LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.

CircPKM2 aggravates the progression of non-small cell lung cancer by regulating MTDH via miR-1298-5p.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer with high morbidity and mortality. The role of dysregulated circular RNAs (circRNAs) in human diseases are receiving more and more attention. In this study, we focused on the role and mechanism of circPKM2 in the progression of NSCLC. METHODS: The expression levels of circPKM2, microRNA-1298-5p (miR-1298-5p) and metadherin (MTDH) in NSCLC were measured by real-time quantitative PCR (qRT-PCR) or Western blot. Cell counting kit-8 (CCK-8), colony formation, 5-ethynyl-2'-deoxyuridine (EdU) staining, flow cytometry, transwell and tube formation assays were conducted to evaluate the effects of circPKM2 on malignant phenotypes of NSCLC. Western blot was used to measure related marker protein levels. RESULTS: CircPKM2 and MTDH were highly expressed in NSCLC tissues and cells, while miR-1298-5p was downregulated. CircPKM2 knockdown effectively suppressed cell proliferation, migration, invasion and tube formation whereas induced apoptosis in vitro. CircPKM2 had a potential targeting site with miR-1298-5p and negatively regulated the expression of miR-1298-5p. MiR-1298-5p inhibitor reversed the effect of circPKM2 knockdown on the progression of NSCLC. CircPKM2 induced MTDH expression via sponging miR-1298-5p to promote the progression of NSCLC. MiR-1298-5p directly targeted MTDH, and the addition of MTDH partially attenuated the inhibition of miR-1298-5p on the progression of NSCLC. In addition, the downregulation of circPKM2 significantly slowed down the growth of xenograft tumors in vivo. CONCLUSION: Our findings demonstrated that circPKM2 mediated NSCLC progression via regulating miR-1298-5p/MTDH axis, providing a novel therapeutic target for NSCLC.

CXCL8 induces M2 macrophage polarization and inhibits CD8+ T cell infiltration to generate an immunosuppressive microenvironment in colorectal cancer.

The poor prognosis of immunotherapy in patients with colorectal cancer (CRC) necessitates a comprehensive understanding of the immunosuppressive mechanisms within tumor microenvironment (TME). Undoubtedly, the anti-tumor immune cells play an indispensable role in immune tolerance. Therefore, it is imperative to investigate novel immune-related factors that have the capacity to enhance anti-tumor immunity. Here, we employed bioinformatic analysis using R and Cytoscape to identify the hub gene chemokine (C-X-C motif) ligand 8 (CXCL8), which is overexpressed in CRC, in the malignant progression of CRC. However, its specific role of CXCL8 in CRC immunity remains to be elucidated. For this purpose, we evaluated how tumor-derived CXCL8 promotes M2 macrophage infiltration by in vivo and in vitro, which can be triggered by IL-1ß within TME. Mechanistically, CXCL8-induced polarization of M2 macrophages depends on the activation of the STAT3 signaling. Finally, immunohistochemistry and multiplexed immunohistochemistry analysis identified that CXCL8 not only enhances PD-L1+ M2 macrophage infiltration but also attenuates the recruitment of PD-1+ CD8+ T cells in murine CRC models. Together, these findings emphasize the critical role for CXCL8 in promoting M2 macrophage polarization and inhibiting CD8+ T cell infiltration, thereby links CXCL8 to the emergency of immunosuppressive microenvironment facilitating tumor evasion. Overall, these findings may provide novel strategy for CRC immunotherapy.

[Calnexin (CNX) enhances the killing effect of CD8+ T cells on colorectal cancer cells by promoting MHC I expression].

