A systems biology approach to discovering pathway signaling dysregulation in metastasis

Mass spectrometry-based proteomic approaches for salivary protein biomarkers discovery and dental caries diagnosis: A critical review

Dental caries is a multifactorial chronic disease resulting from the intricate interplay among acid-generating bacteria, fermentable carbohydrates, and several host factors such as saliva. Saliva comprises several proteins which could be utilized as biomarkers for caries prevention, diagnosis, and prognosis. Mass spectrometry-based salivary proteomics approaches, owing to their sensitivity, provide the opportunity to investigate and unveil crucial cariogenic pathogen activity and host indicators and may demonstrate clinically relevant biomarkers to improve caries diagnosis and management. The present review outlines the published literature of human clinical proteomics investigations on caries and extensively elucidates frequently reported salivary proteins as biomarkers. This review also discusses important aspects while designing an experimental proteomics workflow. The protein-protein interactions and the clinical relevance of salivary proteins as biomarkers for caries, together with uninvestigated domains of the discipline are also discussed critically.

Modulating autophagy to treat diseases: A revisited review on in silico methods

BACKGROUND

Autophagy refers to the conserved cellular catabolic process relevant to lysosome activity and plays a vital role in maintaining the dynamic equilibrium of intracellular matter by degrading harmful and abnormally accumulated cellular components. Accumulating evidence has recently revealed that dysregulation of autophagy by genetic and exogenous interventions may disrupt cellular homeostasis in human diseases. In silico approaches as powerful aids to experiments have also been extensively reported to play their critical roles in the storage, prediction, and analysis of massive amounts of experimental data. Thus, modulating autophagy to treat diseases by in silico methods would be anticipated.

AIM OF REVIEW

Here, we focus on summarizing the updated in silico approaches including databases, systems biology network approaches, omics-based analyses, mathematical models, and artificial intelligence (AI) methods that sought to modulate autophagy for potential therapeutic purposes, which will provide a new insight into more promising therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Autophagy-related databases are the data basis of the in silico method, storing a large amount of information about DNA, RNA, proteins, small molecules and diseases. The systems biology approach is a method to systematically study the interrelationships among biological processes including autophagy from a macroscopic perspective. Omics-based analyses are based on high-throughput data to analyze gene expression at different levels of biological processes involving autophagy. mathematical models are visualization methods to describe the dynamic process of autophagy, and its accuracy is related to the selection of parameters. AI methods use big data related to autophagy to predict autophagy targets, design targeted small molecules, and classify diverse human diseases for potential therapeutic applications.

Types of Cell Death from a Molecular Perspective

The former conventional belief was that cell death resulted from either apoptosis or necrosis; however, in recent years, different pathways through which a cell can undergo cell death have been discovered. Various types of cell death are distinguished by specific morphological alterations in the cell's structure, coupled with numerous biological activation processes. Various diseases, such as cancers, can occur due to the accumulation of damaged cells in the body caused by the dysregulation and failure of cell death. Thus, comprehending these cell death pathways is crucial for formulating effective therapeutic strategies. We focused on providing a comprehensive overview of the existing literature pertaining to various forms of cell death, encompassing apoptosis, anoikis, pyroptosis, NETosis, ferroptosis, autophagy, entosis, methuosis, paraptosis, mitoptosis, parthanatos, necroptosis, and necrosis.

Introduction: Cancer Systems and Integrative Biology

The insights provided by the holistic approaches of systems and integrative biology offer a means to address the multiple levels of complexity evident in cancer biology. Often focused on the use of large-scale, high-dimensional omics data for discovery in silico, integration with lower-dimensional data and lower-throughput wet laboratory studies allows for the development of a more mechanistic understanding of the control, execution, and operation of complex biological systems. While no single volume can cover all of the advances across this broad and rapidly developing field, we here provide reviews, methods, and detailed protocols for several state-of-the-art approaches to probe cancer biology from an integrative systems perspective. The protocols presented are intended for easy implementation in the laboratory and often offer a clear rationale for their development and application. This introduction provides a very brief description of systems and integrative biology as context for the chapters that follow, with a short overview of each chapter to allow the reader to easily and quickly find those protocols of most interest.

