Biology subjects

Jia, E.

Publications and source records attributed to Jia, E..

5 recordsLinked to original sources

bioRxiv2026

Ubiquitous functional synergy partially explains why most transcription factor binding is non-functional

Most genes in whose promotor a transcription factor (TF) binds do not change in expression when the concentration of the TF is perturbed. No existing model can predict which bound promotors will respond and which will not. We hypothesized that a genes response to perturbation of a TF bound in its promotor can depend on which other TFs are bound there, a phenomenon we call functional synergy. This is distinct from cooperative binding, which is already accounted for in the binding location data. To investigate functional synergy, we created a comprehensive dataset on TF binding locations in yeast using a method that is orthogonal to chromatin immunoprecipitation. We then used mathematical modeling to identify high-confidence instances of functional synergy. We found that such synergies are surprisingly common. Responses to perturbations of 44 different TFs were modified by the presence of other TFs. 48 TFs served as modifiers, but some modified responses to many TFs. We conclude that (1) measuring the binding locations of a single TF will not, in general, reveal which genes the TF regulates, and (2) traditional networks linking TFs to their targets must be made substantially more expressive, allowing some TFs to modify the effects of others.

genomics↗

bioRxiv2024

Hyperexcitability precedes CA3 hippocampal neurodegeneration in a dox-regulatable TDP-43 mouse model of ALS-FTD.

Neuronal hyperexcitability is a hallmark of amyotrophic lateral sclerosis (ALS) but its relationship with the TDP-43 aggregates that comprise the predominant pathology in over 90% of ALS cases remains unclear. Emerging evidence in tissue and slice culture models indicate that TDP-43 pathology induces neuronal hyperexcitability suggesting it may be responsible for the excitotoxicity long believed to be a major driver of ALS neuron death. Here, we characterized hyperexcitability and neurodegeneration in the hippocampus of doxycycline-regulatable rNLS8 mice (NEFH-tTA x tetO-hTDP-43{Delta}NLS), followed by treatment with AAV encoded DREADDs and anti-seizure medications to measure the effect on behavioral function and neurodegeneration. We found that approximately half of the CA3 neurons in the dorsal hippocampus are lost between 4 and 6 weeks after TDP-43{Delta}NLS induction. Neurodegeneration was preceded by selective hyperexcitability in the mossy fiber - CA3 circuit, leading us to hypothesize that glutamate excitotoxicity may be a significant contributor to neurodegeneration in this model. Interestingly, hippocampal injection of AAV encoded inhibitory DREADDs (hM4Di) and daily activation with CNO ligand rescued anxiety deficits on elevated zero maze (EZM) but did not reduce neurodegeneration. Therapeutic doses of the anti-seizure medications, valproic acid and levetiracetam, did not improve behavior or prevent neurodegeneration. These results highlight the complexity of TDP-43 - induced alterations to neuronal excitability and suggest that whereas targeting hyperexcitability can meliorate some behavioral deficits, it may not be sufficient to halt or slow neurodegeneration in TDP-43-related proteinopathies. Significance StatementCytoplasmic aggregates of TAR DNA Binding Protein 43 (TDP-43) are the predominant pathology in over 90% of Amyotrophic lateral sclerosis (ALS) and the majority of frontotemporal lobar degeneration (FTLD-TDP) cases. Understanding how TDP-43 pathology promotes neurodegeneration may lead to therapeutic strategies to slow disease progression in humans. Recent reports in mouse and cell culture models suggest loss-of-normal TDP-43 function may drive neuronal hyperexcitability, a key physiological hallmark of ALS and possible contributor to neurodegeneration. In this study, we identified region-specific hyperexcitability that precedes neurodegeneration in the inducible rNLS8 TDP-43 mouse model. Suppressing hyperexcitability with chemogenetics improved behavioral function but did not reduce hippocampal neuron loss. Anti-seizure medications had no beneficial effects suggesting directly targeting hyperexcitability may not be therapeutically effective.

neuroscience↗

bioRxiv2024

Loss of Diurnal Oscillatory Rhythms in Gut Microbiota Correlates with Progression of Atherosclerosis

Circadian rhythms in gut microbiota composition are crucial for metabolic function and disease progression, yet the diurnal oscillation patterns of gut microbiota in atherosclerotic cardiovascular disease (ASCVD) and their role in disease progression remain unknown. Here, we investigate gut bacterial dynamics in ApoE-/- mice within a day, and elucidated the dynamic changes in fecal microbiota composition and function differences among C57BL/6 and ApoE-/- mice with standard chow diet or high-fat, high-cholesterol diet under ad libitum conditions. Compared with C57BL/6 mice, ApoE-/- mice exhibit significant differences in fecal microbial composition. Rhythmic analysis showed that the dynamic changes in the composition and function of fecal microbiota in ApoE-/- mice were significantly different from those in C57BL/6 mice. We further found that the rhythmic strains (Blautia Coccoides) inhibit the progression of ASCVD by improving the intestinal and endothelial barrier function. Our findings demonstrate that diurnal oscillations in gut microbiota are closely related to the progression of ASCVD, and provide a new insight for microbial-targeted therapies for ASCVD.

microbiology↗

bioRxiv2017

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

MotivationLeft-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed.\n\nResultsWe have developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity.\n\nAvailability and implementationThe R code for GSimp, evaluation pipeline, vignette, real-world and simulated targeted metabolomics dataset are available on: https://github.com/WandeRum/GSimp.

bioinformatics↗

bioRxiv2017

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

IntroductionMissing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection of methods can significantly affect following data analyses and interpretations. According to the definition, there are three types of missing values, missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).\n\nObjectivesThe aim of this study was to comprehensively compare common imputation methods for different types of missing values using two separate metabolomics data sets (977 and 198 serum samples respectively) to propose a strategy to deal with missing values in metabolomics studies.\n\nMethodsImputation methods included zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC). Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate the imputation accuracy for MCAR/MAR and MNAR correspondingly. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes sum of squared error were used to evaluate the overall sample distribution. Students t-test followed by Pearson correlation analysis was conducted to evaluate the effect of imputation on univariate statistical analysis.\n\nResultsOur findings demonstrated that RF imputation performed the best for MCAR/MAR and QRILC was the favored one for MNAR.\n\nConclusionCombining with \"modified 80% rule\", we proposed a comprehensive strategy and developed a public-accessible web-tool for missing value imputation in metabolomics data.

bioinformatics↗