Altered resting-state functional connectivity in newborns with hypoxic ischemic encephalopathy assessed using high-density functional near-infrared spectroscopy.

Hypoxic-ischemic encephalopathy (HIE) results from a lack of oxygen to the brain during the perinatal period. HIE can lead to mortality and various acute and long-term morbidities. Improved bedside monitoring methods are needed to identify biomarkers of brain health. Functional near-infrared spectroscopy (fNIRS) can assess resting-state functional connectivity (RSFC) at the bedside. We acquired resting-state fNIRS data from 21 neonates with HIE (postmenstrual age [PMA] = 39.96), in 19 neonates the scans were acquired post-therapeutic hypothermia (TH), and from 20 term-born healthy newborns (PMA = 39.93). Twelve HIE neonates also underwent resting-state functional magnetic resonance imaging (fMRI) post-TH. RSFC was calculated as correlation coefficients amongst the time courses for fNIRS and fMRI data, respectively. The fNIRS and fMRI RSFC maps were comparable. RSFC patterns were then measured with graph theory metrics and compared between HIE infants and healthy controls. HIE newborns showed significantly increased clustering coefficients, network efficiency and modularity compared to controls. Using a support vector machine algorithm, RSFC features demonstrated good performance in classifying the HIE and healthy newborns in separate groups. Our results indicate the utility of fNIRS-connectivity patterns as potential biomarkers for HIE and fNIRS as a new bedside tool for newborns with HIE.

Molecular recognition of ITIM/ITSM domains with SHP2 and their allosteric effect.

Src homology 2-domain-containing tyrosine phosphatase 2 (SHP2) is a non-receptor protein tyrosine phosphatase that is widely expressed in a variety of cells and regulates the immune response of T cells through the PD-1 pathway. However, the activation mechanism and allosteric effects of SHP2 remain unclear, hindering the development of small molecule inhibitors. For the first time, in this study, the complex structure formed by the intact PD-1 tail and SHP2 was modeled. The molecular recognition and conformational changes of inactive/active SHP2 versus ITIM/ITSM were compared based on prolonged MD simulations. The relative flexibility of the two SH2 domains during MD simulations contributes to the recruitment of ITIM/ITSM and supports the subsequent conformational change of SHP2. The binding free energy calculation shows that inactive SHP2 has a higher affinity for ITIM/ITSM than active SHP2, mainly because the former's N-SH2 refers to the α-state. In addition, a significant decrease in the contribution to the binding energy of certain residues (e.g., R32, S34, K35, T42, and K55) of conformationally transformed SHP2 contributes to the above result. These detailed changes during conformational transition will provide theoretical guidance for the molecular design of subsequent novel anticancer drugs.

S288T mutation altering MmpL3 periplasmic domain channel and H-bond network: a novel dual drug resistance mechanism.

CONTEXT: Mycobacterial membrane proteins Large 3 (MmpL3) is responsible for the transport of mycobacterial acids out of cell membrane to form cell wall, which is essential for the survival of Mycobacterium tuberculosis (Mtb) and has become a potent anti-tuberculosis target. SQ109 is an ethambutol (EMB) analogue, as a novel anti-tuberculosis drug, can effectively inhibit MmpL3, and has completed phase 2b-3 clinical trials. Drug resistance has always been the bottleneck problem in clinical treatment of tuberculosis. The S288T mutant of MmpL3 shows significant resistance to the inhibitor SQ109, while the specific action mechanism remains unclear. The results show that MmpL3 S288T mutation causes local conformational change with little effect on the global structure. With MmpL3 bound by SQ109 inhibitor, the distance between D710 and R715 increases resulting in H-bond destruction, but their interactions and proton transfer function are still restored. In addition, the rotation of Y44 in the S288T mutant leads to an obvious bend in the periplasmic domain channel and an increased number of contact residues, reducing substrate transport efficiency. This work not only provides a possible dual drug resistance mechanism of MmpL3 S288T mutant but also aids the development of novel anti-tuberculosis inhibitors. METHODS: In this work, molecular dynamics (MD) and quantum mechanics (QM) simulations both were performed to compare inhibitor (i.e., SQ109) recognition, motion characteristics, and H-bond energy change of MmpL3 after S288T mutation. In addition, the WT_SQ109 complex structure was obtained by molecular docking program (Autodock 4.2); Molecular Mechanics/ Poisson Boltzmann Surface Area (MM-PBSA) and Solvated Interaction Energy (SIE) methods were used to calculate the binding free energies (∆Gbind); Geometric criteria were used to analyze the changes of hydrogen bond networks.

Research Progress on hCNT3 Structure/Function and Nucleoside Anticancer Drugs.

