage, we analyzed 19,843 cases with one or both biological parents and found that known ASD or neurodevelopmental disorder (NDD) risk genes explain nearly 70% of the genetic burden conferred by DNVs. In contrast, less than 20% of genetic risk conferred by rare inherited loss-of-function (LoF) variants are explained by known ASD/NDD genes. We selected 404 genes based on the first stage of analysis and performed a meta-analysis with an additional 22,764 cases and 236,000 population controls. We identified 60 genes with exome-wide significance (p < 2.5e-6), including five new risk genes (NAV3, ITSN1, MARK2, SCAF1, and HNRNPUL2). The association of NAV3 with ASD risk is entirely driven by rare inherited LoFs variants, with an average relative risk of 4, consistent with moderate effect. ASD individuals with LoF variants in the four moderate risk genes (NAV3, ITSN1, SCAF1, and HNRNPUL2, n = 95) have less cognitive impairment compared to 129 ASD individuals with LoF variants in well-established, highly penetrant ASD risk genes (CHD8, SCN2A, ADNP, FOXP1, SHANK3) (59% vs. 88%, p= 1.9e-06). These findings will guide future gene discovery efforts and suggest that much larger numbers of ASD cases and controls are needed to identify additional genes that confer moderate risk of ASD through rare, inherited variants.


2019 ◽  
Vol 6 (7) ◽  
pp. 180643 ◽  
Author(s):  
J. C. Gerlach ◽  
G. Demos ◽  
D. Sornette

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power-Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators , defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious ‘present’ times t 2 before the crashes, we employ a clustering method to group the predicted critical times t c of the LPPLS fits over different time scales, where t c is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.


Cells ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 2500
Author(s):  
Marta Garcia-Forn ◽  
Andrea Boitnott ◽  
Zeynep Akpinar ◽  
Silvia De Rubeis

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder characterized by impairments in social communication and social interaction, and the presence of repetitive behaviors and/or restricted interests. In the past few years, large-scale whole-exome sequencing and genome-wide association studies have made enormous progress in our understanding of the genetic risk architecture of ASD. While showing a complex and heterogeneous landscape, these studies have led to the identification of genetic loci associated with ASD risk. The intersection of genetic and transcriptomic analyses have also begun to shed light on functional convergences between risk genes, with the mid-fetal development of the cerebral cortex emerging as a critical nexus for ASD. In this review, we provide a concise summary of the latest genetic discoveries on ASD. We then discuss the studies in postmortem tissues, stem cell models, and rodent models that implicate recently identified ASD risk genes in cortical development.


2020 ◽  
Vol 17 (5) ◽  
pp. 459-459
Author(s):  
Lin Tang

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