ZMIZ1 enhances ERα-dependent expression of E2F2 in breast cancer.

The estrogen receptor-α (ER) drives 75% of breast cancers. On activation, the ER recruits and assembles a 1-2 MDa transcriptionally active complex. These complexes can modulate tumour growth, and understanding the roles of individual proteins within these complexes can help identify new therapeutic targets. Here, we present the discovery of ER and ZMIZ1 within the same multi-protein assembly by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating a significant decrease in the proliferation of ER-positive cancer cell lines. To establish a role for the ER-ZMIZ1 interaction, we measured the transcriptional changes in the estrogen response post-ZMIZ1 knockdown using an RNA-seq time-course over 24 h. Gene set enrichment analysis of the ZMIZ1-knockdown data identified a specific delay in the response of estradiol-induced cell cycle genes. Integration of ENCODE data with our RNA-seq results identified that ER and ZMIZ1 both bind the promoter of E2F2. We therefore propose that ER and ZMIZ1 interact to enable the efficient estrogenic response at subset of cell cycle genes via a novel ZMIZ1-ER-E2F2 signalling axis. Finally, we show that high ZMIZ1 expression is predictive of worse patient outcome, ER and ZMIZ1 are co-expressed in breast cancer patients in TCGA and METABRIC, and the proteins are co-localised within the nuclei of tumour cell in patient biopsies. In conclusion, we establish that ZMIZ1 is a regulator of the estrogenic cell cycle response and provide evidence of the biological importance of the ER-ZMIZ1 interaction in ER-positive patient tumours, supporting potential clinical relevance.

Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk.

BACKGROUND: Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers. METHODS: We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer. RESULTS: We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. CONCLUSIONS: Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

Direct and indirect responses of the Arabidopsis transcriptome to an induced increase in trehalose 6-phosphate.

Trehalose 6-phosphate (Tre6P) is an essential signal metabolite that regulates the level of sucrose, linking growth and development to the metabolic status. We hypothesized that Tre6P plays a role in mediating the regulation of gene expression by sucrose. To test this, we performed transcriptomic profiling on Arabidopsis (Arabidopsis thaliana) plants that expressed a bacterial TREHALOSE 6-PHOSPHATE SYNTHASE (TPS) under the control of an ethanol-inducible promoter. Induction led to a 4-fold rise in Tre6P levels, a concomitant decrease in sucrose, significant changes (FDR ≤ 0.05) of over 13,000 transcripts, and two-fold or larger changes of over 5000 transcripts. Comparison with nine published responses to sugar availability allowed some of these changes to be linked to the rise in Tre6P, while others were probably due to lower sucrose or other indirect effects. Changes linked to Tre6P included repression of photosynthesis-related gene expression and induction of many growth-related processes including ribosome biogenesis. About 500 starvation-related genes are known to be induced by SUCROSE-NON-FERMENTING-1-RELATED KINASE 1 (SnRK1). They were largely repressed by Tre6P in a manner consistent with SnRK1 inhibition by Tre6P. SnRK1 also represses many genes that are involved in biosynthesis and growth. These responded to Tre6P in a more complex manner, pointing toward Tre6P interacting with other C-signaling pathways. Additionally, elevated Tre6P modified the expression of genes encoding regulatory subunits of the SnRK1 complex and TPS class II and FCS-LIKE ZINC FINGER proteins that are thought to modulate SnRK1 function and genes involved in circadian, TARGET OF RAPAMYCIN-, light, abscisic acid, and other hormone signaling.

Transcriptional and metabolic effects of aspartate-glutamate carrier isoform 1 (AGC1) downregulation in mouse oligodendrocyte precursor cells (OPCs).

Aspartate-glutamate carrier isoform 1 (AGC1) is a carrier responsible for the export of mitochondrial aspartate in exchange for cytosolic glutamate and is part of the malate-aspartate shuttle, essential for the balance of reducing equivalents in the cells. In the brain, mutations in SLC25A12 gene, encoding for AGC1, cause an ultra-rare genetic disease, reported as a neurodevelopmental encephalopathy, whose symptoms include global hypomyelination, arrested psychomotor development, hypotonia and seizures. Among the biological components most affected by AGC1 deficiency are oligodendrocytes, glial cells responsible for myelination processes, and their precursors [oligodendrocyte progenitor cells (OPCs)]. The AGC1 silencing in an in vitro model of OPCs was documented to cause defects of proliferation and differentiation, mediated by alterations of histone acetylation/deacetylation. Disrupting AGC1 activity could possibly reduce the availability of acetyl groups, leading to perturbation of many biological pathways, such as histone modifications and fatty acids formation for myelin production. Here, we explore the transcriptome of mouse OPCs partially silenced for AGC1, reporting results of canonical analyses (differential expression) and pathway enrichment analyses, which highlight a disruption in fatty acids synthesis from both a regulatory and enzymatic stand. We further investigate the cellular effects of AGC1 deficiency through the identification of most affected transcriptional networks and altered alternative splicing. Transcriptional data were integrated with differential metabolite abundance analysis, showing downregulation of several amino acids, including glutamine and aspartate. Taken together, our results provide a molecular foundation for the effects of AGC1 deficiency in OPCs, highlighting the molecular mechanisms affected and providing a list of actionable targets to mitigate the effects of this pathology.