Mathematical Models of Death Signaling Networks

This review provides an overview of the progress made by computational and systems biologists in characterizing different cell death regulatory mechanisms that constitute the cell death network. We define the cell death network as a comprehensive decision-making mechanism that controls multiple death execution molecular circuits. This network involves multiple feedback and feed-forward loops and crosstalk among different cell death-regulating pathways. While substantial progress has been made in characterizing individual cell death execution pathways, the cell death decision network is poorly defined and understood. Certainly, understanding the dynamic behavior of such complex regulatory mechanisms can be only achieved by applying mathematical modeling and system-oriented approaches. Here, we provide an overview of mathematical models that have been developed to characterize different cell death mechanisms and intend to identify future research directions in this field.

Silencing GOLGA8B inhibits cell invasion and metastasis by suppressing STAT3 signaling pathway in lung squamous cell carcinoma

Changes to some Golgi subfamily member proteins are reported to be involved in tumor metastasis. However, the functional role and potential mechanism of the Golgi A8 family member B (GOLGA8B) in lung squamous cell carcinoma (LUSC) remains unknown. In the present study, GOLGA8B expression was detected using qRT-PCR, Western blot, and immunohistochemistry (IHC). In vivo animal experiments and in vitro functional assays were performed to explore the function of GOLGA8B in LUSC. Luciferase assays were performed to investigate the underlying targets of GOLGA8B in LUSC. GOLGA8B was shown to be highly expressed in LUSC metastasis tissue, and significantly associated with the distant metastasis-free survival of LUSC patients. Loss-of-function assays indicated that silencing GOLGA8B suppressed LUSC cell tumorigenesis in vivo and weakened in vitro invasion and migration. GOLGA8B silencing-induced inhibition of invasion and migration was associated with the inactivation of STAT3 signaling. Importantly, these results showed that the number of circulating tumor cells (CTCs) was markedly higher in the GOLGA8B silencing group than in the control vector group. GOLGA8B expression was positively associated with p-STAT3 expression in LUSC tissue. Study findings revealed a novel mechanism by which GOLGA8B promotes tumor metastasis in LUSC cells and suggests that this protein could be a promising target for antitumor metastasis therapy in LUSC patients.

Current State and Challenges of the Global Outcomes of Dental Caries Research in the Meta-Omics Era

Despite significant healthcare advances in the 21st century, the exact etiology of dental caries remains unsolved. The past two decades have witnessed a tremendous growth in our understanding of dental caries amid the advent of revolutionary omics technologies. Accordingly, a consensus has been reached that dental caries is a community-scale metabolic disorder, and its etiology is beyond a single causative organism. This conclusion was based on a variety of microbiome studies following the flow of information along the central dogma of biology from genomic data to the end products of metabolism. These studies were facilitated by the unprecedented growth of the next- generation sequencing tools and omics techniques, such as metagenomics and metatranscriptomics, to estimate the community composition of oral microbiome and its functional potential. Furthermore, the rapidly evolving proteomics and metabolomics platforms, including nuclear magnetic resonance spectroscopy and/or mass spectrometry coupled with chromatography, have enabled precise quantification of the translational outcomes. Although the majority supports 'conserved functional changes' as indicators of dysbiosis, it remains unclear how caries dynamics impact the microbiota functions and vice versa, over the course of disease onset and progression. What compounds the situation is the host-microbiota crosstalk. Genome-wide association studies have been undertaken to elucidate the interaction of host genetic variation with the microbiome. However, these studies are challenged by the complex interaction of host genetics and environmental factors. All these complementary approaches need to be orchestrated to capture the key players in this multifactorial disease. Herein, we critically review the milestones in caries research focusing on the state-of-art singular and integrative omics studies, supplemented with a bibliographic network analysis to address the oral microbiome, the host factors, and their interactions. Additionally, we highlight gaps in the dental literature and shed light on critical future research questions and study designs that could unravel the complexities of dental caries, the most globally widespread disease.

