Recursive Self-Composite Approach Towards Structural Understanding of Boolean Networks.

Boolean networks have been widely used in systems biology to study the dynamical characteristics of biological networks such as steady-states or cycles, yet there has been little attention to the dynamic properties of network structures. Here, we systematically reveal the core network structures using a recursive self-composite of the logic update rules. We find that all Boolean update rules exhibit repeated cyclic logic structures, where each converged logic leads to the same states, defined as kernel states. Consequently, the period of state cycles is upper bounded by the number of logics in the converged logic cycle. In order to uncover the underlying dynamical characteristics by exploiting the repeating structures, we propose leaping and filling algorithms. The algorithms provide a way to avoid large string explosions during the self-composition procedures. Finally, we present three examples-a simple network with a long feedback structure, a T-cell receptor network and a cancer network-to demonstrate the usefulness of the proposed algorithm.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Decoding the principle of cell-fate determination for its reverse control.

Understanding and manipulating cell fate determination is pivotal in biology. Cell fate is determined by intricate and nonlinear interactions among molecules, making mathematical model-based quantitative analysis indispensable for its elucidation. Nevertheless, obtaining the essential dynamic experimental data for model development has been a significant obstacle. However, recent advancements in large-scale omics data technology are providing the necessary foundation for developing such models. Based on accumulated experimental evidence, we can postulate that cell fate is governed by a limited number of core regulatory circuits. Following this concept, we present a conceptual control framework that leverages single-cell RNA-seq data for dynamic molecular regulatory network modeling, aiming to identify and manipulate core regulatory circuits and their master regulators to drive desired cellular state transitions. We illustrate the proposed framework by applying it to the reversion of lung cancer cell states, although it is more broadly applicable to understanding and controlling a wide range of cell-fate determination processes.

Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance.

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.

The fitness trade-off between growth and stress resistance determines the phenotypic landscape.

BACKGROUND: A central challenge in biology is to discover a principle that determines individual phenotypic differences within a species. The growth rate is particularly important for a unicellular organism, and the growth rate under a certain condition is negatively associated with that of another condition, termed fitness trade-off. Therefore, there should exist a common molecular mechanism that regulates multiple growth rates under various conditions, but most studies so far have focused on discovering those genes associated with growth rates under a specific condition. RESULTS: In this study, we found that there exists a recurrent gene expression signature whose expression levels are related to the fitness trade-off between growth preference and stress resistance across various yeast strains and multiple conditions. We further found that the genomic variation of stress-response, ribosomal, and cell cycle regulators are potential causal genes that determine the sensitivity between growth and survival. Intriguingly, we further observed that the same principle holds for human cells using anticancer drug sensitivities across multiple cancer cell lines. CONCLUSIONS: Together, we suggest that the fitness trade-off is an evolutionary trait that determines individual growth phenotype within a species. By using this trait, we can possibly overcome anticancer drug resistance in cancer cells.

Deep learning untangles the resistance mechanism of p53 reactivator in lung cancer cells.

Tumor suppressor p53 plays a pivotal role in suppressing cancer, so various drugs has been suggested to upregulate its function. However, drug resistance is still the biggest hurdle to be overcome. To address this, we developed a deep learning model called AnoDAN (anomalous gene detection using generative adversarial networks and graph neural networks for overcoming drug resistance) that unravels the hidden resistance mechanisms and identifies a combinatorial target to overcome the resistance. Our findings reveal that the TGF-ß signaling pathway, alongside the p53 signaling pathway, mediates the resistance, with THBS1 serving as a core regulatory target in both pathways. Experimental validation in lung cancer cells confirms the effects of THBS1 on responsiveness to a p53 reactivator. We further discovered the positive feedback loop between THBS1 and the TGF-ß pathway as the main source of resistance. This study enhances our understanding of p53 regulation and offers insights into overcoming drug resistance.

Impact of Climate Change and Heat Stress on Milk Production in Korean Holstein Cows: A Large-Scale Data Analysis.

