ABSTRACT
Brain-derived natriuretic factor (BDNF) Val66Met polymorphism has been frequently reported to be associated with Alzheimer's disease (AD) with contrasting results. Numerous studies showed that Met allele increased the risk of AD only in women, while other studies have found worse cognitive performance in Val/Val carriers. We aimed to inquire the effects of Val66Met polymorphism on the progression from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and from MCI to AD and to ascertain if this effect is modulated by demographic and cognitive variables. For this purpose, we followed up 74 subjects (48 SCD, 26 MCI) for a mean time of 9 years. All participants underwent extensive neuropsychological assessment, cognitive reserve estimation, BDNF and apolipoprotein E (ApoE) genotype analysis at baseline. Personality traits and leisure activities were assessed in a subgroup. Each patient underwent clinical-neuropsychological follow-up, during which 18 out of 48 SCD subjects progressed to MCI and 14 out of 26 MCI subjects progressed to AD. We found that Val66Met increased the risk of progression from SCD to MCI and from MCI to AD only in women. Nevertheless, Val/Val carriers who progressed from SCD to MCI had a shorter conversion time compared to Met carriers. We concluded that Val66Met polymorphism might play different roles depending on sex and stage of the disease.
Subject(s)
Alzheimer Disease/genetics , Alzheimer Disease/physiopathology , Brain-Derived Neurotrophic Factor/genetics , Cognitive Dysfunction/genetics , Cognitive Dysfunction/physiopathology , Disease Progression , Aged , Aged, 80 and over , Diagnostic Self Evaluation , Female , Follow-Up Studies , Humans , Male , Middle Aged , Polymorphism, GeneticABSTRACT
Intellectual disability (ID) and autism spectrum disorder (ASD) are clinically and genetically heterogeneous diseases. Recent whole exome sequencing studies indicated that genes associated with different neurological diseases are shared across disorders and converge on common functional pathways. Using the Ion Torrent platform, we developed a low-cost next-generation sequencing gene panel that has been transferred into clinical practice, replacing single disease-gene analyses for the early diagnosis of individuals with ID/ASD. The gene panel was designed using an innovative in silico approach based on disease networks and mining data from public resources to score disease-gene associations. We analyzed 150 unrelated individuals with ID and/or ASD and a confident diagnosis has been reached in 26 cases (17%). Likely pathogenic mutations have been identified in another 15 patients, reaching a total diagnostic yield of 27%. Our data also support the pathogenic role of genes recently proposed to be involved in ASD. Although many of the identified variants need further investigation to be considered disease-causing, our results indicate the efficiency of the targeted gene panel on the identification of novel and rare variants in patients with ID and ASD.
Subject(s)
Autism Spectrum Disorder/diagnosis , Computational Biology/methods , High-Throughput Nucleotide Sequencing/methods , Intellectual Disability/diagnosis , Adolescent , Adult , Autism Spectrum Disorder/genetics , Child , Child, Preschool , Comorbidity , Computer Simulation , Data Mining , Databases, Genetic , Early Diagnosis , Female , Genetic Association Studies , Genetic Predisposition to Disease , High-Throughput Nucleotide Sequencing/economics , Humans , Intellectual Disability/genetics , Male , Mutation , Exome Sequencing/economics , Exome Sequencing/methods , Young AdultABSTRACT
The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.
Subject(s)
Autoantigens/genetics , Cell Cycle Proteins/genetics , Computational Biology/methods , Mutation, Missense , Schizophrenia/genetics , Databases, Genetic , Genetic Predisposition to Disease , Humans , Neural Networks, Computer , Phenotype , Polymorphism, Single NucleotideABSTRACT
The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.
Subject(s)
Autism Spectrum Disorder/genetics , Computational Biology/methods , Intellectual Disability/genetics , Sequence Analysis, DNA/methods , Female , Genetic Predisposition to Disease , Humans , Male , Phenotype , Quantitative Trait LociABSTRACT
The Critical Assessment of Genome Interpretation (CAGI) experiment is the first attempt to evaluate the state-of-the-art in genetic data interpretation. Among the proposed challenges, Crohn disease (CD) risk prediction has become the most classic problem spanning three editions. The scientific question is very hard: can anybody assess the risk to develop CD given the exome data alone? This is one of the ultimate goals of genetic analysis, which motivated most CAGI participants to look for powerful new methods. In the 2016 CD challenge, we implemented all the best methods proposed in the past editions. This resulted in 10 algorithms, which were evaluated fairly by CAGI organizers. We also used all the data available from CAGI 11 and 13 to maximize the amount of training samples. The most effective algorithms used known genes associated with CD from the literature. No method could evaluate effectively the importance of unannotated variants by using heuristics. As a downside, all CD datasets were strongly affected by sample stratification. This affected the performance reported by assessors. Therefore, we expect that future datasets will be normalized in order to remove population effects. This will improve methods comparison and promote algorithms focused on causal variants discovery.