Membrane protein human concentrative nucleoside transporter 3 (hCNT3) can not only transport extracellular nucleosides into the cell but also transport various nucleoside-derived anticancer drugs to the focus of infection for therapeutic effects. Typical nucleoside anticancer drugs, including fludarabine, cladabine, decitabine, and clofarabine, are recognized by hCNT3 and then delivered to the lesion site for their therapeutic effects. hCNT3 is highly conserved during the evolution from lower to higher vertebrates, which contains scaffold and transport domains in structure and delivers substrates by coupling with Na+ and H+ ions in function. In the process of substrate delivery, the transport domain rises from the lower side of transmembrane 9 (TM9) in the inward conformation to the upper side of the outward conformation, accompanied by the collaborative motion of TM7b/ TM4b and hairpin 1b (HP1b)/ HP2b. With the report of a series of three-dimensional structures of homologous CNTs, the structural characteristics and biological functions of hCNT3 have attracted increasing attention from pharmacists and biologists. Our research group has also recently designed an anticancer lead compound with high hCNT3 transport potential based on the structure of 5-fluorouracil. In this work, the sequence evolution, conservation, molecular structure, cationic chelation, substrate recognition, elevator motion pattern and nucleoside derivative drugs of hCNT3 were reviewed, and the differences in hCNT3 transport mode and nucleoside anticancer drug modification were summarized, aiming to provide theoretical guidance for the subsequent molecular design of novel anticancer drugs targeting hCNT3.

Geometric Signatures as Important Factors to Control the Photo-Stabilities of the Phosphorescent Pd(II)/Pt(II) Complexes: A Case Study.

Operation lifetime, as an important parameter, determines the performance of phosphorescent organic light-emitting diodes (OLEDs). Unveiling the intrinsic degradation mechanism of emission material is crucial for improving the operation's lifetime. In this article, the photo-stabilities of tetradentate transition metal complexes, the popular phosphorescent materials, are explored by means of density functional theory (DFT) and time-dependent (TD)-DFT, aiming to illustrate the geometric signatures as important factors to control the photo-stabilities. Results indicate that for the tetradentate Ni(II), Pd(II), and Pt(II) complexes, the coordinate bonds of the Pt(II) complex exhibit stronger strength. It seems that the strengths of coordinate bonds are closely related to the atomic number of the metal center in the same group, which could be attributed to the various electron configurations. The effect of intramolecular and intermolecular interactions on ligand dissociation is also explored here. The large intramolecular steric hindrance and strong π-π interaction between the Pd(II) complexes caused by aggregation could effectively raise the energy barriers of the dissociation reaction, leading to an unfeasible reaction pathway. Moreover, the aggregation of Pd(II) complex can change the photo-deactivation mechanism as compared to that of monomeric Pd(II) complex, which is favored for avoiding the TTA (triplet-triplet annihilation) process.

Finding Community Modules of Brain Networks Based on PSO with Uniform Design.

The brain has the most complex structures and functions in living organisms, and brain networks can provide us an effective way for brain function analysis and brain disease detection. In brain networks, there exist some important neural unit modules, which contain many meaningful biological insights. It is appealing to find the neural unit modules and obtain their affiliations. In this study, we present a novel method by integrating the uniform design into the particle swarm optimization to find community modules of brain networks, abbreviated as UPSO. The difference between UPSO and the existing ones lies in that UPSO is presented first for detecting community modules. Several brain networks generated from functional MRI for studying autism are used to verify the proposed algorithm. Experimental results obtained on these brain networks demonstrate that UPSO can find community modules efficiently and outperforms the other competing methods in terms of modularity and conductance. Additionally, the comparison of UPSO and PSO also shows that the uniform design plays an important role in improving the performance of UPSO.

A network clustering based feature selection strategy for classifying autism spectrum disorder.

BACKGROUND: Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance. METHODS: In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. RESULTS: The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. CONCLUSION: It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

Biomolecular Network Controllability With Drug Binding Information.

Complex networks are ubiquitous in nature. In biological systems, biomolecules interact with each other to form so-called biomolecular networks, which determine the cellular behaviors of living organisms. Controlling the cellular behaviors by regulating certain biomolecules in the network is one of the most concerned problems in systems biology. Recently, the connections between biological networks and structural control theory have been explored, uncovering some interesting biological phenomena. Some researchers have paid attentions to the structural controllability of networks in notion of the minimum steering sets (MSSs). However, because the MSSs for complex networks are not unique and the importance of different MSSs is diverse in real applications, MSSs with certain meanings should be studied. In this paper, we investigated the MSSs of biomolecular networks by considering the drug binding information. The biomolecules in the MSSs with binding preference are enriched with known drug targets and are likely to have more chemical-binding opportunities with existing drugs compared with randomly chosen MSSs, suggesting novel applications for drug target identification and drug repositioning.