Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

Majority of type 2 diabetes mellitus (T2DM) patients are highly susceptible to several forms of cognitive impairments, particularly dementia. However, the underlying neural mechanism of these cognitive impairments remains unclear. We aimed to investigate the correlation between whole brain resting state functional connections (RSFCs) and the cognitive status in 95 patients with T2DM. We constructed an elastic net model to estimate the Montreal Cognitive Assessment (MoCA) scores, which served as an index of the cognitive status of the patients, and to select the RSFCs for further prediction. Subsequently, we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected RSFCs. The estimated and chronological MoCA scores were significantly correlated with R = 0.81 and the mean absolute error (MAE) = 1.20. Additionally, cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54% and the area under the receiver operating characteristic (ROC) curve (AUC) of 0.9737. This connectivity pattern not only included the connections between regions within the default mode network (DMN), but also the functional connectivity between the task-positive networks and the DMN, as well as those within the task-positive networks. The results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.     

High efficiency Agrobacterium‐mediated site‐specific gene integration in maize utilizing the FLP‐FRT recombination system

An efficient Agrobacterium‐mediated site‐specific integration (SSI) technology using the flipase/flipase recognition target (FLP/FRT) system in elite maize inbred lines is described. The system allows precise integration of a single copy of a donor DNA flanked by heterologous FRT sites into a predefined recombinant target line (RTL) containing the corresponding heterologous FRT sites. A promoter‐trap system consisting of a pre‐integrated promoter followed by an FRT site enables efficient selection of events. The efficiency of this system is dependent on several factors including Agrobacterium tumefaciens strain, expression of morphogenic genes Babyboom (Bbm) and Wuschel2 (Wus2) and choice of heterologous FRT pairs. Of the Agrobacterium strains tested, strain AGL1 resulted in higher transformation frequency than strain LBA4404 THY‐ (0.27% vs. 0.05%; per cent of infected embryos producing events). The addition of morphogenic genes increased transformation frequency (2.65% in AGL1; 0.65% in LBA4404 THY‐). Following further optimization, including the choice of FRT pairs, a method was developed that achieved 19%–22.5% transformation frequency. Importantly, >50% of T0 transformants contain the desired full‐length site‐specific insertion. The frequencies reported here establish a new benchmark for generating targeted quality events compatible with commercial product development.     

Combining information from multiple bone turnover markers as diagnostic indices for osteoporosis using support vector machines

Context: Osteoporosis (OP) is a progressive systemic bone disease. Dual-energy X-ray absorptiometry (DXA) is routinely employed and is considered the gold standard method for the diagnosis of OP.

Objective: We aimed to investigate the potential use of combined information from multiple bone turnover markers (BTMs) as a clinical diagnostic tool for OP.

Materials and methods: A total of 9053 Chinese postmenopausal women (2464 primary OP patients and 6589 healthy controls) were recruited. Serum levels of six common BTMs, including BAP, BSP, CTX, OPG, OST and sRANKL were assayed. Models based on support vector machine (SVM) were constructed to explore the efficiency of different combinations of multiple BTMs for OP diagnosis.

Results: Increasing the number of BTMs used in generating the models increased the predictive power of the SVM models for determining the disease status of study subjects. The highest kappa coefficient for the model with one BTM (BAP) compared to DXA was 0.7783. The full model incorporating all six BTMs resulted in a high kappa coefficient of 0.9786.

Conclusion: Our findings showed that although single BTMs were not sufficient for OP diagnosis, appropriate combinations of multiple BTMs incorporated into the SVM models showed almost perfect agreement with the DXA.     

Tropical rainforest flies carrying pathogens form stable associations with social nonhuman primates

Living in groups provides benefits but also incurs costs such as attracting disease vectors. For example, synanthropic flies associate with human settlements, and higher fly densities increase pathogen transmission. We investigated whether such associations also exist in highly mobile nonhuman primate (NHP) Groups. We studied flies in a group of wild sooty mangabeys (Cercocebus atys atys) and three communities of wild chimpanzees (Pan troglodytes verus) in Taï National Park, Côte d'Ivoire. We observed markedly higher fly densities within both mangabey and chimpanzee groups. Using a mark–recapture experiment, we showed that flies stayed with the sooty mangabey group for up to 12 days and for up to 1.3 km. We also tested mangabey‐associated flies for pathogens infecting mangabeys in this ecosystem, Bacillus cereus biovar anthracis (Bcbva), causing sylvatic anthrax, and Treponema pallidum pertenue, causing yaws. Flies contained treponemal (6/103) and Bcbva (7/103) DNA. We cultured Bcbva from all PCR‐positive flies, confirming bacterial viability and suggesting that this bacterium might be transmitted and disseminated by flies. Whole genome sequences of Bcbva isolates revealed a diversity of Bcbva, probably derived from several sources. We conclude that flies actively track mangabeys and carry infectious bacterial pathogens; these associations represent an understudied cost of sociality and potentially expose many social animals to a diversity of pathogens.     

