Williams-Beuren syndrome shapes the gut microbiota metaproteome.

Williams-Beuren syndrome (WBS) is a rare genetic neurodevelopmental disorder with multi-systemic manifestations. The evidence that most subjects with WBS face gastrointestinal (GI) comorbidities, have prompted us to carry out a metaproteomic investigation of their gut microbiota (GM) profile compared to age-matched healthy subjects (CTRLs). Metaproteomic analysis was carried out on fecal samples collected from 41 individuals with WBS, and compared with samples from 45 CTRLs. Stool were extracted for high yield in bacterial protein group (PG) content, trypsin-digested and analysed by nanoLiquid Chromatography-Mass Spectrometry. Label free quantification, taxonomic assignment by the lowest common ancestor (LCA) algorithm and functional annotations by COG and KEGG databases were performed. Data were statistically interpreted by multivariate and univariate analyses. A WBS GM functional dissimilarity respect to CTRLs, regardless age distribution, was reported. The alterations in function of WBSs GM was primarily based on bacterial pathways linked to carbohydrate transport and metabolism and energy production. Influence of diet, obesity, and GI symptoms was assessed, highlighting changes in GM biochemical patterns, according to WBS subsets' stratification. The LCA-derived ecology unveiled WBS-related functionally active bacterial signatures: Bacteroidetes related to over-expressed PGs, and Firmicutes, specifically the specie Faecalibacterium prausnitzii, linked to under-expressed PGs, suggesting a depletion of beneficial bacteria. These new evidences on WBS gut dysbiosis may offer novel targets for tailored interventions.

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.

Background: Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder. Major interplays between the gastrointestinal (GI) tract and the central nervous system (CNS) seem to be driven by gut microbiota (GM). Herein, we provide a GM functional characterization, based on GM metabolomics, mapping of bacterial biochemical pathways, and anamnestic, clinical, and nutritional patient metadata. Methods: Fecal samples collected from children with ASD and neurotypical children were analyzed by gas-chromatography mass spectrometry coupled with solid phase microextraction (GC-MS/SPME) to determine volatile organic compounds (VOCs) associated with the metataxonomic approach by 16S rRNA gene sequencing. Multivariate and univariate statistical analyses assessed differential VOC profiles and relationships with ASD anamnestic and clinical features for biomarker discovery. Multiple web-based and machine learning (ML) models identified metabolic predictors of disease and network analyses correlated GM ecological and metabolic patterns. Results: The GM core volatilome for all ASD patients was characterized by a high concentration of 1-pentanol, 1-butanol, phenyl ethyl alcohol; benzeneacetaldehyde, octadecanal, tetradecanal; methyl isobutyl ketone, 2-hexanone, acetone; acetic, propanoic, 3-methyl-butanoic and 2-methyl-propanoic acids; indole and skatole; and o-cymene. Patients were stratified based on age, GI symptoms, and ASD severity symptoms. Disease risk prediction allowed us to associate butanoic acid with subjects older than 5 years, indole with the absence of GI symptoms and low disease severity, propanoic acid with the ASD risk group, and p-cymene with ASD symptoms, all based on the predictive CBCL-EXT scale. The HistGradientBoostingClassifier model classified ASD patients vs. CTRLs by an accuracy of 89%, based on methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, ethanol, butanoic acid, octadecane, acetic acid, skatole, and tetradecanal features. LogisticRegression models corroborated methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, skatole, and acetic acid as ASD predictors. Conclusion: Our results will aid the development of advanced clinical decision support systems (CDSSs), assisted by ML models, for advanced ASD-personalized medicine, based on omics data integrated into electronic health/medical records. Furthermore, new ASD screening strategies based on GM-related predictors could be used to improve ASD risk assessment by uncovering novel ASD onset and risk predictors.

Functional and Taxonomic Traits of the Gut Microbiota in Type 1 Diabetes Children at the Onset: A Metaproteomic Study.

