Biology subjects

Liao, J.

Publications and source records attributed to Liao, J..

8 recordsLinked to original sources

bioRxiv2018

Differential DNA modification of an enhancer at the IGF2 locus affects dopamine synthesis in patients with major psychosis

Dopamine dysregulation is central to the pathogenesis of diseases with major psychosis, but its molecular origins are unclear. In an epigenome-wide investigation in neurons, individuals with schizophrenia and bipolar disorder showed reduced DNA modifications at an enhancer in IGF2, which disrupted the regulation of the dopamine synthesis enzyme tyrosine hydroxylase and striatal dopamine levels in transgenic mice. Epigenetic control of this enhancer may be an important molecular determinant of psychosis.

neuroscience↗

bioRxiv2018

Animal models of chemotherapy-induced peripheral neuropathy: a machine-assisted systematic review and meta-analysis A comprehensive summary of the field to inform robust experimental design

Background and aimsChemotherapy-induced peripheral neuropathy (CIPN) can be a severely disabling side-effect of commonly used cancer chemotherapeutics, requiring cessation or dose reduction, impacting on survival and quality of life. Our aim was to conduct a systematic review and meta-analysis of research using animal models of CIPN to inform robust experimental design.\n\nMethodsWe systematically searched 5 online databases (PubMed, Web of Science, Citation Index, Biosis Previews and Embase (September 2012) to identify publications reporting in vivo CIPN modelling. Due to the number of publications and high accrual rate of new studies, we ran an updated search November 2015, using machine-learning and text mining to identify relevant studies.\n\nAll data were abstracted by two independent reviewers. For each comparison we calculated a standardised mean difference effect size then combined effects in a random effects meta- analysis. The impact of study design factors and reporting of measures to reduce the risk of bias was assessed. We ran power analysis for the most commonly reported behavioural tests.\n\nResults341 publications were included. The majority (84%) of studies reported using male animals to model CIPN; the most commonly reported strain was Sprague Dawley rat. In modelling experiments, Vincristine was associated with the greatest increase in pain-related behaviour (-3.22 SD [-3.88; -2.56], n=152, p=0). The most commonly reported outcome measure was evoked limb withdrawal to mechanical monofilaments. Pain-related complex behaviours were rarely reported. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. Overall, studies were at moderate risk of bias, with modest reporting of measures to reduce the risk of bias.\n\nConclusionsHere we provide a comprehensive summary of the field of animal models of CIPN and inform robust experimental design by highlighting measures to increase the internal and external validity of studies using animal models of CIPN. Power calculations and other factors, such as clinical relevance, should inform the choice of outcome measure in study design.

neuroscience↗

bioRxiv2018

Automation of citation screening in pre-clinical systematic reviews

BackgroundThe amount of published in vivo studies and the speed researchers are publishing them make it virtually impossible to follow the recent development in the field. Systematic review emerged as a method to summarise and analyse the studies quantitatively and critically but it is often out-of-date due to its lengthy process. MethodWe invited five machine learning and text-mining groups to build classifiers for identifying publications relevant to neuropathic pain (33814 training publications). We kept 1188 publications for the assessment of the performance of different classifiers. Two groups participated in the next stage: testing their algorithm on datasets labeled for psychosis (11777/2944) and datasets labeled for Vitamin D in multiple sclerosis (train/text: 2038/510). ResultThe performances (sensitive/specificity) of the most promising classifier built for neuropathic pain are: 95%/84%. The performance for psychosis and Vitamin D in multiple sclerosis datasets are 95%/73% and 100%/45%. ConclusionsMachine learning can significantly reduce the irrelevant publications in a systematic review, and save the scientists time and money. Classifier algorithms built for one dataset can be reapplied on another dataset in different field. We are building a machine learning service at the back of Systematic Review & Meta-analysis Facility (SyRF).

scientific communication and education↗

bioRxiv2018

The use of text-mining and machine learning algorithms in systematic reviews: reducing workload in preclinical biomedical sciences and reducing human screening error

