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Mitogen-activated protein kinases (MAPKs) play critical roles in the induction of numerous cytokines, chemokines, and inflammatory mediators that mobilize the immune system to counter pathogenic infections. Dual-specificity phosphatase-1 (DUSP1) is a member of dual-specificity phosphatases, which inactivates MAPKs through a negative feedback mechanism. Here we report that in response to viral and bacterial infections, not only DUSP1 transcript but also itsN6-methyladenosine (m6A) level rapidly increase together with the m6A reader protein YTHDF2, resulting in enhanced YTHDF2-mediated DUSP1 transcript degradation. Knockdown of DUSP1 promotes p38 and JNK phosphorylation and activation, thus increasing the expression of innate immune response genes including IL1β, CSF3, TGM2 and SRC. Similarly, knockdown of m6A eraser ALKBH5 increases DUSP1 transcript m6A level resulting in accelerated transcript degradation, activation of p38 and JNK, and enhanced expression of IL1β, CSF3, TGM2 and SRC. These results demonstrate that m6A and reader protein YTHDF2 orchestrate optimal innate immune response during viral and bacterial infections by downregulating the expression of a negative regulator DUSP1 of the p38 and JNK pathways that are central to innate immune response against pathogenic infections.IMPORTANCEInnate immunity is central for controlling pathogenic infections and maintaining the homeostasis of the host. In this study, we have revealed a novel mechanism regulating innate immune response during viral and bacterial infections. We have found thatN6-methyladenosine (m6A) and the reader protein YTHDF2 regulate dual-specificity phosphatase-1, a negative regulator of mitogen-activated protein kinases p38 and JNK, to maximize innate immune response during viral and bacterial infections. These results provide novel insights into the mechanism regulating innate immunity, which could help the development of novel approaches for controlling pathogenic infections.

Purpose: The favorable prognosis of stage I and II nasopharyngeal carcinoma (NPC) has motivated a search for biomarkers for the early detection and risk assessment of Epstein-Barr virus (EBV)–associated NPC. Although EBV seropositivity is ubiquitous among adults, a spike in antibodies against select EBV proteins is a harbinger of NPC. A serologic survey would likely reveal which EBV antibodies could discriminate those at risk of developing NPC. Experimental Design: Lysates from a new EBV mammalian expression library were used in a denaturing multiplex immunoblot assay to survey antibodies against EBV in sera collected from healthy individuals who later developed NPC (incident cases) in a prospective cohort from Singapore and validated in an independent cohort from Shanghai, P.R. China. Results: We show that IgA against EBV nuclear antigen 1 (EBNA1) discriminated incident NPC cases from matched controls with 100% sensitivity and 100% specificity up to 4 years before diagnosis in both Singapore and Shanghai cohorts. Incident NPC cases had a greater IgG repertoire against lytic-classified EBV proteins, and the assortment of IgA against EBV proteins detected by the immunoblot assay increased closer to diagnosis. Conclusions: Although NPC tumors consistently harbor latent EBV, the observed heightened systemic and mucosal immunity against lytic-classified antigens years prior to clinical diagnosis is consistent with enhanced lytic transcription. We conclude that an expanding EBV mucosal reservoir (which can be latent and/or lytic) is a risk factor for NPC. This presents an opportunity to identify those at risk of developing NPC using IgA against EBNA1 as a biomarker.

