Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage.
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ABSTRACT:
SUBMITTER: Zhang C
PROVIDER: S-EPMC8782598 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Developing predictive biomarkers that can detect the tipping point before metastasis of hepatocellular carcinoma (HCC), is critical to prevent further irreversible deterioration. To discover such early-warning signals or biomarkers of pulmonary metastasis in HCC, we analyse time-series gene expression data in spontaneous pulmonary metastasis mice HCCLM3-RFP model with our dynamic network biomarker (DNB) method, and identify CALML3 as a core DNB member. All experimental results of gain-of-function and loss-of-function studies show that CALML3 could indicate metastasis initiation and act as a suppressor of metastasis. We also reveal the biological role of CALML3 in metastasis initiation at a network level, including proximal regulation and cascading influences in dysfunctional pathways. Our further experiments and clinical samples show that DNB with CALML3 reduced pulmonary metastasis in liver cancer. Actually, loss of CALML3 predicts shorter overall and relapse-free survival in postoperative HCC patients, thus providing a prognostic biomarker and therapy target in HCC.
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Project description:A new model-free method has been developed and termed the landscape dynamic network biomarker (l-DNB) methodology. The method is based on bifurcation theory, which can identify tipping points prior to serious disease deterioration using only single-sample omics data. Here, we show that l-DNB provides early-warning signals of disease deterioration on a single-sample basis and also detects critical genes or network biomarkers (i.e. DNB members) that promote the transition from normal to disease states. As a case study, l-DNB was used to predict severe influenza symptoms prior to the actual symptomatic appearance in influenza virus infections. The l-DNB approach was then also applied to three tumor disease datasets from the TCGA and was used to detect critical stages prior to tumor deterioration using an individual DNB for each patient. The individual DNBs were further used as individual biomarkers in the analysis of physiological data, which led to the identification of two biomarker types that were surprisingly effective in predicting the prognosis of tumors. The biomarkers can be considered as common biomarkers for cancer, wherein one indicates a poor prognosis and the other indicates a good prognosis.
| S-EPMC8291500 | biostudies-literature
Project description:Determining mechanism-based biomarkers that distinguish adaptive and adverse cellular processes is critical to understanding the health effects of environmental exposures. Shifting from in vivo, low-throughput toxicity studies to high-throughput screening (HTS) paradigms and risk assessment based on in vitro and in silico testing requires utilizing toxicity pathway information to distinguish adverse outcomes from recoverable adaptive events. Little work has focused on oxidative stresses in human airway for the purposes of predicting adverse responses. We hypothesize that early gene expression-mediated molecular changes could be used to delineate adaptive and adverse responses to environmentally-based perturbations. Here, we examined cellular responses of the tracheobronchial airway to zinc (Zn) exposure, a model oxidant. Airway derived BEAS-2B cells exposed to 2–10 µM Zn2+ elicited concentration- and time-dependent cytotoxicity. Normal, adaptive, and cytotoxic Zn2+ exposure conditions were determined with traditional apical endpoints, and differences in global gene expression around the tipping point of the responses were used to delineate underlying molecular mechanisms. Bioinformatic analyses of differentially expressed genes indicate early enrichment of stress signaling pathways, including those mediated by the transcription factors p53 and NRF2. After 4 h, 154 genes were differentially expressed (p <0.01) between the adaptive and cytotoxic Zn2+ concentrations. Nearly 40% of the biomarker genes were related to the p53 signaling pathway with 30 genes identified as likely direct targets using a database of p53 ChIP-seq studies. Despite similar p53 activation profiles, these data revealed widespread dampening of p53 and NRF2-related genes as early as 4 h after exposure at higher, unrecoverable Zn2+ exposures. Thus, in our model early increased activation of stress response pathways indicated a recoverable adaptive event. Overall, this study highlights the importance of characterizing molecular mechanisms around the tipping point of adverse responses to better inform HTS paradigms.
