Predictors of longitudinal cognitive ageing from age 70 to 82 including APOE e4 status, early-life and lifestyle factors: the Lothian Birth Cohort 1936.

Discovering why some people's cognitive abilities decline more than others is a key challenge for cognitive ageing research. The most effective strategy may be to address multiple risk factors from across the life-course simultaneously in relation to robust longitudinal cognitive data. We conducted a 12-year follow-up of 1091 (at age 70) men and women from the longitudinal Lothian Birth Cohort 1936 study. Comprehensive repeated cognitive measures of visuospatial ability, processing speed, memory, verbal ability, and a general cognitive factor were collected over five assessments (age 70, 73, 76, 79, and 82 years) and analysed using multivariate latent growth curve modelling. Fifteen life-course variables were used to predict variation in cognitive ability levels at age 70 and cognitive slopes from age 70 to 82. Only APOE e4 carrier status was found to be reliably informative of general- and domain-specific cognitive decline, despite there being many life-course correlates of cognitive level at age 70. APOE e4 carriers had significantly steeper slopes across all three fluid cognitive domains compared with non-carriers, especially for memory (ß = -0.234, p < 0.001) and general cognitive function (ß = -0.246, p < 0.001), denoting a widening gap in cognitive functioning with increasing age. Our findings suggest that when many other candidate predictors of cognitive ageing slope are entered en masse, their unique contributions account for relatively small proportions of variance, beyond variation in APOE e4 status. We conclude that APOE e4 status is important for identifying those at greater risk for accelerated cognitive ageing, even among ostensibly healthy individuals.

Third dose of SARS-CoV2 mRNA vaccination produces robust persistent cellular and humoral immune responses in liver transplant recipients.

Weaker responses have been described after two doses of anti-SARS-CoV2 vaccination in liver transplant recipients (LTRs). At the Italian National Institute for Infectious Diseases, 122 LTRs (84% males, median age 64 years) were tested for humoral and cell-mediated immune response after a third doses of anti-SARS-CoV2 mRNA vaccines. Humoral response was measured by quantifying anti-receptor binding domain and neutralizing antibodies; cell-mediated response was measured by quantifying IFN-γ after stimulation of T cells with SARS-CoV-2-specific peptides. Humoral and cellular responses improved significantly compared to the second vaccine dose; 86.4% of previous non-responders to the first 2 vaccine doses (N = 22) became responders. Mycophenolate mofetil-containing regimens were not associated with lower response rates to a third vaccine; shorter time since transplantation (<6 years) was associated with lower humoral and cellular responses to third vaccine. Protective antibodies against Omicron variant were detected in 60% of patients 12 weeks after third vaccine dose.

Neighbourhood deprivation across eight decades and late-life cognitive function in the Lothian Birth Cohort 1936: a life-course study.

INTRODUCTION: although neighbourhood may predict late-life cognitive function, studies mostly rely on measurements at a single time point, with few investigations applying a life-course approach. Furthermore, it is unclear whether the associations between neighbourhood and cognitive test scores relate to specific cognitive domains or general ability. This study explored how neighbourhood deprivation across eight decades contributed to late-life cognitive function. METHODS: data were drawn from the Lothian Birth Cohort 1936 (n = 1,091) with cognitive function measured through 10 tests at ages 70, 73, 76, 79 and 82. Participants' residential history was gathered with 'lifegrid' questionnaires and linked to neighbourhood deprivation in childhood, young adulthood and mid-to-late adulthood. Associations were tested with latent growth curve models for levels and slopes of general (g) and domain-specific abilities (visuospatial ability, memory and processing speed), and life-course associations were explored with path analysis. RESULTS: higher mid-to-late adulthood neighbourhood deprivation was associated with lower age 70 levels (ß = -0.113, 95% confidence intervals [CI]: -0.205, -0.021) and faster decline of g over 12 years (ß = -0.160, 95%CI: -0.290, -0.031). Initially apparent findings with domain-specific cognitive functions (e.g. processing speed) were due to their shared variance with g. Path analyses suggested that childhood neighbourhood disadvantage is indirectly linked to late-life cognitive function through lower education and selective residential mobility. CONCLUSIONS: to our knowledge, we provide the most comprehensive assessment of the life-course neighbourhood deprivation and cognitive ageing relationship. Living in advantaged areas in mid-to-late adulthood may directly contribute to better cognitive function and slower decline, whereas an advantaged childhood neighbourhood likely affects functioning through cognitive reserves.

