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Reprogramming of mRNA translation has a key role in cancer development and drug resistance 1 . However, the molecular mechanisms that are involved in this process remain poorly understood. Wobble tRNA modifications are required for specific codon decoding during translation 2 , 3 . Here we show, in humans, that the enzymes that catalyse modifications of wobble uridine 34 (U 34 ) tRNA (U 34 enzymes) are key players of the protein synthesis rewiring that is induced by the transformation driven by the BRAF V600E oncogene and by resistance to targeted therapy in melanoma. We show that BRAF V600E -expressing melanoma cells are dependent on U 34 enzymes for survival, and that concurrent inhibition of MAPK signalling and ELP3 or CTU1 and/or CTU2 synergizes to kill melanoma cells. Activation of the PI3K signalling pathway, one of the most common mechanisms of acquired resistance to MAPK therapeutic agents, markedly increases the expression of U 34 enzymes. Mechanistically, U 34 enzymes promote glycolysis in melanoma cells through the direct, codon-dependent, regulation of the translation of HIF1A mRNA and the maintenance of high levels of HIF1α protein. Therefore, the acquired resistance to anti-BRAF therapy is associated with high levels of U 34 enzymes and HIF1α. Together, these results demonstrate that U 34 enzymes promote the survival and resistance to therapy of melanoma cells by regulating specific mRNA translation. Enzymes that catalyse modifications of wobble uridine 34 tRNA are essential for the survival of melanoma cells that rely on HIF1α-dependent metabolism through codon-dependent regulation of the translation of HIF1A mRNA.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships 1 , 2 . Here we present a human ‘all-by-all’ reference interactome map of human binary protein interactions, or ‘HuRI’. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome 3 , transcriptome 4 and proteome 5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes. A human binary protein interactome map that includes around 53,000 protein–protein interactions involving more than 8,000 proteins provides a reference for the study of human cellular function in health and disease.

Prolonged cell survival occurs through the expression of specific protein isoforms generated by alternate splicing of mRNA precursors in cancer cells. How alternate splicing regulates tumor development and resistance to targeted therapies in cancer remain poorly understood. Here we show that RNF113A, whose loss-of-function causes the X-linked trichothiodystrophy, is overexpressed in lung cancer and protects from Cisplatin-dependent cell death. RNF113A is a RNA-binding protein which regulates the splicing of multiple candidates involved in cell survival. RNF113A deficiency triggers cell death upon DNA damage through multiple mechanisms, including apoptosis via the destabilization of the prosurvival protein MCL-1, ferroptosis due to enhanced SAT1 expression, and increased production of ROS due to altered Noxa1 expression. RNF113A deficiency circumvents the resistance to Cisplatin and to BCL-2 inhibitors through the destabilization of MCL-1, which thus defines spliceosome inhibitors as a therapeutic approach to treat tumors showing acquired resistance to specific drugs due to MCL-1 stabilization. Alternate splicing of mRNA precursors has been linked to tumor development. Here the authors reveal a role of the E3 ligase RNF113A in spliceosome regulation affecting cell survival upon DNA damage.

Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as ‘nodes’ and ‘edges’, respectively. Better understanding of genotype‐to‐phenotype relationships in human disease will require modeling of how disease‐causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products (‘node removal’) and interaction‐specific or edge‐specific (‘edgetic’) alterations. Global computational analyses of ∼50 000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies. Synopsis Genotype‐to‐phenotype relationships in human genetic disease are often modeled as: ‘mutation in gene X leads to loss of gene product X, which leads to disease A’. However, single ‘gene‐loss’ models cannot explain the increasingly appreciated prevalence of complex genotype‐to‐phenotype relationships, particularly with instances of allelic or locus hetrogeneity (Goh et al , 2007 ). Genes and gene products function not in isolation but as components of complex networks of macromolecules (DNA, RNA, or proteins) and metabolites linked through biochemical or physical interactions, often represented in ‘interactome’ network models as ‘nodes’ and ‘edges’, respectively. Here we use network perturbation models to explain molecular dysfunctions underlying human disease in addition to the gene‐loss model. We hypothesize that different mutations leading to different molecular defects to proteins may cause distinct perturbations of cellular networks, giving rise to distinct phenotypic outcomes (Figure 1 ). For example, truncations close to the start of an open‐reading frame, or mutations that grossly destabilize a protein structure, can be modeled as removing a protein node from the network (‘node removal’). Alternatively, single amino‐acid substitutions that affect specific binding sites, or truncations that preserve certain domains of a protein, may give rise to partially functional gene products with specific changes in distinct molecular interaction(s) (edge‐specific or ‘edgetic’ perturbations) (Figure 1B ). Taking advantage of the large number of known disease‐causing allelic variations in human Mendelian disorders, we investigated how disease‐associated mutations may cause complete loss of gene products or, alternatively, may cause specific loss or gain of individual molecular interaction(s). We examined ∼50 000 Mendelian disease‐causing alleles, affecting over 1900 protein‐coding genes, altogether associated with more than 2000 human disorders available in the Human Gene Mutation Database (HGMD) (Stenson et al , 2003 ), that can be subdivided into two subsets: truncating’ alleles (truncations or frameshifts caused by stop codons, out‐of‐frame insertions or deletions, or defective splicing) versus ‘in‐frame’ alleles (missense mutations and in‐frame insertions or deletions). Over 50% (27 919/52 491) of Mendelian alleles in HGMD correspond to ‘in‐frame’ mutations. Our hypothesis is that, ‘in‐frame’ alleles may affect specific interactions of a given gene product while leaving most other interactions unperturbed. Although exceptions may apply, our hypothesis has several predictions. First, ‘truncating’ versus ‘in‐frame’ alleles may distribute differently among autosomal dominant and autosomal recessive disease, given that dominant mutations are more likely to be edgetic than recessive ones. Indeed, autosomal dominant and autosomal recessive traits annotated in the Online Mendelian Inheritance in Man (OMIM) database (Hamosh et al , 2005 ) show a clear separation with respect to the associated ‘in‐frame’ versus ‘truncating’ mutations. Among genes affected solely by ‘in‐frame’ mutations, the proportion of dominant diseases is ∼10‐fold higher than that of recessive ones, supporting ‘in‐frame’ mutations causing distinct molecular defects as opposed to ‘truncating’ mutations. A proof‐of‐principle characterization of binary protein interaction defects of mutant alleles associated with five genetic disorders supports our hypothesis that ‘in‐frame’ alleles indeed produce mostly functional proteins, preserving many specific protein interactions. As grossly disruptive mutations versus mutations leading to loss or gain of specific interaction(s) probably distribute differently on protein structures, we examined available three‐dimensional structures of all disease proteins. Mutated residues in autosomal dominant disease are significantly more exposed to the surface of the structure than those in autosomal recessive disease, consistent with the idea that disease with distinct modes of inheritance probably involves distinct network perturbations. A second testable prediction of our edgetic perturbation model is that edgetic perturbation versus gene loss for a given gene product might in some cases cause different diseases. We examined 142 genes associated with two or more distinct diseases in which at least five distinct alleles have been reported for each disease. We found ∼30% of the cases for which distribution of ‘in‐frame’ versus ‘truncating’ mutations is significantly different between the two diseases linked to the same gene ( P <0.05). Hence, when affecting the same gene, node removal versus edgetic perturbation can confer strikingly different phenotypes. A third testable prediction is that different edgetic perturbations for a given gene product might cause phenotypically distinguishable diseases (Figure 6 ). We used predicted Pfam domains (Finn et al , 2006 ) as surrogates for functional interaction domains, assuming that ‘in‐frame’ mutations located in distinct Pfam domain‐encoding sequences probably alter distinct interactions. Among 169 genes associated with two or more diseases and encoding proteins containing at least two Pfam domains, nine proteins have at least two Pfam domains significantly enriched with ‘in‐frame’ mutations ( P <0.05). For each of the nine proteins, we found a striking pattern of near mutual exclusivity, whereby different Pfam domains seem to be specifically affected in distinct disorders (Figure 6B ). We conclude that edgetic alleles probably underlie many complex genotype‐to‐phenotype relationships in human disease, such as incomplete penetrance or variable expressivity, as well as allele‐specific phenotypic variations among patients. Edgetic perturbation of human inherited disorders might help explain how seemingly devastating alleles have appeared and persevered in human populations. We present alternative models to explain molecular dysfunctions underlying human inherited disorders based on interaction‐specific or “edgetic” perturbations rather than complete loss of gene products. We find that a substantial fraction of known genetic variants in human Mendelian disorders likely cause edgetic perturbations. We find frequent situations where edgetic perturbation models can explain how different mutations in a single gene can cause distinct disorders. Edgetic perturbation models should provide alternative explanations to complex genotype‐to‐phenotype relationships