2016-04-28 | GSE80733 | GEO
Project description:Understanding tipping point dynamics in harvested ecosystems is of crucial importance for sustainable resource management because ignoring their existence imperils social-ecological systems that depend on them. Fisheries collapses provide the best known examples for realizing tipping points with catastrophic ecological, economic and social consequences. However, present-day fisheries management systems still largely ignore the potential of their resources to exhibit such abrupt changes towards irreversible low productive states. Using a combination of statistical changepoint analysis and stochastic cusp modelling, here we show that Western Baltic cod is beyond such a tipping point caused by unsustainable exploitation levels that failed to account for changing environmental conditions. Furthermore, climate change stabilizes a novel and likely irreversible low productivity state of this fish stock that is not adapted to a fast warming environment. We hence argue that ignorance of non-linear resource dynamics has caused the demise of an economically and culturally important social-ecological system which calls for better adaptation of fisheries systems to climate change.
| S-EPMC8275682 | biostudies-literature
Project description:Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients. The results provided the detection for the pre-resistance state or early signs of endocrine resistance. Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.
| S-EPMC7727267 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a complex disease with a multi-step carcinogenic process from preneoplastic lesions, including cirrhosis, low-grade dysplastic nodules (LGDNs), and high-grade dysplastic nodules (HGDNs) to HCC. There is only an elemental understanding of its molecular pathogenesis, for which a key problem is to identify when and how the critical transition happens during the HCC initiation period at a molecular level. In this work, for the first time, we revealed that LGDNs is the tipping point (i.e., pre-HCC state rather than HCC state) of hepatocarcinogenesis based on a series of gene expression profiles by a new mathematical model termed dynamic network biomarkers (DNB)-a group of dominant genes or molecules for the transition. Different from the conventional biomarkers based on the differential expressions of the observed genes (or molecules) for diagnosing a disease state, the DNB model exploits collective fluctuations and correlations of the observed genes, thereby predicting the imminent disease state or diagnosing the critical state. Our results show that DNB composed of 59 genes signals the tipping point of HCC (i.e., LGDNs). On the other hand, there are a large number of differentially expressed genes between cirrhosis and HGDNs, which highlighted the stark differences or drastic changes before and after the tipping point or LGDNs, implying the 59 DNB members serving as the early-warning signals of the upcoming drastic deterioration for HCC. We further identified the biological pathways responsible for this transition, such as the type I interferon signaling pathway, Janus kinase-signal transducers and activators of transcription (JAK-STAT) signaling pathway, transforming growth factor (TGF)-β signaling pathway, retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathway, cell adhesion molecules, and cell cycle. In particular, pathways related to immune system reactions and cell adhesion were downregulated, and pathways related to cell growth and death were upregulated. Furthermore, DNB was validated as an effective predictor of prognosis for HCV-induced HCC patients by survival analysis on independent data, suggesting a potential clinical application of DNB. This work provides biological insights into the dynamic regulations of the critical transitions during multistep hepatocarcinogenesis.
| S-EPMC5664118 | biostudies-literature
Project description:Determining mechanism-based biomarkers that distinguish adaptive and adverse cellular processes is critical to understanding the health effects of environmental exposures. Shifting from in vivo, low-throughput toxicity studies to high-throughput screening (HTS) paradigms and risk assessment based on in vitro and in silico testing requires utilizing toxicity pathway information to distinguish adverse outcomes from recoverable adaptive events. Little work has focused on oxidative stresses in human airway for the purposes of predicting adverse responses. We hypothesize that early gene expression-mediated molecular changes could be used to delineate adaptive and adverse responses to environmentally-based perturbations. Here, we examined cellular responses of the tracheobronchial airway to zinc (Zn) exposure, a model oxidant. Airway derived BEAS-2B cells exposed to 2-10 μM Zn2+ elicited concentration- and time-dependent cytotoxicity. Normal, adaptive, and cytotoxic Zn2+ exposure conditions were determined with traditional apical endpoints, and differences in global gene expression around the tipping point of the responses were used to delineate underlying molecular mechanisms. Bioinformatic analyses of differentially expressed genes indicate early enrichment of stress signaling pathways, including those mediated by the transcription factors p53 and NRF2. After 4 h, 154 genes were differentially expressed (p < 0.01) between the adaptive and cytotoxic Zn2+ concentrations. Nearly 40% of the biomarker genes were related to the p53 signaling pathway with 30 genes identified as likely direct targets using a database of p53 ChIP-seq studies. Despite similar p53 activation profiles, these data revealed widespread dampening of p53 and NRF2-related genes as early as 4 h after exposure at higher, unrecoverable Zn2+ exposures. Thus, in our model early increased activation of stress response pathways indicated a recoverable adaptive event. Overall, this study highlights the importance of characterizing molecular mechanisms around the tipping point of adverse responses to better inform HTS paradigms.