PURPOSE To study the efficacy and safety of the dienogest and the gonadotropin-releasing hormone agonist (GnRH-a) in symptomatic females with uterine adenomyosis. METHODS A total of 127 patients with adenomyosis with a chief complaint of dysmenorrhea were recruited. The first group received 2 mg of dienogest (DNG) daily, whereas the second group received goserelin acetate (GS) (3.6 mg/4 weeks) for 12 weeks. Outpatient follow-up was undertaken after 12 weeks. RESULTS Among 127 women, 56/63 (88.9%) patients completed the treatment in the DNG group, whereas 62/64 (96.9%) patients completed the treatment in the GS group. A significant decrease in dysmenorrhea symptoms as measured by the visual analog scale (VAS) and Carcinoma antigen125 (CA125) after 12 weeks of treatment was observed in both groups (p

Northern corn leaf blight (NCLB), caused by Setosphaeria turcica, is an important foliar disease in corn. Since 2005, the damage from NCLB has increased in Northeast China, probably from the emergence of new physiological races. In this study, 883 single conidial isolates of S. turcica were obtained from 12 sites across three provinces of Northeast China between 2007 and 2017. The virulence of the isolates was evaluated in five corn lines (B37, B37Ht1, B37Ht2, B37Ht3, and B37HtN). Sixteen physiological races (0, 1, 2, 3, N, 12, 13, 1N, 23, 2N, 3N, 123, 12N, 13N, 23N, and 123N) were obtained, depending on their resistance or susceptibility. Three races (0, 1, and 2) were most prevalent, with frequencies of 40.5, 19.6, and 11.3% in all isolates, respectively. Races varied across provinces and years. Virulence to more than one Ht resistance genes occurred in 21.5% of isolates, with 8.5% virulent to three or more genes. Overall, 41% of isolates were avirulent to all Ht genes, 36% were virulent to Ht1, 28% to Ht2, 11% to Ht3, and 16% to HtN. Isolates from Heilongjiang had a greater frequency of virulence to Ht2 and Ht3, whereas isolates from Jilin and Liaoning were more frequently virulent to Ht1 and HtN, respectively. The frequency of isolate virulence to Ht2 ranged from 8% in 2009 to a maximum of 29% in 2015, and in 2015, isolates were more virulent to Ht2 than Ht1. This study will help growers to purposefully select commercial hybrids with multiple effective Ht resistance genes, and reduce the utilization of Ht1 and Ht2 genes in the process of corn production to strengthen NCLB control.

The mechanisms underlying the reduction in cognitive flexibility associated with reinforcement of addictive substance use are unknown. This reinforcement is mediated by substance-induced synaptic plasticity in direct-pathway medium spiny neurons (dMSNs) that project to the substantia nigra (SNr). Cognitive flexibility is mediated by cholinergic interneurons (CINs), which receive extensive local inhibition from the striatum. Here, we report that cocaine or alcohol administration caused a long-lasting potentiation of local inhibitory dMSN→CIN transmission in the dorsomedial striatum (DMS), a brain region critical for goal-directed behavior and cognitive flexibility. This dMSN→CIN potentiation reduced CIN firing activity. Furthermore, chemogenetic and time-locked optogenetic inhibition of DMS CINs suppressed cognitive flexibility in an instrumental reversal learning task. Importantly, rabies-mediated tracing and physiological studies revealed that SNr-projecting dMSNs, which mediate reinforcement, sent axonal collaterals to inhibit DMS CINs, which mediate flexibility. Our findings demonstrate that the local inhibitory dMSN→CIN circuit mediates a reinforcement-induced reduction in cognitive flexibility.HIGHLIGHTSCocaine reinforcement inhibits striatal cholinergic interneurons (CINs) and impairs cognitive flexibility.Optogenetic and chemogenetic CIN inhibition impairs cognitive flexibility.Reinforcement behaviors potentiate inhibitory transmission from direct-pathway medium spiny neurons (dMSNs) to CINs.Substantia nigra-projecting dMSNs mediate reinforcement and also send collaterals that inhibit CINs.

