Exploring the role of stromal osmoregulation in cancer and disease using executable modelling

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An influx of water molecules can help immune cells called neutrophils to move to where they are needed in the body.

KCNQ potassium channels modulate Wnt activity in gastro-oesophageal adenocarcinomas

Voltage-sensitive potassium channels play an important role in controlling membrane potential and ionic homeostasis in the gut and have been implicated in gastrointestinal (GI) cancers. Through large-scale analysis of 897 patients with gastro-oesophageal adenocarcinomas (GOAs) coupled with in vitro models, we find KCNQ family genes are mutated in ∼30% of patients, and play therapeutically targetable roles in GOA cancer growth. KCNQ1 and KCNQ3 mediate the WNT pathway and MYC to increase proliferation through resultant effects on cadherin junctions. This also highlights novel roles of KCNQ3 in non-excitable tissues. We also discover that activity of KCNQ3 sensitises cancer cells to existing potassium channel inhibitors and that inhibition of KCNQ activity reduces proliferation of GOA cancer cells. These findings reveal a novel and exploitable role of potassium channels in the advancement of human cancer, and highlight that supplemental treatments for GOAs may exist through KCNQ inhibitors.

Potential Theranostic Roles of SLC4 Molecules in Human Diseases

The solute carrier family 4 (SLC4) is an important protein responsible for the transport of various ions across the cell membrane and mediating diverse physiological functions, such as the ion transporting function, protein-to-protein interactions, and molecular transduction. The deficiencies in SLC4 molecules may cause multisystem disease involving, particularly, the respiratory system, digestive, urinary, endocrine, hematopoietic, and central nervous systems. Currently, there are no effective strategies to treat these diseases. SLC4 proteins are also found to contribute to tumorigenesis and development, and some of them are regarded as therapeutic targets in quite a few clinical trials. This indicates that SLC4 proteins have potential clinical prospects. In view of their functional characteristics, there is a critical need to review the specific functions of bicarbonate transporters, their related diseases, and the involved pathological mechanisms. We summarize the diseases caused by the mutations in SLC4 family genes and briefly introduce the clinical manifestations of these diseases as well as the current treatment strategies. Additionally, we illustrate their roles in terms of the physiology and pathogenesis that has been currently researched, which might be the future therapeutic and diagnostic targets of diseases and a new direction for drug research and development.

The Comparison of Serum Exosome Protein Profile in Diagnosis of NSCLC Patients

A thorough study of the exosomal proteomic cargo may enable the identification of proteins that play an important role in cancer development. The aim of this study was to compare the protein profiles of the serum exosomes derived from non-small lung cancer (NSCLC) patients and healthy volunteers (control) using the high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS) method to identify potentially new diagnostic and/or prognostic protein biomarkers. Proteins exclusively identified in NSCLC and control groups were analyzed using several bioinformatic tools and platforms (FunRich, Vesiclepedia, STRING, and TIMER2.0) to find key protein hubs involved in NSCLC progression and the acquisition of metastatic potential. This analysis revealed 150 NSCLC proteins, which are significantly involved in osmoregulation, cell-cell adhesion, cell motility, and differentiation. Among them, 3 proteins: Interleukin-34 (IL-34), HLA class II histocompatibility antigen, DM alpha chain (HLA-DMA), and HLA class II histocompatibility antigen, DO beta chain (HLA-DOB) were shown to be significantly involved in the cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs) infiltration processes. Additionally, detected proteins were analyzed according to the presence of lymph node metastasis, showing that differences in frequency of detection of protein FAM166B, killer cell immunoglobulin-like receptor 2DL1, and olfactory receptor 52R1 correlate with the N feature according to the TNM Classification of Malignant Tumors. These results prove their involvement in NSCLC lymph node spread and metastasis. However, this study requires further investigation.

Prediction of hearing preservation after acoustic neuroma surgery based on SMOTE-XGBoost

Prior to the surgical removal of an acoustic neuroma, the majority of patients anticipate that their hearing will be preserved to the greatest possible extent following surgery. This paper proposes a postoperative hearing preservation prediction model for the characteristics of class-imbalanced hospital real data based on the extreme gradient boost tree (XGBoost). In order to eliminate sample imbalance, the synthetic minority oversampling technique (SMOTE) is applied to increase the number of underclass samples in the data. Multiple machine learning models are also used for the accurate prediction of surgical hearing preservation in acoustic neuroma patients. In comparison to research results from existing literature, the experimental results found the model proposed in this paper to be superior. In summary, the method this paper proposes can make a significant contribution to the development of personalized preoperative diagnosis and treatment plans for patients, leading to effective judgment for the hearing retention of patients with acoustic neuroma following surgery, a simplified long medical treatment process and saved medical resources.