Type 1 diabetes (T1D) is a chronic autoimmune metabolic disorder with onset in pediatric/adolescent age, characterized by insufficient insulin production, due to a progressive destruction of pancreatic ß-cells. Evidence on the correlation between the human gut microbiota (GM) composition and T1D insurgence has been recently reported. In particular, 16S rRNA-based metagenomics has been intensively employed in the last decade in a number of investigations focused on GM representation in relation to a pre-disease state or to a response to clinical treatments. On the other hand, few works have been published using alternative functional omics, which is more suitable to provide a different interpretation of such a relationship. In this work, we pursued a comprehensive metaproteomic investigation on T1D children compared with a group of siblings (SIBL) and a reference control group (CTRL) composed of aged matched healthy subjects, with the aim of finding features in the T1D patients' GM to be related with the onset of the disease. Modulated metaproteins were found either by comparing T1D with CTRL and SIBL or by stratifying T1D by insulin need (IN), as a proxy of ß-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity.

Gut Microbiota Functional Traits, Blood pH, and Anti-GAD Antibodies Concur in the Clinical Characterization of T1D at Onset.

Alterations of gut microbiota have been identified before clinical manifestation of type 1 diabetes (T1D). To identify the associations amongst gut microbiome profile, metabolism and disease markers, the 16S rRNA-based microbiota profiling and 1H-NMR metabolomic analysis were performed on stool samples of 52 T1D patients at onset, 17 T1D siblings and 57 healthy subjects (CTRL). Univariate, multivariate analyses and classification models were applied to clinical and -omic integrated datasets. In T1D patients and their siblings, Clostridiales and Dorea were increased and Dialister and Akkermansia were decreased compared to CTRL, while in T1D, Lachnospiraceae were higher and Collinsella was lower, compared to siblings and CTRL. Higher levels of isobutyrate, malonate, Clostridium, Enterobacteriaceae, Clostridiales, Bacteroidales, were associated to T1D compared to CTRL. Patients with higher anti-GAD levels showed low abundances of Roseburia, Faecalibacterium and Alistipes and those with normal blood pH and low serum HbA1c levels showed high levels of purine and pyrimidine intermediates. We detected specific gut microbiota profiles linked to both T1D at the onset and to diabetes familiarity. The presence of specific microbial and metabolic profiles in gut linked to anti-GAD levels and to blood acidosis can be considered as predictive biomarker associated progression and severity of T1D.

The Relationship Between Pediatric Gut Microbiota and SARS-CoV-2 Infection.

This is the first study on gut microbiota (GM) in children affected by coronavirus disease 2019 (COVID-19). Stool samples from 88 patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and 95 healthy subjects were collected (admission: 3-7 days, discharge) to study GM profile by 16S rRNA gene sequencing and relationship to disease severity. The study group was divided in COVID-19 (68), Non-COVID-19 (16), and MIS-C (multisystem inflammatory syndrome in children) (4). Correlations among GM ecology, predicted functions, multiple machine learning (ML) models, and inflammatory response were provided for COVID-19 and Non-COVID-19 cohorts. The GM of COVID-19 cohort resulted as dysbiotic, with the lowest α-diversity compared with Non-COVID-19 and CTRLs and by a specific ß-diversity. Its profile appeared enriched in Faecalibacterium, Fusobacterium, and Neisseria and reduced in Bifidobacterium, Blautia, Ruminococcus, Collinsella, Coprococcus, Eggerthella, and Akkermansia, compared with CTRLs (p < 0.05). All GM paired-comparisons disclosed comparable results through all time points. The comparison between COVID-19 and Non-COVID-19 cohorts highlighted a reduction of Abiotrophia in the COVID-19 cohort (p < 0.05). The GM of MIS-C cohort was characterized by an increase of Veillonella, Clostridium, Dialister, Ruminococcus, and Streptococcus and a decrease of Bifidobacterium, Blautia, Granulicatella, and Prevotella, compared with CTRLs. Stratifying for disease severity, the GM associated to "moderate" COVID-19 was characterized by lower α-diversity compared with "mild" and "asymptomatic" and by a GM profile deprived in Neisseria, Lachnospira, Streptococcus, and Prevotella and enriched in Dialister, Acidaminococcus, Oscillospora, Ruminococcus, Clostridium, Alistipes, and Bacteroides. The ML models identified Staphylococcus, Anaerostipes, Faecalibacterium, Dorea, Dialister, Streptococcus, Roseburia, Haemophilus, Granulicatella, Gemmiger, Lachnospira, Corynebacterium, Prevotella, Bilophila, Phascolarctobacterium, Oscillospira, and Veillonella as microbial markers of COVID-19. The KEGG ortholog (KO)-based prediction of GM functional profile highlighted 28 and 39 KO-associated pathways to COVID-19 and CTRLs, respectively. Finally, Bacteroides and Sutterella correlated with proinflammatory cytokines regardless disease severity. Unlike adult GM profiles, Faecalibacterium was a specific marker of pediatric COVID-19 GM. The durable modification of patients' GM profile suggested a prompt GM quenching response to SARS-CoV-2 infection since the first symptoms. Faecalibacterium and reduced fatty acid and amino acid degradation were proposed as specific COVID-19 disease traits, possibly associated to restrained severity of SARS-CoV-2-infected children. Altogether, this evidence provides a characterization of the pediatric COVID-19-related GM.