| S-EPMC4873291 | biostudies-literature
Project description:BackgroundAlthough risk factors for poor outcomes and complications have been studied, there remain limited objective criteria to guide surgeons about the timing of arthroplasty. The purpose of this study was to further characterize the tipping-point scores for a group of patient-reported outcome measures (PROMs) in patients undergoing primary shoulder arthroplasty.MethodsWe retrospectively reviewed 5670 primary shoulder arthroplasties (1833 anatomic total shoulder arthroplasties and 3837 reverse shoulder arthroplasties [RSAs]) performed over a 10-year period. Preoperative range of motion, PROMs (American Shoulder and Elbow Surgeons, Simple Shoulder Test, and Shoulder Pain and Disability Index scores), and Constant scores were evaluated. The tipping point for each PROM was evaluated. Univariate and multivariate analyses were performed to assess risk factors for lower tipping points.ResultsPatients undergoing RSA demonstrated lower tipping points for all range-of-motion parameters as well as American Shoulder and Elbow Surgeons, Shoulder Pain and Disability Index, and Simple Shoulder Test scores. Female sex was predictive of a lower tipping point prior to shoulder arthroplasty, regardless of implant type. When the total shoulder arthroplasty subgroup was evaluated, both female sex and a higher body mass index were shown to be associated with a lower tipping point.DiscussionThe choice to undergo shoulder arthroplasty is a multifactorial decision that encompasses both physical and social factors. Female patients and patients undergoing RSA are more likely to accept slightly worse shoulder function prior to making the decision to undergo shoulder arthroplasty.
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Project description:Metastatic and locally advanced prostate cancer is treated by pharmacological targeting of androgen synthesis and androgen response via androgen signaling inhibitors (ASI), most of which target the androgen receptor (AR). However, ASI therapy invariably fails after 1-2 years. Emerging clinical evidence indicates that in response to ASI therapy, the AR-positive prostatic adenocarcinoma can transdifferentiate into AR-negative neuroendocrine prostate cancer (NEPC) in 17-25 % treated patients, likely through a process called neuroendocrine differentiation (NED). Despite high clinical incidence, the epigenetic pathways underlying NED and ASI therapy-induced NED remain unclear. By utilizing a combinatorial single cell and bulk mRNA sequencing workflow, we demonstrate in a time-resolved manner that following AR inhibition with enzalutamide, prostate cancer cells exhibit immediate loss of canonical AR signaling activity and simultaneous morphological change from epithelial to NE-like (NEL) morphology, followed by activation of specific neuroendocrine (NE)-associated transcriptional programs. Additionally, we observed that activation of NE-associated pathways occurs prior to complete repression of epithelial or canonical AR pathways, a phenomenon also observed clinically via heterogenous AR status in clinical samples. Our model indicates that, mechanistically, ASI therapy induces NED with initial morphological change followed by deactivation of canonical AR target genes and subsequent de-repression of NE-associated target genes, while retaining AR expression and transcriptional shift towards non-canonical AR activity. Coupled with scRNA-seq and CUT&RUN analysis, our model system can provide a platform for screening of potential therapeutic agents that may prevent ASI-induced NED or reverse the NED process.
| S-EPMC9636493 | biostudies-literature
Project description:The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.
| S-EPMC8907966 | biostudies-literature