SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Epithelial-Mesenchymal Transition in Acute Leukemias

Epithelial-mesenchymal transition (EMT) is a metabolic process that confers phenotypic flexibility to cells and the ability to adapt to new functions. This transition is critical during embryogenesis and is required for the differentiation of many tissues and organs. EMT can also be induced in advanced-stage cancers, leading to further malignant behavior and chemotherapy resistance, resulting in an unfavorable prognosis for patients. Although EMT was long considered and studied only in solid tumors, it has been shown to be involved in the pathogenesis of hematological malignancies, including acute leukemias. Indeed, there is increasing evidence that EMT promotes the progression of acute leukemias, leading to the emergence of a more aggressive phenotype of the disease, and also causes chemotherapy resistance. The current literature suggests that the levels and activities of EMT inducers and markers can be used to predict prognosis, and that targeting EMT in addition to conventional therapies may increase treatment success in acute leukemias.

System Biology Approach in Investigating Epithelial-Mesenchymal Transition (EMT)

Epithelial-mesenchymal transition (EMT) is a trans-differentiating and reversible process that leads to dramatic cell phenotypic changes, enabling epithelial cells in acquiring mesenchymal phenotypes and behaviors. EMT plays a crucial role during embryogenesis, and occurs in several para-physiologic and pathological conditions, as during fibrosis or cancer development. EMT displays some hallmarks of critical transitions, as a sudden change in the overall configuration of a system in correspondence of specific tipping point around which a "catastrophic bifurcation" happens. The transition occurs when external conditions breach specific thresholds. This definition helps in highlighting two main aspects: (1) the change involves the overall system, rather than single, discrete components; (2) cues from the microenvironment play an irreplaceable role in triggering the transition. This evidence implies that critical transition should be ascertained focusing the investigation at the system level (rather than investigating only molecular parameters) in a well-defined context, as the transition is strictly dependent on the microenvironment in which it occurs. Therefore, we need a systems biology approach to investigate EMT across the Waddington-like epigenetic landscape wherein the participation of both internal and external cues can be studied to follow the extent and the main characteristics of the phenotypic transition. Herein, we suggest a set of systems parameters (motility, invasiveness) altogether with specific molecular/histological markers to identify those critical observables, which can be integrated into a comprehensive mechanistic model.

Trehalose metabolism coordinates transcriptional regulatory control and metabolic requirements to trigger the onset of cassava storage root initiation

Cassava storage roots (SR) are an important source of food energy and raw material for a wide range of applications. Understanding SR initiation and the associated regulation is critical to boosting tuber yield in cassava. Decades of transcriptome studies have identified key regulators relevant to SR formation, transcriptional regulation and sugar metabolism. However, there remain uncertainties over the roles of the regulators in modulating the onset of SR development owing to the limitation of the widely applied differential gene expression analysis. Here, we aimed to investigate the regulation underlying the transition from fibrous (FR) to SR based on Dynamic Network Biomarker (DNB) analysis. Gene expression analysis during cassava root initiation showed the transition period to SR happened in FR during 8 weeks after planting (FR8). Ninety-nine DNB genes associated with SR initiation and development were identified. Interestingly, the role of trehalose metabolism, especially trehalase1 (TRE1), in modulating metabolites abundance and coordinating regulatory signaling and carbon substrate availability via the connection of transcriptional regulation and sugar metabolism was highlighted. The results agree with the associated DNB characters of TRE1 reported in other transcriptome studies of cassava SR initiation and Attre1 loss of function in literature. The findings help fill the knowledge gap regarding the regulation underlying cassava SR initiation.