This study investigated the effects of heat stress on milk production in Korean Holstein cows using large-scale data. Heat stress was assessed using the temperature-humidity index (THI). Weather records (2016 to 2020) were collected from 70 regional weather stations using an installed automated surface observing system (ASOS). A dataset of 2,094,436 milk production records from 215,276 Holstein cows obtained from the Dairy Cattle Genetic Improvement Center was analyzed. Stepwise selection was used to select the input variables, including the daily maximum THI (THI_max). Least-squares means were calculated for milk yield, fat and protein corrected milk (FPCM), fat and protein yield, fat-to-protein ratio, solids not fat, and lactation persistency. Segmented linear regression analysis determined the break points (BPs) of the THI_max. Over the five years, heat stress exposure increased, particularly from May to September. This study identified BPs around THI_max of 80-82 for milk yield and FPCM. Similar patterns for other milk traits were observed, which significantly decreased beyond their respective BPs. These findings indicate that THI variations adversely affect milk yield and composition in dairy cows, highlighting the importance of appropriate feeding management strategies to ensure the optimal productivity of Holstein cows under varying climatic conditions.

Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy.

Accumulated genetic alterations in cancer cells distort cellular stimulus-response (or input-output) relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within a cell implicates a possibility of restoring such distorted input-output relationships by rewiring the signal flow through controlling hidden molecular switches. Here, a system framework of analyzing cellular input-output relationships in consideration of various genetic alterations and identifying possible molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis is presented. Such reversion is demonstrated by the analysis of a number of cancer molecular networks together with a focused case study on bladder cancer with in vitro experiments and patient survival data analysis. The origin of reversibility from an evolutionary point of view based on the redundancy and robustness intrinsically embedded in complex molecular regulatory networks is further discussed.

Critical transition and reversion of tumorigenesis.

Cancer is caused by the accumulation of genetic alterations and therefore has been historically considered to be irreversible. Intriguingly, several studies have reported that cancer cells can be reversed to be normal cells under certain circumstances. Despite these experimental observations, conceptual and theoretical frameworks that explain these phenomena and enable their exploration in a systematic way are lacking. In this review, we provide an overview of cancer reversion studies and describe recent advancements in systems biological approaches based on attractor landscape analysis. We suggest that the critical transition in tumorigenesis is an important clue for achieving cancer reversion. During tumorigenesis, a critical transition may occur at a tipping point, where cells undergo abrupt changes and reach a new equilibrium state that is determined by complex intracellular regulatory events. We introduce a conceptual framework based on attractor landscapes through which we can investigate the critical transition in tumorigenesis and induce its reversion by combining intracellular molecular perturbation and extracellular signaling controls. Finally, we present a cancer reversion therapy approach that may be a paradigm-changing alternative to current cancer cell-killing therapies.

Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity represented by point attractors.

The underlying genetic networks of cells give rise to diverse behaviors known as phenotypes. Control of this cellular phenotypic diversity (CPD) may reveal key targets that govern differentiation during development or drug resistance in cancer. This work establishes an approach to control CPD that encompasses practical constraints, including model limitations, the number of simultaneous control targets, which targets are viable for control, and the granularity of control. Cellular networks are often limited to the structure of interactions, due to the practical difficulty of modeling interaction dynamics. However, these dynamics are essential to CPD. In response, our statistical control approach infers the CPD directly from the structure of a network, by considering an ensemble average function over all possible Boolean dynamics for each node in the network. These ensemble average functions are combined with an acyclic form of the network to infer the number of point attractors. Our approach is applied to several known biological models and shown to outperform existing approaches. Statistical control of CPD offers a new avenue to contend with systemic processes such as differentiation and cancer, despite practical limitations in the field.

The infusion of ex vivo, interleukin-15 and -21-activated donor NK cells after haploidentical HCT in high-risk AML and MDS patients-a randomized trial.

