Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory ...to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to "diagnose disease", sDNB is based on the information of differential associations, thereby having the ability to "predict disease" or "diagnose near-future disease". Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Integrating different omics profiles is a challenging task, which provides a comprehensive way to understand complex diseases in a multi-view manner. One key for such an integration is to extract ...intrinsic patterns in concordance with data structures, so as to discover consistent information across various data types even with noise pollution. Thus, we proposed a novel framework called 'pattern fusion analysis' (PFA), which performs automated information alignment and bias correction, to fuse local sample-patterns (e.g. from each data type) into a global sample-pattern corresponding to phenotypes (e.g. across most data types). In particular, PFA can identify significant sample-patterns from different omics profiles by optimally adjusting the effects of each data type to the patterns, thereby alleviating the problems to process different platforms and different reliability levels of heterogeneous data.
To validate the effectiveness of our method, we first tested PFA on various synthetic datasets, and found that PFA can not only capture the intrinsic sample clustering structures from the multi-omics data in contrast to the state-of-the-art methods, such as iClusterPlus, SNF and moCluster, but also provide an automatic weight-scheme to measure the corresponding contributions by data types or even samples. In addition, the computational results show that PFA can reveal shared and complementary sample-patterns across data types with distinct signal-to-noise ratios in Cancer Cell Line Encyclopedia (CCLE) datasets, and outperforms over other works at identifying clinically distinct cancer subtypes in The Cancer Genome Atlas (TCGA) datasets.
PFA has been implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/PFApackage_0.1.rar .
lnchen@sibs.ac.cn , liujuan@whu.edu.cn or zengtao@sibs.ac.cn.
Supplementary data are available at Bioinformatics online.
Unlike traditional diagnosis of an existing disease state, detecting the pre-disease state just before the serious deterioration of a disease is a challenging task, because the state of the system ...may show little apparent change or symptoms before this critical transition during disease progression. By exploring the rich interaction information provided by high-throughput data, the dynamical network biomarker (DNB) can identify the pre-disease state, but this requires multiple samples to reach a correct diagnosis for one individual, thereby restricting its clinical application.
In this article, we have developed a novel computational approach based on the DNB theory and differential distributions between the expressions of DNB and non-DNB molecules, which can detect the pre-disease state reliably even from a single sample taken from one individual, by compensating insufficient samples with existing datasets from population studies. Our approach has been validated by the successful identification of pre-disease samples from subjects or individuals before the emergence of disease symptoms for acute lung injury, influenza and breast cancer.
Targeted therapy is the foundation of personalized medicine in cancer, which is often understood as the right patient using the right drug. Thinking from the viewpoint of determinants during ...personalized drug treatment, the genetics, epigenetics and metagenomics would provide individual-specific biological elements to characterize the personalized responses for therapy.
Such personalized determinants should be not only understood as specific to one person, while they should have certain replicate observations in a group of individuals but not all, which actually provide more credible and reproducible personalized biological features. The requirement of detecting personalized determinants is well supported by novel high-throughput sequencing technologies and newly temporal-spatial experimental protocols, which quickly produce the omics big data.
In this mini-review, we would like to give a brief introduction firstly on the advanced drug or drug-like therapy with genetics, epigenetics and metagenomics, respectively, from the viewpoint of personalized determinants; then summarize the computational methods helpful to analyze the corresponding omics data under the consideration of personalized biological context; and particularly focus on metagenomics to discuss current data, method, and opportunity for personalized medicine.
Totally, detecting personalized determinants during drug treatment from omics big data will bring the precision medicine or personalized medicine from concept to application. More and more inspiring biotechnologies, data resources, and analytic approaches will benefit All of US in the near future.
Mammary gland is present in all mammals and usually functions in producing milk to feed the young offspring. Mammogenesis refers to the growth and development of mammary gland, which begins at ...puberty and ends after lactation. Pregnancy is regulated by various cytokines, which further contributes to mammary gland development. Epithelial cells, including basal and luminal cells, are one of the major components of mammary gland cells. The development of basal and luminal cells has been observed to significantly differ at different stages. However, the underlying mechanisms for differences between basal and luminal cells have not been fully studied. To explore the mechanisms underlying the differentiation of mammary progenitors or their offspring into luminal and myoepithelial cells, the single-cell sequencing data on mammary epithelia cells of virgin and pregnant mouse was deeply investigated in this work. We evaluated features by using Monte Carlo feature selection and plotted the incremental feature selection curve with support vector machine or RIPPER to find the optimal gene features and rules that can divide epithelial cells into four clusters with different cell subtypes like basal and luminal cells and different phases like pregnancy and virginity. As representations, the feature genes Cldn7, Gjb6, Sparc, Cldn3, Cited1, Krt17, Spp1, Cldn4, Gjb2 and Cldn19 might play an important role in classifying the epithelial mammary cells. Notably, seven most important rules based on the combination of cell-specific and tissue-specific expressions of feature genes effectively classify the epithelial mammary cells in a quantitative and interpretable manner.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the ...Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into ...the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Along with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be ...some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes.
Assuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states.
Collectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
G-protein-signaling modulator 1 (GPSM1) exhibits strong genetic association with Type 2 diabetes (T2D) and Body Mass Index in population studies. However, how GPSM1 carries out such control and in ...which types of cells are poorly understood. Here, we demonstrate that myeloid GPSM1 promotes metabolic inflammation to accelerate T2D and obesity development. Mice with myeloid-specific GPSM1 ablation are protected against high fat diet-induced insulin resistance, glucose dysregulation, and liver steatosis via repression of adipose tissue pro-inflammatory states. Mechanistically, GPSM1 deficiency mainly promotes TNFAIP3 transcription via the Gα
/cAMP/PKA/CREB axis, thus inhibiting TLR4-induced NF-κB signaling in macrophages. In addition, we identify a small-molecule compound, AN-465/42243987, which suppresses the pro-inflammatory phenotype by inhibiting GPSM1 function, which could make it a candidate for metabolic therapy. Furthermore, GPSM1 expression is upregulated in visceral fat of individuals with obesity and is correlated with clinical metabolic traits. Overall, our findings identify macrophage GPSM1 as a link between metabolic inflammation and systemic homeostasis.
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