An ontology-based approach for modelling and querying Alzheimer's disease data.

BACKGROUND: The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained. METHODS: Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner. RESULTS: We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease. CONCLUSIONS: The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

Metabolic Cardiomyopathies and Cardiac Defects in Inherited Disorders of Carbohydrate Metabolism: A Systematic Review.

Heart failure (HF) is a progressive chronic disease that remains a primary cause of death worldwide, affecting over 64 million patients. HF can be caused by cardiomyopathies and congenital cardiac defects with monogenic etiology. The number of genes and monogenic disorders linked to development of cardiac defects is constantly growing and includes inherited metabolic disorders (IMDs). Several IMDs affecting various metabolic pathways have been reported presenting cardiomyopathies and cardiac defects. Considering the pivotal role of sugar metabolism in cardiac tissue, including energy production, nucleic acid synthesis and glycosylation, it is not surprising that an increasing number of IMDs linked to carbohydrate metabolism are described with cardiac manifestations. In this systematic review, we offer a comprehensive overview of IMDs linked to carbohydrate metabolism presenting that present with cardiomyopathies, arrhythmogenic disorders and/or structural cardiac defects. We identified 58 IMDs presenting with cardiac complications: 3 defects of sugar/sugar-linked transporters (GLUT3, GLUT10, THTR1); 2 disorders of the pentose phosphate pathway (G6PDH, TALDO); 9 diseases of glycogen metabolism (GAA, GBE1, GDE, GYG1, GYS1, LAMP2, RBCK1, PRKAG2, G6PT1); 29 congenital disorders of glycosylation (ALG3, ALG6, ALG9, ALG12, ATP6V1A, ATP6V1E1, B3GALTL, B3GAT3, COG1, COG7, DOLK, DPM3, FKRP, FKTN, GMPPB, MPDU1, NPL, PGM1, PIGA, PIGL, PIGN, PIGO, PIGT, PIGV, PMM2, POMT1, POMT2, SRD5A3, XYLT2); 15 carbohydrate-linked lysosomal storage diseases (CTSA, GBA1, GLA, GLB1, HEXB, IDUA, IDS, SGSH, NAGLU, HGSNAT, GNS, GALNS, ARSB, GUSB, ARSK). With this systematic review we aim to raise awareness about the cardiac presentations in carbohydrate-linked IMDs and draw attention to carbohydrate-linked pathogenic mechanisms that may underlie cardiac complications.

In Vitro Skeletal Muscle Model of PGM1 Deficiency Reveals Altered Energy Homeostasis.

Phosphoglucomutase 1 (PGM1) is a key enzyme for the regulation of energy metabolism from glycogen and glycolysis, as it catalyzes the interconversion of glucose 1-phosphate and glucose 6-phosphate. PGM1 deficiency is an autosomal recessive disorder characterized by a highly heterogenous clinical spectrum, including hypoglycemia, cleft palate, liver dysfunction, growth delay, exercise intolerance, and dilated cardiomyopathy. Abnormal protein glycosylation has been observed in this disease. Oral supplementation with D-galactose efficiently restores protein glycosylation by replenishing the lacking pool of UDP-galactose, and rescues some symptoms, such as hypoglycemia, hepatopathy, and growth delay. However, D-galactose effects on skeletal muscle and heart symptoms remain unclear. In this study, we established an in vitro muscle model for PGM1 deficiency to investigate the role of PGM1 and the effect of D-galactose on nucleotide sugars and energy metabolism. Genome-editing of C2C12 myoblasts via CRISPR/Cas9 resulted in Pgm1 (mouse homologue of human PGM1, according to updated nomenclature) knockout clones, which showed impaired maturation to myotubes. No difference was found for steady-state levels of nucleotide sugars, while dynamic flux analysis based on 13C6-galactose suggested a block in the use of galactose for energy production in knockout myoblasts. Subsequent analyses revealed a lower basal respiration and mitochondrial ATP production capacity in the knockout myoblasts and myotubes, which were not restored by D-galactose. In conclusion, an in vitro mouse muscle cell model has been established to study the muscle-specific metabolic mechanisms in PGM1 deficiency, which suggested that galactose was unable to restore the reduced energy production capacity.

Adolescence in lockdown: The protective role of mentalizing and epistemic trust.