Crosstalk between Plk1, p53, cell cycle, and G2/M DNA damage checkpoint regulation in cancer: computational modeling and analysis

Different cancer cell lines can have varying responses to the same perturbations or stressful conditions. Cancer cells that have DNA damage checkpoint-related mutations are often more sensitive to gene perturbations including altered Plk1 and p53 activities than cancer cells without these mutations. The perturbations often induce a cell cycle arrest in the former cancer, whereas they only delay the cell cycle progression in the latter cancer. To study crosstalk between Plk1, p53, and G2/M DNA damage checkpoint leading to differential cell cycle regulations, we developed a computational model by extending our recently developed model of mitotic cell cycle and including these key interactions. We have used the model to analyze the cancer cell cycle progression under various gene perturbations including Plk1-depletion conditions. We also analyzed mutations and perturbations in approximately 1800 different cell lines available in the Cancer Dependency Map and grouped lines by genes that are represented in our model. Our model successfully explained phenotypes of various cancer cell lines under different gene perturbations. Several sensitivity analysis approaches were used to identify the range of key parameter values that lead to the cell cycle arrest in cancer cells. Our resulting model can be used to predict the effect of potential treatments targeting key mitotic and DNA damage checkpoint regulators on cell cycle progression of different types of cancer cells.

HNRNPA2B1 regulates tamoxifen- and fulvestrant-sensitivity and hallmarks of endocrine resistance in breast cancer cells

Despite new combination therapies improving survival of breast cancer patients with estrogen receptor α (ER+) tumors, the molecular mechanisms for endocrine-resistant disease remain unresolved. Previously we demonstrated that expression of the RNA binding protein and N6-methyladenosine (m6A) reader HNRNPA2B1 (A2B1) is higher in LCC9 and LY2 tamoxifen (TAM)-resistant ERα breast cancer cells relative to parental TAM-sensitive MCF-7 cells. Here we report that A2B1 protein expression is higher in breast tumors than paired normal breast tissue. Modest stable overexpression of A2B1 in MCF-7 cells (MCF-7-A2B1 cells) resulted in TAM- and fulvestrant- resistance whereas knockdown of A2B1 in LCC9 and LY2 cells restored TAM and fulvestrant, endocrine-sensitivity. MCF-7-A2B1 cells gained hallmarks of TAM-resistant metastatic behavior: increased migration and invasion, clonogenicity, and soft agar colony size, which were attenuated by A2B1 knockdown in MCF-7-A2B1 and the TAM-resistant LCC9 and LY2 cells. MCF-7-A2B1, LCC9, and LY2 cells have a higher proportion of CD44+/CD24-/low cancer stem cells (CSC) compared to MCF-7 cells. MCF-7-A2B1 cells have increased ERα and reduced miR-222-3p that targets ERα. Like LCC9 cells, MCF-7-A2B1 have activated AKT and MAPK that depend on A2B1 expression and are growth inhibited by inhibitors of these pathways. These data support that targeting A2B1 could provide a complimentary therapeutic approach to reduce acquired endocrine resistance.

Experimental models of endocrine responsive breast cancer: strengths, limitations, and use

Breast cancers characterized by expression of estrogen receptor-alpha (ER; ESR1) represent approximately 70% of all new cases and comprise the largest molecular subtype of this disease. Despite this high prevalence, the number of adequate experimental models of ER+ breast cancer is relatively limited. Nonetheless, these models have proved very useful in advancing understanding of how cells respond to and resist endocrine therapies, and how the ER acts as a transcription factor to regulate cell fate signaling. We discuss the primary experimental models of ER+ breast cancer including 2D and 3D cultures of established cell lines, cell line- and patient-derived xenografts, and chemically induced rodent models, with a consideration of their respective general strengths and limitations. What can and cannot be learned easily from these models is also discussed, and some observations on how these models may be used more effectively are provided. Overall, despite their limitations, the panel of models currently available has enabled major advances in the field, and these models remain central to the ability to study mechanisms of therapy action and resistance and for hypothesis testing that would otherwise be intractable or unethical in human subjects.