The conditions that led to the formation of the first organisms and the ways that life originates from a lifeless chemical soup are poorly understood. The recent hypothesis of “RNA-peptide coevolution” suggests that the current close relationship between amino acids and nucleobases may well have extended to the origin of life. We now show how the interplay between these compound classes can give rise to new self-replicating molecules using a dynamic combinatorial approach. We report two strategies for the fabrication of chimeric amino acid/nucleobase self-replicating macrocycles capable of exponential growth. The first one relies on mixing nucleobase- and peptide-based building blocks, where the ligation of these two gives rise to highly specific chimeric ring structures. The second one starts from peptide nucleic acid (PNA) building blocks in which nucleobases are already linked to amino acids from the start. While previously reported nucleic acid-based self-replicating systems rely on presynthesis of (short) oligonucleotide sequences, self-replication in the present systems start from units containing only a single nucleobase. Self-replication is accompanied by self-assembly, spontaneously giving rise to an ordered one-dimensional arrangement of nucleobase nanostructures.

Recent studies have established a concept of tumour necrosis factor‐α (TNF‐α)/Fas signalling crosstalk, highlighting TNF‐α as a critical cytokine in sensitizing hepatocytes to death induced by Fas activation. However, in the exact inflammatory response, besides TNF‐α, many other mediators, that might modulate apoptotic response differentially, are released. To resolve the issue, we studied the effects of lipopolysaccharide (LPS), one of the crucial inductors of inflammation in the liver, on apoptotic outcome. We show that LPS‐induced inflammation diminishes the sensitivity of hepatocytes to Fas stimulus in vivo at caspase‐8 level. Analysis of molecular mechanisms revealed an increased expression of various pro‐inflammatory cytokines in non‐parenchymal liver cells and hepatocyte‐specific increase in Bcl‐xL, associated with signal transducer and activator of transcription 3 (Stat3) phosphorylation. Pre‐treatment with ruxolitinib, a selective Janus kinase (JAK) 1/2 inhibitor, prevented the LPS‐induced Stat3 phosphorylation and restored the sensitivity of hepatocytes to Fas‐mediated apoptosis. Furthermore, ruxolitinib pre‐treatment diminished the LPS‐induced Bcl‐xL up‐regulation without an inhibitory effect on LPS‐induced expression of pro‐inflammatory cytokines. In summary, although the reports are showing that the effects of isolated pro‐inflammatory mediators, such as TNF‐α or neutrophils, are pro‐apoptotic, the overall effect of inflammatory milieu on hepatocytes in vivo is Stat3‐dependent desensitization to Fas‐mediated apoptosis.

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3–5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes. Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity. The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

Abstract As smartphone use continues to become more embedded within daily life, identifying the factors driving their use in extreme environments may have numerous meaningful implications. Little is currently known about mountaineers’ intentions to use smartphones in high-alpine environments. Therefore, the purpose of this study was to examine the extent to which attitude, subjective norm, and perceived behavioral control predicted mountaineers’ intentions to use smartphones in high-alpine environments. A sample of 167 mountaineers from 37 countries completed a brief questionnaire about their intentions to use smartphones during their next high-alpine expedition. A series of multiple regression analyses were used to determine the salient beliefs influencing mountaineers’ smartphone use in high-alpine environments. The study findings provide a better understanding of the potential factors driving mountaineers’ use of smartphones. More broadly, these findings add to the growing body of literature regarding smartphone use in extreme environments.

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis. Joana Carlevaro-Fita, Andrés Lanzós et al. present the Cancer LncRNA Census (CLC), a manually curated dataset of 122 long noncoding RNAs (lncRNAs) with experimentally-validated functions in cancer based on data from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. CLC lncRNAs have unique gene features, and a number display evidence for cancer-driving functions that are conserved from humans to mice.

Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations. Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery. Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.

Abstract Methadone eliminates heroin use, reduces death rates and criminality associated with heroin use, and improves patients’ health and social productivity. This study included long-term addicts who completed a methadone therapy program as well as relapsed patients. Liver and renal markers important for methadone metabolism were analyzed. Renal markers included urea and creatinine, while hepatic markers included total bilirubin, AST, ALT, γGT, and LDH as nonspecific but significant parameters of liver metabolism. The study included 34 male and 6 female heroin-dependent patients undergoing a rehabilitation program with methadone maintenance treatment (MMT). During therapy, average values of all parameters remained within the reference interval but individual parameters in some patients were very high. Significant differences for urea (0.00) and very high individual variations in all parameters, especially γGT and LDH, were found in patients who were in relapse. Age of the patients did not show a correlation with the presence of significant differences in serum biochemical parameters during therapy. Prolonged use of methadone therapy stabilizes high variations of liver and renal markers. MMT achieves a stabilization of serum indicators relevant for methadone metabolism that correlates with the duration of consumption and the type of opioid substance. The most important hepato-renal markers as indicators of therapy success are γGT, LDH, and creatinine. The validity of former enzymatic tests (AST, ALP, and ALT) should be seriously reconsidered in terms of MTT treatment success and monitoring the health of heroin addicts.

Hydrops fetalis is a serious condition indicating a bad prognosis of affected fetuses. Incidence of immune hydropsfetalis is significantly decreasing, whereas more and more non-immune hydropsfetalisis are identified. We described a case of the most difficult manifestation of hemolytic disease of a newborn due to rhesus incompatibility. Immune hydrops fetalis occurred due to inadequate immune prophylaxis. While treating the newborn, we applied exchange transfusion, additional transfusion and immunoglobulin therapy. With sensitized pregnant patients, it is necessary to regularly monitor the condition of fetus and titer of mother’s antibodies. Considering a difficulty of affected fetuses’ disease, it is necessary to strengthen preventive measures by application of rhesus immunoglobulin with affected Rh negative mothers.

A noninvasive diagnostic procedure that allows for in vivo microscopic examination of the epidermis, the dermoepidermal junction, and the papillary dermis. This aids in the identification of specific diagnostic patterns related to color and cell structure to aid in differentiating malignant and benign lesions.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1–4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5′ region of TP53, in the 3′ untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available. Analyses of 2,658 whole genomes across 38 types of cancer identify the contribution of non-coding point mutations and structural variants to driving cancer.