Genomic and transcriptomic analysis of the recent Mpox outbreak.

The Mpox (formerly named Monkeypox) virus is the etiological cause of a recent multi-country outbreak, with thousands of distinct cases detected outside the endemic areas of Africa as of December 2023. In this article, we analyze the sequences of full genomes of Mpox virus from Europe and compare them with all available Mpox sequences of historical relevance, annotated by year and geographic origin, as well as related Cowpox and Variola (smallpox) virus sequences. Our results show that the recent outbreak is most likely originating from the West African clade of Mpox, with >99 % sequence identity with sequences derived from historical and recent cases, dating from 1971 to 2017. We analyze specific mutations occurring in viral proteins between the current outbreak, previous Mpox and Cowpox sequences, and the historical Variola virus. Genome-wide sequence analysis of the recent outbreak and other Mpox/Cowpox/Variola viruses shows a very high conservation, with 97.9 % (protein-based) and 97.8 % (nucleotide-based) sequence identity. We identified significant correlation in human transcriptional responses as well, with a conserved immune pathway response induced in human cell cultures by the three families of Pox virus. The similarities identified between the major strains of Pox viruses, as well as within the Mpox clades, both at the genomic and transcriptomic levels, provide a molecular basis for the observed efficacy of Variola vaccines in other Poxviruses.

Quantitative and qualitative detection of tRNAs, tRNA halves and tRFs in human cancer samples: Molecular grounds for biomarker development and clinical perspectives.

Transfer RNAs (tRNAs) are small non-coding RNAs playing a central role during protein synthesis. Besides translation, growing evidence suggests that in many contexts, precursor or mature tRNAs can also be processed into smaller fragments playing many non-canonical regulatory roles in different biological pathways with oncogenic relevance. Depending on the source, these molecules can be classified as tRNA halves (also known as tiRNAs) or tRNA-derived fragments (tRFs), and furtherly divided into 5'-tRNA and 3'-tRNA halves, or tRF-1, tRF-2, tRF-3, tRF-5, and i-tRF, respectively. Unlike DNA and mRNA, high-throughput sequencing of tRNAs is challenging, because of technical limitations of currently developed sequencing methods. In recent years, different sequencing approaches have been proposed allowing the quantification and identification of an increasing number of tRNA fragments with critical functions in distinct physiological and pathophysiological processes. In the present review, we discussed pros and cons of recent advances in different sequencing methods, also introducing the expanding repertoire of bioinformatics tool and resources specifically focused on tRNA research and discussing current issues in the study of these small RNA molecules. Furthermore, we discussed the potential value of tRNA fragments as diagnostic and prognostic biomarkers for different types of cancers.

Coronary Revascularization after Transcatheter and Surgical Aortic Valve Replacement.

INTRODUCTION: Due to the selective criteria and short-term follow-up of previous transcatheter aortic valve implantation (TAVI) trials, the coronary revascularization incidence after TAVI has been difficult to determine. This study investigated the epidemiology of coronary revascularization after surgical aortic valve replacement (SAVR) and TAVI in patients with severe aortic valve stenosis (AS), with and without coronary artery disease (CAD), in a mid-term follow-up, single-center, real-world setting. METHODS: Between 2010 to 2020, 1486 patients with AS underwent SAVR or TAVI with balloon-expandable Edwards® transcatheter heart valves (THVs). Using hospital discharge records, we could estimate for each patient resident in Emilia Romagna the rate of ischemic events treated with percutaneous coronary intervention (PCI). A subgroup without CAD was also analyzed. RESULTS: The 5-year overall survival was 78.2%. Freedom from PCI after AVR and TAVI at 5 years was 96.9% and 96.9%, respectively, with previous PCI as a predictor (HR 4.86, 95% CI 2.57-9.21 p < 0.001). The freedom from PCI curves were not significantly different. CONCLUSIONS: Notwithstanding the aged population, the revascularization incidence was only 2.4%, requiring further evaluation even in younger patients with longer follow-up. Despite the profile frame raise due to the evolution of Edwards® balloon-expandable THVs, PCI or coronarography feasibility were not compromised in our population.

