Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

A mathematical model of synaptotagmin 7 revealing functional importance of short-term synaptic plasticity

Synaptotagmin 7(Syt7), a presynaptic calcium sensor, has a significant role in the facilitation in shortterm synaptic plasticity: Syt7 knock out mice show a significant reduction in the facilitation. The functional importance of short-term synaptic plasticity such as facilitation is not well understood. In this study, we attempt to investigate the potential functional relationship between the short-term synaptic plasticity and postsynaptic response by developing a mathematical model that captures the responses of both wild-type and Syt7 knock-out mice. We then studied the model behaviours of wild-type and Syt7 knock-out mice in response to multiple input action potentials. These behaviors could establish functional importance of short-term plasticity in regulating the postsynaptic response and related synaptic properties. In agreement with previous modeling studies, we show that release sites are governed by non-uniform release probabilities of neurotransmitters. The structure of non-uniform release of neurotransmitters makes shortterm synaptic plasticity to act as a high-pass filter. We also propose that Syt7 may be a modulator for the long-term changes of postsynaptic response that helps to train the target frequency of the filter. We have developed a mathematical model of short-term plasticity which explains the experimental data.

What can computational modeling offer for studying the Ca^(2+) dysregulation in Alzheimer's disease:current research and future directions

Ca^2＋ dysregulation is an early event observed in Alzheimer＇s disease（AD） patients preceding the presence of its clinical symptoms.Dysregulation of neuronalCa^2＋ will cause synaptic loss and neuronal death,eventually leading to memory impairments and cognitive decline.Treatments targetingCa^2＋ signaling pathways are potential therapeutic strategies against AD.The complicated interactions make it challenging and expensive to study the underlying mechanisms as to how Ca^2＋ signaling contributes to the pathogenesis of AD.Computational modeling offers new opportunities to study the signaling pathway and test proposed mechanisms.In this mini-review,we present some computational approaches that have been used to study Ca^2＋ dysregulation of AD by simulating Ca^2＋signaling at various levels.We also pointed out the future directions that computational modeling can be done in studying the Ca^2＋ dysregulation in AD.

Functional repertoire of protein kinases and phosphatases in synaptic plasticity and associated neurological disorders

Protein phosphorylation and dephosphorylation are two essential and vital cellular mechanisms that regulate many receptors and enzymes through kinases and phosphatases.Ca^2+- dependent kinases and phosphatases are responsible for controlling neuronal processing;balance is achieved through opposition.During molecular mechanisms of learning and memory,kinases generally modulate positively while phosphatases modulate negatively.This review outlines some of the critical physiological and structural aspects of kinases and phosphatases involved in maintaining postsynaptic structural plasticity.It also explores the link between neuronal disorders and the deregulation of phosphatases and kinases.

Modeling protein-protein interactions in axon initial segment to understand their potential impact on action potential initiation

The axon initial segment(AIS)region is crucial for action potential initiation due to the presence of high-density AIS protein voltage-gated sodium channels(Nav).Nav channels comprise several serine residues responsible for the recruitment of Nav channels into the structure of AIS through interactions with ankyrin-G(AnkG).In this study,a series of computational experiments are performed to understand the role of AIS proteins casein kinase 2 and AnkG on Nav channel recruitment into the AIS.The computational simulation results using Virtual cell software indicate that Nav channels with all serine sites available for phosphorylation bind to AnkG with strong affinity.At the low initial concentration of AnkG and casein kinase 2,the concentration of Nav channels reduces significantly,suggesting the importance of casein kinase 2 and AnkG in the recruitment of Nav channels.