Development of a 32-gene signature using machine learning for accurate prediction of inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic inflammatory condition caused by multiple genetic and environmental factors. Numerous genes are implicated in the etiology of IBD, but the diagnosis of IBD is challenging. Here, XGBoost, a machine learning prediction model, has been used to distinguish IBD from healthy cases following elaborative feature selection. Using combined unsupervised clustering analysis and the XGBoost feature selection method, we successfully identified a 32-gene signature that can predict IBD occurrence in new cohorts with 0.8651 accuracy. The signature shows enrichment in neutrophil extracellular trap formation and cytokine signaling in the immune system. The probability threshold of the XGBoost-based classification model can be adjusted to fit personalized lifestyle and health status. Therefore, this study reveals potential IBD-related biomarkers that facilitate an effective personalized diagnosis of IBD.

Glucose and Inositol Transporters, SLC5A1 and SLC5A3, in Glioblastoma Cell Migration

(1) Background: The recurrence of glioblastoma multiforme (GBM) is mainly due to invasion of the surrounding brain tissue, where organic solutes, including glucose and inositol, are abundant. Invasive cell migration has been linked to the aberrant expression of transmembrane solute-linked carriers (SLC). Here, we explore the role of glucose (SLC5A1) and inositol transporters (SLC5A3) in GBM cell migration. (2) Methods: Using immunofluorescence microscopy, we visualized the subcellular localization of SLC5A1 and SLC5A3 in two highly motile human GBM cell lines. We also employed wound-healing assays to examine the effect of SLC inhibition on GBM cell migration and examined the chemotactic potential of inositol. (3) Results: While GBM cell migration was significantly increased by extracellular inositol and glucose, it was strongly impaired by SLC transporter inhibition. In the GBM cell monolayers, both SLCs were exclusively detected in the migrating cells at the monolayer edge. In single GBM cells, both transporters were primarily localized at the leading edge of the lamellipodium. Interestingly, in GBM cells migrating via blebbing, SLC5A1 and SLC5A3 were predominantly detected in nascent and mature blebs, respectively. (4) Conclusion: We provide several lines of evidence for the involvement of SLC5A1 and SLC5A3 in GBM cell migration, thereby complementing the migration-associated transportome. Our findings suggest that SLC inhibition is a promising approach to GBM treatment.

Albumins as Extracellular Protein Nanoparticles Collaborate with Plasma Ions to Control Biological Osmotic Pressure

Introduction

Plasma albumins as protein nanoparticles (PNs) exert essential functions in the control of biological osmotic pressure (OP), being involved in regulating water metabolism, cell morphology and cell tension. Understanding how plasma albumins and different electrolytes co-determine biological OP effects is crucial for correct interpretation of hemodynamic disorders, and practical treatment of hypo/hyper-proteinemia.

Methods

Optical measurement based on intermediate filament (IF) tension probe was used for real-time evaluation of transmembrane osmotic effects in live cells. Ion fluorescent probes were employed to evaluate intracellular ion levels, and a current clamp was used to measure membrane potential, thus exploring association of electrochemical and osmotic effects.

Results

Albumins are involved in regulation of intracellular osmolarity by a quantitative relationship. Extracellular PNs can alter membrane potentials by adsorbing counterions, induce production of intracellular PNs and further control the opening of ion channels and ion flow, contributing to electrochemical and osmotic re-equilibrium. Furthermore, various ions interplay with extracellular PNs, showing different osmotic effects: increased levels of calcium ions result in a hypotonic effect, whereas potassium ions induce hyper-osmolarity.

Conclusion

Extracellular PNs and Ca²⁺/K⁺ display antagonistic or synergetic effects in regulating biological OP. Live cells can spontaneously regulate osmotic effects through changing membrane potential and controlling intracellular ion content. Various plasma components need to be comprehensively analyzed, further developing a diagnostic index that considers the biological OP effects of various blood components and improves the evaluation of symptoms and diseases, such as calcium/potassium-hemodynamic disorders and edema.

Osmotic pressure modulates single cell cycle dynamics inducing reversible growth arrest and reactivation of human metastatic cells

Biophysical cues such as osmotic pressure modulate proliferation and growth arrest of bacteria, yeast cells and seeds. In tissues, osmotic regulation takes place through blood and lymphatic capillaries and, at a single cell level, water and osmoregulation play a critical role. However, the effect of osmotic pressure on single cell cycle dynamics remains poorly understood. Here, we investigate the effect of osmotic pressure on single cell cycle dynamics, nuclear growth, proliferation, migration and protein expression, by quantitative time-lapse imaging of single cells genetically modified with fluorescent ubiquitination-based cell cycle indicator 2 (FUCCI2). Single cell data reveals that under hyperosmotic stress, distinct cell subpopulations emerge with impaired nuclear growth, delayed or growth arrested cell cycle and reduced migration. This state is reversible for mild hyperosmotic stress, where cells return to regular cell cycle dynamics, proliferation and migration. Thus, osmotic pressure can modulate the reversible growth arrest and reactivation of human metastatic cells.