Background Elevated aerobic glycolysis rate is a biochemical alteration associated with malignant transformation and cancer progression. This metabolic shift unavoidably generates methylglyoxal (MG), a potent inducer of dicarbonyl stress through the formation of advanced glycation end products (AGEs). We have previously shown that the silencing of glyoxalase 1 (GLO1), the main MG detoxifying enzyme, generates endogenous dicarbonyl stress resulting in enhanced growth and metastasis in vivo. However, the molecular mechanisms through which MG stress promotes metastasis development remain to be unveiled. Methods In this study, we used RNA sequencing analysis to investigate gene-expression profiling of GLO1-depleted breast cancer cells and we validated the regulated expression of selected genes of interest by RT-qPCR. Using in vitro and in vivo assays, we demonstrated the acquisition of a pro-metastatic phenotype related to dicarbonyl stress in MDA-MB-231, MDA-MB-468 and MCF7 breast cancer cellular models. Hyperactivation of MEK/ERK/SMAD1 pathway was evidenced using western blotting upon endogenous MG stress and exogenous MG treatment conditions. MEK and SMAD1 regulation of MG pro-metastatic signature genes in breast cancer cells was demonstrated by RT-qPCR. Results High-throughput transcriptome profiling of GLO1-depleted breast cancer cells highlighted a pro-metastatic signature that establishes novel connections between MG dicarbonyl stress, extracellular matrix (ECM) remodeling by neoplastic cells and enhanced cell migration. Mechanistically, we showed that these metastasis-related processes are functionally linked to MEK/ERK/SMAD1 cascade activation in breast cancer cells. We showed that sustained MEK/ERK activation in GLO1-depleted cells notably occurred through the down-regulation of the expression of dual specificity phosphatases in MG-stressed breast cancer cells. The use of carnosine and aminoguanidine, two potent MG scavengers, reversed MG stress effects in in vitro and in vivo experimental settings. Conclusions These results uncover for the first time the key role of MG dicarbonyl stress in the induction of ECM remodeling and the activation of migratory signaling pathways, both in favor of enhanced metastatic dissemination of breast cancer cells. Importantly, the efficient inhibition of mitogen-activated protein kinase (MAPK) signaling using MG scavengers further emphasizes the need to investigate their therapeutic potential across different malignancies.