Gut Microbiota Ecology and Inferred Functions in Children With ASD Compared to Neurotypical Subjects.

Autism spectrum disorders (ASDs) is a multifactorial neurodevelopmental disorder. The communication between the gastrointestinal (GI) tract and the central nervous system seems driven by gut microbiota (GM). Herein, we provide GM profiling, considering GI functional symptoms, neurological impairment, and dietary habits. Forty-one and 35 fecal samples collected from ASD and neurotypical children (CTRLs), respectively, (age range, 3-15 years) were analyzed by 16S targeted-metagenomics (the V3-V4 region) and inflammation and permeability markers (i.e., sIgA, zonulin lysozyme), and then correlated with subjects' metadata. Our ASD cohort was characterized as follows: 30/41 (73%) with GI functional symptoms; 24/41 (58%) picky eaters (PEs), with one or more dietary needs, including 10/41 (24%) with food selectivity (FS); 36/41 (88%) presenting high and medium autism severity symptoms (HMASSs). Among the cohort with GI symptoms, 28/30 (93%) showed HMASSs, 17/30 (57%) were picky eaters and only 8/30 (27%) with food selectivity. The remaining 11/41 (27%) ASDs without GI symptoms that were characterized by HMASS for 8/11 (72%) and 7/11 (63%) were picky eaters. GM ecology was investigated for the overall ASD cohort versus CTRLs; ASDs with GI and without GI, respectively, versus CTRLs; ASD with GI versus ASD without GI; ASDs with HMASS versus low ASSs; PEs versus no-PEs; and FS versus absence of FS. In particular, the GM of ASDs, compared to CTRLs, was characterized by the increase of Proteobacteria, Bacteroidetes, Rikenellaceae, Pasteurellaceae, Klebsiella, Bacteroides, Roseburia, Lactobacillus, Prevotella, Sutterella, Staphylococcus, and Haemophilus. Moreover, Sutterella, Roseburia and Fusobacterium were associated to ASD with GI symptoms compared to CTRLs. Interestingly, ASD with GI symptoms showed higher value of zonulin and lower levels of lysozyme, which were also characterized by differentially expressed predicted functional pathways. Multiple machine learning models classified correctly 80% overall ASDs, compared with CTRLs, based on Bacteroides, Lactobacillus, Prevotella, Staphylococcus, Sutterella, and Haemophilus features. In conclusion, in our patient cohort, regardless of the evaluation of many factors potentially modulating the GM profile, the major phenotypic determinant affecting the GM was represented by GI hallmarks and patients' age.

A Shaving Proteomic Approach to Unveil Surface Proteins Modulation of Multi-Drug Resistant Pseudomonas aeruginosa Strains Isolated From Cystic Fibrosis Patients.

Cystic fibrosis (CF) is the most common rare disease caused by a mutation of the CF transmembrane conductance regulator gene encoding a channel protein of the apical membrane of epithelial cells leading to alteration of Na+ and K+ transport, hence inducing accumulation of dense and sticky mucus and promoting recurrent airway infections. The most detected bacterium in CF patients is Pseudomonas aeruginosa (PA) which causes chronic colonization, requiring stringent antibiotic therapies that, in turn induces multi-drug resistance. Despite eradication attempts at the first infection, the bacterium is able to utilize several adaptation mechanisms to survive in hostile environments such as the CF lung. Its adaptive machinery includes modulation of surface molecules such as efflux pumps, flagellum, pili and other virulence factors. In the present study we compared surface protein expression of PA multi- and pan-drug resistant strains to wild-type antibiotic-sensitive strains, isolated from the airways of CF patients with chronic colonization and recent infection, respectively. After shaving with trypsin, microbial peptides were analyzed by tandem-mass spectrometry on a high-resolution platform that allowed the identification of 174 differentially modulated proteins localized in the region from extracellular space to cytoplasmic membrane. Biofilm assay was performed to characterize all 26 PA strains in term of biofilm production. Among the differentially expressed proteins, 17 were associated to the virulome (e.g., Tse2, Tse5, Tsi1, PilF, FliY, B-type flagellin, FliM, PyoS5), six to the resistome (e.g., OprJ, LptD) and five to the biofilm reservoir (e.g., AlgF, PlsD). The biofilm assay characterized chronic antibiotic-resistant isolates as weaker biofilm producers than wild-type strains. Our results suggest the loss of PA early virulence factors (e.g., pili and flagella) and later expression of virulence traits (e.g., secretion systems proteins) as an indicator of PA adaptation and persistence in the CF lung environment. To our knowledge, this is the first study that, applying a shaving proteomic approach, describes adaptation processes of a large collection of PA clinical strains isolated from CF patients in early and chronic infection phases.

Structural Bioinformatic Survey of Protein-Small Molecule Interfaces Delineates the Role of Glycine in Surface Pocket Formation.

With a structural bioinformatic approach, we have explored amino acid compositions at PISA defined interfaces between small molecules and proteins that are contained in an optimized subset of 11,351 PDB files. The use of a series of restrictions, to prevent redundancy and biases from interactions between amino acids with charged side chains and ions, yielded a final data set of 45,230 protein-small molecule interfaces. We have compared occurrences of natural amino acids in surface exposed regions and binding sites for all the proteins of our data set. From our structural bioinformatic survey, the most relevant signal arose from the unexpected Gly abundance at enzyme catalytic sites. This finding suggested that Gly must have a fundamental role in stabilizing concave protein surface moieties. Subsequently, we have tried to predict the effect of in silico Gly mutations in hen egg white lysozyme to optimize those conditions that can reshape the protein surface with the appearance of new pockets. Replacing amino acids having bulky side chains with Gly in specific protein regions seems a feasible way for designing proteins with additional surface pockets, which can alter protein surface dynamics, therefore, representing controllable switches for protein activity.

Cross-correlation of virome-bacteriome-host-metabolome to study respiratory health.

A comprehensive understanding of the microbiome-host relationship in respiratory diseases can be elucidated by exploring the landscape of virome-bacteriome-host metabolome data through unsupervised 'multi-omics' approaches. Here, we describe how the composition and function of airway and gut virome and bacteriome may contribute to pathogen establishment and propagation in airway districts and how the virome-bacteriome communities may react to respiratory diseases. A new systems medicine approach, including the characterization of respiratory and gut microbiome, may be crucial to demonstrate the likelihood and odds of respiratory disease pathophysiology, opening new avenues to the discovery of a chain of causation for key bacteria and viruses in disease severity.

A metaproteomic-based gut microbiota profiling in children affected by autism spectrum disorders.