On electrostatic interactions of adenosine triphosphate-insulin-degrading enzyme revealed by quantum mechanics/molecular mechanics and molecular dynamics

The insulin-degrading enzyme(IDE)plays a significant role in the degradation of the amyloid beta(Aβ),a peptide found in the brain regions of the patients with early Alzheimer’s disease.Adenosine triphosphate(ATP)allosterically regulates the Aβ-degrading activity of IDE.The present study investigates the electrostatic interactions between ATP-IDE at the allosteric site of IDE,including thermostabilities/flexibilities of IDE residues,which have not yet been explored systematically.This study applies the quantum mechanics/molecular mechanics(QM/MM)to the proposed computational model for exploring electrostatic interactions between ATP and IDE.Molecular dynamic(MD)simulations are performed at different temperatures for identifying flexible and thermostable residues of IDE.The proposed computational model predicts QM/MM energy-minimised structures providing the IDE residues(Lys530 and Asp385)with high binding affinities.Considering root mean square fluctuation values during the MD simulations at 300.00 K including heat-shock temperatures(321.15 K and 315.15 K)indicates that Lys530 and Asp385 are also the thermostable residues of IDE,whereas Ser576 and Lys858 have high flexibilities with compromised thermostabilities.The present study sheds light on the phenomenon of biological recognition and interactions at the ATP-binding domain,which may have important implications for pharmacological drug design.The proposed computational model may facilitate the development of allosteric IDE activators/inhibitors,which mimic ATP interactions.

Alleviating the unwanted effects of oxidative stress on Aβclearance:a review of related concepts and strategies for the development of computational modelling

Treatment for Alzheimer’s disease(AD)can be more effective in the early stages.Although we do not completely understand the aetiology of the early stages of AD,potential pathological factors(amyloid beta[Aβ]and tau)and other co-factors have been identified as causes of AD,which may indicate some of the mechanism at work in the early stages of AD.Today,one of the primary techniques used to help delay or prevent AD in the early stages involves alleviating the unwanted effects of oxidative stress on Aβclearance.4-Hydroxynonenal(HNE),a product of lipid peroxidation caused by oxidative stress,plays a key role in the adduction of the degrading proteases.This HNE employs a mechanism which decreases catalytic activity.This process ultimately impairs Aβclearance.The degradation of HNE-modified proteins helps to alleviate the unwanted effects of oxidative stress.Having a clear understanding of the mechanisms associated with the degradation of the HNE-modified proteins is essential for the development of strategies and for alleviating the unwanted effects of oxidative stress.The strategies which could be employed to decrease the effects of oxidative stress include enhancing antioxidant activity,as well as the use of nanozymes and/or specific inhibitors.One area which shows promise in reducing oxidative stress is protein design.However,more research is needed to improve the effectiveness and accuracy of this technique.This paper discusses the interplay of potential pathological factors and AD.In particular,it focuses on the effect of oxidative stress on the expression of the Aβ-degrading proteases through adduction of the degrading proteases caused by HNE.The paper also elucidates other strategies that can be used to alleviate the unwanted effects of oxidative stress on Aβclearance.To improve the effectiveness and accuracy of protein design,we explain the application of quantum mechanical/molecular mechanical approach.

Fundamental Boolean network modelling for childhood acute lymphoblastic leukaemia pathways

Background:A novel data-driven Boolean model,namely,the fundamental Boolean model(FBM),has been proposed to draw genetic regulatory insights into gene activation,inhibition,and protein decay,published in 2018.This novel Boolean model facilitates the analysis of the activation and inhibition pathways.However,the novel model does not handle the situation well,where genetic regulation might require more time steps to complete.Methods:Here,we propose extending the fundamental Boolean modelling to address the issue that some gene regulations might require more time steps to complete than others.We denoted this extension model as the temporal fundamental Boolean model(TFBM)and related networks as the temporal fundamental Boolean networks(TFBNs).The leukaemia microarray datasets downloaded from the National Centre for Biotechnology Information have been adopted to demonstrate the utility of the proposed TFBM and TFBNs.Results:We developed the TFBNs that contain 285 components and 2775 Boolean rules based on TFBM on the leukaemia microarray datasets,which are in the form of short-time series.The data contain gene expression measurements for 13 GC-sensitive children under therapy for acute lymphoblastic leukaemia,and each sample has three time points:0 hour(before GC treatment),6/8 hours(after GC treatment)and 24 hours(after GC treatment).Conclusion:We conclude that the proposed TFBM unlocks their predecessor’s limitation,Le.,FBM,that could help pharmaceutical agents identify any side effects on clinic-related data.New hypotheses could be identified by analysing the extracted fundamental Boolean networks and analysing their up-regulatory and down-regulatory pathways.