Giα proteins exhibit functional differences in the activation of ERK1/2, Akt and mTORC1 by growth factors in normal and breast cancer cells

Background In a classic model, Giα proteins including Gi1α, Gi2α and Gi3α are important for transducing signals from Giα protein-coupled receptors (GiαPCRs) to their downstream cascades in response to hormones and neurotransmitters. Our previous study has suggested that Gi1α, Gi2α and Gi3α are also important for the activation of the PI3K/Akt/mTORC1 pathway by epidermal growth factor (EGF) and its family members. However, a genetic role of these Giα proteins in the activation of extracellular signal-regulated protein kinase 1 and 2 (ERK1/2) by EGF is largely unknown. Further, it is not clear whether these Giα proteins are also engaged in the activation of both the Akt/mTORC1 and ERK1/2 pathways by other growth factor family members. Additionally, a role of these Giα proteins in breast cancer remains to be elucidated. Results We found that Gi1/3 deficient MEFs with the low expression level of Gi2α showed defective ERK1/2 activation by EGFs, IGF-1 and insulin, and Akt and mTORC1 activation by EGFs and FGFs. Gi1/2/3 knockdown breast cancer cells exhibited a similar defect in the activations and a defect in in vitro growth and invasion. The Giα proteins associated with RTKs, Gab1, FRS2 and Shp2 in breast cancer cells and their ablation impaired Gab1’s interactions with Shp2 in response to EGF and IGF-1, or with FRS2 and Grb2 in response to bFGF. Conclusions Giα proteins differentially regulate the activation of Akt, mTORC1 and ERK1/2 by different families of growth factors. Giα proteins are important for breast cancer cell growth and invasion.Fil: Wang, Zhanwei. University of Hawaii Cancer Center. Honolulu; Estados UnidosFil: Dela Cruz, Rica. University of Hawaii Cancer Center. Honolulu; Estados UnidosFil: Ji, Fang. Shanghai Jiao Tong University . Sahnghai; ChinaFil: Guo, Sheng. University of Hawaii Cancer Center. Honolulu; Estados Unidos. Shanghai Jiaotong University. Shangha; Estados UnidosFil: Zhang, Jianhua. Shanghai Jiaotong University. Shangha; Estados Unidos. University of Hawaii Cancer Center. Honolulu; Estados UnidosFil: Wang, Ying. David Geffen School of Medicine at UCLA. Los Angeles; Estados UnidosFil: Feng, Gen-Sheng. University of California at San Diego; Estados UnidosFil: Birnbaumer, Lutz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. National Institutes of Health; Estados UnidosFil: Jiang, Meisheng. David Geffen School of Medicine at UCLA. Los Angeles; Estados UnidosFil: Chu, Wen Ming. University of Hawaii Cancer Center. Honolulu; Estados Unido

Risk for Transportation of Coronavirus Disease from Wuhan to Other Cities in China.

On January 23, 2020, China quarantined Wuhan to contain coronavirus disease (COVID-19). We estimated the probability of transportation of COVID-19 from Wuhan to 369 other cities in China before the quarantine. Expected COVID-19 risk is >50% in 130 (95% CI 89-190) cities and >99% in the 4 largest metropolitan areas

Serial Interval of COVID-19 among Publicly Reported Confirmed Cases.

We estimate the distribution of serial intervals for 468 confirmed cases of coronavirus disease reported in China as of February 8, 2020. The mean interval was 3.96 days (95% CI 3.53-4.39 days), SD 4.75 days (95% CI 4.46-5.07 days); 12.6% of case reports indicated presymptomatic transmission

Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions.

Studies of novel coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have reported varying estimates of epidemiological parameters, including serial interval distributions-i.e., the time between illness onset in successive cases in a transmission chain-and reproduction numbers. By compiling a line-list database of transmission pairs in mainland China, we show that mean serial intervals of COVID-19 shortened substantially from 7.8 to 2.6 days within a month (9 January to 13 February 2020). This change was driven by enhanced nonpharmaceutical interventions, particularly case isolation. We also show that using real-time estimation of serial intervals allowing for variation over time provides more accurate estimates of reproduction numbers than using conventionally fixed serial interval distributions. These findings could improve our ability to assess transmission dynamics, forecast future incidence, and estimate the impact of control measures

IASM: A System for the Intelligent Active Surveillance of Malaria

Malaria, a life-threatening infectious disease, spreads rapidly via parasites. Malaria prevention is more effective and efficient than treatment. However, the existing surveillance systems used to prevent malaria are inadequate, especially in areas with limited or no access to medical resources. In this paper, in order to monitor the spreading of malaria, we develop an intelligent surveillance system based on our existing algorithms. First, a visualization function and active surveillance were implemented in order to predict and categorize areas at high risk of infection. Next, socioeconomic and climatological characteristics were applied to the proposed prediction model. Then, the redundancy of the socioeconomic attribute values was reduced using the stepwise regression method to improve the accuracy of the proposed prediction model. The experimental results indicated that the proposed IASM predicted malaria outbreaks more close to the real data and with fewer variables than other models. Furthermore, the proposed model effectively identified areas at high risk of infection