SMAD Proteins in TGF-β Signalling Pathway in Cancer: Regulatory Mechanisms and Clinical Applications

Suppressor of mother against decapentaplegic (SMAD) family proteins are central to one of the most versatile cytokine signalling pathways in metazoan biology, the transforming growth factor-β (TGF-β) pathway. The TGF-β pathway is widely known for its dual role in cancer progression as both an inhibitor of tumour cell growth and an inducer of tumour metastasis. This is mainly mediated through SMAD proteins and their cofactors or regulators. SMAD proteins act as transcription factors, regulating the transcription of a wide range of genes, and their rich post-translational modifications are influenced by a variety of regulators and cofactors. The complex role, mechanisms, and important functions of SMAD proteins in tumours are the hot topics in current oncology research. In this paper, we summarize the recent progress on the effects and mechanisms of SMAD proteins on tumour development, diagnosis, treatment and prognosis, and provide clues for subsequent research on SMAD proteins in tumours.

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

Background

Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity.

Methods

We integrated DNB analysis with graph convolutional neural networks (GCN) to identify critical transitions during hepatocellular carcinoma development in a mouse model. A DNB-GCN model was constructed using transcriptomic data and gene expression levels as node features.

Results

DNB analysis identified a critical transition point at 7 weeks of age despite histological examinations being unable to detect cancerous changes at that time point. The DNB-GCN model achieved 100% accuracy in classifying healthy and cancerous mice, and was able to accurately predict the health status of newly introduced mice.

Conclusion

The integration of DNB analysis and GCN demonstrates potential for the early detection of complex diseases by capturing network structures and molecular features that conventional biomarker discovery methods overlook. The approach warrants further development and validation.

Laminaria japonica polysaccharide attenuates podocyte epithelial-mesenchymal transformation via TGF-β1-mediated Smad3 and p38MAPK pathways

In the present work, we explored the interventional effect and potential mechanism of a purified Laminaria japonica polysaccharide (LJP61A) on podocyte epithelial-mesenchymal transition (EMT) in TGF-β1-induced podocytes and adriamycin-treated mice. Results showed that compared to the model groups, LJP61A significantly up-regulated the levels of epithelial markers (Nephrin, WT-1, podocin) and down-regulated the levels of mesenchymal markers (α-SMA, FN1) in vitro and in vivo, thus preventing EMT-like morphological changes of podocytes, proteinuria and kidney injury. Smad3 and p38MAPK are two central pathways mediating podocyte EMT activated by TGF-β1. We found that LJP61A suppressed TGF-β1-induced activation of Smad3, Smad4 and p38MAPK in vitro and in vivo. Moreover, the inhibitory actions of LJP61A on podocyte EMT were synergistically strengthened by Smad3 inhibitor SIS3 and p38MAPK inhibitor SB203580. Taken together, these findings revealed that LJP61A could prevent podocyte EMT, which might be related to the inhibition of TGF-β1-mediated Smad3 and p38MAPK pathways.

MAPKAPK2, a potential dynamic network biomarker of α-synuclein prior to its aggregation in PD patients

One of the important pathological features of Parkinson's disease (PD) is the pathological aggregation of α-synuclein (α-Syn) in the substantia nigra. Preventing the aggregation of α-Syn has become a potential strategy for treating PD. However, the molecular mechanism of α-Syn aggregation is unclear. In this study, using the dynamic network biomarker (DNB) method, we first identified the critical time point when α-Syn undergoes pathological aggregation based on a SH-SY5Y cell model and found that DNB genes encode transcription factors that regulated target genes that were differentially expressed. Interestingly, we found that these DNB genes and their neighbouring genes were significantly enriched in the cellular senescence pathway and thus proposed that the DNB genes HSF1 and MAPKAPK2 regulate the expression of the neighbouring gene SERPINE1. Notably, in Gene Expression Omnibus (GEO) data obtained from substantia nigra, prefrontal cortex and peripheral blood samples, the expression level of MAPKAPK2 was significantly higher in PD patients than in healthy people, suggesting that MAPKAPK2 has potential as an early diagnostic biomarker of diseases related to pathological aggregation of α-Syn, such as PD. These findings provide new insights into the mechanisms underlying the pathological aggregation of α-Syn.