Targeted quantitative metabolic profiling of brain-derived cell cultures by semi-automated MEPS and LC-MS/MS.

The accurate characterisation of metabolic profiles is an important prerequisite to determine the rate and the efficiency of the metabolic pathways taking place in the cells. Changes in the balance of metabolites involved in vital processes such as glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), as well as in the biochemical pathways related to amino acids, lipids, nucleotides, and their precursors reflect the physiological condition of the cells and may contribute to the development of various human diseases. The feasible and reliable measurement of a wide array of metabolites and biomarkers possesses great potential to elucidate physiological and pathological mechanisms, aid preclinical drug development and highlight potential therapeutic targets. An effective, straightforward, sensitive, and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach was developed for the simultaneous quali-quantitative analysis of 41 compounds in both cell pellet and cell growth medium obtained from brain-derived cell cultures. Sample pretreatment miniaturisation was achieved thanks to the development and optimisation of an original extraction/purification approach based on digitally programmed microextraction by packed sorbent (eVol®-MEPS). MEPS allows satisfactory and reproducible clean-up and preconcentration of both low-volume homogenate cell pellet lysate and cell growth medium with advantages including, but not limited to, minimal sample handling and method sustainability in terms of sample, solvents, and energy consumption. The MEPS-LC-MS/MS method showed good sensitivity, selectivity, linearity, and precision. As a proof of concept, the developed method was successfully applied to the analysis of both cell pellet and cell growth medium obtained from a line of mouse immortalised oligodendrocyte precursor cells (OPCs; Oli-neu cell line), leading to the unambiguous determination of all the considered target analytes. This method is thus expected to be suitable for targeted, quantitative metabolic profiling in most brain cell models, thus allowing accurate investigations on the biochemical pathways that can be altered in central nervous system (CNS) neuropathologies, including e.g., mitochondrial respiration and glycolysis, or use of specific nutrients for growth and proliferation, or lipid, amino acid and nucleotide metabolism.

Cross-Species Transcriptomics Analysis Highlights Conserved Molecular Responses to Per- and Polyfluoroalkyl Substances.

In recent decades, per- and polyfluoroalkyl substances (PFASs) have garnered widespread public attention due to their persistence in the environment and detrimental effects on the health of living organisms, spurring the generation of several transcriptome-centered investigations to understand the biological basis of their mechanism. In this study, we collected 2144 publicly available samples from seven distinct animal species to examine the molecular responses to PFAS exposure and to determine if there are conserved responses. Our comparative transcriptional analysis revealed that exposure to PFAS is conserved across different tissues, molecules and species. We identified and reported several genes exhibiting consistent and evolutionarily conserved transcriptional response to PFASs, such as ESR1, HADHA and ID1, as well as several pathways including lipid metabolism, immune response and hormone pathways. This study provides the first evidence that distinct PFAS molecules induce comparable transcriptional changes and affect the same metabolic processes across inter-species borders. Our findings have significant implications for understanding the impact of PFAS exposure on living organisms and the environment. We believe that this study offers a novel perspective on the molecular responses to PFAS exposure and provides a foundation for future research into developing strategies for mitigating the detrimental effects of these substances in the ecosystem.

Copper chelation suppresses epithelial-mesenchymal transition by inhibition of canonical and non-canonical TGF-ß signaling pathways in cancer.

BACKGROUND: Metastatic cancer cells exploit Epithelial-mesenchymal-transition (EMT) to enhance their migration, invasion, and resistance to treatments. Recent studies highlight that elevated levels of copper are implicated in cancer progression and metastasis. Clinical trials using copper chelators are associated with improved patient survival; however, the molecular mechanisms by which copper depletion inhibits tumor progression and metastasis are poorly understood. This remains a major hurdle to the clinical translation of copper chelators. Here, we propose that copper chelation inhibits metastasis by reducing TGF-ß levels and EMT signaling. Given that many drugs targeting TGF-ß have failed in clinical trials, partly because of severe side effects arising in patients, we hypothesized that copper chelation therapy might be a less toxic alternative to target the TGF-ß/EMT axis. RESULTS: Our cytokine array and RNA-seq data suggested a link between copper homeostasis, TGF-ß and EMT process. To validate this hypothesis, we performed single-cell imaging, protein assays, and in vivo studies. Here, we used the copper chelating agent TEPA to block copper trafficking. Our in vivo study showed a reduction of TGF-ß levels and metastasis to the lung in the TNBC mouse model. Mechanistically, TEPA significantly downregulated canonical (TGF-ß/SMAD2&3) and non-canonical (TGF-ß/PI3K/AKT, TGF-ß/RAS/RAF/MEK/ERK, and TGF-ß/WNT/ß-catenin) TGF-ß signaling pathways. Additionally, EMT markers of MMP-9, MMP-14, Vimentin, ß-catenin, ZEB1, and p-SMAD2 were downregulated, and EMT transcription factors of SNAI1, ZEB1, and p-SMAD2 accumulated in the cytoplasm after treatment. CONCLUSIONS: Our study suggests that copper chelation therapy represents a potentially effective therapeutic approach for targeting TGF-ß and inhibiting EMT in a diverse range of cancers.