During the last decade, the evidences on the relationship between neurodevelopmental disorders and the microbial communities of the intestinal tract have considerably grown. Particularly, the role of gut microbiota (GM) ecology and predicted functions in Autism Spectrum Disorders (ASD) has been especially investigated by 16S rRNA targeted and shotgun metagenomics, trying to assess disease signature and their correlation with cognitive impairment or gastrointestinal (GI) manifestations of the disease. Herein we present a metaproteomic approach to point out the microbial gene expression profiles, their functional annotations, and the taxonomic distribution of gut microbial communities in ASD children. We pursued a LC-MS/MS based investigation, to compare the GM profiles of patients with those of their respective relatives and aged-matched controls, providing a quantitative evaluation of bacterial metaproteins by SWATH analysis. All data were managed by a multiple step bioinformatic pipeline, including network analysis. In particular, comparing ASD subjects with CTRLs, up-regulation was found for some metaproteins associated with Clostridia and with carbohydrate metabolism (glyceraldehyde-3-phosphate and glutamate dehydrogenases), while down-regulation was observed for others associated with Bacteroidia (SusC and SusD family together with the TonB dependent receptor). Moreover, network analysis highlighted specific microbial correlations among ASD subgroups characterized by different functioning levels and GI symptoms. SIGNIFICANCE: To the best of our knowledge, this study represents the first metaproteomic investigation on the gut microbiota of ASD children compared with relatives and age-matched CTRLs. Remarkably, the applied SWATH methodology allowed the attribution of differentially regulated functions to specific microbial taxa, offering a novel and complementary point of view with respect to previous studies.

Clinical Parasitology and Parasitome Maps as Old and New Tools to Improve Clinical Microbiomics.

A growing body of evidence shows that dysbiotic gut microbiota may correlate with a wide range of disorders; hence, the clinical use of microbiota maps and fecal microbiota transplantation (FMT) can be exploited in the clinic of some infectious diseases. Through direct or indirect ecological and functional competition, FMT may stimulate decolonization of pathogens or opportunistic pathogens, modulating immune response and colonic inflammation, and restoring intestinal homeostasis, which reduces host damage. Herein, we discuss how diagnostic parasitology may contribute to designing clinical metagenomic pipelines and FMT programs, especially in pediatric subjects. The consequences of more specialized diagnostics in the context of gut microbiota communities may improve the clinical parasitology and extend its applications to the prevention and treatment of several communicable and even noncommunicable disorders.

Structural bioinformatics survey on disease-inducing missense mutations.

Understanding the molecular mechanisms that correlate pathologies with missense mutations is of critical importance for disease risk estimations and for devising personalized therapies. Thus, we have performed a bioinformatic survey of ClinVar, a database of human genomic variations, to find signals that can account for missense mutation pathogenicity. Arginine resulted as the most frequently replaced amino acid both in benign and pathogenic mutations. By adding the structural dimension to this investigation to increase its resolution, we found that arginine mutations occurring at the protein-DNA interface increase pathogenicity 6.5 times with respect to benign variants. Glycine is the second amino acid among all the pathological missense mutations. Necessarily replaced by larger amino acids, glycine substitutions perturb the structural stability of proteins and, therefore, their functions, being mostly located in buried protein moieties. Arginine and glycine appear as representative of missense mutations causing respective changes in interaction processes and protein structural features, the two main molecular mechanisms of genome-induced pathologies.

Fecal microbiota signatures of insulin resistance, inflammation, and metabolic syndrome in youth with obesity: a pilot study.