Subject(s)
Computational Biology/methods , Crohn Disease/genetics , Algorithms , Genetic Predisposition to Disease , High-Throughput Nucleotide Sequencing , Humans , Practice Guidelines as Topic , Exome SequencingABSTRACT
The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.
Subject(s)
Computational Biology/methods , Sequence Analysis, DNA/methods , Databases, Genetic , Genetic Predisposition to Disease , Genetic Testing , Humans , PhenotypeABSTRACT
Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.
Subject(s)
Computational Biology/methods , Cyclin-Dependent Kinase Inhibitor p18/genetics , Genetic Variation , Cell Line, Tumor , Cell Proliferation , Computer Simulation , Cyclin-Dependent Kinase Inhibitor p16 , Cyclin-Dependent Kinase Inhibitor p18/chemistry , Databases, Genetic , Genetic Predisposition to Disease , Humans , Machine Learning , Protein StabilityABSTRACT
The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.
Subject(s)
High-Throughput Nucleotide Sequencing/methods , Whole Genome Sequencing/methods , Area Under Curve , Genetic Predisposition to Disease , Human Genome Project , Humans , Phenotype , Quantitative Trait LociABSTRACT
Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.
Subject(s)
Bipolar Disorder/genetics , Crohn Disease/genetics , Exome Sequencing/methods , Precision Medicine/methods , Warfarin/therapeutic use , Computational Biology/methods , Databases, Genetic , Genetic Predisposition to Disease , Humans , Information Dissemination , Pharmacogenomic Variants , Phenotype , Warfarin/pharmacologyABSTRACT
Preparative liquid chromatography in reversed phase conditions (RPLC) is the most common approach adopted in the downstream processing for the purification of therapeutic peptides at industrial level. Due to the strict requirements on the quality imposed by the Regulatory Agencies, routinary methods based on the use of aqueous buffers and acetonitrile (ACN) as organic modifier are commonly used, where ACN is practically the only available choice for the purification of peptide derivatives. However, ACN is known to suffers of many shortcomings, such as drastic shortage in the market, high costs and, most importantly, it shows unwanted toxicity for human health and environment, which led it among the less environmentally friendly ones. For this reason, the selection of a suitable alternative becomes crucial for the sustainable downstream processing of peptides and biopharmaceuticals in general. In this paper, a promising green solvent, namely dimethyl carbonate (DMC) has been used for the separation of a peptide not only in linear conditions but also for its purification through non-linear overloaded chromatography. The performance of the process has been compared to that achievable with the common method where ACN is used as organic modifier and to that obtained with two additional solvents (namely ethanol and isopropanol), already used as greener alternatives to ACN. This proof-of-concept study showed that, thanks to its higher elution strength, DMC can be considered a green alternative to ACN, since it allows to reduce method duration while reaching good purities and recoveries. Indeed, at a target purity fixed to 98.5 %, DMC led to the best productivity with respect to all the other solvents tested, confirming its suitability as a sustainable alternative to ACN for the purification of complex biopharmaceutical products.
Subject(s)
Chromatography, Reverse-Phase , Peptides , Humans , Chromatography, Reverse-Phase/methods , Solvents/chemistry , Acetonitriles/chemistry , Chromatography, High Pressure Liquid/methodsABSTRACT
Nowadays, environmental problems are drawing the attention of governments and international organisations, which are therefore encouraging the transition to green industrial processes and approaches. In this context, chemists can help indicate a suitable direction. Beside the efforts focused on greening synthetic approaches, currently also analytical techniques and separations are under observation, especially those employing large volumes of organic solvents, such as reversed-phase liquid chromatography (RPLC). Acetonitrile has always been considered the best performing organic modifier for RPLC applications, due to its chemical features (complete miscibility in water, UV transparency, low viscosity etc); nevertheless, it suffers of severe shortcomings, and most importantly, it does not fully comply with Environmental, Health and Safety (EHS) requirements. For these reasons, alternative greener solvents are being investigated, especially easily available alcohols. In this work, chromatographic performance of the most common solvents used in reversed-phase chromatography, i.e., acetonitrile, ethanol and isopropanol, have been compared to a scarcely used solvent, dimethyl carbonate (DMC). The analytes of interest were two small molecules, caffeine and paracetamol, whose kinetics and retention behaviour obtained with the four solvents have been compared, and all contributions to band broadening have been assessed. Results about kinetic performance are very promising, indicating that a small amount (7 % v/v) of DMC is able to produce the same efficiency as a 2.5-times larger ACN volume (18 % v/v), and larger efficiency than alcohols. This paper reports, for the first time, fundamental studies concerning the mass transfer phenomena when DMC is used as an organic solvent in RPLC, and, together with the companion paper, represents the results of a research whose final aim was to discover whether DMC is suitable for chromatographic applications both in linear and preparative conditions.
