Search

Mutational activation of the KRAS gene occurs in almost all pancreatic ductal adenocarcinoma (PDAC) and is the earliest molecular event in their carcinogenesis. Evidence has accumulated of the metabolic reprogramming in PDAC, such as amino acid homeostasis and autophagic flux. However, the biological effects of KRAS mutation on metabolic reprogramming at the earlier stages of PDAC carcinogenesis are unclear. Here we report dynamic metabolic reprogramming in immortalized human non-cancerous pancreatic ductal epithelial cells, in which a KRAS mutation was induced by gene-editing, which may mimic early pancreatic carcinogenesis. Similar to the cases of PDAC, KRAS gene mutation increased the dependency on glucose and glutamine for maintaining the intracellular redox balance. In addition, the intracellular levels of amino acids were significantly decreased because of active protein synthesis, and the cells required greater autophagic flux to maintain their viability. The lysosomal inhibitor chloroquine significantly inhibited cell proliferation. Therefore, metabolic reprogramming is an early event in carcinogenesis initiated by KRAS gene mutation, suggesting a rationale for the development of nutritional interventions that suppress or delay the development of PDAC.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease and highly resistant to all forms of therapy. PDAC cells reprogram their metabolism extensively to promote their survival and growth. Reflecting the vital role of altered metabolism, experimental and clinical trials targeting the rewired metabolism are currently underway. In this review, we summarize the vital role of metabolic reprogramming in the development of PDAC and the future of novel therapeutic applications., Various types of metabolic reprogramming occur in pancreatic cancer cells, including metabolism of glucose, amino acids, and nutrient acquisition such as autophagy. Increased knowledge of these features can lead to the development of novel therapeutics.

Circular RNA (circRNA) is a type of non-coding RNA that forms a covalently closed continuous loop. The expression pattern of circRNA varies among cell types and tissues, and many circRNAs are aberrantly expressed in various cancers. Aberrantly expressed circRNAs have been shown to play crucial roles in carcinogenesis, functioning as microRNA sponges or new templates for protein translation. Recent research has shown that circRNAs are enriched in exosomes. Exosomes are secretory vesicles that mediate intercellular communication through the delivery of cargo, including proteins, lipids, DNA, and RNA. Exosome-mediated crosstalk between cancer cells and the tumor microenvironment promotes the epithelial-mesenchymal transition, angiogenesis, and immune escape, and thus may contribute to cancer invasion and metastasis. In this review, we discuss the biological functions of exosomal circRNAs and their significance in cancer progression. Additionally, we discuss the potential clinical applications of exosomal circRNAs as biomarkers and in cancer therapy.

Circular RNAs (circRNAs) are single-stranded, covalently closed RNA molecules that are produced from pre-mRNAs through a process known as back-splicing. Although circRNAs are expressed under specific conditions, current understanding of their comprehensive expression status is still limited. Here, we performed a large-scale circRNA profiling analysis in human pancreatic ductal adenocarcinoma (PDAC) tissues, using circular RNA-specific RNA sequencing. We identified more than 40,000 previously unknown circRNAs, some of which were upregulated in PDAC tissues, compared with normal pancreatic tissues. We determined the full-length sequence of a circRNA upregulated in PDAC, which was derived from two noncoding RNA loci on chromosome 12. The novel circRNA, named circPDAC RNA, was not expressed in normal human cells, but was expressed in PDAC and other carcinoma cells. While postulated biological functions, such as peptide production from the circPDAC RNA, were not detected, its aberrant expression was confirmed in other PDAC tissues and in serum from a PDAC patient. These results demonstrate that comprehensive studies are necessary to reveal the expression status of circRNAs and that the circPDAC RNA identified here might serve as a novel biomarker for cancers, including PDAC.

Pancreatic cancer is a malignancy with an extremely poor prognosis. Chronic pancreatitis is a well-known risk factor for pancreatic cancer. Inflammation is thought to influence carcinogenesis through DNA damage and activation of intracellular signaling pathways. Many transcription factors and signaling pathways co-operate to determine and maintain cell identity at each phase of pancreatic organogenesis and cell differentiation. Recent studies have shown that carcinogenesis is promoted through the suppression of transcription factors related to differentiation. Pancreatitis also demonstrates transcriptional changes, suggesting that multifactorial epigenetic changes lead to impaired differentiation. Taken together, these factors may constitute an important framework for pancreatic carcinogenesis. In this review, we discuss the role of inflammation and de-differentiation in the development of pancreatic cancer, as well as the future of novel therapeutic applications.

