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The ability of SARS-CoV-2 to rapidly mutate represents a remarkable complicancy. Quantitative evaluations of the effects that these mutations have on the virus structure/function is of great relevance and the availability of a large number of SARS-CoV-2 sequences since the early phases of the pandemic represents a unique opportunity to follow the adaptation of the virus to humans. Here, we evaluated the SARS-CoV-2 amino acid mutations and their progression by analyzing publicly available viral genomes at three stages of the pandemic (2020 March 15th and October 7th, 2021 February 7th). Mutations were classified in conservative and non-conservative based on the probability to be accepted during the evolution according to the Point Accepted Mutation substitution matrices and on the similarity of the encoding codons. We found that the most frequent substitutions are T > I, L > F, and A > V and we observe accumulation of hydrophobic residues. These findings are consistent among the three stages analyzed. We also found that non-conservative mutations are less frequent than conservative ones. This finding may be ascribed to a progressive adaptation of the virus to the host. In conclusion, the present study provides indications of the early evolution of the virus and tools for the global and genome-specific evaluation of the possible impact of mutations on the structure/function of SARS-CoV-2 variants.

Background Tumor-initiating cells (TIC), also known as cancer stem cells, are considered a specific subpopulation of cells necessary for cancer initiation and metastasis; however, the mechanisms by which they acquire metastatic traits are not well understood. Methods LAMC2 transcriptional levels were evaluated using publicly available transcriptome data sets, and LAMC2 immunohistochemistry was performed using a tissue microarray composed of PDAC and normal pancreas tissues. Silencing and tracing of LAMC2 was performed using lentiviral shRNA constructs and CRISPR/Cas9-mediated homologous recombination, respectively. The contribution of LAMC2 to PDAC tumorigenicity was explored in vitro by tumor cell invasion, migration, sphere-forming and organoids assays, and in vivo by tumor growth and metastatic assays. mRNA sequencing was performed to identify key cellular pathways upregulated in LAMC2 expressing cells. Metastatic spreading induced by LAMC2- expressing cells was blocked by pharmacological inhibition of transforming growth factor beta (TGF-β) signaling. Results We report a LAMC2-expressing cell population, which is endowed with enhanced self-renewal capacity, and is sufficient for tumor initiation and differentiation, and drives metastasis. mRNA profiling of these cells indicates a prominent squamous signature, and differentially activated pathways critical for tumor growth and metastasis, including deregulation of the TGF-β signaling pathway. Treatment with Vactosertib, a new small molecule inhibitor of the TGF-β type I receptor (activin receptor-like kinase-5, ALK5), completely abrogated lung metastasis, primarily originating from LAMC2-expressing cells. Conclusions We have identified a highly metastatic subpopulation of TICs marked by LAMC2. Strategies aimed at targeting the LAMC2 population may be effective in reducing tumor aggressiveness in PDAC patients. Our results prompt further study of this TIC population in pancreatic cancer and exploration as a potential therapeutic target and/or biomarker.

This study proposes a novel machine learning (ML)-based stacking technique that integrates Single Nucleotide Polymorphisms (SNPs) and inferred local ancestry (LA) to improve predictive accuracy in clinical outcomes. Asthma, particularly severe asthma (SA) with poor response to inhaled corticosteroids (ICS), serves as the case study to illustrate this approach. Using data from the Biorepository and Integrative Genomics (BIG) Initiative, which includes whole-exome sequenced data from a self-reported African American pediatric cohort (N=248), we develop an ML framework to predict ICS response. After SNP data preprocessing and LA estimation, we employ stratified 10-fold cross-validation, creating base pipelines for SNP and LA data, which are then combined in stacked pipelines to assess the effectiveness of integrating these distinct data types. The stacked SNP pipeline yields an AUC of 0.693 ± 0.066 and the stacked LA pipeline yields an AUC of 0.625 ± 0.103. The integration of LA with SNP data significantly improves predictive performance, boosting the AUC to 0.729 ± 0.048 (paired t -test p -value = 0.005). Pipelines using LA data alone shows comparable performance to those using SNP data alone. However, the most important contributing features are distinct between LA and SNP data demonstrating that these data types capture distinct sources of variation and could provide complementary insights. This study highlights the potential of stacking ML pipelines, based on feature selection techniques and along with logistic regression and random forest predictive models, to integrate SNP and LA data. Such holistic approach has the promise to improve predictive performance of medication response in complex conditions like SA. This approach has broader implications for advancing personalized medicine through the effective use of multifactorial data.