Molecular biomarkers, network biomarkers, and dynamic network biomarkers for diagnosis and prediction of rare diseases

The difficulty of converting scientific research findings into novel pharmacological treatments for rare and life-threatening diseases is enormous. Biomarkers related to multiple biological processes involved in cell growth, proliferation, and disease occurrence have been identified in recent years with the development of immunology, molecular biology, and genomics technologies. Biomarkers are capable of reflecting normal physiological processes, pathological processes, and the response to therapeutic intervention; as such, they play vital roles in disease diagnosis, prevention, drug response, and other aspects of biomedicine. The discovery of valuable biomarkers has become a focal point of current research. Numerous studies have identified molecular biomarkers based on the differential expression/concentration of molecules (e.g., genes/proteins) for disease state diagnosis, characterization, and treatment. Although technological breakthroughs in molecular analysis platforms have enabled the identification of a large number of candidate biomarkers for rare diseases, only a small number of these candidates have been properly validated for use in patient treatment. The traditional molecular biomarkers may lose vital information by ignoring molecular associations/interactions, and thus the concept of network biomarkers based on differential associations/correlations of molecule pairs has been established. This approach promises to be more stable and reliable in diagnosing disease states. Furthermore, the newly-emerged dynamic network biomarkers (DNBs) based on differential fluctuations/correlations of molecular groups are able to recognize pre-disease states or critical states instead of disease states, thereby achieving rare disease prediction or predictive/preventative medicine and providing deep insight into the dynamic characteristics of disease initiation and progression.

Intrinsic entropy model for feature selection of scRNA-seq data

Recent advances of single-cell RNA sequencing (scRNA-seq) technologies have led to extensive study of cellular heterogeneity and cell-to-cell variation. However, the high frequency of dropout events and noise in scRNA-seq data confounds the accuracy of the downstream analysis, i.e. clustering analysis, whose accuracy depends heavily on the selected feature genes. Here, by deriving an entropy decomposition formula, we propose a feature selection method, i.e. an intrinsic entropy (IE) model, to identify the informative genes for accurately clustering analysis. Specifically, by eliminating the 'noisy' fluctuation or extrinsic entropy (EE), we extract the IE of each gene from the total entropy (TE), i.e. TE = IE + EE. We show that the IE of each gene actually reflects the regulatory fluctuation of this gene in a cellular process, and thus high-IE genes provide rich information on cell type or state analysis. To validate the performance of the high-IE genes, we conduct computational analysis on both simulated datasets and real single-cell datasets by comparing with other representative methods. The results show that our IE model is not only broadly applicable and robust for different clustering and classification methods, but also sensitive for novel cell types. Our results also demonstrate that the intrinsic entropy/fluctuation of a gene serves as information rather than noise in contrast to its total entropy/fluctuation.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development

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.

Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage

Skin, as the outmost layer of human body, is frequently exposed to environmental stressors including pollutants and ultraviolet (UV), which could lead to skin disorders. Generally, skin response process to ultraviolet B (UVB) irradiation is a nonlinear dynamic process, with unknown underlying molecular mechanism of critical transition. Here, the landscape dynamic network biomarker (l-DNB) analysis of time series transcriptome data on 3D skin model was conducted to reveal the complicated process of skin response to UV irradiation at both molecular and network levels. The advanced l-DNB analysis approach showed that: (i) there was a tipping point before critical transition state during pigmentation process, validated by 3D skin model; (ii) 13 core DNB genes were identified to detect the tipping point as a network biomarker, supported by computational assessment; (iii) core DNB genes such as COL7A1 and CTNNB1 can effectively predict skin lightening, validated by independent human skin data. Overall, this study provides new insights for skin response to repetitive UVB irradiation, including dynamic pathway pattern, biphasic response, and DNBs for skin lightening change, and enables us to further understand the skin resilience process after external stress.

Dynamical network biomarkers: Theory and applications

This paper reviews theory of DNB (Dynamical Network Biomarkers) and its applications including both modern medicine and traditional medicine. We show that omics data such as gene/protein expression profiles can be effectively used to detect pre-disease states before critical transitions from healthy states to disease states by using the DNB theory. The DNB theory with big biological data is expected to lead to ultra-early precision and preventive medicine.