OBJECTIVE: Mentalizing is the ability to interpret one's own and others' behavior as driven by intentional mental states. Epistemic trust (openness to interpersonally transmitted information) has been associated with mentalizing. Balanced mentalizing abilities allow people to cope with external and internal stressors. Studies show that social isolation imposed by the COVID-19 pandemic was highly stressful for most people, especially for adolescents. Here we examine whether mentalizing and epistemic trust were protective factors in relation to emotional distress during the lockdown. METHOD: A total of 131 nonclinical adolescents, aged between 12 and 18 years, were evaluated during the lockdown using the Reflective Functioning Questionnaire for Youth, Inventory of Parent and Peer Attachment, Perceived Stress Scale, and Difficulties in Emotion Regulation Scale. RESULTS: Results from network analysis showed that epistemic trust and mentalizing were negatively associated with perceived stress and emotion dysregulation. Epistemic trust in fathers was associated with level of perceived stress, and epistemic trust in mothers with emotion dysregulation. CONCLUSION: These findings suggest that epistemic trust and the capacity to mentalize were low in adolescents during lockdown, and this was associated with high levels of stress. However, robust levels of epistemic trust and mentalizing may have acted as protective factors that buffered individuals from the risk of emotional dysregulation during the lockdown.

StaRTrEK:in silico estimation of RNA half-lives from genome-wide time-course experiments without transcriptional inhibition.

BACKGROUND: Gene expression is the result of the balance between transcription and degradation. Recent experimental findings have shown fine and specific regulation of RNA degradation and the presence of various molecular machinery purposely devoted to this task, such as RNA binding proteins, non-coding RNAs, etc. A biological process can be studied by measuring time-courses of RNA abundance in response of internal and/or external stimuli, using recent technologies, such as the microarrays or the Next Generation Sequencing devices. Unfortunately, the picture provided by looking only at the transcriptome abundance may not gain insight into its dynamic regulation. By contrast, independent simultaneous measurement of RNA expression and half-lives could provide such valuable additional insight. A computational approach to the estimation of RNAs half-lives from RNA expression time profiles data, can be a low-cost alternative to its experimental measurement which may be also affected by various artifacts. RESULTS: Here we present a computational methodology, called StaRTrEK (STAbility Rates ThRough Expression Kinetics), able to estimate half-life values basing only on genome-wide gene expression time series without transcriptional inhibition. The StaRTrEK algorithm makes use of a simple first order kinetic model and of a [Formula: see text]-norm regularized least square optimization approach to find its parameter values. Estimates provided by StaRTrEK are validated using simulated data and three independent experimental datasets of two short (6 samples) and one long (48 samples) time-courses. CONCLUSIONS: We believe that our algorithm can be used as a fast valuable computational complement to time-course experimental gene expression studies by adding a relevant kinetic property, i.e. the RNA half-life, with a strong biological interpretation, thus providing a dynamic picture of what is going in a cell during the biological process under study.

Dynamic tracing of sugar metabolism reveals the mechanisms of action of synthetic sugar analogs.

Synthetic sugar analogs are widely applied in metabolic oligosaccharide engineering (MOE) and as novel drugs to interfere with glycoconjugate biosynthesis. However, mechanistic insights on their exact cellular metabolism over time are mostly lacking. We combined ion-pair ultrahigh performance liquid chromatography-triple quadrupole mass spectrometry mass spectrometry using tributyl- and triethylamine buffers for sensitive analysis of sugar metabolites in cells and organisms and identified low abundant nucleotide sugars, such as UDP-arabinose in human cell lines and CMP-sialic acid (CMP-NeuNAc) in Drosophila. Furthermore, MOE revealed that propargyloxycarbonyl (Poc)-labeled ManNPoc was metabolized to both CMP-NeuNPoc and UDP-GlcNPoc. Finally, time-course analysis of the effect of antitumor compound 3Fax-NeuNAc by incubation of B16-F10 melanoma cells with N-acetyl-D-[UL-13C6]glucosamine revealed full depletion of endogenous ManNAc 6-phosphate and CMP-NeuNAc within 24 h. Thus, dynamic tracing of sugar metabolic pathways provides a general approach to reveal time-dependent insights into the metabolism of synthetic sugars, which is important for the rational design of analogs with optimized effects.

Cognitive Change Before Old Age (11 to 70) Predicts Cognitive Change During Old Age (70 to 82).