Objective To investigate the killing effect and molecular mechanism of aberrant expression of calnexin (CNX) in the colorectal cancer (CRC) on the CD8+ T immune cells. Methods Immunohistochemistry was used to detect CNX protein level in 102 pairs of CRC cancer and adjacent non-cancerous tissues. Western blotting was employed to examine the protein expression of MHC I in the HCT-15 cells overexpressed with CNX or in the SW480 cells whose CNX expressions were knockdown by siRNA. Murine CD8+ T cells isolated from the spleen were cocultured with CT-26 murine CRC cells infected with lentivirus-mediated CNX overexpression. The killing effect of CD8+ T cells on CT-26 cells was determined by cytotoxicity kit. The secretion of interferon γ (IFN-γ) and tumor necrosis factor α (TNF-α) in the culture medium were examined by ELISA. Results The protein level of CNX in colorectal cancer tissues were significantly lower than that in non-cancerous tissues. CNX overexpressed in HCT-15 cells was upregulated and CNX knockdown in SW480 cells downregulated the MHC I expression in these cells. Furthermore, the overexpression of CNX could not only enhance the killing effect of CD8+ T cells on CT-26 cells, but also promote the secretion of IFN-γ and TNF-α from these cells. Conclusion CNX can enhance the killing potential of CD8+ T cells on tumor cells through upregulating the MHC I expression in colorectal cancer cells.

Infinite-Horizon Optimal Control of Switched Boolean Control Networks With Average Cost: An Efficient Graph-Theoretical Approach.

This study investigates the infinite-horizon optimal control (IHOC) problem for switched Boolean control networks with an average cost criterion. A primary challenge of this problem is the prohibitively high computational cost when dealing with large-scale networks. We attempt to develop a more efficient approach from a novel graph-theoretical perspective. First, a weighted directed graph structure called the optimal state transition graph (OSTG) is established, whose edges encode the optimal action for each admissible state transition between states reachable from a given initial state subject to various constraints. Then, we reduce the IHOC problem into a minimum-mean cycle (MMC) problem in the OSTG. Finally, we develop an algorithm that can quickly find a particular MMC by resorting to Karp's algorithm in the graph theory and construct an optimal switching control law based on state feedback. The time complexity analysis shows that our algorithm, albeit still running in exponential time, can outperform all the existing methods in terms of time efficiency. A 16-state-3-input signaling network in leukemia is used as a benchmark to test its effectiveness. Results show that the proposed graph-theoretical approach is much more computationally efficient and can reduce the running time dramatically: it runs hundreds or even thousands of times faster than the existing methods. The Python implementation of the algorithm is available at https://github.com/ShuhuaGao/sbcn_mmc.

Learning Asynchronous Boolean Networks From Single-Cell Data Using Multiobjective Cooperative Genetic Programming.

Recent advances in high-throughput single-cell technologies provide new opportunities for computational modeling of gene regulatory networks (GRNs) with an unprecedented amount of gene expression data. Current studies on the Boolean network (BN) modeling of GRNs mostly depend on bulk time-series data and focus on the synchronous update scheme due to its computational simplicity and tractability. However, such synchrony is a strong and rarely biologically realistic assumption. In this study, we adopt the asynchronous update scheme instead and propose a novel framework called SgpNet to infer asynchronous BNs from single-cell data by formulating it into a multiobjective optimization problem. SgpNet aims to find BNs that can match the asynchronous state transition graph (STG) extracted from single-cell data and retain the sparsity of GRNs. To search the huge solution space efficiently, we encode each Boolean function as a tree in genetic programming and evolve all functions of a network simultaneously via cooperative coevolution. Besides, we develop a regulator preselection strategy in view of GRN sparsity to further enhance learning efficiency. An error threshold estimation heuristic is also proposed to ease tedious parameter tuning. SgpNet is compared with the state-of-the-art method on both synthetic data and experimental single-cell data. Results show that SgpNet achieves comparable inference accuracy, while it has far fewer parameters and eliminates artificial restrictions on the Boolean function structures. Furthermore, SgpNet can potentially scale to large networks via straightforward parallelization on multiple cores.

Finite-Horizon Optimal Control of Boolean Control Networks: A Unified Graph-Theoretical Approach.