Subject(s)
Chromatography, Reverse-Phase , Ethanol , Chromatography, Reverse-Phase/methods , Solvents/chemistry , Ethanol/chemistry , Acetonitriles/chemistry , Chromatography, High Pressure Liquid/methodsABSTRACT
The approved combination of Tixagevimab/Cilgavimab has been shown to decrease the rate of symptomatic SARS-CoV-2 infection in patients at increased risk of inadequate response to vaccination. However, Tixagevimab/Cilgavimab was tested in a few studies that included patients with hematological malignancies, even if this population has shown an increased risk of unfavorable outcomes following infection (with high rates of hospitalization, intensive care unit admission, and mortality) and poor significant immunization following vaccines. We performed a real-life prospective cohort study to evaluate the rate of SARS-CoV-2 infection following pre-exposure prophylaxis with Tixagevimab/Cilgavimab in anti-spike seronegative patients compared to a cohort of seropositive patients who were observed or received a fourth vaccine dose. We recruited 103 patients with a mean age of 67 years: 35 (34%) received Tixagevimab/Cilgavimab and were followed from March 17, 2022, until November 15, 2022. After a median follow-up of 4.24 months, the 3-month cumulative incidence of infection was 20% versus 12% in the Tixagevimab/Cilgavimab and observation/vaccine groups respectively (HR 1.57; 95% CI: 0.65-3.56; p = 0.34). In this study, we report our experience with Tixagevimab/Cilgavimab and a tailored approach to SARS-CoV-2 infection prevention in patients with hematological malignancies during the SARS-CoV-2 omicron surge.
ABSTRACT
Like in other parts of the world, in Italy internet is an increasingly used source of diseases-related informations, used both by health professionals and by the patients. In recent years, besides the most common search engines, sanitary informations contained in video sharing sites have gained an increased influence, either in the search and in the choice phases. The aim of the present paper is to review the literature on the use of online videos in the health field. Experimental studies, reviews, and reports published by English magazines and scientific journals on this subject since the year 2000 have been included. 179 articles were identified, but only 45 were considered as relevant. The articles are schematically divided into three major categories according to their subject: videos for health information/patient education, videos for educational use and other uses of the video. We describe the critical aspects of the different video types and suggestions for their use, as well as some guidelines and hints for the preparation of new health information material in video format, specifically designed for spreading via the web. New technologies are nowadays an innovative form of worldwide communication. Unlike traditional videos, these resources stimulate the interaction among users and so they have become a sort of an hybrid between public and personal tools. The potential of these new tools, however, implies that all the traditional institutions dedicated to the research and education acquire the skill to use them.
Subject(s)
Education, Medical/methods , Internet , Patient Education as Topic/methods , Video Recording , HumansABSTRACT
The SARS-CoV-2 pandemic is maintained by the emergence of successive variants, highlighting the flexibility of the protein sequences of the virus. We show that experimentally determined intrinsically disordered regions (IDRs) are abundant in the SARS-CoV-2 viral proteins, making up to 28% of disorder content for the S1 subunit of spike and up to 51% for the nucleoprotein, with the vast majority of mutations occurring in the 13 major variants mapped to these IDRs. Strikingly, antigenic sites are enriched in IDRs, in the receptor-binding domain (RBD) and in the N-terminal domain (NTD), suggesting a key role of structural flexibility in the antigenicity of the SARS-CoV-2 protein surface. Mutations occurring in the S1 subunit and nucleoprotein (N) IDRs are critical for immune evasion and antibody escape, suggesting potential additional implications for vaccines and monoclonal therapeutic strategies. Overall, this suggests the presence of variable regions on S1 and N protein surfaces, which confer sequence and antigenic flexibility to the virus without altering its protein functions.