Identifying predictors of cognitive decline in old age helps us understand its mechanisms and identify those at greater risk. Here, we examined how cognitive change from ages 11 to 70 is associated with cognitive change at older ages (70 to 82 years) in the Lothian Birth Cohort 1936 longitudinal study (N = 1,091 at recruitment). Using latent-growth-curve models, we estimated rates of change from ages 70 to 82 in general cognitive ability (g) and in three cognitive domains: visuospatial, memory, and processing speed. We found that g accounted for 71.3% of interindividual change variance. Greater cognitive gain from ages 11 to 70 predicted slower decline in g over 12 subsequent years (ß = 0.163, p = .001), independently of cognitive level in childhood and at age 70, and domain-specific change beyond g. These results contribute to the goal of identifying people at higher risk of age-related cognitive decline.

SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19.

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1ß, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.

A Comparison of Network-Based Methods for Drug Repurposing along with an Application to Human Complex Diseases.

Drug repurposing strategy, proposing a therapeutic switching of already approved drugs with known medical indications to new therapeutic purposes, has been considered as an efficient approach to unveil novel drug candidates with new pharmacological activities, significantly reducing the cost and shortening the time of de novo drug discovery. Meaningful computational approaches for drug repurposing exploit the principles of the emerging field of Network Medicine, according to which human diseases can be interpreted as local perturbations of the human interactome network, where the molecular determinants of each disease (disease genes) are not randomly scattered, but co-localized in highly interconnected subnetworks (disease modules), whose perturbation is linked to the pathophenotype manifestation. By interpreting drug effects as local perturbations of the interactome, for a drug to be on-target effective against a specific disease or to cause off-target adverse effects, its targets should be in the nearby of disease-associated genes. Here, we used the network-based proximity measure to compute the distance between the drug module and the disease module in the human interactome by exploiting five different metrics (minimum, maximum, mean, median, mode), with the aim to compare different frameworks for highlighting putative repurposable drugs to treat complex human diseases, including malignant breast and prostate neoplasms, schizophrenia, and liver cirrhosis. Whilst the standard metric (that is the minimum) for the network-based proximity remained a valid tool for efficiently screening off-label drugs, we observed that the other implemented metrics specifically predicted further interesting drug candidates worthy of investigation for yielding a potentially significant clinical benefit.

Repurposing Histaminergic Drugs in Multiple Sclerosis.

Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1-H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.

COVID-Q: Validation of the first COVID-19 questionnaire based on patient-rated symptom gravity.

OBJECTIVES: The aim of the present study was to develop and validate the CoronaVirus-Disease-2019 (COVID-19) Questionnaire (COVID-Q), a novel symptom questionnaire specific for COVID-19 patients, to provide a comprehensive evaluation that may be helpful for physicians, and evaluate the questionnaire's performance in identifying subjects at higher risk of testing positive. MATERIALS AND METHODS: Consecutive non-hospitalised adults who underwent nasopharyngeal-throat swab for severe-acute-respiratory-syndrome-coronavirus-2 (SARS-CoV-2) detection at Treviso Hospital in March 2020, were enrolled. Subjects were divided into positive (cases) and negative (controls). All subjects answered the COVID-Q. Patients not able to answer COVID-Q because of clinical conditions were excluded. Parallel Analysis and Principal Component Analysis identified items measuring the same dimension. The Item Response Theory (IRT)-based analyses evaluated the functioning of item categories, the presence of clusters of local dependence among items, item fit within the model and model fit to the data. RESULTS: Answers obtained from 230 cases (113 males; mean age 55 years, range 20-99) and 230 controls (61 males; mean age 46 years, range 21-89) were analysed. Six components were extracted with parallel analysis: asthenia, influenza-like symptoms, ear and nose symptoms, breathing issues, throat symptoms, and anosmia/ageusia. The final IRT models retained 27 items as significant for symptom assessment. The total questionnaire's score was significantly associated with positivity to the molecular test: subjects with multiple symptoms were more likely to be affected (P < .001). Older age, male gender presence of breathing issues and anosmia/ageusia were significantly related to positivity (P < .001). Comorbidities had not a significant association with the COVID-19 diagnosis. CONCLUSION: COVID-Q could be validated since the evaluated aspects were overall significantly related to infection. The application of the questionnaire to clinical practice may help to identify subjects who are likely to be affected by COVID-19 and address them to a nasopharyngeal swab in order to achieve an early diagnosis.