This article investigates the finite-horizon optimal control (FHOC) problem of Boolean control networks (BCNs) from a graph theory perspective. We first formulate two general problems to unify various special cases studied in the literature: 1) the horizon length is a priori fixed and 2) the horizon length is unspecified but finite for given destination states. Notably, both problems can incorporate time-variant costs, which are rarely considered in existing work, and a variety of constraints. The existence of an optimal control sequence is analyzed under mild assumptions. Motivated by BCNs' finite state space and control space, we approach the two general problems intuitively and efficiently under a graph-theoretical framework. A weighted state transition graph and its time-expanded variants are developed, and the equivalence between the FHOC problem and the shortest-path (SP) problem in specific graphs is established rigorously. Two algorithms are developed to find the SP and construct the optimal control sequence for the two problems with reduced computational complexity, though technically, a classical SP algorithm in graph theory is sufficient for all problems. Compared with existing algebraic methods, our graph-theoretical approach can achieve state-of-the-art time efficiency while targeting the most general problems. Furthermore, our approach is the first one capable of solving Problem 2) with time-variant costs. Finally, a genetic network in the bacterium E. coli and a signaling network involved in human leukemia are used to validate the effectiveness of our approach. The results of two common tasks for both networks show that our approach can dramatically reduce the running time. Python implementation of our algorithms is available at GitHub https://github.com/ShuhuaGao/FHOC.

miR-148a-3p silences the CANX/MHC-I pathway and impairs CD8+ T cell-mediated immune attack in colorectal cancer.

Nonresponse, or acquired resistance to immune checkpoint inhibitors in colorectal cancer (CRC) highlight the importance of finding potential tolerance mechanisms. Low expression of major histocompatibility complex, class I (MHC-I) on the cell surface of the tumor is one of the main mechanisms of tumor escape from T-cell recognition and destruction. In this study, we demonstrated that a high level of calnexin (CANX) in the tumors is positively correlated with the overall survival in colorectal cancer patients. CANX is a chaperone protein involved in the folding and assembly of MHC-I molecules. Using miRNA target prediction databases and luciferase assays, we identified miR-148a-3p as a potential regulator of CANX. Inhibition of miR-148a-3p restores surface levels of MHC-I and significantly enhanced the effects of CD8+ T-cell-mediated immune attack in vitro and in vivo by promoting CANX expression. These results reveal that miR-148a-3p can function as a tumor promotor in CRC by targeting the CANX/MHC-I axis, which provides a rationale for immunotherapy through targeting the miR-148a-3p/CANX/MHC-I pathway in patients with CRC.

Mathematical Modeling Reveals the Role of Hypoxia in the Promotion of Human Mesenchymal Stem Cell Long-Term Expansion.

Many experimental studies have found that human mesenchymal stem cells (MSCs) in long-term culture exhibited enhanced cell proliferation and prolonged lifespan under hypoxia (around 1%-7% oxygen) against the normoxic condition (about 21% oxygen). Inspired by the experimental findings, we aimed to investigate the hypoxic effects on MSC expansion quantitatively through mathematical modeling to elucidate the corresponding biological mechanism. A two-compartment model based on ordinary differential equations (ODEs), which incorporate cellular division and senescence via state transition, was developed to describe the MSC expansion process. Parameters of this model were fitted to experimental data and used to interpret the different proliferative capacities of MSCs under hypoxia and normoxia along with model sensitivity analysis. The proposed model was tested on data from two separate experimental studies, and it could reproduce the observed growth characteristics in both conditions. Overall, this compartmental model with a logistic state transition rate was sufficient to explain the experimental findings and highlighted the promotive role of hypoxia in MSC proliferation. This in silico study suggests that hypoxia can enhance MSC long-term expansion mainly by delaying replicative senescence, which is indicated by the slowdown of the state transition rate in our model. Therefore, this explanatory model may provide theoretical proof for the experimentally observed MSC growth superiority under hypoxia and has the potential to further optimize MSC culture protocols for regenerative medicine applications.