Miscarriage is the spontaneous termination of a pregnancy before 24 weeks of gestation. We studied the genome of euploid miscarried embryos from mothers in the range of healthy adult individuals to understand genetic susceptibility to miscarriage not caused by chromosomal aneuploidies. We developed gp , a pipeline that we used to prioritize 439 unique variants in 399 genes, including genes known to be associated with miscarriages. Among the prioritized genes we found STAG2 coding for the cohesin complex subunit, for which inactivation in mouse is lethal, and TLE4 a target of Notch and Wnt, physically interacting with a region on chromosome 9 associated to miscarriages.

The genetic features of isolated populations can boost power in complex-trait association studies, and an in-depth understanding of how their genetic variation has been shaped by their demographic history can help leverage these advantageous characteristics. Here, we perform a comprehensive investigation using 3,059 newly generated low-depth whole-genome sequences from eight European isolates and two matched general populations, together with published data from the 1000 Genomes Project and UK10K. Sequencing data give deeper and richer insights into population demography and genetic characteristics than genotype-chip data, distinguishing related populations more effectively and allowing their functional variants to be studied more fully. We demonstrate relaxation of purifying selection in the isolates, leading to enrichment of rare and low-frequency functional variants, using novel statistics, DVxy and SVxy . We also develop an isolation-index ( Isx ) that predicts the overall level of such key genetic characteristics and can thus help guide population choice in future complex-trait association studies. Isolated populations often have special genetic compositions that can be leveraged for genetic association studies. Here, Xue and colleagues generate and analyse 3,059 low-depth whole-genome sequences from eight European isolated populations and two matched general populations.

Background Phlebotomine sand flies (Diptera, Nematocera) are important vectors of several pathogens, including Leishmania parasites, causing serious diseases of humans and dogs. Despite their importance as disease vectors, most aspects of sand fly biology remain unknown including the molecular basis of their reproduction and sex determination, aspects also relevant for the development of novel vector control strategies. Results Using comparative genomics/transcriptomics data mining and transcriptional profiling, we identified the sex determining genes in phlebotomine sand flies and proposed the first model for the sex determination cascade of these insects. For all the genes identified, we produced manually curated gene models, developmental gene expression profile and performed evolutionary molecular analysis. We identified and characterized, for the first time in a Nematocera species, the transformer ( tra ) homolog which exhibits both conserved and novel features. The analysis of the tra locus in sand flies and its expression pattern suggest that this gene is able to autoregulate its own splicing, as observed in the fruit fly Ceratitis capitata and several other insect species. Conclusions Our results permit to fill the gap about sex determination in sand flies, contribute to a better understanding of this developmental pathway in Nematocera and open the way for the identification of sex determining orthologs in other species of this important Diptera sub-order. Furthermore, the sex determination genes identified in our work also provide the opportunity of future biotechnological applications to control natural population of sand flies, reducing their impact on public health.

Background Pancreatic ductal adenocarcinoma (PDAC) is the most lethal cancer with an aggressive metastatic phenotype and very poor clinical prognosis. Interestingly, a lower occurrence of PDAC has been described in individuals with severe and long-standing asthma. Here we explored the potential link between PDAC and the glucocorticoid (GC) budesonide, a first-line therapy to treat asthma. Methods We tested the effect of budesonide and the classical GCs on the morphology, proliferation, migration and invasiveness of patient-derived PDAC cells and pancreatic cancer cell lines, using 2D and 3D cultures in vitro. Furthermore, a xenograft model was used to investigate the effect of budesonide on PDAC tumor growth in vivo. Finally, we combined genome-wide transcriptome analysis with genetic and pharmacological approaches to explore the mechanisms underlying budesonide activities in the different environmental conditions. Results We found that in 2D culture settings, high micromolar concentrations of budesonide reduced the mesenchymal invasive/migrating features of PDAC cells, without affecting proliferation or survival. This activity was specific and independent of the Glucocorticoid Receptor (GR). Conversely, in a more physiological 3D environment, low nanomolar concentrations of budesonide strongly reduced PDAC cell proliferation in a GR-dependent manner. Accordingly, we found that budesonide reduced PDAC tumor growth in vivo. Mechanistically, we demonstrated that the 3D environment drives the cells towards a general metabolic reprogramming involving protein, lipid, and energy metabolism (e.g., increased glycolysis dependency). This metabolic change sensitizes PDAC cells to the anti-proliferative effect of budesonide, which instead induces opposite changes (e.g., increased mitochondrial oxidative phosphorylation). Finally, we provide evidence that budesonide inhibits PDAC growth, at least in part, through the tumor suppressor CDKN1C/p57Kip2. Conclusions Collectively, our study reveals that the microenvironment influences the susceptibility of PDAC cells to GCs and provides unprecedented evidence for the anti-proliferative activity of budesonide on PDAC cells in 3D conditions, in vitro and in vivo. Our findings may explain, at least in part, the reason for the lower occurrence of pancreatic cancer in asthmatic patients and suggest a potential suitability of budesonide for clinical trials as a therapeutic approach to fight pancreatic cancer.