Genetic, epigenetic and genomic effects on variation of gene expression among grape varieties.

The transcriptional regulatory structure of plant genomes is still relatively unexplored, and little is known about factors that influence expression variation in plants. We used a genetic system consisting of 10 heterozygous grape varieties with high consanguinity and high haplotypic diversity to: (i) identify regions of haplotype sharing through whole-genome resequencing and single-nucleotide polymorphism (SNP) genotyping; (ii) analyse gene expression through RNA-seq in four stages of berry development; and (iii) associate gene expression variation with genetic and epigenetic properties. We found that haplotype sharing in and around genes was positively correlated with similarity in expression and was negatively correlated with the fraction of differentially expressed genes. Genetic and epigenetic properties of the gene and the surrounding region showed significant effects on the extent of expression variation, with negative associations for the level of gene body methylation and mean expression level, and with positive associations for nucleotide diversity, structural diversity and ratio of non-synonymous to synonymous nucleotide diversity. We also observed a spatial dependency of covariation of gene expression among varieties. These results highlight relevant roles for cis-acting factors, selective constraints and epigenetic features of the gene, and the regional context in which the gene is located, in the determination of expression variation. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA385116; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA392287; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA373967 (released upon publication); https://www.ncbi.nlm.nih.gov/bioproject/PRJNA490160 (released upon publication); https://www.ncbi.nlm.nih.gov/bioproject/PRJNA265039; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA265040.

Phosphoglucomutase-1 deficiency: Early presentation, metabolic management and detection in neonatal blood spots.

Phosphoglucomutase 1 deficiency is a congenital disorder of glycosylation (CDG) with multiorgan involvement affecting carbohydrate metabolism, N-glycosylation and energy production. The metabolic management consists of dietary D-galactose supplementation that ameliorates hypoglycemia, hepatic dysfunction, endocrine anomalies and growth delay. Previous studies suggest that D-galactose administration in juvenile patients leads to more significant and long-lasting effects, stressing the urge of neonatal diagnosis (0-6 months of age). Here, we detail the early clinical presentation of PGM1-CDG in eleven infantile patients, and applied the modified Beutler test for screening of PGM1-CDG in neonatal dried blood spots (DBSs). All eleven infants presented episodic hypoglycemia and elevated transaminases, along with cleft palate and growth delay (10/11), muscle involvement (8/11), neurologic involvement (5/11), cardiac defects (2/11). Standard dietary measures for suspected lactose intolerance in four patients prior to diagnosis led to worsening of hypoglycemia, hepatic failure and recurrent diarrhea, which resolved upon D-galactose supplementation. To investigate possible differences in early vs. late clinical presentation, we performed the first systematic literature review for PGM1-CDG, which highlighted respiratory and gastrointestinal symptoms as significantly more diagnosed in neonatal age. The modified Butler-test successfully identified PGM1-CDG in DBSs from seven patients, including for the first time Guthrie cards from newborn screening, confirming the possibility of future inclusion of PGM1-CDG in neonatal screening programs. In conclusion, severe infantile morbidity of PGM1-CDG due to delayed diagnosis could be prevented by raising awareness on its early presentation and by inclusion in newborn screening programs, enabling early treatments and galactose-based metabolic management.

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.

Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNA generated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as Akkermansia muciniphila, Rikenellaceae, Bacteroides, Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.

The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes.

Breast cancer (BC) is a heterogeneous and complex disease as witnessed by the existence of different subtypes and clinical characteristics that poses significant challenges in disease management. The complexity of this tumor may rely on the highly interconnected nature of the various biological processes as stated by the new paradigm of Network Medicine. We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules. To characterize BC phenotypes, we constructed protein-protein interaction modules based on "hub genes", called switch genes, both common and specific to the four tumor subtypes. Transcriptomic profiles of patients were stratified according to both clinical (immunohistochemistry) and genetic (PAM50) classifications. 266 and 372 switch genes were identified from immunohistochemistry and PAM50 classifications, respectively. Moreover, the identified switch genes were functionally characterized to select an interconnected pathway of disease genes. By intersecting the common switch genes of the two classifications, we selected a unique signature of 28 disease genes that were BC subtype-independent and classification subtype-independent. Data were validated both in vitro (10 BC cell lines) and ex vivo (66 BC tissues) experiments. Results showed that four of these hub proteins (AURKA, CDC45, ESPL1, and RAD54L) were over-expressed in all tumor subtypes. Moreover, the inhibition of one of the identified switch genes (AURKA) similarly affected all BC subtypes. In conclusion, using a network-based approach, we identified a common BC disease module which might reflect its pathological signature, suggesting a new vision to face with the disease heterogeneity.

MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis.

BACKGROUND: miRNAs regulate the expression of several genes with one miRNA able to target multiple genes and with one gene able to be simultaneously targeted by more than one miRNA. Therefore, it has become indispensable to shorten the long list of miRNA-target interactions to put in the spotlight in order to gain insight into understanding the regulatory mechanism orchestrated by miRNAs in various cellular processes. A reasonable solution is certainly to prioritize miRNA-target interactions to maximize the effectiveness of the downstream analysis. RESULTS: We propose a new and easy-to-use web tool MIENTURNET (MicroRNA ENrichment TURned NETwork) that receives in input a list of miRNAs or mRNAs and tackles the problem of prioritizing miRNA-target interactions by performing a statistical analysis followed by a fully featured network-based visualization and analysis. The statistics is used to assess the significance of an over-representation of miRNA-target interactions and then MIENTURNET filters based on the statistical significance associated with each miRNA-target interaction. In addition, the holistic approach of the network theory is used to infer possible evidences of miRNA regulation by capturing emergent properties of the miRNA-target regulatory network that would be not evident through a pairwise analysis of the individual components. CONCLUSION: MIENTURNET offers the possibility to consistently perform both statistical and network-based analyses by using only a single tool leading to a more effective prioritization of the miRNA-target interactions. This has the potential to avoid researchers without computational and informatics skills to navigate multiple websites and thus to independently investigate miRNA activity in every cellular process of interest in an easy and at the same time exhaustive way thanks to the intuitive web interface. The web application along with a well-documented and comprehensive user guide are freely available at http://userver.bio.uniroma1.it/apps/mienturnet/ without any login requirement.

SWIM tool application to expression data of glioblastoma stem-like cell lines, corresponding primary tumors and conventional glioma cell lines.

BACKGROUND: It is well-known that glioblastoma contains self-renewing, stem-like subpopulation with the ability to sustain tumor growth. These cells - called cancer stem-like cells - share certain phenotypic characteristics with untransformed stem cells and are resistant to many conventional cancer therapies, which might explain the limitations in curing human malignancies. Thus, the identification of genes controlling the differentiation of these stem-like cells is becoming a successful therapeutic strategy, owing to the promise of novel targets for treating malignancies. METHODS: Recently, we developed SWIM, a software able to unveil a small pool of genes - called switch genes - critically associated with drastic changes in cell phenotype. Here, we applied SWIM to the expression profiling of glioblastoma stem-like cells and conventional glioma cell lines, in order to identify switch genes related to stem-like phenotype. RESULTS: SWIM identifies 171 switch genes that are all down-regulated in glioblastoma stem-like cells. This list encompasses genes like CAV1, COL5A1, COL6A3, FLNB, HMMR, ITGA3, ITGA5, MET, SDC1, THBS1, and VEGFC, involved in "ECM-receptor interaction" and "focal adhesion" pathways. The inhibition of switch genes highly correlates with the activation of genes related to neural development and differentiation, such as the 4-core OLIG2, POU3F2, SALL2, SOX2, whose induction has been shown to be sufficient to reprogram differentiated glioblastoma into stem-like cells. Among switch genes, the transcription factor FOSL1 appears as the brightest star since: it is down-regulated in stem-like cells; it highly negatively correlates with the 4-core genes that are all up-regulated in stem-like cells; the promoter regions of the 4-core genes harbor a consensus binding motif for FOSL1. CONCLUSIONS: We suggest that the inhibition of switch genes in stem-like cells could induce the deregulation of cell communication pathways, contributing to neoplastic progression and tumor invasiveness. Conversely, their activation could restore the physiological equilibrium between cell adhesion and migration, hampering the progression of cancer. Moreover, we posit FOSL1 as promising candidate to orchestrate the differentiation of cancer stem-like cells by repressing the 4-core genes' expression, which severely halts cancer growth and might affect the therapeutic outcome. We suggest FOSL1 as novel putative therapeutic and prognostic biomarker, worthy of further investigation.