Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of “hills” (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or “drive” tumorigenesis. A typical tumor contains two to eight of these “driver gene” mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

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Cancer Genome Landscapes

Interest in the topic of tumour metabolism has waxed and waned over the past century of cancer research. The early observations of Warburg and his contemporaries established that there are fundamental differences in the central metabolic pathways operating in malignant tissue. However, the initial hypotheses that were based on these observations proved inadequate to explain tumorigenesis, and the oncogene revolution pushed tumour metabolism to the margins of cancer research. In recent years, interest has been renewed as it has become clear that many of the signalling pathways that are affected by genetic mutations and the tumour microenvironment have a profound effect on core metabolism, making this topic once again one of the most intense areas of research in cancer biology.

/pdf/regulation-of-cancer-cell-metabolism-1dhy51t2fl.pdf

Regulation of cancer cell metabolism

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Mutations in the enzyme cytosolic isocitrate dehydrogenase 1 (IDH1) are a common feature of a major subset of primary human brain cancers. These mutations occur at a single amino acid residue of the IDH1 active site, resulting in loss of the enzyme's ability to catalyse conversion of isocitrate to alpha-ketoglutarate. However, only a single copy of the gene is mutated in tumours, raising the possibility that the mutations do not result in a simple loss of function. Here we show that cancer-associated IDH1 mutations result in a new ability of the enzyme to catalyse the NADPH-dependent reduction of alpha-ketoglutarate to R(-)-2-hydroxyglutarate (2HG). Structural studies demonstrate that when arginine 132 is mutated to histidine, residues in the active site are shifted to produce structural changes consistent with reduced oxidative decarboxylation of isocitrate and acquisition of the ability to convert alpha-ketoglutarate to 2HG. Excess accumulation of 2HG has been shown to lead to an elevated risk of malignant brain tumours in patients with inborn errors of 2HG metabolism. Similarly, in human malignant gliomas harbouring IDH1 mutations, we find markedly elevated levels of 2HG. These data demonstrate that the IDH1 mutations result in production of the onco-metabolite 2HG, and indicate that the excess 2HG which accumulates in vivo contributes to the formation and malignant progression of gliomas.

/pdf/cancer-associated-idh1-mutations-produce-2-hydroxyglutarate-3hnshr7hh1.pdf

Cancer-associated IDH1 mutations produce 2-hydroxyglutarate

Metabolic Reprogramming: A Cancer Hallmark Even Warburg Did Not Anticipate

Facing the increasingly serious traffic congestion problem, the existing traffic signal control technology has been unable to meet the demands of urban smart traffic construction. As one of the most promising methods, Reinforcement Learning (RL) has been widely exploited for solving the adaptive traffic signal control (ATSC) problem. However, centralized RL control mode is impractical due to its poor robustness and high computational complexity. In this paper, we propose two distributed Multi-agent Reinforcement Learning (MARL) control modes as well as a Federated Learning (FL) framework to solve the ATSC problem, where the former is based on Advantage Actor-Critic (A2C) algorithm and the latter is based on Federated Averaging (FedAvg) algorithm. By comparing in a small grid of traffic network, the experimental results reveal that our proposed algorithms outperform the centralized solution in terms of both the optimality and the learning efficiency.

Adaptive Traffic Signal Control Using Distributed MARL and Federated Learning

Mode-Independent Stabilization of Markovian Jump Systems With Time-Varying Delays: A Sliding Mode Approach

Contamination of water streams by dyes and heavy metals has become a major problem due to their persistence, accumulation, and toxicity. Therefore, it is essential to eliminate and/or reduce these contaminants before discharge into the natural environment. In recent years, 3D graphene has drawn intense research interests owing to its large surface area, superior charge conductivity, and thermal conductivity properties. Due to their unique surface and structural properties, 3D graphene-based materials (3D GBMs) are regarded as ideal adsorbents for decontamination and show great potential in wastewater or exhaust gas treatment. Here, this minireview summarizes the recent progress on 3D GBMs synthesis and their applications for adsorbing dyes and heavy metals from wastewater based on the structures and properties of 3D GBMs, which provides valuable insights into 3D GBMs' application in the environmental field.

Synthesis of 3D graphene-based materials and their applications for removing dyes and heavy metals

Synthesis of sulfonate of alkylated extract of FCC slurry for enhancing oil recovery

Recognition of protein-coding genes, a classical bioinformatics issue, is an absolutely needed step for annotating newly sequenced genomes. The Z-curve algorithm, as one of the most effective methods on this issue, has been successfully applied in annotating or re-annotating many genomes, including those of bacteria, archaea and viruses. Two Z-curve based ab initio gene-finding programs have been developed: ZCURVE (for bacteria and archaea) and ZCURVE_V (for viruses and phages). ZCURVE_C (for 57 bacteria) and Zfisher (for any bacterium) are web servers for re-annotation of bacterial and archaeal genomes. The above four tools can be used for genome annotation or re-annotation, either independently or combined with the other gene-finding programs. In addition to recognizing protein-coding genes and exons, Z-curve algorithms are also effective in recognizing promoters and translation start sites. Here, we summarize the applications of Z-curve algorithms in gene finding and genome annotation.

Yan Lin

Papers

Adaptive Traffic Signal Control Using Distributed MARL and Federated Learning

Mode-Independent Stabilization of Markovian Jump Systems With Time-Varying Delays: A Sliding Mode Approach

Synthesis of 3D graphene-based materials and their applications for removing dyes and heavy metals

Synthesis of sulfonate of alkylated extract of FCC slurry for enhancing oil recovery

Recognition of Protein-coding Genes Based on Z-curve Algorithms.