Clinical effect of donor-derived natural killer cell infusion (DNKI) after HLA-haploidentical hematopoietic cell transplantation (HCT) was evaluated in high-risk myeloid malignancy in phase 2, randomized trial. Seventy-six evaluable patients (aged 21-70 years) were randomized to receive DNKI (N = 40) or not (N = 36) after haploidentical HCT. For the HCT conditioning, busulfan, fludarabine, and anti-thymocyte globulin were administered. DNKI was given twice 13 and 20 days after HCT. Four patients in the DNKI group failed to receive DNKI. In the remaining 36 patients, median DNKI doses were 1.0 × 108/kg and 1.4 × 108/kg on days 13 and 20, respectively. Intention-to-treat analysis showed a lower disease progression for the DNKI group (30-month cumulative incidence, 35% vs 61%, P = 0.040; subdistribution hazard ratio, 0.50). Furthermore, at 3 months after HCT, the DNKI patients showed a 1.8- and 2.6-fold higher median absolute blood count of NK and T cells, respectively. scRNA-sequencing analysis in seven study patients showed that there was a marked increase in memory-like NK cells in DNKI patients which, in turn, expanded the CD8+ effector-memory T cells. In high-risk myeloid malignancy, DNKI after haploidentical HCT reduced disease progression. This enhanced graft-vs-leukemia effect may be related to the DNKI-induced, post-HCT expansion of NK and T cells. Clinical trial number: NCT02477787.

A Cell-Fate Reprogramming Strategy Reverses Epithelial-to-Mesenchymal Transition of Lung Cancer Cells While Avoiding Hybrid States.

The epithelial-to-mesenchymal transition (EMT) of primary cancer contributes to the acquisition of lethal properties, including metastasis and drug resistance. Blocking or reversing EMT could be an effective strategy to improve cancer treatment. However, it is still unclear how to achieve complete EMT reversal (rEMT), as cancer cells often transition to hybrid EMT states with high metastatic potential. To tackle this problem, we employed a systems biology approach and identified a core-regulatory circuit that plays the primary role in driving rEMT without hybrid properties. Perturbation of any single node was not sufficient to completely revert EMT. Inhibition of both SMAD4 and ERK signaling along with p53 activation could induce rEMT in cancer cells even with TGFß stimulation, a primary inducer of EMT. Induction of rEMT in lung cancer cells with the triple combination approach restored chemosensitivity. This cell-fate reprogramming strategy based on attractor landscapes revealed potential therapeutic targets that can eradicate metastatic potential by subverting EMT while avoiding hybrid states. SIGNIFICANCE: Network modeling unravels the highly complex and plastic process regulating epithelial and mesenchymal states in cancer cells and discovers therapeutic interventions for reversing epithelial-to-mesenchymal transition and enhancing chemosensitivity.

Inhibition of Tolaasin Cytotoxicity Causing Brown Blotch Disease in Cultivated Mushrooms Using Tolaasin Inhibitory Factors.

Tolaasin, a pore-forming bacterial peptide toxin secreted by Pseudomonas tolaasii, causes brown blotch disease in cultivated mushrooms by forming membrane pores and collapsing the membrane structures. Tolaasin is a lipodepsipeptide, MW 1985, and pore formation by tolaasin molecules is accomplished by hydrophobic interactions and multimerizations. Compounds that inhibit tolaasin toxicity have been isolated from various food additives. Food detergents, sucrose esters of fatty acids, and polyglycerol esters of fatty acids can effectively inhibit tolaasin cytotoxicity. These chemicals, named tolaasin-inhibitory factors (TIF), were effective at concentrations ranging from 10-4 to 10-5 M. The most effective compound, TIF 16, inhibited tolaasin-induced hemolysis independent of temperature and pH, while tolaasin toxicity increased at higher temperatures. When TIF 16 was added to tolaasin-pretreated erythrocytes, the cytotoxic activity of tolaasin immediately stopped, and no further hemolysis was observed. In the artificial lipid bilayer, the single-channel activity of the tolaasin channel was completely and irreversibly blocked by TIF 16. When TIF 16 was sprayed onto pathogen-treated oyster mushrooms growing on the shelves of cultivation houses, the development of disease was completely suppressed, and normal growth of oyster mushrooms was observed. Furthermore, the treatment with TIF 16 did not show any adverse effect on the growth of oyster mushrooms. These results indicate that TIF 16 is a good candidate for the biochemical control of brown blotch disease.

