Arginine Methylation of DDX3 by PRMT1 Mediates Mitochondrial Homeostasis to Promote Breast Cancer Metastasis.

Dysregulated mitochondrial dynamics and metabolism play important roles in tumorigenesis. Metastasizing tumor cells predominantly utilize mitochondrial metabolism, and regulators of metabolic reprogramming may provide reliable biomarkers for diagnosing cancer metastasis. Here, we identified a type I arginine methyltransferase-DEAD-box polypeptide 3, X-linked (PRMT1-DDX3) axis that promotes breast cancer metastasis by coordinating mitochondrial biogenesis and mitophagy to ensure mitochondrial quality control. Mechanistically, PRMT1 induces arginine methylation of DDX3, which enhances its protein stability and prevents proteasomal degradation. DDX3 mediates mitochondrial homeostasis by translocating to mitochondria where it facilitates phosphatase and tensin homology-induced kinase 1 translation in response to mitochondrial stress. Inhibition of DDX3 suppresses mitochondrial biogenesis and mitophagy, resulting in diminished cancer stemness and metastatic properties. Overall, this study uncovers a mechanism by which the PRMT1-DDX3 axis regulates mitochondrial homeostasis to support breast cancer metastasis, suggesting strategies for targeting metabolic vulnerabilities to treat metastatic breast cancer. Significance: DDX3 is stabilized by PRMT1-mediated arginine methylation and coordinates mitophagy and mitochondrial biogenesis by upregulating PINK1 to facilitate breast cancer progression.

PRMT1 Confers Resistance to Olaparib via Modulating MYC Signaling in Triple-Negative Breast Cancer.

Treatment of triple-negative breast cancer (TNBC) remains an unmet clinical need owing to its lack of an efficient therapeutic target. The targeting of DNA repair by poly(ADP-ribose) polymerase (PARP) inhibitors has shown benefit for patients with the BRCA variation. However, sensitivities to the PARP inhibitors were reported regardless of BRCA status. Thus, exploring the underlying mechanisms is imperative. Herein, we identified that breast cancer cells with an elevated expression of protein arginine methyl transferase 1 (PRMT1) was associated with therapeutic sensitivity to the PARP inhibitor olaparib. The results of cell viability and colony formation assays indicated that the suppression of PRMT1 by small hairpin RNA or by the chemical inhibitor increased sensitivity to olaparib in human TNBC MDA-MB-231 and BT549 cells. Bioinformatic analysis revealed that PRMT1 expression was significantly associated with the MYC signature, and TNBC cells with higher PRMT1 and the MYC signature were associated with therapeutic sensitivity to olaparib. Mechanistic studies further demonstrated that knockdown of PRMT1 reduced the c-Myc protein level and downregulated the expression of MYC downstream targets, whereas overexpression of PRMT1 enhanced c-Myc protein expression. Moreover, the overexpression of PRMT1 promoted c-Myc protein stability, and the inhibition of PRMT1 downregulated c-Myc protein stability. Accordingly, the knockdown of PRMT1 inhibited homologous recombination gene expression. These data indicate that PRMT1 is instrumental in regulating DNA repair, at least in part, by modulating c-Myc signaling. Our data highlighted the PRMT1/c-Myc network as a potential therapeutic target in patients with TNBC.