AIMS: To identify fecal microbiota profiles associated with metabolic abnormalities belonging to the metabolic syndrome (MS), high count of white blood cells (WBCs) and insulin resistance (IR). METHODS: Sixty-eight young patients with obesity were stratified for percentile distribution of MS abnormalities. A MS risk score was defined as low, medium, and high MS risk. High WBCs were defined as a count ≥ 7.0 103/µL; severe obesity as body mass index Z-score ≥ 2 standard deviations; IR as homeostatic assessment model algorithm of IR (HOMA) ≥ 3.7. Stool samples were analyzed by 16S rRNA-based metagenomics. RESULTS: We found reduced bacterial richness of fecal microbiota in patients with IR and high diastolic blood pressure (BP). Distinct microbial markers were associated to high BP (Clostridium and Clostridiaceae), low high-density lipoprotein cholesterol (Lachnospiraceae, Gemellaceae, Turicibacter), and high MS risk (Coriobacteriaceae), WBCs (Bacteroides caccae, Gemellaceae), severe obesity (Lachnospiraceae), and impaired glucose tolerance (Bacteroides ovatus and Enterobacteriaceae). Conversely, taxa such as Faecalibacterium prausnitzii, Parabacterodes, Bacteroides caccae, Oscillospira, Parabacterodes distasonis, Coprococcus, and Haemophilus parainfluenzae were associated to low MS risk score, triglycerides, fasting glucose and HOMA-IR, respectively. Supervised multilevel analysis grouped clearly "variable" patients based on the MS risk. CONCLUSIONS: This was a proof-of-concept study opening the way at the identification of fecal microbiota signatures, precisely associated with cardiometabolic risk factors in young patients with obesity. These evidences led us to infer, while some gut bacteria have a detrimental role in exacerbating metabolic risk factors some others are beneficial ameliorating cardiovascular host health.

Gut Microbiota Profile in Children with IgE-Mediated Cow's Milk Allergy and Cow's Milk Sensitization and Probiotic Intestinal Persistence Evaluation.

Food allergy (FA) and, in particular, IgE-mediated cow's milk allergy is associated with compositional and functional changes of gut microbiota. In this study, we compared the gut microbiota of cow's milk allergic (CMA) infants with that of cow's milk sensitized (CMS) infants and Healthy controls. The effect of the intake of a mixture of Bifidobacterium longum subsp. longum BB536, Bifidobacterium breve M-16V and Bifidobacterium longum subsp. infantis M-63 on gut microbiota modulation of CMA infants and probiotic persistence was also investigated. Gut microbiota of CMA infants resulted to be characterized by a dysbiotic status with a prevalence of some bacteria as Haemophilus, Klebsiella, Prevotella, Actinobacillus and Streptococcus. Among the three strains administered, B.longum subsp. infantis colonized the gastrointestinal tract and persisted in the gut microbiota of infants with CMA for 60 days. This colonization was associated with perturbations of the gut microbiota, specifically with the increase of Akkermansia and Ruminococcus. Multi-strain probiotic formulations can be studied for their persistence in the intestine by monitoring specific bacterial probes persistence and exploiting microbiota profiling modulation before the evaluation of their therapeutic effects.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery.

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Novel Compound Heterozygous Mutations in IL-7 Receptor α Gene in a 15-Month-Old Girl Presenting With Thrombocytopenia, Normal T Cell Count and Maternal Engraftment.

Patients with severe combined immunodeficiency (SCID) exhibit T lymphopenia and profound impairments in cellular and humoral immunity. IL-7 receptor α (IL-7Rα) deficiency is a rare form of SCID that usually presents in the first months of life with severe and opportunistic infections, failure to thrive and high risk of mortality unless treated. Here, we reported an atypical and delayed onset of IL7Rα-SCID in a 15-month-old girl presenting with thrombocytopenia. Immunological investigations showed a normal lymphocyte count with isolated CD4-penia, absence of naïve T cells, marked hypergammaglobulinemia, and maternal T cell engraftment. Targeted next generation sequencing (NGS) revealed two novel compound heterozygous mutations in the IL-7Rα gene: c.160T>C (p.S54P) and c.245G>T (p.C82F). The atypical onset and the unusual immunological phenotype expressed by our patient highlights the diagnostic challenge in the field of primary immunodeficiencies (PID) and in particular in SCID patients where prompt diagnosis and therapy greatly affects survival.

Structural investigation of Rett-inducing MeCP2 mutations.