Global stabilizing control of large-scale biomolecular regulatory networks.

MOTIVATION: Cellular behavior is determined by complex non-linear interactions between numerous intracellular molecules that are often represented by Boolean network models. To achieve a desired cellular behavior with minimal intervention, we need to identify optimal control targets that can drive heterogeneous cellular states to the desired phenotypic cellular state with minimal node intervention. Previous attempts to realize such global stabilization were based solely on either network structure information or simple linear dynamics. Other attempts based on non-linear dynamics are not scalable. RESULTS: Here, we investigate the underlying relationship between structurally identified control targets and optimal global stabilizing control targets based on non-linear dynamics. We discovered that optimal global stabilizing control targets can be identified by analyzing the dynamics between structurally identified control targets. Utilizing these findings, we developed a scalable global stabilizing control framework using both structural and dynamic information. Our framework narrows down the search space based on strongly connected components and feedback vertex sets then identifies global stabilizing control targets based on the canalization of Boolean network dynamics. We find that the proposed global stabilizing control is superior with respect to the number of control target nodes, scalability, and computational complexity. AVAILABILITY AND IMPLEMENTATION: We provide a GitHub repository that contains the DCGS framework written in Python as well as biological random Boolean network datasets (https://github.com/sugyun/DCGS). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hidden patterns of gene expression provide prognostic insight for colorectal cancer.

Cancer tissue samples contain cancer cells and non-cancer cells with each biopsied site containing distinct proportions of these populations. Consequently, assigning useful tumor subtypes based on gene expression measurements from clinical samples is challenging. We applied a blind source separation approach to extract cancer cell-intrinsic gene expression patterns within clinical tumor samples of colorectal cancer. After a blind source separation, we found that a cancer cell-intrinsic gene expression program unique to each patient exists in the "residual" expression profile remaining after separation of the gene expression data. We performed a consensus clustering analysis of the extracted gene expression profiles to identify novel and robust cancer cell-intrinsic subtypes. We validated the identified subtypes using an independent clinical gene expression dataset. The cancer cell-intrinsic subtypes are independent of biopsy site and provided prognostic information in addition to currently available clinical and molecular variables. After validating this approach in colorectal cancer, we further identified novel tumor subtypes with unique clinical information across multiple types of cancer. These cancer cell-intrinsic molecular subtypes provide novel prognostic value for clinical assessment of cancer.

Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics.

The response variation to anti-cancer drugs originates from complex intracellular network dynamics of cancer. Such dynamic networks present challenges to determining optimal drug targets and stratifying cancer patients for precision medicine, although several cancer genome studies provided insights into the molecular characteristics of cancer. Here, we introduce a network dynamics-based approach based on attractor landscape analysis to evaluate the therapeutic window of a drug from cancer signaling networks combined with genomic profiles. This approach allows for effective screening of drug targets to explore potential target combinations for enhancing the therapeutic window of drug responses. We also effectively stratify patients into desired/undesired response groups using critical genomic determinants, which are network-specific origins of variability to drug response, and their dominance relationship. Our methods provide a viable and quantitative framework to connect genotype information to the phenotypes of drug response with regard to network dynamics determining the therapeutic window.

Network Dynamics Caused by Genomic Alteration Determine the Therapeutic Response to FGFR Inhibitors for Lung Cancer.

Recently, FGFR inhibitors have been highlighted as promising targeted drugs due to the high prevalence of FGFR1 amplification in cancer patients. Although various potential biomarkers for FGFR inhibitors have been suggested, their functional effects have been shown to be limited due to the complexity of the cancer signaling network and the heterogenous genomic conditions of patients. To overcome such limitations, we have reconstructed a lung cancer network model by integrating a cell line genomic database and analyzing the model in order to understand the underlying mechanism of heterogeneous drug responses. Here, we identify novel genomic context-specific candidates that can increase the efficacy of FGFR inhibitors. Furthermore, we suggest optimal targets that can induce more effective therapeutic responses than that of FGFR inhibitors in each of the FGFR-resistant lung cancer cells through computational simulations at a system level. Our findings provide new insights into the regulatory mechanism of differential responses to FGFR inhibitors for optimal therapeutic strategies in lung cancer.