Dynamic network biomarker analysis discovers IbNAC083 in the initiation and regulation of sweet potato root tuberization

The initiation and development of storage roots (SRs) are intricately regulated by a transcriptional regulatory network. One key challenge is to accurately pinpoint the tipping point during the transition from pre-swelling to SRs and to identify the core regulators governing such a critical transition. To solve this problem, we performed a dynamic network biomarker (DNB) analysis of transcriptomic dynamics during root development in Ipomoea batatas (sweet potato). First, our analysis identified stage-specific expression patterns for a significant proportion (>9%) of the sweet potato genes and unraveled the chronology of events that happen at the early and later stages of root development. Then, the results showed that different root developmental stages can be depicted by co-expressed modules of sweet potato genes. Moreover, we identified the key components and transcriptional regulatory network that determine root development. Furthermore, through DNB analysis an early stage, with a root diameter of 3.5 mm, was identified as the critical period of SR swelling initiation, which is consistent with morphological and metabolic changes. In particular, we identified a NAM/ATAF/CUC (NAC) domain transcription factor, IbNAC083, as a core regulator of this initiation in the DNB-associated network. Further analyses and experiments showed that IbNAC083, along with its associated differentially expressed genes, induced dysfunction of metabolism processes, including the biosynthesis of lignin, flavonol and starch, thus leading to the transition to swelling roots.

Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT), a basic developmental process that might promote cancer metastasis, has been studied from various perspectives. Recently, the early warning theory has been used to anticipate critical transitions in EMT from mathematical modeling. However, the underlying mechanisms of EMT involving complex molecular networks remain to be clarified. Especially, how to quantify the global stability and stochastic transition dynamics of EMT and what the underlying mechanism for early warning theory in EMT is remain to be fully clarified. To address these issues, we constructed a comprehensive gene regulatory network model for EMT and quantified the corresponding potential landscape. The landscape for EMT displays multiple stable attractors, which correspond to E, M, and some other intermediate states. Based on the path-integral approach, we identified the most probable transition paths of EMT, which are supported by experimental data. Correspondingly, the results of transition actions demonstrated that intermediate states can accelerate EMT, consistent with recent studies. By integrating the landscape and path with early warning concept, we identified the potential barrier height from the landscape as a global and more accurate measure for early warning signals to predict critical transitions in EMT. The landscape results also provide an intuitive and quantitative explanation for the early warning theory. Overall, the landscape and path results advance our mechanistic understanding of dynamical transitions and roles of intermediate states in EMT, and the potential barrier height provides a new, to our knowledge, measure for critical transitions and quantitative explanations for the early warning theory.

Single-cell transcriptomics reveal DHX9 in mature B cell as a dynamic network biomarker before lymph node metastasis in CRC

Increasing evidence indicates that mature B cells in the adjacent tumor tissue, both as an intermediate state, are vital in advanced colorectal cancer (CRC), which is associated with a low survival rate. Developing predictive biomarkers that detect the tipping point of mature B cells before lymph node metastasis in CRC is critical to prevent irreversible deterioration. We analyzed B cells in the adjacent tissues of CRC samples from different stages using the dynamic network biomarker (DNB) method. Single-cell profiling of 725 CRC-derived B cells revealed the emergence of a mature B cell subtype. Using the DNB method, we identified stage II as a critical period before lymph node metastasis and that reversed difference genes triggered by DNBs were enriched in the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway involving B cell immune capability. DHX9 (DEAH-box helicase 9) was a specific para-cancerous tissue DNB key gene. The dynamic expression levels of DHX9 and its proximate network genes involved in B cell-related pathways were reversed at the network level from stage I to III. In summary, DHX9 in mature B cells of CRC-adjacent tissues may serve as a predictable biomarker and a potential immune target in CRC progression.