A History of Repeated Alcohol Intoxication Promotes Cognitive Impairment and Gene Expression Signatures of Disease Progression in the 3xTg Mouse Model of Alzheimer's Disease.

The impact of alcohol abuse on Alzheimer's disease (AD) is poorly understood. Here, we show that the onset of neurocognitive impairment in a mouse model of AD is hastened by repeated alcohol intoxication through exposure to alcohol vapor, and we provide a comprehensive gene expression dataset of the prefrontal cortex by the single-nucleus RNA sequencing of 113,242 cells. We observed a broad dysregulation of gene expression that involves neuronal excitability, neurodegeneration, and inflammation, including interferon genes. Several genes previously associated with AD in humans by genome-wide association studies were differentially regulated in specific neuronal populations. The gene expression signatures of AD mice with a history of alcohol intoxication showed greater similarity to the signatures of older AD mice with advanced disease and cognitive impairment than did the gene expression signatures of AD mice not exposed to alcohol, suggesting that alcohol promotes transcriptional changes consistent with AD progression. Our gene expression dataset at the single-cell level provides a unique resource for investigations of the molecular bases of the detrimental role of excessive alcohol intake in AD.

Neuronopathic Gaucher disease models reveal defects in cell growth promoted by Hippo pathway activation.

Gaucher Disease (GD), the most common lysosomal disorder, arises from mutations in the GBA1 gene and is characterized by a wide spectrum of phenotypes, ranging from mild hematological and visceral involvement to severe neurological disease. Neuronopathic patients display dramatic neuronal loss and increased neuroinflammation, whose molecular basis are still unclear. Using a combination of Drosophila dGBA1b loss-of-function models and GD patient-derived iPSCs differentiated towards neuronal precursors and mature neurons we showed that different GD- tissues and neuronal cells display an impairment of growth mechanisms with an increased cell death and reduced proliferation. These phenotypes are coupled with the downregulation of several Hippo transcriptional targets, mainly involved in cells and tissue growth, and YAP exclusion from nuclei. Interestingly, Hippo knock-down in the GBA-KO flies rescues the proliferative defect, suggesting that targeting the Hippo pathway can be a promising therapeutic approach to neuronopathic GD.

Single-Cell Sequencing Identifies Master Regulators Affected by Panobinostat in Neuroblastoma Cells.

The molecular mechanisms and gene regulatory networks sustaining cell proliferation in neuroblastoma (NBL) cells are still not fully understood. In this tumor context, it has been proposed that anti-proliferative drugs, such as the pan-HDAC inhibitor panobinostat, could be tested to mitigate tumor progression. Here, we set out to investigate the effects of panobinostat treatment at the unprecedented resolution offered by single-cell sequencing. We identified a global senescence signature paired with reduction in proliferation in treated Kelly cells and more isolated transcriptional responses compatible with early neuronal differentiation. Using master regulator analysis, we identified BAZ1A, HCFC1, MAZ, and ZNF146 as the transcriptional regulators most significantly repressed by panobinostat. Experimental silencing of these transcription factors (TFs) confirmed their role in sustaining NBL cell proliferation in vitro.

Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data.

Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as 'signaling hubs'. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called 'SURFACER'. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.

The R Language: An Engine for Bioinformatics and Data Science.

The R programming language is approaching its 30th birthday, and in the last three decades it has achieved a prominent role in statistics, bioinformatics, and data science in general. It currently ranks among the top 10 most popular languages worldwide, and its community has produced tens of thousands of extensions and packages, with scopes ranging from machine learning to transcriptome data analysis. In this review, we provide an historical chronicle of how R became what it is today, describing all its current features and capabilities. We also illustrate the major tools of R, such as the current R editors and integrated development environments (IDEs), the R Shiny web server, the R methods for machine learning, and its relationship with other programming languages. We also discuss the role of R in science in general as a driver for reproducibility. Overall, we hope to provide both a complete snapshot of R today and a practical compendium of the major features and applications of this programming language.