The expression of CDR1‑AS, a representative circular RNA, is closely linked with poor prognosis in gastrointestinal cancers, such as colon, liver, and pancreatic cancers. Although it is well known that CDR1‑AS antagonizes microRNA‑7 function through its sequence similarities in the brain, its biological function and link with the malignant potential of cancer cells remain unclear, partly due to the difficulties of ectopic expression of circular RNAs. In the present study, SW620, a colon cancer cell line that stably expresses CDR1‑AS RNA circularized, was established using the laccase 2 gene cassette, and its biological function associated with malignant behavior was determined. In contrast to previous studies, cell growth or invasion ability was not altered by CDR1‑AS expression. However, the expression levels of CMTM4 and CMTM6, which were recently recognized as critical regulators of PD‑L1 protein expression at the cell surface, were significantly increased. Accordingly, the cell surface PD‑L1 protein levels were increased in CDR1‑AS‑expressing cells. Notably, the effects were not canceled out by overexpressing microRNA‑7, indicating that the increase in cell surface PD‑L1 in CDR1‑AS‑expressing cells was not dependent on microRNA‑7 function. These results indicated that expression of this circular RNA in cancer cells may lead to poor prognosis by increasing cell surface PD‑L1 levels through microRNA‑7‑independent mechanisms.

In statistical analysis and data mining, change-point detection that identifies the change-points which are times when the probability distribution of time series changes has been used for various purposes, such as anomaly detections on network traffic and transaction data. However, computation cost of a conventional AR (Auto-Regression) model based approach is too high and infeasible for online. In this paper, an AR model based online change-point detection algorithm, called ChangeFinder, is implemented on an FPGA (Field Programmable Gate Array) based NIC (Network Interface Card). The proposed system computes the change-point score from time series data received from 10 GbE (10 Gbit Ethernet). More specifically, it computes the change-point score at the 10 GbE NIC in advance of host applications. This paper aims to reduce the host workload and improve change-point detection performance by offloading ChangeFinder algorithm from host to the NIC. As evaluations, change-point detection in the FPGA NIC is compared with a baseline software implementation and those enhanced by two network optimization techniques using DPDK and Netfilter in terms of throughput. The result demonstrates 16.8x improvement in change-point detection throughput compared to the baseline software implementation. The throughput achieves 83.4% of the 10 GbE line rate.

Toward machine learning based prediction services, the prediction server has multiple predictors and selects an appropriate one based on past feedbacks from the clients. In this case, three messages including request, reply, and feedback, are required for each prediction request. Packets are typically transmitted and received via a network protocol stack in OS kernel, and performance improvement can be expected by avoiding the protocol stack since it degrades the communication performance especially for small packets. We implement the prediction server using network optimization approaches including kernel-bypassing and in-NIC processing approaches. Evaluation results show that these network optimizations are beneficial to improve the prediction server performance compared to a baseline prediction server using a standard network protocol stack.

名古屋コーチン肉を特徴づけているおいしさの要因を明らかにするため，ブロイラーと合鴨肉を比較対象として，官能評価と遊離アミノ酸などの分析を行い，以下の結果を得た．名古屋コーチンとブロイラーの加熱もも肉の2点嗜好試験では，味は両者間で差はなかったが，香り，食感および総合評価で前者が有意に好ましいと判定された．両者の2点識別試験では，うま味の強さは両者間で差がなかったが，品種特異臭と推定される名古屋コーチン臭と硬さが名古屋コーチンが有意に上位にあると判定された．両鶏のもも肉から調製したスープの2点識別試験では，うま味の強度はブロイラーが強い傾向にあった．コク味はブロイラーが有意に強かった．両スープにおける遊離アミノ酸の総モル数はブロイラーが多い傾向にあり，グリシン，ヒドロキシプロリン，セリン，アスパラギン，β-アラニン，アラニンおよびプロリンはブロイラーが有意に多かった．その他のアミノ酸は有意差がなかったが，ブロイラーが多い傾向にあった．名古屋コーチン加熱もも肉と合鴨加熱むね肉の2点識別試験では，うま味強度は名古屋コーチンが大きい傾向にあった．合鴨臭と硬さは合鴨が有意に上位にあると判定された．重量比でブロイラーもも挽肉8に合鴨むね挽肉2を混合したパティは，名古屋コーチンもも挽肉パティとは香りを根拠にした3点識別試験で識別できなかった．以上の結果より，名古屋コーチンと合鴨を特徴づけているおいしさの要因は，味ではなく，両者の互いに類似した特有香と豊かな噛みごたえであり，ブロイラーはうま味とコク味の強いスープを与える特性を有していると結論された．

To realize technological innovation based on advanced university technologies, it is important to establish collaborative research projects between universities and industry in the early stages of R&D. One way to stimulate the creation of university-industry collaborative research is to have a systematical coordinating mechanism between a university and industry. We analyzed the effectiveness of a university-industry coordination program at creating university-industry collaborative research projects. We used data from the matching program called Proprius21 at the University of Tokyo and found out that the matching program has increased collaborative studies at the University of Tokyo. We also analyzed the R&D characteristics of various firms using the relation between the firm's R&D intensity and the range of firm's R&D areas. The matching program is especially effective for large companies in ICT-related fields, although it is not so effective for efforts involving life science technologies, which are overall the most active area for university-industry collaboration. Results show that the matching program is most effective for creating collaborative studies with technology-oriented firms engaged in various business domains.