Biology subjects

Ji, J.

Publications and source records attributed to Ji, J..

6 recordsLinked to original sources

bioRxiv2018

Dynamic Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model

MotivationNowadays brain connectivity analysis has attracted tremendous attention and has been at the foreground of neuroscience research. Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activity. Growing evidence now suggests that the brain connectivity network experiences alternations with the presence of numerous neurological disorders, thus differential brain network analysis may provides new insights into disease pathologies. For the matrix-valued data in brain connectivity analysis, existing graphical model estimation methods assume a vector normal distribution that in essence requires the columns of the matrix data to be independent. It is obviously not true, they have limited applications. Among the few solutions on graphical model estimation under a matrix normal distribution, none of them tackle the estimation of differential graphs across different populations. This motivates us to consider the differential network for matrix-variate data to detect the brain connectivity alternation.\n\nResultsThe primary interest is to detect spatial locations where the connectivity, in terms of the spatial partial correlation, differ across the two groups. To detect the brain connectivity alternation, we innovatively propose a Matrix-Variate Differential Network (MVDN) model. MVDN assumes that the matrix-variate data follows a matrix-normal distribution. We exploit the D-trace loss function and a Lasso-type penalty to directly estimate the spatial differential partial correlation matrix where the temporal information is fully excavated. We propose an ADMM algorithm for the Lasso penalized D-trace loss optimization problem. We investigate theoretical properties of the estimator. We show that under mild and regular conditions, the proposed method can identify all differential edges accurately with probability tending to 1 in high-dimensional setting where dimensions of matrix-valued data p, q and sample size n are all allowed to go to infinity. Simulation studies demonstrate that MVDN provides more accurate differential network estimation than that achieved by other state-of-the-art methods. We apply MVDN to Electroencephalography (EEG) dataset, which consists of 77 alcoholic individuals and 45 controls. The hub genes and differential interaction patterns identified are consistent with existing experimental studies.\n\nContactheyong@sdufe.edu.cn\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2018

Inhibitor of the spindle assembly checkpoint surpasses apoptosis sensitizer in synergy with taxanes

The antitumor effect of taxanes have been attributed to their ability to induce mitotic arrest through activation of the spindle assembly checkpoint. Cell death following prolonged mitotic arrest is mediated by the intrinsic apoptosis pathway. Thus, apoptosis sensitizers which inhibit antiapoptotic Bcl-2 family proteins has been shown to enhance taxanes-induced cell death. By contrast, spindle checkpoint disruption facilitates mitotic slippage and is thought to promote taxanes resistance. Notably, other modes of cell death also contribute to treatment outcomes. Here we show that inhibition of the spindle checkpoint suppresses taxanes induced apoptosis but increases terminal growth arrest of tumor cells with features of cellular senescence. By using clonogenic assay which measures the net result of multiple forms of cell death and is more reflective of therapeutic response, our finding suggests apoptosis is not a major determinant of antitumor efficacy of taxanes, whereas spindle checkpoint inhibitor displays a long-term advantage over apoptosis sensitizer in blocking colony outgrowth of tumor cells when combined with different microtubule toxins, therefore represents a superior therapeutic strategy.\n\nSIGNIFICANCEApoptosis has long been regarded as the primary mechanism of anti-cancer efficacy of taxanes, while the role of the spindle assembly checkpoint (SAC) in treatment response to taxanes has been controversial. Either apoptosis sensitizer or inhibitor of SAC has been reported to synergize with taxanes. While inhibitor of antiapoptotic proteins potentiates taxanes induced apoptosis, inhibitor of SAC suppresses apoptosis by facilitating mitotic slippage, that is why it is implicated in taxanes resistance. By demonstrating that apoptotic rates are not associate with long-term treatment response, not only do we find that inhibitor of SAC displays a long-term advantage over apoptosis sensitizer in combination with taxanes, but we also resolve the dispute around the role of SAC in cellular response to taxanes.

cancer biology↗

bioRxiv2018

A new insight into underlying disease mechanism through semi-parametric latent differential network model

BackgroundIn genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types.\n\nResultsWe propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive estimation approaches do not require precision matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Theoretical analysis shows that the proposed methods achieve the same parametric convergence rate for both the difference of the precision matrices estimation and differential structure recovery, which means that the extra modeling flexibility comes at almost no cost of statistical efficiency. Besides theoretical analysis, thorough numerical simulations are conducted to compare the empirical performance of the proposed methods with some other state-of-the-art methods. The result shows that the proposed methods work quite well for various data types. The proposed method is then applied on gene expression data associated with lung cancer to illustrate its empirical usefulness.\n\nConclusionsThe proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended.

bioinformatics↗

bioRxiv2017

The role of Cdx2 as a lineage specific transcriptional repressor for pluripotent network during trophectoderm and inner cell mass specification

The first cellular differentiation event in mouse development leads to the formation of the blastocyst consisting of the inner cell mass (ICM) and an outer functional epithelium called trophectoderm (TE). The lineage specific transcription factor CDX2 is required for proper TE specification, where it promotes expression of TE genes, and represses expression of Pou5f1 (OCT4) by inhibiting OCT4 from promoting its own expression. However its downstream network in the developing early embryo is not fully characterized. Here, we performed high-throughput single embryo qPCR analysis in Cdx2 null embryos to identify components of the CDX2-regulated network in vivo. To identify genes likely to be regulated by CDX2 directly, we performed CDX2 ChIP-Seq on trophoblast stem (TS) cells, derived from the TE. In addition, we examined the dynamics of gene expression changes using an inducible CDX2 embryonic stem (ES) cell system, so that we could predict which CDX2-bound genes are activated or repressed by CDX2 binding. By integrating these data with observations of chromatin modifications, we were able to identify novel regulatory elements that are likely to repress gene expression in a lineage-specific manner. Interestingly, we found CDX2 binding sites within regulatory elements of key pluripotent genes such as Pou5f1 and Nanog, pointing to the existence of a novel mechanism by which CDX2 maintains repression of OCT4 in trophoblast. Our study proposes a general mechanism in regulating lineage segregation during mammalian development.

cell biology↗

bioRxiv2017

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

MotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.\n\nResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.\n\nAvailabilityR scripts available at https://github.com/jijiadong/JDINAC\n\nContactlxie@iscb.org\n\nSupplementary informationSupplementary data are available at bioRxiv online.

systems biology↗

bioRxiv2016

DNA damage in 3D constricted migration or after lamin-A depletion in 2D: shared mechanisms of repair factor mis-localization under nuclear stress

Cells that migrate through small, rigid pores and that have normal levels of the nuclear structure protein lamin-A exhibit an increase in DNA damage, which is also observed with lamin-A depletion in diseases such as cancer and with many lamin-A mutations. Here we show nuclear envelope rupture is a shared feature that increases in standard culture after lamin-A knockdown, which causes nuclear loss of multiple DNA repair factors and increased DNA damage. Some repair factors are merely mis-localized to cytoplasm whereas others are partially depleted unless rescued by lamin-A expression. Compared to standard cultures on rigid glass coverslips, the growth of lamin-A low cells on soft matrix relaxes cytoskeletal stress on the nucleus, suppresses the mis-localization of DNA repair factors, and minimizes DNA damage nearly to wildtype levels. Conversely, constricted migration of the lamin-A low cells causes abnormally high levels of DNA damage, consistent with sustained loss of repair factors. The findings add insight into why monogenic progeroid syndromes that often associate with increased DNA damage and predominantly impact cells in stiff tissues result from mutations only in lamin-A or DNA repair factors.

cell biology↗