Viral infections are known to hijack the transcription and translation of the host cell. However, the extent to which viral proteins coordinate these perturbations remains unclear. Here we used a model system, the human T-cell leukemia virus type 1 (HTLV-1), and systematically analyzed the transcriptome and interactome of key effectors oncoviral proteins Tax and HBZ. We showed that Tax and HBZ target distinct but also common transcription factors. Unexpectedly, we also uncovered a large set of interactions with RNA-binding proteins, including the U2 auxiliary factor large subunit (U2AF2), a key cellular regulator of pre-mRNA splicing. We discovered that Tax and HBZ perturb the splicing landscape by altering cassette exons in opposing manners, with Tax inducing exon inclusion while HBZ induces exon exclusion. Among Tax- and HBZ-dependent splicing changes, we identify events that are also altered in Adult T cell leukemia/lymphoma (ATLL) samples from two independent patient cohorts, and in well-known cancer census genes. Our interactome mapping approach, applicable to other viral oncogenes, has identified spliceosome perturbation as a novel mechanism coordinated by Tax and HBZ to reprogram the transcriptome.

Newborn screening (NBS) has evolved significantly since its inception, yet many treatable rare diseases remain unscreened due to technical limitations. The BabyDetect study used gene panel sequencing to expand NBS to treatable conditions not covered by conventional biochemical screening. We present here the analytical validation of this workflow, assessing sensitivity, precision, and reproducibility using dried blood spots from newborns. We implemented strict quality control thresholds for sequencing, coverage, and contamination, ensuring high reliability. Longitudinal monitoring confirmed consistent performance across more than 5900 samples. Automation of DNA extraction improved scalability, and a panel redesign enhanced the coverage and selection of targeted regions. By focusing on known pathogenic/likely pathogenic variants, we minimized false positives and maintained clinical actionability. Our findings demonstrate that gene panel sequencing-based NBS is feasible, accurate, and scalable, addressing critical gaps in current screening programs.

Non-coding RNAs (ncRNA) represent 1/5 of the mammalian transcript number, and 90% of the genome length is transcribed. Many ncRNAs play a role in cancer. Among them, non-coding natural antisense transcripts (ncNAT) are RNA sequences that are complementary and overlapping to those of either protein-coding (PCT) or non-coding transcripts. Several ncNATs were described as regulating protein coding gene expression on the same loci, and they are expected to act more frequently in cis compared to other ncRNAs that commonly function in trans . In this work, 22 breast cancers expressing estrogen receptors and their paired adjacent non-malignant tissues were analyzed by strand-specific RNA sequencing. To highlight ncNATs potentially playing a role in protein coding gene regulations that occur in breast cancer, three different data analysis methods were used: differential expression analysis of ncNATs between tumor and non-malignant tissues, differential correlation analysis of paired ncNAT/PCT between tumor and non-malignant tissues, and ncNAT/PCT read count ratio variation between tumor and non-malignant tissues. Each of these methods yielded lists of ncNAT/PCT pairs that were enriched in survival-associated genes. This work highlights ncNAT lists that display potential to affect the expression of protein-coding genes involved in breast cancer pathology.

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical‐versus‐functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an “inter‐interactome” approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter‐interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra‐species networks. Although substantially reduced relative to intra‐species networks, the levels of functional overlap in the yeast–human inter‐interactome network uncover significant remnants of co‐functionality widely preserved in the two proteomes beyond human–yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co‐functionality. Such non‐functional interactions, however, represent a reservoir from which nascent functional interactions may arise. Synopsis An inter‐species “inter‐interactome” was generated by systematic mapping protein–protein interactions between human and yeast proteomes. Comparisons of the inter‐species interactome with the two “parent” intra‐species human and yeast networks reveal evolutionary constraints and plasticity of biological systems. The human and yeast proteomes widely retain the ability to form inter‐species protein–protein interactions. Inter‐species interactions significantly but not exclusively correspond to ancestral binding properties preserved in human and yeast proteins. Ancestral binding properties appear to underlie conserved and species‐specific functions. Graphical Abstract An inter‐species “inter‐interactome” was generated by systematic mapping protein–protein interactions between human and yeast proteomes. Comparisons of the inter‐species interactome with the two “parent” intra‐species human and yeast networks reveal evolutionary constraints and plasticity of biological systems.