Escalated (Dependent) Oxycodone Self-Administration Is Associated with Cognitive Impairment and Transcriptional Evidence of Neurodegeneration in Human Immunodeficiency Virus (HIV) Transgenic Rats.

Substance use disorder is associated with accelerated disease progression in people with human immunodeficiency virus (HIV; PWH). Problem opioid use, including high-dose opioid therapy, prescription drug misuse, and opioid abuse, is high and increasing in the PWH population. Oxycodone is a broadly prescribed opioid in both the general population and PWH. Here, we allowed HIV transgenic (Tg) rats and wildtype (WT) littermates to intravenously self-administer oxycodone under short-access (ShA) conditions, which led to moderate, stable, "recreational"-like levels of drug intake, or under long-access (LgA) conditions, which led to escalated (dependent) drug intake. HIV Tg rats with histories of oxycodone self-administration under LgA conditions exhibited significant impairment in memory performance in the novel object recognition (NOR) paradigm. RNA-sequencing expression profiling of the medial prefrontal cortex (mPFC) in HIV Tg rats that self-administered oxycodone under ShA conditions exhibited greater transcriptional evidence of inflammation than WT rats that self-administered oxycodone under the same conditions. HIV Tg rats that self-administered oxycodone under LgA conditions exhibited transcriptional evidence of an increase in neuronal injury and neurodegeneration compared with WT rats under the same conditions. Gene expression analysis indicated that glucocorticoid-dependent adaptations contributed to the gene expression effects of oxycodone self-administration. Overall, the present results indicate that a history of opioid intake promotes neuroinflammation and glucocorticoid dysregulation, and excessive opioid intake is associated with neurotoxicity and cognitive impairment in HIV Tg rats.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

Corticosteroid sensitization drives opioid addiction.

The global crisis of opioid overdose fatalities has led to an urgent search to discover the neurobiological mechanisms of opioid use disorder (OUD). A driving force for OUD is the dysphoric and emotionally painful state (hyperkatifeia) that is produced during acute and protracted opioid withdrawal. Here, we explored a mechanistic role for extrahypothalamic stress systems in driving opioid addiction. We found that glucocorticoid receptor (GR) antagonism with mifepristone reduced opioid addiction-like behaviors in rats and zebrafish of both sexes and decreased the firing of corticotropin-releasing factor neurons in the rat amygdala (i.e., a marker of brain stress system activation). In support of the hypothesized role of glucocorticoid transcriptional regulation of extrahypothalamic GRs in addiction-like behavior, an intra-amygdala infusion of an antisense oligonucleotide that blocked GR transcriptional activity reduced addiction-like behaviors. Finally, we identified transcriptional adaptations of GR signaling in the amygdala of humans with OUD. Thus, GRs, their coregulators, and downstream systems may represent viable therapeutic targets to treat the "stress side" of OUD.

Structural genetics of circulating variants affecting the SARS-CoV-2 spike/human ACE2 complex.

SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and the human ACE2 receptor. This mechanism evolved from the ancestor bat coronavirus and is currently one of the main targets for antiviral strategies. However, there currently exist several Spike protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these variants may exert a specific effect on the affinity with ACE2 which, in turn, is also characterized by multiple alleles in the human population. In the current study, the GBPM analysis, originally developed for highlighting host-guest interaction features, has been applied to define the key amino acids responsible for the Spike/ACE2 molecular recognition, using four different crystallographic structures. Then, we intersected these structural results with the current mutational status, based on more than 295,000 sequenced cases, in the SARS-CoV-2 population. We identified several Spike mutations interacting with ACE2 and mutated in at least 20 distinct patients: S477N, N439K, N501Y, Y453F, E484K, K417N, S477I and G476S. Among these, mutation N501Y in particular is one of the events characterizing SARS-CoV-2 lineage B.1.1.7, which has recently risen in frequency in Europe. We also identified five ACE2 rare variants that may affect interaction with Spike and susceptibility to infection: S19P, E37K, M82I, E329G and G352V.Communicated by Ramaswamy H. Sarma.

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.

BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patients with breast cancer, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed 'SURFACER'. METHODS: Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA versus normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2 and Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity. RESULTS: Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of five BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms. CONCLUSIONS: BRCA patients can be stratified into five surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk.