SUMMARY N6-methyladenosine (m6A) represents the most prevalent internal modification in mammalian mRNAs. Despite its functional importance in various fundamental bioprocesses, the studies of m6A in cancer have been limited. Here we show that FTO, as an m6A demethylase, plays a critical oncogenic role in acute myeloid leukemia (AML). FTO is highly expressed in AMLs with t(11q23)/MLL-rearrangements, t(15;17)/PML-RARA, FLT3-ITD and/or NPM1 mutations. FTO enhances leukemic oncogene-mediated cell transformation and leukemogenesis, and inhibits all-trans-retinoic acid (ATRA)-induced AML cell differentiation, through regulating expression of targets such as ASB2 and RARA by reducing m6A levels in these mRNA transcripts. Collectively, our study demonstrates the functional importance of the m6A methylation and the corresponding proteins in cancer, and provides profound insights into leukemogensis and drug response.

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Carbon nanotubes have many material properties that make them attractive for applications. In the context of nanoelectronics, interest has focused on single-walled carbon nanotubes (SWNTs) because slight changes in tube diameter and wrapping angle, defined by the chirality indices (n, m), will shift their electrical conductivity from one characteristic of a metallic state to one characteristic of a semiconducting state, and will also change the bandgap. However, this structure-function relationship can be fully exploited only with structurally pure SWNTs. Solution-based separation methods yield tubes within a narrow structure range, but the ultimate goal of producing just one type of SWNT by controlling its structure during growth has proved to be a considerable challenge over the last two decades. Such efforts aim to optimize the composition or shape of the catalyst particles that are used in the chemical vapour deposition synthesis process to decompose the carbon feedstock and influence SWNT nucleation and growth. This approach resulted in the highest reported proportion, 55 per cent, of single-chirality SWNTs in an as-grown sample. Here we show that SWNTs of a single chirality, (12, 6), can be produced directly with an abundance higher than 92 per cent when using tungsten-based bimetallic alloy nanocrystals as catalysts. These, unlike other catalysts used so far, have such high melting points that they maintain their crystalline structure during the chemical vapour deposition process. This feature seems crucial because experiment and simulation both suggest that the highly selective growth of (12, 6) SWNTs is the result of a good structural match between the carbon atom arrangement around the nanotube circumference and the arrangement of the catalytically active atoms in one of the planes of the nanocrystal catalyst. We anticipate that using high-melting-point alloy nanocrystals with optimized structures as catalysts paves the way for total chirality control in SWNT growth and will thus promote the development of SWNT applications.

Summary Development of life-threatening cancer metastases at distant organs requires disseminated tumor cells’ adaptation to and co-evolution with the drastically different microenvironments of metastatic sites1. Cancer cells of common origin manifest distinct gene expression patterns after metastasizing to different organs2. Clearly, the dynamic interplay between metastatic tumor cells and extrinsic signals at individual metastatic organ sites critically impacts the subsequent metastatic outgrowth3,4. Yet, it is unclear when and how disseminated tumor cells acquire the essential traits from the microenvironment of metastatic organs that prime their subsequent outgrowth. Here we show that primary tumor cells with normal expression of PTEN, an important tumor suppressor, lose PTEN expression after dissemination to the brain, but not to other organs. PTEN level in PTEN-loss brain metastatic tumor cells is restored after leaving brain microenvironment. This brain microenvironment-dependent, reversible PTEN mRNA and protein down-regulation is epigenetically regulated by microRNAs (miRNAs) from astrocytes. Mechanistically, astrocyte-derived exosomes mediate an intercellular transfer of PTEN-targeting miRNAs to metastatic tumor cells, while astrocyte-specific depletion of PTEN-targeting miRNAs or blockade of astrocyte exosome secretion rescues the PTEN loss and suppresses brain metastasis in vivo. Furthermore, this adaptive PTEN loss in brain metastatic tumor cells leads to an increased secretion of cytokine chemokine (C-C motif) ligand 2 (CCL2), which recruits Iba1+ myeloid cells that reciprocally enhance outgrowth of brain metastatic tumor cells via enhanced proliferation and reduced apoptosis. Our findings demonstrate a remarkable plasticity of PTEN expression in metastatic tumor cells in response to different organ microenvironments, underpinning an essential role of co-evolution between the metastatic cells and their microenvironment during the adaptive metastatic outgrowth. Our findings signify the dynamic and reciprocal cross-talk between tumor cells and the metastatic niche; importantly, they provide new opportunities for effective anti-metastasis therapies, especially of consequence for those brain metastasis patients who are in dire need.