Characterizing human collective behaviours of COVID-19 in Hong Kong

People are likely to engage in collective behaviour online during extreme events, such as the COVID-19 crisis, to express their awareness, actions and concerns. Hong Kong has implemented stringent public health and social measures (PHSMs) to curb COVID-19 epidemic waves since the first COVID-19 case was confirmed on 22 January 2020. People are likely to engage in collective behaviour online during extreme events, such as the COVID-19 crisis, to express their awareness, actions and concerns. Here, we offer a framework to evaluate interactions among individuals emotions, perception, and online behaviours in Hong Kong during the first two waves (February to June 2020) and found a strong correlation between online behaviours of Google search and the real-time reproduction numbers. To validate the model output of risk perception, we conducted 10 rounds of cross-sectional telephone surveys from February 1 through June 20 in 2020 to quantify risk perception levels over time. Compared with the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people risk perception (individuals who worried about being infected) during the studied period. We may need to reinvigorate the public by engaging people as part of the solution to live their lives with reduced risk

Joint Effect of Genotypic and Phenotypic Features of Reproductive Factors on Endometrial Cancer Risk

Prolonged estrogen exposure is believed to be the major cause of endometrial cancer. As possible markers of estrogen exposure, various menstrual and reproductive features, e.g., ages at menarche and menopause, are found to be associated with endometrial cancer risk. In order to assess their combined effects on endometrial cancer, we created the total number of menstrual cycles (TNMC) that a woman experienced during her life or up to the time of study and two genetic risk scores, GRS1 for age at menarche and GRS2 for age at menopause. Comparing 482 endometrial cancer patients with 571 population controls, we found TNMC was associated with endometrial cancer risk and that the association remained statistically significant after adjustment for obesity and other potential confounders. Risk increased by about 2.5% for every additional 10 menstrual-cycles. The study also showed that high GRS1 was associated with increased risk. This relationship, however, was attenuated after adjustment for obesity. Our study further indicated women with high TNMC and GRS1 had twice the risk of endometrial cancer compared to those low in both indices. Our results provided additional support to the involvement of estrogen exposure in endometrial cancer risk with regard to genetic background and lifestyle features

Pandemic fatigue impedes mitigation of COVID-19 in Hong Kong

Hong Kong has implemented stringent public health and social measures (PHSMs) to curb each of the four COVID-19 epidemic waves since January 2020. The third wave between July and September 2020 was brought under control within 2 m, while the fourth wave starting from the end of October 2020 has taken longer to bring under control and lasted at least 5 mo. Here, we report the pandemic fatigue as one of the potential reasons for the reduced impact of PHSMs on transmission in the fourth wave. We contacted either 500 or 1,000 local residents through weekly random-digit dialing of landlines and mobile telephones from May 2020 to February 2021. We analyze the epidemiological impact of pandemic fatigue by using the large and detailed cross-sectional telephone surveys to quantify risk perception and self-reported protective behaviors and mathematical models to incorporate population protective behaviors. Our retrospective prediction suggests that an increase of 100 daily new reported cases would lead to 6.60% (95% CI: 4.03, 9.17) more people worrying about being infected, increase 3.77% (95% CI: 2.46, 5.09) more people to avoid social gatherings, and reduce the weekly mean reproduction number by 0.32 (95% CI: 0.20, 0.44). Accordingly, the fourth wave would have been 14% (95% CI%: −53%, 81%) smaller if not for pandemic fatigue. This indicates the important role of mitigating pandemic fatigue in maintaining population protective behaviors for controlling COVID-19

Exploring the Fecal Microbial Composition and Metagenomic Functional Capacities Associated With Feed Efficiency in Commercial DLY Pigs

Gut microbiota has indispensable roles in nutrient digestion and energy harvesting, especially in processing the indigestible components of dietary polysaccharides. Searching for the microbial taxa and functional capacity of the gut microbiome associated with feed efficiency (FE) can provide important knowledge to increase profitability and sustainability of the swine industry. In the current study, we performed a comparative analysis of the fecal microbiota in 50 commercial Duroc × (Landrace × Yorkshire) (DLY) pigs with polarizing FE using 16S rRNA gene sequencing and shotgun metagenomic sequencing. There was a different microbial community structure in the fecal microbiota of pigs with different FE. Random forest analysis identified 24 operational taxonomic units (OTUs) as potential biomarkers to improve swine FE. Multiple comparison analysis detected 8 OTUs with a significant difference or tendency toward a difference between high- and low-FE pigs (P < 0.01, q < 0.1). The high-FE pigs had a greater abundance of OTUs that were from the Lachnospiraceae and Prevotellaceae families and the Escherichia-Shigella and Streptococcus genera than low-FE pigs. A sub-species Streptococcus gallolyticus subsp. gallolyticus could be an important candidate for improving FE. The functional capacity analysis found 18 KEGG pathways and CAZy EC activities that were different between high- and low-FE pigs. The fecal microbiota in high FE pigs have greater functional capacity to degrade dietary cellulose, polysaccharides, and protein and may have a greater abundance of microbes that can promote intestinal health. These results provided insights for improving porcine FE through modulating the gut microbiome