MET is a new confirmed gene responsible for familial distal arthrogryposis

In this Correspondence, F. Mari and colleagues report a second two-generation family with distal arthrogryposis caused by a mutation in MET tyrosine kinase. (Figure presented.). © The Author(s) 2024

The Personalized Inherited Signature Predisposing to Non-Small-Cell Lung Cancer in Non-Smokers

Simple Summary Building on the idea of a germline oligogenic origin of lung cancer, we performed WES of DNA from patients' peripheral blood and their unaffected sibs. Filtering for rare variants and potentially damaging effects, we identified 40 deleterious variants mapping in genes previously associated with cancer exclusively identified in patients. Transcriptome profiling on both tumor and normal lung tissues revealed that, among the selected mutated genes, 16 variants mapping in 16 genes were either down- or upregulated in cancer specimens. Among the downregulated genes, 9 variants in 9 genes carried the mutated allele suggesting a loss of heterozygosity. Notably, the group of mutated genes was unique for each patient, pinpointing to a "private" oligogenic germline signature. In the era of precision medicine, this report emphasizes the importance of an "omic" approach to uncover an oligogenic germline signature underlying cancer development and identify suitable therapeutic targets.Abstract Lung cancer (LC) continues to be an important public health problem, being the most common form of cancer and a major cause of cancer deaths worldwide. Despite the great bulk of research to identify genetic susceptibility genes by genome-wide association studies, only few loci associated to nicotine dependence have been consistently replicated. Our previously published study in few phenotypically discordant sib-pairs identified a combination of germline truncating mutations in known cancer susceptibility genes in never-smoker early-onset LC patients, which does not present in their healthy sib. These results firstly demonstrated the presence of an oligogenic combination of disrupted cancer-predisposing genes in non-smokers patients, giving experimental support to a model of a "private genetic epidemiology". Here, we used a combination of whole-exome and RNA sequencing coupled with a discordant sib's model in a novel cohort of pairs of never-smokers early-onset LC patients and in their healthy sibs used as controls. We selected rare germline variants predicted as deleterious by CADD and SVM bioinformatics tools and absent in the healthy sib. Overall, we identified an average of 200 variants per patient, about 10 of which in cancer-predisposing genes. In most of them, RNA sequencing data reinforced the pathogenic role of the identified variants showing: (i) downregulation in LC tissue (indicating a "second hit" in tumor suppressor genes); (ii) upregulation in cancer tissue (likely oncogene); and (iii) downregulation in both normal and cancer tissue (indicating transcript instability). The combination of the two techniques demonstrates that each patient has an average of six (with a range from four to eight) private mutations with a functional effect in tumor-predisposing genes. The presence of a unique combination of disrupting events in the affected subjects may explain the absence of the familial clustering of non-small-cell lung cancer. In conclusion, these findings indicate that each patient has his/her own "predisposing signature" to cancer development and suggest the use of personalized therapeutic strategies in lung cancer

Gain- and Loss-of-Function CFTR Alleles Are Associated with COVID-19 Clinical Outcomes

Carriers of single pathogenic variants of the CFTR (cystic fibrosis transmembrane conductance regulator) gene have a higher risk of severe COVID-19 and 14-day death. The machine learning post-Mendelian model pinpointed CFTR as a bidirectional modulator of COVID-19 outcomes. Here, we demonstrate that the rare complex allele [G576V;R668C] is associated with a milder disease via a gain-of-function mechanism. Conversely, CFTR ultra-rare alleles with reduced function are associated with disease severity either alone (dominant disorder) or with another hypomorphic allele in the second chromosome (recessive disorder) with a global residual CFTR activity between 50 to 91%. Furthermore, we characterized novel CFTR complex alleles, including [A238V;F508del], [R74W;D1270N;V201M], [I1027T;F508del], [I506V;D1168G], and simple alleles, including R347C, F1052V, Y625N, I328V, K68E, A309D, A252T, G542*, V562I, R1066H, I506V, I807M, which lead to a reduced CFTR function and thus, to more severe COVID-19. In conclusion, CFTR genetic analysis is an important tool in identifying patients at risk of severe COVID-19

