Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target.

The genomic landscape of colorectal cancer (CRC) is shaped by inactivating mutations in tumour suppressors such as APC, and oncogenic mutations such as mutant KRAS. Here we used genetically engineered mouse models, and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that untargeted metabolic profiling can be applied to stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-deficient CRC. Loss of Apc in the mouse intestine was found to be sufficient to drive expression of one of its enzymes, adenosylhomocysteinase (AHCY), which was also found to be transcriptionally upregulated in human CRC. Targeting of AHCY function impaired growth of APC-deficient organoids in vitro, and prevented the characteristic hyperproliferative/crypt progenitor phenotype driven by acute deletion of Apc in vivo, even in the context of mutant Kras. Finally, pharmacological inhibition of AHCY reduced intestinal tumour burden in ApcMin/+ mice indicating its potential as a metabolic drug target in CRC.

Atmospheric-Pressure Infrared Laser-Ablation Plasma-Postionization Mass Spectrometry Imaging of Formalin-Fixed Paraffin-Embedded (FFPE) and Fresh-Frozen Tissue Sections with No Sample Preparation.

Mass spectrometry imaging (MSI) encompasses a powerful suit of techniques which provide spatially resolved atomic and molecular information from almost any sample type. MSI is now widely used in preclinical research to provide insight into metabolic phenotypes of disease. Typically, fresh-frozen tissue preparations are considered optimal for biological MSI and other traditional preservation methods such as formalin fixation, alone or with paraffin embedding (FFPE), are considered less optimal or even incompatible. Due to the prevalence of FFPE tissue storage, particularly for rare and therefore high-value tissue samples, there is substantial motivation for optimizing MSI methods for analysis of FFPE tissue. Here, we present a novel modality, atmospheric-pressure infrared laser-ablation plasma postionization (AP-IR-LA-PPI), with the first proof-of-concept examples of MSI for FFPE and fresh-frozen tissues, with no post-sectioning sample preparation. We present ion images from FFPE and fresh tissues in positive and negative ion modes. Molecular annotations (via the Metaspace annotation engine) and on-tissue MS/MS provide additional confidence that the detected ions arise from a broad range of metabolite and lipid classes from both FFPE and fresh-frozen tissues.

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities.

Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Huffman based on deep learning concepts using weights, pruning, and pooling in the neural network. The proposed algorithm is believed to obtain a better compression ratio. Additionally, in this paper, we also discuss the challenges of applying the proposed algorithm to IoT data compression due to the limitations of IoT memory and IoT processor, which later it can be implemented in IoT networks.

Evolutionary Persistence of DNA Methylation for Millions of Years after Ancient Loss of a De Novo Methyltransferase.

Cytosine methylation of DNA is a widespread modification of DNA that plays numerous critical roles. In the yeast Cryptococcus neoformans, CG methylation occurs in transposon-rich repeats and requires the DNA methyltransferase Dnmt5. We show that Dnmt5 displays exquisite maintenance-type specificity in vitro and in vivo and utilizes similar in vivo cofactors as the metazoan maintenance methylase Dnmt1. Remarkably, phylogenetic and functional analysis revealed that the ancestral species lost the gene for a de novo methylase, DnmtX, between 50-150 mya. We examined how methylation has persisted since the ancient loss of DnmtX. Experimental and comparative studies reveal efficient replication of methylation patterns in C. neoformans, rare stochastic methylation loss and gain events, and the action of natural selection. We propose that an epigenome has been propagated for >50 million years through a process analogous to Darwinian evolution of the genome.

Oral vitamin C supplementation to patients with myeloid cancer on azacitidine treatment: Normalization of plasma vitamin C induces epigenetic changes.

BACKGROUND: Patients with haematological malignancies are often vitamin C deficient, and vitamin C is essential for the TET-induced conversion of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), the first step in active DNA demethylation. Here, we investigate whether oral vitamin C supplementation can correct vitamin C deficiency and affect the 5hmC/5mC ratio in patients with myeloid cancers treated with DNA methyltransferase inhibitors (DNMTis). RESULTS: We conducted a randomized, double-blinded, placebo-controlled pilot trial (NCT02877277) in Danish patients with myeloid cancers performed during 3 cycles of DNMTi-treatment (5-azacytidine, 100 mg/m2/d for 5 days in 28-day cycles) supplemented by oral dose of 500 mg vitamin C (n = 10) or placebo (n = 10) daily during the last 2 cycles. Fourteen patients (70%) were deficient in plasma vitamin C (< 23 µM) and four of the remaining six patients were taking vitamin supplements at inclusion. Global DNA methylation was significantly higher in patients with severe vitamin C deficiency (< 11.4 µM; 4.997 vs 4.656% 5mC relative to deoxyguanosine, 95% CI [0.126, 0.556], P = 0.004). Oral supplementation restored plasma vitamin C levels to the normal range in all patients in the vitamin C arm (mean increase 34.85 ± 7.94 µM, P = 0.0004). We show for the first time that global 5hmC/5mC levels were significantly increased in mononuclear myeloid cells from patients receiving oral vitamin C compared to placebo (0.037% vs - 0.029%, 95% CI [- 0.129, - 0.003], P = 0.041). CONCLUSIONS: Normalization of plasma vitamin C by oral supplementation leads to an increase in the 5hmC/5mC ratio compared to placebo-treated patients and may enhance the biological effects of DNMTis. The clinical efficacy of oral vitamin C supplementation to DNMTis should be investigated in a large randomized, placebo-controlled clinical trial. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02877277 . Registered on 9 August 2016, retrospectively registered.