Robust Stabilizing Control of Perturbed Biological Networks via Coordinate Transformation and Algebraic Analysis.

This article investigates robust stabilizing control of biological systems modeled by Boolean networks (BNs). A population of BNs is considered where a majority of BNs have the same BN dynamics, but some BNs are inflicted by mutations damaging particular nodes, leading to perturbed dynamics that prohibit global stabilization to the desired attractor. The proposed control strategy consists of two steps. First, the nominal BN is transformed and curtailed into a sub-BN via a simple coordinate transformation and network reduction associated with the desired attractor. The feedback vertex set (FVS) control is then applied to the reduced BN to determine the control inputs for the nominal BN. Next, the control inputs derived in the first step and mutated nodes are applied to the nominal BN so as to identify residual dynamics of perturbed BNs, and additional control inputs are selected according to the canalization effect of each node. The overall control inputs are applied to the BN population, so that the nominal BN converges to the desired attractor and perturbed BNs to their own attractors that are the closest possible to the desired attractor. The performance of the proposed robust control scheme is validated through numerical experiments on random BNs and a complex biological network.

Multi-Omics-Based Autophagy-Related Untypical Subtypes in Patients with Cerebral Amyloid Pathology.

Recent multi-omics analyses paved the way for a comprehensive understanding of pathological processes. However, only few studies have explored Alzheimer's disease (AD) despite the possibility of biological subtypes within these patients. For this study, unsupervised classification of four datasets (genetics, miRNA transcriptomics, proteomics, and blood-based biomarkers) using Multi-Omics Factor Analysis+ (MOFA+), along with systems-biological approaches following various downstream analyses are performed. New subgroups within 170 patients with cerebral amyloid pathology (Aß+) are revealed and the features of them are identified based on the top-rated targets constructing multi-omics factors of both whole (M-TPAD) and immune-focused models (M-IPAD). The authors explored the characteristics of subtypes and possible key-drivers for AD pathogenesis. Further in-depth studies showed that these subtypes are associated with longitudinal brain changes and autophagy pathways are main contributors. The significance of autophagy or clustering tendency is validated in peripheral blood mononuclear cells (PBMCs; n = 120 including 30 Aß- and 90 Aß+), induced pluripotent stem cell-derived human brain organoids/microglia (n = 12 including 5 Aß-, 5 Aß+, and CRISPR-Cas9 apolipoprotein isogenic lines), and human brain transcriptome (n = 78). Collectively, this study provides a strategy for precision medicine therapy and drug development for AD using integrative multi-omics analysis and network modelling.

PRRX1 is a master transcription factor of stromal fibroblasts for myofibroblastic lineage progression.

Although stromal fibroblasts play a critical role in cancer progression, their identities remain unclear as they exhibit high heterogeneity and plasticity. Here, a master transcription factor (mTF) constructing core-regulatory circuitry, PRRX1, which determines the fibroblast lineage with a myofibroblastic phenotype, is identified for the fibroblast subgroup. PRRX1 orchestrates the functional drift of fibroblasts into myofibroblastic phenotype via TGF-ß signaling by remodeling a super-enhancer landscape. Such reprogrammed fibroblasts have myofibroblastic functions resulting in markedly enhanced tumorigenicity and aggressiveness of cancer. PRRX1 expression in cancer-associated fibroblast (CAF) has an unfavorable prognosis in multiple cancer types. Fibroblast-specific PRRX1 depletion induces long-term and sustained complete remission of chemotherapy-resistant cancer in genetically engineered mice models. This study reveals CAF subpopulations based on super-enhancer profiles including PRRX1. Therefore, mTFs, including PRRX1, provide another opportunity for establishing a hierarchical classification system of fibroblasts and cancer treatment by targeting fibroblasts.