Project description:The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.
Project description:Photoinduced enhanced Raman spectroscopy (PIERS) is a new surface enhanced Raman spectroscopy (SERS) modality with a 680% Raman signal enhancement of adsorbed analytes over that of SERS. Despite the explosion in recent demonstrations, the PIERS mechanism remains undetermined. Using X-ray and time-resolved optical spectroscopies, electron microscopy, cyclic voltammetry, and density functional theory simulations, we elucidate the atomic-scale mechanism behind PIERS. Stable PIERS substrates were fabricated using self-organized arrays of TiO2 nanotubes with controlled oxygen vacancy doping and size-controlled silver nanoparticles. The key source of PIERS vs SERS enhancement is an increase in the Raman polarizability of the adsorbed analyte upon photoinduced charge transfer. A balance between improved crystallinity, which enhances charge transfer due to higher electron mobility but decreases light absorption, and increased oxygen vacancy defect concentration, which increases light absorption, is critical. This work enables the rational design of PIERS substrates for sensing.
Project description:The RNA polymerase II transcription subunit 12 homolog (MED12) is a member of the mediator complex, which plays a critical role in RNA transcription. Mutations in MED12 cause X-linked intellectual disability and other anomalies collectively grouped as MED12-related disorders. While MED12 mutations have been most commonly reported in male patients, we present the case of a 1-year-old girl with clinical characteristics similar to MED12-related disorders. To explore the clinical characteristics of the condition and its possible pathogenesis, we analyzed the patient's clinical data; genetic testing by whole-exome sequencing revealed a de novo heterozygous mutation (c.1249-1G > C) in MED12. Further cDNA experiments revealed that the patient had an abnormal splicing at the skipping of exon9, which may have produced a truncated protein. qPCR showed decreased MED12 gene expression level in the patient, and an X-chromosome inactivation test confirmed a skewed inactivation of the X-chromosome. The lymphoblast transcription levels of the genes involved in the Gli3-dependent sonic hedgehog (SHH) signaling pathway, namely, CREB5, BMP4, and NEUROG2, were found to be significantly elevated compared with those of her parents and sex- and age-matched controls. Our results support the view that MED12 mutations may dysregulate the SHH signaling pathway, which may have accounted for the aberrant craniofacial morphology of our patient.
Project description:Phosphorylation is one of the most frequent post-translational modifications on proteins. It regulates many cellular processes by modulation of phosphorylation on protein structure and dynamics. However, the mechanism of phosphorylation-induced conformational changes of proteins is still poorly understood. Here, we report a computational study of three representative groups of tyrosine in ADP-ribosylhydrolase 1, serine in BTG2, and serine in Sp100C by using six molecular dynamics (MD) simulations and quantum chemical calculations. Added phosphorylation was found to disrupt hydrogen bond, and increase new weak interactions (hydrogen bond and hydrophobic interaction) during MD simulations, leading to conformational changes. Quantum chemical calculations further indicate that the phosphorylation on tyrosine, threonine, and serine could decrease the optical band gap energy (Egap), which can trigger electronic transitions to form or disrupt interactions easily. Our results provide an atomic and electronic description of how phosphorylation facilitates conformational and dynamic changes in proteins, which may be useful for studying protein function and protein design.
Project description:Furin cleaves diverse types of protein precursors in the secretory pathway. The substrates for furin cleavage possess a specific 20-residue recognition sequence motif. In this report, based on the functional characterisation of the 20-residue sequence motif, we developed a furin cleavage site prediction tool, PiTou, using a hybrid method composed of a hidden Markov model and biological knowledge-based cumulative probability score functions. PiTou can accurately predict the presence and location of furin cleavage sites in protein sequences with high sensitivity (96.9%) and high specificity (97.3%). PiTou's prediction scores are biological meaningful and reflect binding strength and solvent accessibility of furin substrates. A prediction result is interpreted within cellular contexts: subcellular localisation, cellular function and interference by other dynamic protein modifications. Combining next-generation sequencing, PiTou can help with elucidating the molecular mechanism of furin cleavage-associated human diseases. PiTou has been made freely available at the associated website.
Project description:BackgroundGlobal influenza surveillance in humans and animals is a critical component of pandemic preparedness. The FluChip-8G Insight assay was developed to subtype both seasonal and potentially pandemic influenza viruses in a single assay with a same day result. FluChip-8G Insight uses whole gene segment RT-PCR-based amplification to provide robustness against genetic drift and subsequent microarray detection with artificial neural network-based data interpretation.ObjectivesThe objective of this study was to verify and validate the performance of the FluChip-8G Insight assay for the detection and positive identification of human and animal origin non-seasonal influenza A specimens.MethodsWe evaluated the ability of the FluChip-8G Insight technology to type and HA and NA subtype a sample set consisting of 297 results from 180 unique non-seasonal influenza A strains (49 unique subtypes).ResultsFluChip-8G Insight demonstrated a positive percent agreement ≥93% for 5 targeted HA and 5 targeted NA subtypes except for H9 (88%), and negative percent agreement exceeding 95% for all targeted subtypes.ConclusionsThe FluChip-8G Insight neural network-based algorithm used for virus identification performed well over a data set of 297 naïve sample results, and can be easily updated to improve performance on emerging strains without changing the underlying assay chemistry.