Prognostic Value of a Glycolytic Signature and Its Regulation by Y-Box-Binding Protein 1 in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer as it shows a high capacity for metastasis and poor prognoses. Metabolic reprogramming is one of the hallmarks of cancer, and aberrant glycolysis was reported to be upregulated in TNBC. Thus, identifying metabolic biomarkers for diagnoses and investigating cross-talk between glycolysis and invasiveness could potentially enable the development of therapeutics for patients with TNBC. In order to determine novel and reliable metabolic biomarkers for predicting clinical outcomes of TNBC, we analyzed transcriptome levels of glycolysis-related genes in various subtypes of breast cancer from public databases and identified a distinct glycolysis gene signature, which included ENO1, SLC2A6, LDHA, PFKP, PGAM1, and GPI, that was elevated and associated with poorer prognoses of TNBC patients. Notably, we found a transcription factor named Y-box-binding protein 1 (YBX1) to be strongly associated with this glycolysis gene signature, and it was overexpressed in TNBC. A mechanistic study further validated that YBX1 was upregulated in TNBC cell lines, and knockdown of YBX1 suppressed expression of those glycolytic genes. Moreover, YBX1 expression was positively associated with epithelial-to-mesenchymal transition (EMT) genes in breast cancer patients, and suppression of YBX1 downregulated expressions of EMT-related genes and tumor migration and invasion in MDA-MB-231 and BT549 TNBC cells. Our data revealed an YBX1-glycolysis-EMT network as an attractive diagnostic marker and metabolic target in TNBC patients.

Upregulation of CD109 Promotes the Epithelial-to-Mesenchymal Transition and Stemness Properties of Lung Adenocarcinomas via Activation of the Hippo-YAP Signaling.

Metastasis is the leading cause of death in lung adenocarcinomas. Identifying potential prognostic biomarkers and exploiting regulatory mechanisms could improve the diagnosis and treatment of lung cancer patients. We previously found that cluster of differentiation 109 (CD109) was upregulated in lung tumor tissues, and CD109 overexpression was correlated with the invasive and metastatic capacities of lung adenocarcinoma cells. However, the contribution of CD109 to lung tumorigenesis remains to be elucidated. In the present study, we identified that CD109 was upregulated in metastatic lung adenocarcinoma cells, and elevation of CD109 was correlated with epithelial-to-mesenchymal transition (EMT) traits in patients with lung adenocarcinoma. Functionally, CD109 expression was crucial for EMT gene expressions, tumor invasiveness, and cancer stemness properties. Moreover, elevation of CD109 was accompanied by upregulation of the yes-associated protein (YAP) signature in metastatic lung cancer cells and lung cancer patients, and activation of YAP was demonstrated to participate in CD109-elicited EMT gene expressions and tumor invasiveness. Our study reveals the molecular mechanism underlying CD109 in lung tumor aggressiveness, and CD109 could be a potential diagnostic and therapeutic target for lung cancer patients.

Fucoidan-based, tumor-activated nanoplatform for overcoming hypoxia and enhancing photodynamic therapy and antitumor immunity.

Multifunctional nanoplatforms combined with photodynamic therapy (PDT) and anticancer drugs have shown great promising in cancer therapy. However, their efficacy is limited by the low specificity, low oxygen levels, and a tolerant tumor immune microenvironment. Herein, we developed a biocompatible theranostic nanoplatform (FM@VP) based on co-assembly of a nanocomplex formed by a functional polysaccharide fucoidan and a bioreducible polyamidoamine (PAMAM) dendrimer, a photosensitizer verteporfin (VP), and MnO2 nanoparticles (a tumor microenvironment responsive oxygen evolving nanomaterial) into a multifunctional nanoparticle cluster. The dendrimer-fucoidan polyionic nanocomplex (DFPN) specifically targeted P-selectin-overexpressed triple-negative breast cancer (TNBC) and the tumor-associated vasculature, and was sensitive to glutathione (GSH) in tumor. More importantly, this FM@VP nanocomplex simultaneously overcame tumor hypoxia, suppressed oncogenic signaling, and attenuated tumor-mediated immunosuppression, resulting in improving therapeutic efficacy of PDT while enhancing antitumor immunity and anti-metastasis. This discovery provides a powerful strategy for synergetic cancer targeting/photodynamic/immunotherapy and could serve as a safe clinical translational approach.

Fucoidan from Laminaria japonica exerts antitumor effects on angiogenesis and micrometastasis in triple-negative breast cancer cells.