Carriers of ADAMTS13 Rare Variants Are at High Risk of Life-Threatening COVID-19

Thrombosis of small and large vessels is reported as a key player in COVID-19 severity. However, host genetic determinants of this susceptibility are still unclear. Congenital Thrombotic Thrombocytopenic Purpura is a severe autosomal recessive disorder characterized by uncleaved ultra-large vWF and thrombotic microangiopathy, frequently triggered by infections. Carriers are reported to be asymptomatic. Exome analysis of about 3000 SARS-CoV-2 infected subjects of different severities, belonging to the GEN-COVID cohort, revealed the specific role of vWF cleaving enzyme ADAMTS13 (A disintegrin-like and metalloprotease with thrombospondin type 1 motif, 13). We report here that ultra-rare variants in a heterozygous state lead to a rare form of COVID-19 characterized by hyper-inflammation signs, which segregates in families as an autosomal dominant disorder conditioned by SARS-CoV-2 infection, sex, and age. This has clinical relevance due to the availability of drugs such as Caplacizumab, which inhibits vWF-platelet interaction, and Crizanlizumab, which, by inhibiting P-selectin binding to its ligands, prevents leukocyte recruitment and platelet aggregation at the site of vascular damage

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

: The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways

Pathogen-sugar interactions revealed by universal saturation transfer analysis

Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily modified pathogen proteins can be confounded by overlapping sugar signals and/or compounded with known experimental constraints. Universal saturation transfer analysis (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin-lineage severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike trimer binds sialoside sugars in an “end-on” manner. uSTA-guided modeling and a high-resolution cryo–electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar binding in SARS-CoV-2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins deep in the human lung as potentially relevant to virulence and/or zoonosis

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores ((Formula presented.) and (Formula presented.)). By applying a logistic regression with both IPGS, ((Formula presented.) (or indifferently (Formula presented.)) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity.. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGSph1 and IPGSph2). By applying a logistic regression with both IPGS, (IPGSph2 (or indifferently IPGSph1) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

Blood circulating miRNAs as pancreatic cancer biomarkers: An evidence from pooled analysis and bioinformatics study

Pancreatic cancer (PC) is one of the deadliest cancers, characterized by a poor prognosis. Currently, there are no screening programs for the early detection of PC, and existing diagnostic methods are primarily limited to high-risk individuals. Biomarkers such as CA19–9 have not significantly improved early diagnosis, making the identification of new potential biomarkers crucial for routine clinical practice. Among the candidate biomarkers, miRNAs have been most extensively studied due to their role in regulating gene expression (either as oncomiRs or tumor suppressor miRNAs) and their potential for minimally invasive analysis through liquid biopsy techniques. This review aims to summarize the current literature on blood-circulating miRNAs and their diagnostic value in PC detection, considering the context of CA19–9 and benign pancreatic diseases. The data from the collected studies were curated through both statistical and bioinformatics analyses to identify the most promising miRNAs with optimal diagnostic accuracy for PC detection and to assess their role in the molecular processes leading to tumor development

From asbestos exposure to carcinogenesis: Transcriptomic signatures in malignant pleural mesothelioma

Background: The incidence of malignant pleural mesothelioma (MPM) has surged due to widespread asbestos exposure, particularly since the mid-20th century. Despite significant advancements in cancer treatment, an effective cure for MPM remains elusive, largely due to a limited understanding of the molecular mechanisms underlying asbestos-related carcinogenesis. This exploratory study aims to uncover gene expression patterns uniquely altered in mesothelioma patients with documented asbestos exposure, providing a solid foundation for future research focused on identifying novel prognostic and predictive biomarkers. Methods: Publicly available RNA sequencing data were analyzed through a bioinformatics pipeline to perform differential gene expression analysis. Additionally, functional enrichment analysis was applied to highlight significantly enriched Gene Ontology (GO) terms related to biological processes, molecular functions, and cellular components, offering insights into the molecular pathways involved in MPM development. Results: The analysis uncovered a set of differentially expressed genes (DEGs) in MPM patients with documented asbestos exposure, as well as key GO terms. These enriched biological terms reflect processes such as ion homeostasis and oxidative stress response, providing crucial information on the cellular alterations driven by asbestos exposure. Conclusion: This study's findings deepen our understanding of the molecular landscape underlying asbestos-induced carcinogenesis in MPM. The identification of specific DEGs and enriched GO terms lays the foundation for future investigations, including the development of biomarkers, with potential implications for the diagnostic and prognostic assessment of MPM