X-ray structure of methyl-CpG binding domain (MBD) of MeCP2, an intrinsically disordered protein (IDP) involved in Rett syndrome, offers a rational basis for defining the spatial distribution for most of the sites where mutations responsible of Rett syndrome, RTT, occur. We have ascribed pathogenicity for mutations of amino acids bearing positively charged side chains, all located at the protein-DNA interface, as positive charge removal cause reduction of the MeCP2-DNA adduct lifetime. Pathogenicity of the frequent proline replacements, outside the DNA contact moiety of MBD, can be attributed to the role of this amino acid for maintaining both unfolded states for unbound MeCP2 and, at the same time, to favor some higher conformational order for stabilizing structural determinants required by protein activity. These hypotheses can be extended to transcription repressor domain, TRD, the other MeCP2-DNA interaction site and, in general, to all the IDP that interact with nucleic acids.

A structural bioinformatics investigation on protein-DNA complexes delineates their modes of interaction.

The lifetimes of protein-DNA adducts are strictly related to the various protein functions. This feature must be encoded by the amino acids located at the protein-DNA interface. The large number of structurally characterized protein-DNA complexes now available from the Protein Data Bank (PDB) allows extensive structural bioinformatics investigations on protein-DNA interfaces. The modes of protein binding to DNA have been explored by dividing 629 non-redundant PDB files of protein-DNA complexes into separate classes for structural proteins, transcription factors and DNA-related enzymes. From the selected PDB structures, we could define 2953 protein-DNA contact regions. A systematic analysis of amino acid occurrences at these protein-DNA contact regions yielded composition profiles, which are typical for each of the three protein classes. The critical role of some amino acids to influence intermolecular contact lifetimes is discussed here. The occurrence of arginine at the protein-DNA interface, by far the most abundant amino acid in this protein moiety, is found to be the main feature that differentiates proteins from the three classes. Structural proteins and, to a lesser extent, transcription factors exhibit the highest Arg occurrence at protein-DNA contact regions. Reduced Arg/Lys ratios together with increased contents of Asp and Glu are observed in all the DNA-interacting enzymes. The amount of negatively charged side chains, highly conserved among homologous DNA-related enzymes at protein-DNA interfaces, is suggested as a tool to modulate protein mobility along DNA chains. Arg/Lys, Asp/Asn and Glu/Gln substitutions at protein-DNA interfaces may represent a very feasible way to control protein motion on DNA rails.

Hot spot mapping of protein surfaces with TEMPOL: Bovine pancreatic RNase A as a model system.

TEMPOL spin-label has been used to identify surface exposure of protein nuclei from NMR analysis of the induced paramagnetic relaxation enhancements (PRE). The absence of linear dependence between atom depths and observed PRE reveals that specific mechanisms drive the approach of the paramagnet to the protein surface. RNase A represents a unique protein system to explore the fine details of the information offered by TEMPOL induced PRE, due to the abundance of previous results, obtained in solution and in the crystal, dealing with surface dynamics behavior of this protein. MD simulations in explicit solvent have been performed, also in the presence of TEMPOL, in order to delineate the role of intermolecular hydrogen bonds (HB) on PRE extents. Comparison of our results with the ones obtained from multiple solvent crystal structure (MSCS) studies yields information on the specificities that these two techniques have for characterizing protein-ligand interactions, a fundamental step in the development of reliable surface druggability predictors.

On Nature's Strategy for Assigning Genetic Code Multiplicity.

Genetic code redundancy would yield, on the average, the assignment of three codons for each of the natural amino acids. The fact that this number is observed only for incorporating Ile and to stop RNA translation still waits for an overall explanation. Through a Structural Bioinformatics approach, the wealth of information stored in the Protein Data Bank has been used here to look for unambiguous clues to decipher the rationale of standard genetic code (SGC) in assigning from one to six different codons for amino acid translation. Leu and Arg, both protected from translational errors by six codons, offer the clearest clue by appearing as the most abundant amino acids in protein-protein and protein-nucleic acid interfaces. Other SGC hidden messages have been sought by analyzing, in a protein structure framework, the roles of over- and under-protected amino acids.