BackgroundHere we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies, with the aim of achieving a high performing algorithm comparable to human screening.\n\nMethodsWe applied ML approaches to a broad systematic review of animal models of depression at the citation screening stage. We tested two independently developed ML approaches which used different classification models and feature sets. We recorded the performance of the ML approaches on an unseen validation set of papers using sensitivity, specificity and accuracy. We aimed to achieve 95% sensitivity and to maximise specificity. The classification model providing the most accurate predictions was applied to the remaining unseen records in the dataset and will be used in the next stage of the preclinical biomedical sciences systematic review. We used a cross validation technique to assign ML inclusion likelihood scores to the human screened records, to identify potential errors made during the human screening process (error analysis).\n\nResultsML approaches reached 98.7% sensitivity based on learning from a training set of 5749 records, with an inclusion prevalence of 13.2%. The highest level of specificity reached was 86%. Performance was assessed on an independent validation dataset. Human errors in the training and validation sets were successfully identified using assigned the inclusion likelihood from the ML model to highlight discrepancies. Training the ML algorithm on the corrected dataset improved the specificity of the algorithm without compromising sensitivity. Error analysis correction leads to a 3% improvement in sensitivity and specificity, which increases precision and accuracy of the ML algorithm.\n\nConclusionsThis work has confirmed the performance and application of ML algorithms for screening in systematic reviews of preclinical animal studies. It has highlighted the novel use of ML algorithms to identify human error. This needs to be confirmed in other reviews, , but represents a promising approach to integrating human decisions and automation in systematic review methodology.

neuroscience↗

bioRxiv2018

Estimating the statistical performance of different approaches to meta-analysis of data from animal studies in identifying the impact of aspects of study design

BackgroundMeta-analysis is increasingly used to summarise the findings identified in systematic reviews of animal studies modelling human disease. Such reviews typically identify a large number of individually small studies, testing efficacy under a variety of conditions. This leads to substantial heterogeneity, and identifying potential sources of this heterogeneity is an important function of such analyses. However, the statistical performance of different approaches (normalised compared with standardised mean difference estimates of effect size; stratified meta-analysis compared with meta-regression) is not known.\n\nMethodsUsing data from 3116 experiments in focal cerebral ischaemia to construct a linear model predicting observed improvement in outcome contingent on 25 independent variables. We used stochastic simulation to attribute these variables to simulated studies according to their prevalence. To ascertain the ability to detect an effect of a given variable we introduced in addition this \"variable of interest\" of given prevalence and effect. To establish any impact of a latent variable on the apparent influence of the variable of interest we also introduced a \"latent confounding variable\" with given prevalence and effect, and allowed the prevalence of the variable of interest to be different in the presence and absence of the latent variable.\n\nResultsGenerally, the normalised mean difference (NMD) approach had higher statistical power than the standardised mean difference (SMD) approach. Even when the effect size and the number of studies contributing to the meta-analysis was small, there was good statistical power to detect the overall effect, with a low false positive rate. For detecting an effect of the variable of interest, stratified meta-analysis was associated with a substantial false positive rate with NMD estimates of effect size, while using an SMD estimate of effect size had very low statistical power. Univariate and multivariable meta-regression performed substantially better, with low false positive rate for both NMD and SMD approaches; power was higher for NMD than for SMD. The presence or absence of a latent confounding variables only introduced an apparent effect of the variable of interest when there was substantial asymmetry in the prevalence of the variable of interest in the presence or absence of the confounding variable.\n\nConclusionsIn meta-analysis of data from animal studies, NMD estimates of effect size should be used in preference to SMD estimates, and meta-regression should, where possible, be chosen over stratified meta-analysis. The power to detect the influence of the variable of interest depends on the effect of the variable of interest and its prevalence, but unless effects are very large adequate power is only achieved once at least 100 experiments are included in the meta-analysis.

bioinformatics↗

bioRxiv2018

Structural and functional influences of urban and rural childhoods on the medial prefrontal cortex