The highly fibrotic microenvironment of pancreatic ductal adenocarcinoma (PDAC) poses significant challenges for effective treatment, particularly in drug delivery and tumor progression. Our study investigates the role of collagen dynamics in PDAC, revealing that TGF-β1 negatively regulates the expression of L1 cell adhesion molecule (L1CAM), leading to a more invasive tumor phenotype. We identify a subset of PDAC cells with low L1CAM expression (L1 low ) that actively influences collagen deposition and remodeling, as evidenced by the upregulation of collagen 17A1 (COL17A1) and matrix metalloproteinase 2 (MMP2), both associated with poor prognosis. In vivo studies demonstrate that L1 low cells correlate with increased collagen deposition, reduced sensitivity to gemcitabine, and heightened liver metastasis. The secretion of COL17A1 and MMP2 by these cells enhances their migratory capabilities and contributes to the formation of a fibrotic stroma that facilitates tumor progression. This interaction underscores the critical role of collagen in shaping the tumor microenvironment and promoting aggressive tumor behavior. Notably, treatment with Tranilast significantly reduced collagen deposition and MMP2 levels while promoting L1CAM expression, suggesting a therapeutic avenue for counteracting the aggressive characteristics of L1 low cells. By modulating collagen dynamics and enhancing drug delivery, Tranilast may improve treatment outcomes for patients with low L1CAM-expressing tumors. Understanding the mechanisms by which L1 low cells contribute to collagen secretion and tumor aggressiveness is essential for developing effective interventions in pancreatic cancer.

Human papillomavirus (HPV) is a sexually transmitted infection common among men and women of reproductive age worldwide. HPV viruses are associated with epithelial lesions and cancers. HPV infections have been shown to be significantly associated with many adverse effects in reproductive function. Infection with HPVs, specifically of high-oncogenic risk types (HR HPVs), affects different stages of human reproduction, resulting in a series of adverse outcomes: 1) reduction of male fertility (male infertility), characterized by qualitative and quantitative semen alterations; 2) impairment of couple fertility with increase of blastocyst apoptosis and reduction of endometrial implantation of trophoblastic cells; 3) defects of embryos and fetal development, with increase of spontaneous abortion and spontaneous preterm birth. The actual molecular mechanism(s) by which HPV infection is involved remain unclear. HPV-associated infertility as Janus, has two faces: one reflecting anti-HPV immunity, and the other, direct pathogenic effects of HPVs, specifically, of HR HPVs on the infected/HPV-replicating cells. Adverse effects observed for HR HPVs differ depending on the genotype of infecting virus, reflecting differential response of the host immune system as well as functional differences between HPVs and their individual proteins/antigens, including their ability to induce genetic instability/DNA damage. Review summarizes HPV involvement in all reproductive stages, evaluate the adverse role(s) played by HPVs, and identifies mechanisms of viral pathogenicity, common as well as specific for each stage of the reproduction process.

A number of studies carried out since the early '70s has investigated the effects of isolation on genetic variation within and among human populations in diverse geographical contexts. However, no extensive analysis has been carried out on the heterogeneity among genomes within isolated populations. This issue is worth exploring since events of recent admixture and/or subdivision could potentially disrupt the genetic homogeneity which is to be expected when isolation is prolonged and constant over time. Here, we analyze literature data relative to 87,815 autosomal single-nucleotide polymorphisms, which were obtained from a total of 28 European populations. Our results challenge the traditional paradigm of population isolates as structured as genetically (and genomically) uniform entities. In fact, focusing on the distribution of variance of intra-population diversity measures across individuals, we show that the inter-individual heterogeneity of isolated populations is at least comparable to the open ones. More in particular, three small and highly inbred isolates (Sappada, Sauris and Timau in Northeastern Italy) were found to be characterized by levels of inter-individual heterogeneity largely exceeding that of all other populations, possibly due to relatively recent events of genetic introgression. Finally, we propose a way to monitor the effects of inter-individual heterogeneity in disease-gene association studies.