Fucoidan is a fucose-rich polysaccharide that has gained attention for its various anticancer properties. However, the effect and underlying mechanism of fucoidan on triple-negative breast cancer (TNBC) are still unknown. Herein, we investigated the anticancer potential of fucoidan from Laminaria japonica. We found that fucoidan showed modest antiproliferative activity against TNBC cells, while it effectively reduced migratory and invasive capacities. Mechanistically, fucoidan suppressed activation of MAPK and PI3K followed by inhibition of AP-1 and NF-κB signaling in TNBC. Additionally, fucoidan downregulated expressions of proangiogenic factors in TNBC cells, and fucoidan blocked tumor-elicited tube formation by human umbilical vascular endothelial cells (HUVECs). We also observed that fucoidan blocked tumor adhesion and invasion towards HUVECs. Surprisingly, fucoidan robustly suppressed tube formation on HUVECs. Moreover, fucoidan inhibited in vivo angiogenesis and micrometastasis in a transgenic zebrafish model. Together, L. japonica fucoidan exhibits potent antitumor effects by its attenuation of invasiveness and proangiogenesis in TNBC.

Estimating mean first passage time of biased random walks with short relaxation time on complex networks.

Biased random walk has been studied extensively over the past decade especially in the transport and communication networks communities. The mean first passage time (MFPT) of a biased random walk is an important performance indicator in those domains. While the fundamental matrix approach gives precise solution to MFPT, the computation is expensive and the solution lacks interpretability. Other approaches based on the Mean Field Theory relate MFPT to the node degree alone. However, nodes with the same degree may have very different local weight distribution, which may result in vastly different MFPT. We derive an approximate bound to the MFPT of biased random walk with short relaxation time on complex network where the biases are controlled by arbitrarily assigned node weights. We show that the MFPT of a node in this general case is closely related to not only its node degree, but also its local weight distribution. The MFPTs obtained from computer simulations also agree with the new theoretical analysis. Our result enables fast estimation of MFPT, which is useful especially to differentiate between nodes that have very different local node weight distribution even though they share the same node degrees.

Tree-structured algorithm for long weak motif discovery.

MOTIVATION: Motifs in DNA sequences often appear in degenerate form, so there has been an increased interest in computational algorithms for weak motif discovery. Probabilistic algorithms are unable to detect weak motifs while exact methods have been able to detect only short weak motifs. This article proposes an exact tree-based motif detection (TreeMotif) algorithm capable of discovering longer and weaker motifs than by the existing methods. RESULTS: TreeMotif converts the graphical representation of motifs into a tree-structured representation in which a tree that branches with nodes from every sequence represents motif instances. The method of tree construction is novel to motif discovery based on graphical representation. TreeMotif is more efficient and scalable in handling longer and weaker motifs than the existing algorithms in terms of accuracy and execution time. The performances of TreeMotif were demonstrated on synthetic data as well as on real biological data. AVAILABILITY: https://sites.google.com/site/shqssw/treemotif CONTACT: sunh0013@e.ntu.edu.sg SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RecMotif: a novel fast algorithm for weak motif discovery.

BACKGROUND: Weak motif discovery in DNA sequences is an important but unresolved problem in computational biology. Previous algorithms that aimed to solve the problem usually require a large amount of memory or execution time. In this paper, we proposed a fast and memory efficient algorithm, RecMotif, which guarantees to discover all motifs with specific (l, d) settings (where l is the motif length and d is the maximum number of mutations between a motif instance and the true motif). RESULTS: Comparisons with several recently proposed algorithms have shown that RecMotif is more scalable for handling longer and weaker motifs. For instance, it can solve the open challenge cases such as (40, 14) within 5 hours while the other algorithms compared failed due to either longer execution times or shortage of memory space. For real biological sequences, such as E.coli CRP, RecMotif is able to accurately discover the motif instances with (l, d) as (18, 6) in less than 1 second, which is faster than the other algorithms compared. CONCLUSIONS: RecMotif is a novel algorithm that requires only a space complexity of O(m²n) (where m is the number of sequences in the data and n is the length of the sequences).