Subject(s)
COVID-19 , Intrinsically Disordered Proteins , Humans , Immune Evasion/genetics , Intrinsically Disordered Proteins/genetics , Nucleoproteins , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/metabolismABSTRACT
Multiple myeloma (MM) is a malignant plasma cell (PC) neoplasm, which also displays pathological bone involvement. Clonal expansion of MM cells in the bone marrow causes a perturbation of bone homeostasis that culminates in MM-associated bone disease (MMABD). We previously demonstrated that the S/T kinase CK1α sustains MM cell survival through the activation of AKT and ß-catenin signaling. CK1α is a negative regulator of the Wnt/ß-catenin cascade, the activation of which promotes osteogenesis by directly stimulating the expression of RUNX2, the master gene regulator of osteoblastogenesis. In this study, we investigated the role of CK1α in the osteoblastogenic potential of mesenchymal stromal cells (MSCs) and its involvement in MM-MSC cross-talk. We found that CK1α silencing in in vitro co-cultures of MMs and MSCs modulated RUNX2 expression differently in PCs and in MSCs, mainly through the regulation of Wnt/ß-catenin signaling. Our findings suggest that the CK1α/RUNX2 axis could be a potential therapeutic target for constraining malignant PC expansion and supporting the osteoblastic transcriptional program of MSCs, with potential for ameliorating MMABD. Moreover, considering that Lenalidomide treatment leads to MM cell death through Ikaros, Aiolos and CK1α proteasomal degradation, we examined its effects on the osteoblastogenic potential of MSC compartments.
ABSTRACT
Mantle Cell Lymphoma (MCL) is still an incurable B-cell malignancy characterized by poor prognosis and frequent relapses. B Cell Receptor (BCR) signaling inhibitors, in particular of the kinases BTK and PI3Kγ/δ, have demonstrated clinically meaningful anti-proliferative effects in B cell tumors. However, refractoriness to these drugs may develop, portending a dismal prognosis. Protein kinase CK1α is an emerging pro-growth enzyme in B cell malignancies. In multiple myeloma, this kinase sustains ß-catenin and AKT-dependent survival and is involved in the activation of NF-κB in B cells. In this study, we analyzed the role of CK1α on MCL cell survival and proliferation, on the regulation of BCR-related BTK, NF-κB, PI3K/AKT signaling cascades and the effects of CK1α chemical inhibition or gene silencing in association with the BTK inhibitor Ibrutinib or the PI3Kγ/δ inhibitor Duvelisib. CK1α was found highly expressed in MCL cells as compared to normal B cells. The inactivation/loss of CK1α caused MCL cell apoptosis and proliferation arrest. CK1α sustained BCR signaling, in particular the NF-κB, AKT and BTK pathways by modulating the phosphorylation of Ser 652 on CARD11, Ser 536 p65 on NF-κB, Ser 473 on AKT, Tyr 223 on BTK, as well as the protein levels. We also provided evidence that CK1α-mediated regulation of CARD11 and BTK likely implicates a physical interaction. The combination of CK1α inhibition with Ibrutinib or Duvelisib synergistically increased cytotoxicity, leading to a further decrease of the activation of BCR signaling pathways. Therefore, CK1α sustains MCL growth through the regulation of BCR-linked survival signaling cascades and protects from Ibrutinib/Duvelisib-induced apoptosis. Thus, CK1α could be considered as a rational molecular target for the treatment of MCL, in association with novel agents.
ABSTRACT
In this work we present a framework for blood cholesterol levels prediction from genotype data. The predictor is based on an algorithm for cholesterol metabolism simulation available in literature, implemented and optimized by our group in the R language. The main weakness of the former simulation algorithm was the need of experimental data to simulate mutations in genes altering the cholesterol metabolism. This caveat strongly limited the application of the model in the clinical practice. In this work we present how this limitation could be bypassed thanks to an optimization of model parameters based on patient cholesterol levels retrieved from literature. Prediction performance has been assessed taking into consideration several scoring indices currently used for performance evaluation of machine learning methods. Our assessment shows how the optimization phase improved model performance, compared to the original version available in literature.
Subject(s)
Cholesterol/blood , Computer Simulation , Genotype , Machine Learning , HumansABSTRACT
We aimed to detail language profiles, brain metabolic patterns and proportion of Alzheimer's disease biomarkers in a cohort of patients with mixed primary progressive aphasia (mPPA). We considered 58 patients with PPA: 10 with non-fluent/agrammatic variant (nfvPPA), 16 with semantic variant (svPPA), 21 with logopenic variant (lvPPA) and 9 with mPPA. Patients with mPPA were further classified as 4 nf/lvPPA (with prevailing features for nfvPPA and lvPPA) and 5 s/lvPPA (with prevailing features for svPPA and lvPPA). Nf/lvPPA patients were characterized by higher proportion of Naming impairment compared to nfvPPA and more frequent Grammatical Errors and Phonologic Errors than lvPPA. S/lvPPA had higher proportion of impairment in Sentences Repetition compared to svPPA and in Single-word Comprehension compared to lvPPA. 100% of nf/lvPPA and 40% of s/lvPPA had Aß positive biomarkers. Brain hypometabolic pattern in Nf/lvPPA was consistent with lvPPA, while s/lvPPA had a brain metabolism resembling svPPA. We concluded that nf/lvPPA patients might be considered as PPA variant due to Alzheimer's disease and s/lvPPA group mainly included patients with svPPA.