A retrospective analysis of nosocomial viral gastrointestinal and respiratory tract infections

BACKGROUND: Viral nosocomial infections (NIs) in children are common and most frequently affect the gastrointestinal or respiratory tract. Few studies are dedicated to this topic. We aimed to determine incidence and characteristics of these specific viral NIs at our hospital. METHODS: This was a retrospective analysis of nosocomial gastroenteritis and respiratory tract infections (RTIs) of hospitalized patients at the University Children's Hospital Basel over a 12-month period. Patients with new-onset gastroenteritis or RTI during hospitalization or a physician diagnosis of NI on discharge were included for analysis. NIs were defined by use of Centers for Disease Control and Prevention recommendations and specific agents' incubation periods. RESULTS: Overall, 5493 patients were hospitalized accounting for 22,251 hospital days. Forty-five (0.8%) patients acquired an NI: 15 cases of gastroenteritis (mean age, 9.9 months; range: 3-24; NI incidence: 0.7 per 1000 hospitalization days) and 30 cases of RTI (mean age, 63.7 months; range: 1-174; NI incidence: 1.3 per 1000 hospitalization days). Main agents were rotavirus (10/15 gastroenteritis, 67%) and rhinovirus (22/30 nosocomial RTI; 73%). Physicians reported 9 cases of NI, of which 2 (22%) did not fulfill the criteria for an NI, 3 were surgical site infections, 1 was a case of rotavirus gastroenteritis and 3 were RTIs by rhinovirus. CONCLUSIONS: Viral NIs, especially caused by rotavirus and rhinovirus, are frequent in children of all ages but underestimated if exclusively reported by physicians. Prospective studies should further investigate the role and epidemiology of rhinovirus in nosocomial RTI, ideally by use of automated information technology support

The role of side-chain interactions in the early steps of aggregation: Molecular dynamics simulations of an amyloid-forming peptide from the yeast prion Sup35

Understanding the early steps of aggregation at atomic detail might be crucial for the rational design of therapeutics preventing diseases associated with amyloid deposits. In this paper, aggregation of the heptapeptide GNNQQNY, from the N-terminal prion-determining domain of the yeast protein Sup35, was studied by 20 molecular dynamics runs for a total simulation time of 20 μs. The simulations generate in-register parallel packing of GNNQQNY β-strands that is consistent with x-ray diffraction and Fourier transform infrared data. The statistically preferred aggregation pathway does not correspond to a purely downhill profile of the energy surface because of the presence of enthalpic barriers that originate from out-of-register interactions. The parallel β-sheet arrangement is favored over the antiparallel because of side-chain contacts; in particular, stacking interactions of the tyrosine rings and hydrogen bonds between amide groups. No ordered aggregation was found in control simulations with the mutant sequence SQNGNQQRG in accord with experimental data and the strong sequence dependence of aggregation

T-Cell Receptors Binding Orientation over Peptide/MHC Class I Is Driven by Long-Range Interactions.

Crystallographic data about T-Cell Receptor - peptide - major histocompatibility complex class I (TCRpMHC) interaction have revealed extremely diverse TCR binding modes triggering antigen recognition. Understanding the molecular basis that governs TCR orientation over pMHC is still a considerable challenge. We present a simplified rigid approach applied on all non-redundant TCRpMHC crystal structures available. The CHARMM force field in combination with the FACTS implicit solvation model is used to study the role of long-distance interactions between the TCR and pMHC. We demonstrate that the sum of the coulomb interactions and the electrostatic solvation energies is sufficient to identify two orientations corresponding to energetic minima at 0° and 180° from the native orientation. Interestingly, these results are shown to be robust upon small structural variations of the TCR such as changes induced by Molecular Dynamics simulations, suggesting that shape complementarity is not required to obtain a reliable signal. Accurate energy minima are also identified by confronting unbound TCR crystal structures to pMHC. Furthermore, we decompose the electrostatic energy into residue contributions to estimate their role in the overall orientation. Results show that most of the driving force leading to the formation of the complex is defined by CDR1,2/MHC interactions. This long-distance contribution appears to be independent from the binding process itself, since it is reliably identified without considering neither short-range energy terms nor CDR induced fit upon binding. Ultimately, we present an attempt to predict the TCR/pMHC binding mode for a TCR structure obtained by homology modeling. The simplicity of the approach and the absence of any fitted parameters make it also easily applicable to other types of macromolecular protein complexes

Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/

Structural basis for inhibiting β-amyloid oligomerization by a non-coded β-breaker-substituted endomorphin analogue

The distribution of endomorphins (EM) 1 and 2 in the human brain inversely correlates with cerebral neurodegeneration in Alzheimer's disease (AD), implying a protective role. These endogenous opioid peptides incorporate aromatic residues and a β-breaker motif, as seen in several optimized inhibitors of Aβ aggregation. The activity of native endomorphins was studied, as well as the rationally designed analogue Aib-1, which includes a remarkably efficient β-breaker, α-aminoisobutyric acid (Aib). In vitro and GFP fusion protein assays showed that Aib-1 interacted with Aβ and markedly inhibited the formation of toxic oligomer and fibril growth. Moreover, Aib-1 prevented the toxicity of Aβ toward neuronal PC12 cells and markedly rectified reduced longevity of an AD fly model. Atomistic simulations and NMR-derived solution structures revealed that Aib-1 significantly reduced the propensity of Aβ to aggregate due to multimode interactions including aromatic, hydrophobic, and polar contacts. We suggest that hindering the self-assembly process by interfering with the aromatic core of amyloidogenic peptides may pave the way toward developing therapeutic agents to treat amyloid-associated diseases