238 research outputs found

    RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

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    We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes

    FOXP3 Inhibitory Peptide P60 Increases Efficacy of Cytokine-induced Killer Cells against Renal and Pancreatic Cancer Cells

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    Background/Aim: Cytokine-induced killer (CIK) cells are ex vivo expanded major histocompatibility complex (MHC)-unrestricted cytotoxic cells with promising effects against a variety of cancer types. Regulatory T-cells (T-reg) have been shown to reduce the effectiveness of CIK cells against tumor cells. Peptide P60 has been shown to inhibit the immunosuppressive functions of T-regs. This study aimed at examining the effect of p60 on CIK cells efficacy against renal and pancreatic cancer cells. Materials and Methods: The effect of P60 on CIK cytotoxicity was examined using flow cytometry, WST-8-based cell viability assay and interferon γ (IFNγ) ELISA. Results: P60 treatment resulted in a significant decrease in the viability of renal and pancreatic cancer cell lines co-cultured with CIK cells. No increase in IFNγ secretion from CIK cells was detected following treatment with P60. P60 caused no changes in the distribution of major effector cell populations in CIK cell cultures. Conclusion: P60 may potentiate CIK cell cytotoxicity against tumor cells

    Fluorescent Probes for Ecto-5′-nucleotidase (CD73)

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    Ecto-5′-nucleotidase (CD73) catalyzes the hydrolysis of AMP to anti-inflammatory, immunosuppressive adenosine. It is expressed on vascular endothelial, epithelial, and also numerous cancer cells where it strongly contributes to an immunosuppressive microenvironment. In the present study we designed and synthesized fluorescent-labeled CD73 inhibitors with low nanomolar affinity and high selectivity based on N6-benzyl-α,β-methylene-ADP (PSB-12379) as a lead structure. Fluorescein was attached to the benzyl residue via different linkers resulting in PSB-19416 (14b, Ki12.6 nM) and PSB-18332 (14a, Ki2.98 nM) as fluorescent high-affinity probes for CD73. These compounds are anticipated to become useful tools for biological studies, drug screening, and diagnostic applications

    Contrasting Computational Models of Mate Preference Integration Across 45 Countries

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    Humans express a wide array of ideal mate preferences. Around the world, people desire romantic partners who are intelligent, healthy, kind, physically attractive, wealthy, and more. In order for these ideal preferences to guide the choice of actual romantic partners, human mating psychology must possess a means to integrate information across these many preference dimensions into summaries of the overall mate value of their potential mates. Here we explore the computational design of this mate preference integration process using a large sample of n = 14,487 people from 45 countries around the world. We combine this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets. Across cultures, people higher in mate value appear to experience greater power of choice on the mating market in that they set higher ideal standards, better fulfill their preferences in choice, and pair with higher mate value partners. Furthermore, we find that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.The work of Truong Thi Khanh Ha was supported by grants 501.01–2016.02 from the Vietnam National Foundation for Science and Technology Development (NAFOSTED). Anna Oleszkiewicz was supported by the Ministry of Science and Higher Education (#626/STYP/12/2017). This study was conducted in line with project NIR No. 01201370995 “Cross-cultural and interdisciplinary researches. Biosocial and cross-cultural analysis of models of tolerance and basic values of culture in modern society” (Marina Butovskaya and Daria Dronova). Agnieszka Sorokowska and Piotr Sorokowski were supported by the National Science Center—Poland (2014/13/B/HS6/02644). Petra Gyuris, András Láng, and Norbert Meskó were supported by the Hungarian Scientific Research Fund — OTKA (K125437). Feng Jiang was supported by the National Nature Science Foundation of China, grant No. 71971225
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