AbstrAct

BackgroundAging is a fundamental biological process. Characterization of genetic and environmental factors that influence lifespan is a crucial step toward understanding the mechanisms of aging at the organism level. To capture the different effects of genetic and environmental factors on lifespan, appropriate statistical analyses are needed.Methodology/Principal FindingsWe developed an online application for survival analysis (OASIS) that helps conduct various novel statistical tasks involved in analyzing survival data in a user-friendly manner. OASIS provides standard survival analysis results including Kaplan-Meier estimates and mean/median survival time by taking censored survival data. OASIS also provides various statistical tests including comparison of mean survival time, overall survival curve, and survival rate at specific time point. To visualize survival data, OASIS generates survival and log cumulative hazard plots that enable researchers to easily interpret their experimental results. Furthermore, we provide statistical methods that can analyze variances among survival datasets. In addition, users can analyze proportional effects of risk factors on survival.Conclusions/SignificanceOASIS provides a platform that is essential to facilitate efficient statistical analyses of survival data in the field of aging research. Web application and a detailed description of algorithms are accessible from http://sbi.postech.ac.kr/oasis.

The GnRH neurons represent the output cells of the neuronal network controlling gonadal function. Their activation initiates the onset of puberty, but the underlying mechanisms remain unclear. Using a GnRH-green fluorescent protein mouse model, we have been able to fill individual GnRH neurons with biocytin in the acute brain slice preparation to examine their morphological characteristics across puberty. GnRH neurons in prepubertal male mice [postnatal d 10-15 (PND10-15)] exhibited half as many dendritic and somal spines as adult male mice (>PND60; P < 0.05) but, surprisingly, a much more complex dendritic tree with 5-fold greater branch points (P < 0.05). Experiments examining somal and proximal dendritic spine numbers in vivo, in perfusion-fixed tissue from GnRH-green fluorescent protein mice, revealed the same pattern of approximately twice as many spines on adult GnRH neurons compared with PND10 male mice (P < 0.01). In contrast to the spine density alterations, reflecting changing excitatory input, confocal immunofluorescence studies revealed no differences in the numbers of vesicular gamma-aminobutyric acid transporter-immunoreactive elements adjacent to GnRH soma or proximal dendrites in prepubertal and adult male mice. Experiments evaluating dendritic tree structure in vivo (PND3, -10, and -35 and adult) revealed that GnRH neurons located in the rostral preoptic area, but not the medial septum, exhibited a more complex branching pattern at PND10, but that this was adult-like by PND35. These studies demonstrate unexpected dendritic tree remodeling in the GnRH neurons and provide evidence for an increase in direct excitatory inputs to GnRH neurons across the time of puberty.

The newly emerging Middle East respiratory syndrome coronavirus (MERS-CoV) causes a severe respiratory infection with a high mortality rate (~35%). MERS-CoV has been a global threat due to continuous outbreaks in the Arabian peninsula and international spread by infected travelers since 2012. From May to July 2015, a large outbreak initiated by an infected traveler from the Arabian peninsula swept South Korea and resulted in 186 confirmed cases with 38 deaths (case fatality rate, 20.4%). Here, we show the rapid emergence and spread of a mutant MERS-CoV with reduced affinity to the human CD26 receptor during the South Korean outbreak. We isolated 13 new viral genomes from 14 infected patients treated at a hospital and found that 12 of these genomes possess a point mutation in the receptor-binding domain (RBD) of viral spike (S) protein. Specifically, 11 of these genomes have an I529T mutation in RBD, and 1 has a D510G mutation. Strikingly, both mutations result in reduced affinity of RBD to human CD26 compared to wild-type RBD, as measured by surface plasmon resonance analysis and cellular binding assay. Additionally, pseudotyped virus bearing an I529T mutation in S protein showed reduced entry into host cells compared to virus with wild-type S protein. These unexpected findings suggest that MERS-CoV adaptation during human-to-human spread may be driven by host immunological pressure such as neutralizing antibodies, resulting in reduced affinity to host receptor, and thereby impairs viral fitness and virulence, rather than positive selection for a better affinity to CD26.

Noradrenaline (NA) is a major neurotransmitter that regulates many neuroendocrine and sympathetic autonomic functions of the hypothalamic paraventricular nucleus (PVN). Previously NA has been shown to increase the frequency of excitatory synaptic activity of parvocellular neurons within the PVN, but little is known about its effects on inhibitory synaptic activity. In this work, we studied the effects of NA (1-100 microM) on the spontaneous inhibitory synaptic currents (sIPSC) of type II PVN neurons in brain slices of the rat using the whole cell patch-clamp technique. Spontaneous IPSCs were observed from most type II neurons (n = 121) identified by their anatomical location within the PVN and their electrophysiological properties. Bath application of NA (100 microM) increased sIPSC frequency by 256% in 59% of the neurons. This effect was blocked by prazosin (2-20 microM), the alpha(1)-adrenoceptor antagonist and mimicked by phenylephrine (10-100 microM), the alpha(1)-adrenoceptor agonist. However, in 33% of the neurons, NA decreased sIPSC frequency by 54%, and this effect was blocked by yohimbine (2-20 microM), the alpha(2)-adrenoceptor antagonist and mimicked by clonidine (50 microM), the alpha(2)-adrenoceptor agonist. The Na(+) channel blocker, tetrodotoxin (0.1 microM) blocked the alpha(1)-adrenoceptor-mediated effect, but not the alpha(2)-adreonoceptor-mediated one. Both of the stimulatory and inhibitory effects of NA on sIPSC frequency were observed in individual neurons when tested with NA alone, or both phenylephrine and clonidine. Furthermore, in most neurons that showed the stimulatory effects, the inhibitory effects of NA were unmasked after blocking the stimulatory effects by prazosin or tetrodotoxin. These data indicate that tonic GABAergic inputs to the majority of type II PVN neurons are under a dual noradrenergic modulation, the increase in sIPSC frequency via somatic or dendritic alpha(1)-adrenoceptors and the decrease in sIPSC frequency via axonal terminal alpha(2)-adrenoceptors on the presynaptic GABAergic neurons.

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Invariant natural killer T (iNKT), mucosal-associated invariant T (MAIT), and γδ T cells are innate T cells that acquire memory phenotype in the thymus and share similar biological characteristics. However, how their effector differentiation is developmentally regulated is still unclear. Here, we identify analogous effector subsets of these three innate T cell types in the thymus that share transcriptional profiles. Using single-cell RNA sequencing, we show that iNKT, MAIT and γδ T cells mature via shared, branched differentiation rather than linear maturation or TCR-mediated instruction. Simultaneous TCR clonotyping analysis reveals that thymic maturation of all three types is accompanied by clonal selection and expansion. Analyses of mice deficient of TBET, GATA3 or RORγt and additional in vivo experiments corroborate the predicted differentiation paths, while human innate T cells from liver samples display similar features. Collectively, our data indicate that innate T cells share effector differentiation processes in the thymus.

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.