Global increases in urbanization have brought dramatic economic, environmental and social changes. However, less is understood about how these may influence disease-related brain mechanisms underlying epidemiological observations that urban birth and childhoods may increase the risk for neuropsychiatric disorders, including increased social stress and depression. In a genetically homogeneous Han Chinese adult population with divergent urban and rural birth and childhoods, we examined the structural and functional MRI neural correlates of childhood urbanicity, focusing on behavioral traits responding to social status threats, and polygenic risk for depression. Subjects with divergent rural and urban childhoods were similar in adult socioeconomic status and were genetically homogeneous. Urban childhoods, however, were associated with higher trait anxiety-depression. On structural MRI, urban childhoods were associated with relatively reduced medial prefrontal gray matter volumes. Functional medial prefrontal engagement under social status threat during working memory correlated with trait anxiety-depression in subjects with urban childhoods, to a significantly greater extent than in their rural counterparts, implicating an exaggerated physiological response to the threat context. Stress-associated medial prefrontal engagement also interacted with polygenic risk for depression, significantly predicting a differential response in individuals with urban but not rural childhoods. Developmental urbanicity thus differentially influenced medial prefrontal structure and function, at least in part through mechanisms associated with the neural processing of social status threat, trait anxiety, and genetic risk for depression, which may be factors in the association of urbanicity with adult psychopathology.\n\nSignificance StatementUrban living has been associated with social inequalities and stress. However, less is understood about the neural underpinnings by which these stressors affect disease risk, and in particular, genetic risk for depression. Leveraging urbanization in China, we studied adults with diverse urban and rural upbringings, who were genetically homogeneous and with similar current socioeconomic status, to isolate the effects of childhood urbanicity. At medial prefrontal cortex, a region critical for processing emotional stressors and social status, genetic risk for depression resulted in more deleterious function under stress in individuals with urban, but not rural childhoods. This implicates medial prefrontal cortexs critical role in brain development, integrating genetic mechanisms of stress and depression with the childhood environment.

neuroscience↗

bioRxiv2017

Single-cell RNA-Seq Resolves Cellular Heterogeneity and Transcriptional Dynamics during Spermatogonia Stem Cells Establishment and Differentiation

The transition of gonocytes to spermatogonia and subsequent differentiation provide the foundation of spermatogenesis. However, systematic understanding on the cellular and molecular basis of this process is still limited, mainly impeded by the asynchrony in development and the lack of stage-specific markers. Using single-cell RNA sequencing on Oct4-GFP+/KIT- cells isolated from PND5.5 mice, we dissected the cellular heterogeneity and established molecular regulations. We demonstrated that gonocyte-spermatogonial transition was characterized by gene expression change related to apoptosis, cell cycle progression, and regulation of migration processes. Pseudotime analysis reconstructed developmental dynamics of the spermatogonial populations and unraveled sequential cellular and molecular transitions. We also identified CD87 as a neonatal stem cell marker which are potentially involved in the intial establishment of SSC pool. Lastly, we uncovered an unexpected subpopulation of spermatogonia primed to differentiation within the undifferentiated compartment, which is characterized by the lack of self-renewal genes and enhanced Oct4 expression and retinoic acid signaling response. Our study thus provides a novel understanding of cellular and molecular changes during spermatogonial establishment.

developmental biology↗

bioRxiv2017

CAM: A Quality Control Pipeline For MNase-Seq Data

Nucleosome organization affects the accessibility of cis-elements to trans-acting factors. Micrococcal nuclease digestion followed by high-throughput sequencing (MNase-seq) is the most popular technology used to profile nucleosome organization on a genome-wide scale. Evaluating the data quality of MNase-seq data remains challenging, especially in mammalian. There is a strong need for a convenient and comprehensive approach to obtain dedicated quality control (QC) for MNase-seq data analysis. Here we developed CAM, which is a comprehensive QC pipeline for MNase-seq data. The CAM pipeline provides multiple informative QC measurements and nucleosome organization profiles on different potentially functional regions for given MNase-seq data. CAM also includes 268 historical MNase-seq datasets from human and mouse as a reference atlas for unbiased assessment. CAM is freely available at: http://www.tongji.edu.cn/~zhanglab/CAM

bioinformatics↗