Network embedding assigns nodes in a network to lowdimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure-and property-preserving network embedding methods, the network embedding methods with side information and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions. IntroductionMany complex systems take the form of networks, such as social networks, biological networks, and information networks. It is well recognized that network data is often sophisticated and thus is challenging to deal with. To process network data effectively, the first critical challenge is to find effective network data representation, that is, how to represent networks concisely so that advanced analytic tasks, such as pattern discovery, analysis and prediction, can be conducted efficiently in both time and space.Traditionally, we usually represent a network as a graph G = V, E , where V is a vertex set representing the nodes in a network, and E is an edge set representing the relationships among the nodes. For large networks, such as those with billions of nodes, the traditional network representation poses several challenges to network processing and analysis.• High computational complexity. The nodes in a network are related to each other to a certain degree, encoded by the edge set E in the traditional network representation.

We present stellar velocity dispersion profiles for seven Milky Way dwarf spheroidal (dSph) satellite galaxies. We have measured 8394 line-of-sight velocities (+/- 2.5 km/s) for 6804 stars from high-resolution spectra obtained at the Magellan and MMT telescopes. We combine these new data with previously published velocities to obtain the largest available kinematic samples, which include more than 5500 dSph members. All the measured dSphs have stellar velocity dispersion of order 10 km/s that remains approximately constant with distance from the dSph center, out to and in some cases beyond the radius at which the mean surface brightness falls to the background level. Assuming dSphs reside within dark matter halos characterized by the NFW density profile, we obtain reasonable fits to the empirical velocity dispersion profiles. These fits imply that, among the seven dSphs, M_vir ~ 10^[8-9] M_sun. The mass enclosed at a radius of 600 pc, the region common to all data sets, ranges from (2-7) x 10^7 M_sun .Comment: Accepted for publication in the Astrophysical Journal Letter

N6-methyladenosine (m6A) is a prevalent internal modification in mRNA and non- coding RNA affecting various cellular pathways. Here we report the discovery of two additional modifications, N6-hydroxymethyladenosine (hm6A) and N6- formyladenosine (f6A), in mammalian mRNA. We show that FeII- and α-ketoglutarate (α-KG)-dependent fat mass and obesity associated (FTO) protein oxidizes m6A to generates hm6A as an intermediate modification and f6A as a further oxidized product. hm6A and f6A have half-life times of ~3 h in aqueous solution under physiological relevant conditions, and are present in isolated mRNA from human cells as well as mouse tissues. These previously unknown modifications derived from the prevalent m6A in mRNA, formed through oxidative RNA demethylation, may dynamically modulate RNA-protein interactions to affect gene expression regulation.

The semiconductor industry is increasingly of the view that Moore's law-which predicts the biennial doubling of the number of transistors per microprocessor chip-is nearing its end. Consequently, the pursuit of alternative semiconducting materials for nanoelectronic devices, including single-walled carbon nanotubes (SWNTs), continues. Arrays of horizontal nanotubes are particularly appealing for technological applications because they optimize current output. However, the direct growth of horizontal SWNT arrays with controlled chirality, that would enable the arrays to be adapted for a wider range of applications and ensure the uniformity of the fabricated devices, has not yet been achieved. Here we show that horizontal SWNT arrays with predicted chirality can be grown from the surfaces of solid carbide catalysts by controlling the symmetries of the active catalyst surface. We obtained horizontally aligned metallic SWNT arrays with an average density of more than 20 tubes per micrometre in which 90 per cent of the tubes had chiral indices of (12, 6), and semiconducting SWNT arrays with an average density of more than 10 tubes per micrometre in which 80 per cent of the nanotubes had chiral indices of (8, 4). The nanotubes were grown using uniform size MoC and WC solid catalysts. Thermodynamically, the SWNT was selectively nucleated by matching its structural symmetry and diameter with those of the catalyst. We grew nanotubes with chiral indices of (2m, m) (where m is a positive integer), the yield of which could be increased by raising the concentration of carbon to maximize the kinetic growth rate in the chemical vapour deposition process. Compared to previously reported methods, such as cloning, seeding and specific-structure-matching growth, our strategy of controlling the thermodynamics and kinetics offers more degrees of freedom, enabling the chirality of as-grown SWNTs in an array to be tuned, and can also be used to predict the growth conditions required to achieve the desired chiralities.