Project description:The study of infectious diseases holds significant scientific and societal importance, yet current research on the mechanisms of disease emergence and prediction methods still face challenging issues. This research uses the landscape and flux theoretical framework to reveal the non-equilibrium dynamics of adaptive infectious diseases and uncover its underlying physical mechanism. This allows the quantification of dynamics, characterizing the system with two basins of attraction determined by gradient and rotational flux forces. Quantification of entropy production rates provides insights into the system deviating from equilibrium and associated dissipative costs. The study identifies early warning indicators for the critical transition, emphasizing the advantage of observing time irreversibility from time series over theoretical entropy production and flux. The presence of rotational flux leads to an irreversible pathway between disease states. Through global sensitivity analysis, we identified the key factors influencing infectious diseases. In summary, this research offers valuable insights into infectious disease dynamics and presents a practical approach for predicting the onset of critical transition, addressing existing research gaps.
Project description:AimsThere is currently no gold-standard definition or method for identifying suicide clusters, resulting in considerable heterogeneity in the types of suicide clusters that are detected. This study sought to identify the characteristics, mechanisms and parameters of suicide clusters using three cluster detection methods. Specifically, the study aimed to: (1) determine the overlap in suicide clusters among each method, (2) compare the spatial and temporal parameters associated with different suicide clusters and (3) identify the demographic characteristics and rates of exposure to suicide among cluster and non-cluster members.MethodsSuicide data were obtained from the National Coronial Information System. N = 3027 Australians, aged 10-24 who died by suicide in 2006-2015 were included. Suicide clusters were determined using: (1) poisson scan statistics, (2) a systematic search of coronial inquests and (3) descriptive network analysis. These methods were chosen to operationalise three different definitions of suicide clusters, namely clusters that are: (1) statistically significant, (2) perceived to be significant and (3) characterised by social links among three or more suicide descendants. For each method, the demographic characteristics and rates of exposure to suicide were identified, in addition to the maximum duration of suicide clusters, the geospatial overlap between suicide clusters, and the overlap of individual cluster members.ResultsEight suicide clusters (69 suicides) were identified from the scan statistic, seven (40 suicides) from coronial inquests; and 11 (37 suicides) from the descriptive network analysis. Of the eight clusters detected using the scan statistic, two overlapped with clusters detected using the descriptive network analysis and one with clusters identified from coronial inquests. Of the seven clusters from coronial inquests, four overlapped with clusters from the descriptive network analysis and one with clusters from the scan statistic. Overall, 9.2% (12 suicides) of individuals were identified by more than one method. Prior exposure to suicide was 10.1% (N = 7) in clusters from the scan statistic, 32.5% (N = 13) in clusters from coronial inquest and 56.8% (N = 21) in clusters from the descriptive network analysis.ConclusionEach method identified markedly different suicide clusters. Evidence of social links between cluster members typically involved clusters detected using the descriptive network analysis. However, these data were limited to the availability information collected as part of the police and coroner investigation. Communities tasked with detecting and responding to suicide clusters may benefit from using the spatial and temporal parameters revealed in descriptive studies to inform analyses of suicide clusters using inferential methods.
Project description:Ceratonia siliqua L. is a Mediterranean medicinal plant traditionally cultivated for its ethnopharmacological benefits, such as antidiarrheal, antidiabetic, enhance acetylcholine, antioxidant, antiatherosclerotic, and for its possible anti-neurodegenerative potential. The aim of the present study was to evaluate the chemical composition, as well as the cognitive-enhancing, anxiolytic, and antioxidant activities of the aqueous extract from C. siliqua (CsAE) leaves against 6-hydroxydopamine (6-OHDA) zebrafish Parkinson's disease (PD) model. CsAE (0.1, 0.3, and 1 mg/L) was administered by immersion to zebrafish (Danio rerio) for eight consecutive days and one hour before each behavioral test of each day, while 6-OHDA (250 µM) treatment was supplied one day before the novel tank diving test (NTT). Qualitative and quantitative analyses were performed by the ultra-high-performance liquid chromatography (UHPLC) analysis. The memory performance was evaluated through the NTT and Y-maze tests. Additionally, the in vitro and in vivo antioxidant status and acetylcholinesterase (AChE) activity was also assessed. Our finds demonstrated that CsAE presented positive antioxidant and anti-AChE activities, which contributed to the improvement of cognitive function in the 6-OHDA zebrafish PD model.
Project description:The petrochemical industry is composed of several interconnected processes that use fossil-based feedstock for producing chemicals. These processes are typically geographically clustered and often belong to different parties. Reducing the environmental impacts of the petrochemical industry is not straightforward due to, on the one hand, their reliance on fossil fuels for energy and as a feedstock and, on the other hand, the significant level of interconnected energy and material flows among processes. Current methods for analyzing changes to existing processes cannot capture the multitude and level of interactions. The goal of this paper is to create a model of a petrochemical cluster and analyze its physical characteristics and performance. This paper addresses this goal by developing an assessment method that combines process simulations, multiplex graph analysis, and key performance indicators. The method is applied to a case study based on the petrochemical cluster in the Port of Rotterdam, resulting in a uniquely highly detailed model of a petrochemical cluster. The network analysis results show that only some of the processes are very interconnected. From the performance analysis, it can be observed that the olefins process is the most carbon-intense and has high CO2 emissions. Additionally, the results showed the importance of considering existing interconnections when assessing the current performance of existing petrochemical clusters or the performance due to future changes to chemical processes. For instance, some changes would occur to an industrial cluster by introducing alternative carbon sources, such as biomass or CO2.Supplementary informationThe online version contains supplementary material available at 10.1007/s43615-024-00410-5.