m6A mRNA demethylase FTO regulates melanoma tumorigenicity and response to anti-PD-1 blockade

Melanoma is one of the most deadly and therapy-resistant cancers. Here we show that N6-methyladenosine (m6A) mRNA demethylation by fat mass and obesity-associated protein (FTO) increases melanoma growth and decreases response to anti-PD-1 blockade immunotherapy. FTO level is increased in human melanoma and enhances melanoma tumorigenesis in mice. FTO is induced by metabolic starvation stress through the autophagy and NF-κB pathway. Knockdown of FTO increases m6A methylation in the critical protumorigenic melanoma cell-intrinsic genes including PD-1 (PDCD1), CXCR4, and SOX10, leading to increased RNA decay through the m6A reader YTHDF2. Knockdown of FTO sensitizes melanoma cells to interferon gamma (IFNγ) and sensitizes melanoma to anti-PD-1 treatment in mice, depending on adaptive immunity. Our findings demonstrate a crucial role of FTO as an m6A demethylase in promoting melanoma tumorigenesis and anti-PD-1 resistance, and suggest that the combination of FTO inhibition with anti-PD-1 blockade may reduce the resistance to immunotherapy in melanoma. © 2019, The Author(s)

The diploid genome sequence of an Asian individual

Here we present the first diploid genome sequence of an Asian individual. The genome was sequenced to 36-fold average coverage using massively parallel sequencing technology. We aligned the short reads onto the NCBI human reference genome to 99.97% coverage, and guided by the reference genome, we used uniquely mapped reads to assemble a high-quality consensus sequence for 92% of the Asian individual's genome. We identified approximately 3 million single-nucleotide polymorphisms (SNPs) inside this region, of which 13.6% were not in the dbSNP database. Genotyping analysis showed that SNP identification had high accuracy and consistency, indicating the high sequence quality of this assembly. We also carried out heterozygote phasing and haplotype prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese, respectively), sequence comparison with the two available individual genomes (J. D. Watson and J. C. Venter), and structural variation identification. These variations were considered for their potential biological impact. Our sequence data and analyses demonstrate the potential usefulness of next-generation sequencing technologies for personal genomics

Bioinformatic Analysis and Post-Translational Modification Crosstalk Prediction of Lysine Acetylation

Recent proteomics studies suggest high abundance and a much wider role for lysine acetylation (K-Ac) in cellular functions. Nevertheless, cross influence between K-Ac and other post-translational modifications (PTMs) has not been carefully examined. Here, we used a variety of bioinformatics tools to analyze several available K-Ac datasets. Using gene ontology databases, we demonstrate that K-Ac sites are found in all cellular compartments. KEGG analysis indicates that the K-Ac sites are found on proteins responsible for a diverse and wide array of vital cellular functions. Domain structure prediction shows that K-Ac sites are found throughout a wide variety of protein domains, including those in heat shock proteins and those involved in cell cycle functions and DNA repair. Secondary structure prediction proves that K-Ac sites are preferentially found in ordered structures such as alpha helices and beta sheets. Finally, by mutating K-Ac sites in silico and predicting the effect on nearby phosphorylation sites, we demonstrate that the majority of lysine acetylation sites have the potential to impact protein phosphorylation, methylation, and ubiquitination status. Our work validates earlier smaller-scale studies on the acetylome and demonstrates the importance of PTM crosstalk for regulation of cellular function

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Bioinformatic Analysis and Post-Translational Modification Crosstalk Prediction of Lysine Acetylation

Recent proteomics studies suggest high abundance and a much wider role for lysine acetylation (K-Ac) in cellular functions. Nevertheless, cross influence between K-Ac and other post-translational modifications (PTMs) has not been carefully examined. Here, we used a variety of bioinformatics tools to analyze several available K-Ac datasets. Using gene ontology databases, we demonstrate that K-Ac sites are found in all cellular compartments. KEGG analysis indicates that the K-Ac sites are found on proteins responsible for a diverse and wide array of vital cellular functions. Domain structure prediction shows that K-Ac sites are found throughout a wide variety of protein domains, including those in heat shock proteins and those involved in cell cycle functions and DNA repair. Secondary structure prediction proves that K-Ac sites are preferentially found in ordered structures such as alpha helices and beta sheets. Finally, by mutating K-Ac sites in silico and predicting the effect on nearby phosphorylation sites, we demonstrate that the majority of lysine acetylation sites have the potential to impact protein phosphorylation, methylation, and ubiquitination status. Our work validates earlier smaller-scale studies on the acetylome and demonstrates the importance of PTM crosstalk for regulation of cellular function.</p

<i>N</i>⁶-methyladenosine of HIV-1 RNA regulates viral infection and HIV-1 Gag protein expression

The internal N6-methyladenosine (m6A) methylation of eukaryotic nuclear RNA controls post-transcriptional gene expression, which is regulated by methyltransferases (writers), demethylases (erasers), and m6A-binding proteins (readers) in cells. The YTH domain family proteins (YTHDF1-3) bind to m6A-modified cellular RNAs and affect RNA metabolism and processing. Here, we show that YTHDF1-3 proteins recognize m6A-modified HIV-1 RNA and inhibit HIV-1 infection in cell lines and primary CD4+ T-cells. We further mapped the YTHDF1-3 binding sites in HIV-1 RNA from infected cells. We found that the overexpression of YTHDF proteins in cells inhibited HIV-1 infection mainly by decreasing HIV-1 reverse transcription, while knockdown of YTHDF1-3 in cells had the opposite effects. Moreover, silencing the m6A writers decreased HIV-1 Gag protein expression in virus-producing cells, while silencing the m6A erasers increased Gag expression. Our findings suggest an important role of m6A modification of HIV-1 RNA in viral infection and HIV-1 protein synthesis

Optimized minimal genome-wide human sgRNA library

Abstract Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)-based knockout screening is revolting the genetic analysis of a cellular or molecular phenotype in question but is challenged by the large size of single-guide RNA (sgRNA) library. Here we designed a minimal genome-wide human sgRNA library, H-mLib, which is composed of 21,159 sgRNA pairs assembled based on a dedicated selection strategy from all potential SpCas9/sgRNAs in the human genome. These sgRNA pairs were cloned into a dual-gRNA vector each targeting one gene, resulting in a compact library size nearly identical to the number of human protein-coding genes. The performance of the H-mLib was benchmarked to other CRISPR libraries in a proliferation screening conducted in K562 cells. We also identified groups of core essential genes and cell-type specific essential genes by comparing the screening results from the K562 and Jurkat cells. Together, the H-mLib exemplified high specificity and sensitivity in identifying essential genes while containing minimal library complexity, emphasizing its advantages and applications in CRISPR screening with limited cell numbers