The population genomics of multiple tsetse fly (Glossina fuscipes fuscipes) admixture zones in Uganda

Understanding the mechanisms that enforce, maintain or reverse the process of speciation is an important challenge in evolutionary biology. This study investigates the patterns of divergence and discusses the processes that form and maintain divergent lineages of the tsetse fly Glossina fuscipes fuscipes in Uganda. We sampled 251 flies from 18 sites spanning known genetic lineages and the four admixture zones between them. We apply population genomics, hybrid zone and approximate Bayesian computation to the analysis of three types of genetic markers: 55,267 double‐digest restriction site‐associated DNA (ddRAD) SNPs to assess genome‐wide admixture, 16 microsatellites to provide continuity with published data and accurate biogeographic modelling, and a 491‐bp fragment of mitochondrial cytochrome oxidase I and II to infer maternal inheritance patterns. Admixture zones correspond with regions impacted by the reorganization of Uganda's river networks that occurred during the formation of the West African Rift system over the last several hundred thousand years. Because tsetse fly population distributions are defined by rivers, admixture zones likely represent both old and new regions of secondary contact. Our results indicate that older hybrid zones contain mostly parental types, while younger zones contain variable hybrid types resulting from multiple generations of interbreeding. These findings suggest that reproductive barriers are nearly complete in the older admixture zones, while nearly absent in the younger admixture zones. Findings are consistent with predictions of hybrid zone theory: Populations in zones of secondary contact transition rapidly from early to late stages of speciation or collapse all together.     

Precise Gene Editing Preserves Hematopoietic Stem Cell Function following Transient p53-Mediated DNA Damage Response

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Enhanced preparation of adeno-associated viral vectors by using high hydrostatic pressure to selectively inactivate helper adenovirus

Gene delivery vectors based on adeno-associated virus (AAV) have significant therapeutic potential, but much room for improvement remains in the areas of vector engineering and production. AAV production requires complementation with either helper virus, such as adenovirus, or plasmids containing helper genes, and helper virus-based approaches have distinct advantages in the use of bioreactors to produce large quantities of AAV vectors for clinical applications. However, helper viruses must eventually be inactivated and removed from AAV preparations to ensure safety. The current practice of thermally inactivating adenovirus is problematic as it can also inactivate AAV. Here, we report a novel method using high hydrostatic pressure (HHP) to selectively and completely inactivate helper adenovirus without any detectable loss of functional AAV vectors. The pressure inactivation kinetics of human adenovirus serotype 5 and the high-pressure stabilities of AAV serotypes 2 and 5 (AAV2, AAV5), which were previously unknown, were characterized. Adenovirus was inactivated beyond detection at 260 MPa or higher, whereas AAV2 was stable up to approximately 450 MPa, and surprisingly, AAV5 was stable up to at least 700 MPa. The viral genomic DNA of pressure-inactivated AAV2 was made sensitive to DNAse I digestion, suggesting that gross changes in particle structure had occurred, and this hypothesis was further supported by transmission electron microscopy. This approach should be useful in the laboratory- and clinical-scale production of AAV gene delivery vectors. Moreover, HHP provides a tool for probing the biophysical properties of AAV, which may facilitate understanding and improving the functions of this important virus.     

Effects of in vivo exposure to DEET on blood feeding behavior and fecundity in Anopheles quadrimaculatus (Diptera: Culicidae)

This study determined the effects of contact with DEET on guinea pig skin on mortality, probing time, blood feeding rate, engorgement time, and fecundity responses in female Anopheles quadrimaculatus Say. Exposure, in this manner, to 10% DEET (in ethanol) for 5 min, resulted in 98% mortality in mosquitoes after 24h. The median probing time (PT(50)) required by females, when exposed to 0.1%, 1.0%, and 10% DEET, was significantly (P<0.0001) longer (12.5, 12.1, and 19.1s, respectively) than the 6.8s required by females to probe ethanol-treated skin (control). Similarly, mean blood feeding rates in populations of females exposed to 1.0% DEET for < or = 5 min (14.4%) was 6x lower (P<0.001) (85.5%) than in females exposed to ethanol-treated skin, whereas the mean engorgement time on skin treated with 1.0% DEET (66.3s) was significantly shorter (P<0.0001) than for females feeding on the control guinea pigs (105.9s). The mean number of mature o?cytes per female (fecundity) in treatment (1.0% DEET) and control mosquitoes was not significantly different. The responses to DEET observed in this study suggest that repeated exposure of female A. quadrimaculatus populations to this repellent, in laboratory bioassays, could result in confounding of toxicant and repellent effects and inaccurate estimates of DEET repellency.     

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.