Several industries that provide services to customers (e.g., public utility and transportation) charge higher prices during peak hours to smooth demand. With technologies (e.g., electronic shelf labels) enabling retailers to change prices easily within each day, should supermarkets use peak-hour pricing? To examine this question formally, we introduce a stylized duopoly model in the presence of “negative externality,” where firms compete for congestion-averse customers. We characterize how customers endogenously segment themselves regarding when and where to shop, and then use the equilibrium outcomes to examine whether the firms should implement peak-hour pricing for varying types of customer flexibility and competitive asymmetry. Our analysis shows that, if customers are not flexible in their store choice, then both firms would always use peak-hour pricing. However, if store choice flexibility is present, then firms’ decisions depend on the competitive asymmetry as follows. If one firm has a clear competitive advantage (in terms of value or price) over the other firm, then the dominant firm will use peak-hour pricing, whereas the other firm will not. Otherwise, both firms will use peak-hour pricing if they engage in symmetric competition (in terms of similar value and price), or neither firm will use it if they engage in differentiated competition (high value versus low cost). Through our analysis of different extensions, we find that a firm’s ability to set its regular price would dampen the effect of peak-period pricing. Also, we obtain consistent results when there is heterogeneity in customer valuation and customer congestion aversion level.

Large-scale genetic and pharmacologic dependency maps are generated to reveal genetic vulnerabilities and drug sensitivities of cancer. However, user-friendly software is needed to systematically link such maps. Here we present DepLink, an R Shiny server to identify genetic and pharmacologic perturbations that induce similar effects on cell viability or molecular changes. DepLink integrates heterogeneous datasets of genome-wide CRISPR loss-of-function screens, high-throughput pharmacologic screens, and perturbation expression signatures. The datasets are systematically connected by four complementary modules tailored for different query scenarios. In summary, DepLink enables easy navigation, visualization, and linkage of rapidly evolving cancer dependency maps.

Let $ G $ be a simple graph with edge set $ E(G) $. The modified Sombor index is defined as $ ^{m}SO(G) = \sum\limits_{uv\in E(G)}\frac{1}{\sqrt{d_{u}^{2}~~+~~d_{v}^{2}}} $, where $ d_{u} $ (resp. $ d_{v} $) denotes the degree of vertex $ u $ (resp. $ v $). In this paper, we determine some bounds for the modified Sombor indices of graphs with given some parameters (e.g., maximum degree $ \Delta $, minimum degree $ \delta $, diameter $ d $, girth $ g $) and the Nordhaus-Gaddum-type results. We also obtain the relationship between modified Sombor index and some other indices. At last, we obtain some bounds for the modified spectral radius and energy.

The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs generated by correlation analysis, GeneMania database, and correlation + GeneMania were trained with and without clinical data to predict the risk score (RS). The performance of the 6 Surv_GCNN models was compared with two other existing models, Cox-PH and Cox-nnet. The results showed that Cox-PH has the worst performance among 8 tested models across the 13 cancer types while Surv_GCNN models with clinical data reported the best overall performance, outperforming other competing models in 7 out of 13 cancer types including BLCA, BRCA, COAD, LUSC, SARC, STAD, and UCEC. A novel network-based interpretation of Surv_GCNN was also proposed to identify potential gene markers for breast cancer. The signatures learned by the nodes in the hidden layer of Surv_GCNN were identified and were linked to potential gene markers by network modularization. The identified gene markers for breast cancer have been compared to a total of 213 gene markers from three widely cited lists for breast cancer survival analysis. About 57% of gene markers obtained by Surv_GCNN with correlation + GeneMania graph either overlap or directly interact with the 213 genes, confirming the effectiveness of the identified markers by Surv_GCNN.

We develop a novel computational method, NucHMM, to identify functional nucleosome states associated with cell type-specific combinatorial histone marks and nucleosome organization features such as phasing, spacing and positioning. We test it on publicly available MNase-seq and ChIP-seq data in MCF7, H1, and IMR90 cells and identify 11 distinct functional nucleosome states. We demonstrate these nucleosome states are distinctly associated with the splicing potentiality of skipping exons. This advances our understanding of the chromatin function at the nucleosome level and offers insights into the interplay between nucleosome organization and splicing processes. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02465-1.