CRISPR activation screen in mice identifies novel membrane proteins enhancing pulmonary metastatic colonisation

Melanoma represents ~5% of all cutaneous malignancies, yet accounts for the majority of skin cancer deaths due to its propensity to metastasise. To develop new therapies, novel target molecules must to be identified and the accessibility of cell surface proteins makes them attractive targets. Using CRISPR activation technology, we screened a library of guide RNAs targeting membrane protein-encoding genes to identify cell surface molecules whose upregulation enhances the metastatic pulmonary colonisation capabilities of tumour cells in vivo. We show that upregulated expression of the cell surface protein LRRN4CL led to increased pulmonary metastases in mice. Critically, LRRN4CL expression was elevated in melanoma patient samples, with high expression levels correlating with decreased survival. Collectively, our findings uncover an unappreciated role for LRRN4CL in the outcome of melanoma patients and identifies a potential therapeutic target and biomarker.

Roles of hsa-miR-12462 and SLC9A1 in acute myeloid leukemia

MicroRNAs (miRNAs) play important roles in cell proliferation, differentiation, and survival and may be useful for acute myeloid leukemia (AML) diagnosis and prognosis. In this study, we defined a novel miRNA, hsa-miR-12462, through small RNA sequencing of the bone marrow (BM) cells from 128 AML patients. Overexpression of hsa-miR-12462 in AML cells (U937 and HL-60) significantly decreased their growth rate when compared with those of the wild-type and MOCK controls. In a xenograft mouse model, tumor weight and size in the mice bearing the U937 cells with hsa-miR-12462 overexpression were significantly reduced when compared with those bearing the mock cells. The AML cells overexpressing hsa-miR-12462 had increased sensitivity to cytarabine chemotherapy. Combining the data from the MiRDB, an online microRNA database (http://mirdb.org), with the RNA-sequencing results, SLC9A1 was predicted to be one of the targets of hsa-miR-12462. hsa-miR-12462 was further confirmed to bind exclusively to the 3′UTR of SLC9A1 in U937 cells, leading to downregulation of SLC9A1. In summary, a higher level of hsa-miR-12462 in AML cells is associated with increased sensitivity to cytarabine chemotherapy via downregulation of SLC9A1.

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.

A framework for exhaustive modelling of genetic interaction patterns using Petri nets

MOTIVATION

Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs.

RESULTS

In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets.

AVAILABILITY AND IMPLEMENTATION

The framework is available at http://www.ibi.vu.nl/programs/ExhMod.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Constructing and Analyzing Computational Models of Cell Signaling with BioModelAnalyzer

BioModelAnalyzer (BMA) is an open-source graphical tool for the development of executable models of protein and gene networks within cells. Based upon the Qualitative Networks formalism, the user can rapidly construct large networks, either manually or by connecting motifs selected from a built-in library. After the appropriate functions for each variable are defined, the user has access to three analysis engines to test the model. In addition to standard simulation tools, BMA includes an interface to the stability-testing algorithm and to a graphical Linear Temporal Logic (LTL) editor and analysis tool. Alongside this, we have developed a novel ChatBot to aid users constructing LTL queries and to explain the interface and run through tutorials. Here we present worked examples of model construction and testing via the interface. As an initial example, we discuss fate decisions in Dictyostelium discoidum and cAMP signaling. We go on to describe the workflow leading to the construction of a published model of the germline of C. elegans. Finally, we demonstrate how to construct simple models from the built-in network motif library. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Modeling the signaling network of Dictyostelium discoidum Basic Protocol 2: Modeling the germline progression of Caenorhabditis elegans Basic Protocol 3: Constructing a model of the cell cycle using motifs.

MicroRNA-Mediated Metabolic Reprograming in Renal Cancer

Metabolic reprogramming is one of the hallmarks of renal cell cancer (RCC). We hypothesized that altered metabolism of RCC cells results from dysregulation of microRNAs targeting metabolically relevant genes. Combined large-scale transcriptomic and metabolic analysis of RCC patients tissue samples revealed a group of microRNAs that contribute to metabolic reprogramming in RCC. miRNAs expressions correlated with their predicted target genes and with gas chromatography-mass spectrometry (GC-MS) metabolome profiles of RCC tumors. Assays performed in RCC-derived cell lines showed that miR-146a-5p and miR-155-5p targeted genes of PPP (the pentose phosphate pathway) (G6PD and TKT), the TCA (tricarboxylic acid cycle) cycle (SUCLG2), and arginine metabolism (GATM), respectively. miR-106b-5p and miR-122-5p regulated the NFAT5 osmoregulatory transcription factor. Altered expressions of G6PD, TKT, SUCLG2, GATM, miR-106b-5p, miR-155-5p, and miR-342-3p correlated with poor survival of RCC patients. miR-106b-5p, miR-146a-5p, and miR-342-3p stimulated proliferation of RCC cells. The analysis involving >6000 patients revealed that miR-34a-5p, miR-106b-5p, miR-146a-5p, and miR-155-5p are PanCancer metabomiRs possibly involved in global regulation of cancer metabolism. In conclusion, we found that microRNAs upregulated in renal cancer contribute to disturbed expression of key genes involved in the regulation of RCC metabolome. miR-146a-5p and miR-155-5p emerge as a key “metabomiRs” that target genes of crucial metabolic pathways (PPP (the pentose phosphate pathway), TCA cycle, and arginine metabolism).