RPH1 and GIS1 Are Damage-Responsive Repressors of PHR1

The Saccharomyces cerevisiae DNA repair gene PHR1 encodes a photolyase that catalyzes the light-dependent repair of pyrimidine dimers. PHR1 expression is induced at the level of transcription by a variety of DNA-damaging agents. The primary regulator of the PHR1 damage response is a 39-bp sequence called URSPHR1 which is the binding site for a protein(s) that constitutes the damage-responsive repressor PRP. In this communication, we report the identification of two proteins, Rph1p and Gis1p, that regulate PHR1 expression through URSPHR1. Both proteins contain two putative zinc fingers that are identical throughout the DNA binding region, and deletion of both RPH1 and GIS1 is required to fully derepress PHR1 in the absence of damage. Derepression of PHR1 increases the rate and extent of photoreactivation in vivo, demonstrating that the damage response of PHR1 enhances cellular repair capacity. In vitro footprinting and binding competition studies indicate that the sequence AG4 (C4T) within URSPHR1 is the binding site for Rph1p and Gis1p and suggests that at least one additional DNA binding component is present in the PRP complex

Identification of antiviral roles for the exon-junction complex and nonsense-mediated decay in flaviviral infection.

West Nile virus (WNV) is an emerging mosquito-borne flavivirus, related to dengue virus and Zika virus. To gain insight into host pathways involved in WNV infection, we performed a systematic affinity-tag purification mass spectrometry (APMS) study to identify 259 WNV-interacting human proteins. RNA interference screening revealed 26 genes that both interact with WNV proteins and influence WNV infection. We found that WNV, dengue and Zika virus capsids interact with a conserved subset of proteins that impact infection. These include the exon-junction complex (EJC) recycling factor PYM1, which is antiviral against all three viruses. The EJC has roles in nonsense-mediated decay (NMD), and we found that both the EJC and NMD are antiviral and the EJC protein RBM8A directly binds WNV RNA. To counteract this, flavivirus infection inhibits NMD and the capsid-PYM1 interaction interferes with EJC protein function and localization. Depletion of PYM1 attenuates RBM8A binding to viral RNA, suggesting that WNV sequesters PYM1 to protect viral RNA from decay. Together, these data suggest a complex interplay between the virus and host in regulating NMD and the EJC

The Howl - Fall 2016

The Howl is a magazine that is planned, researched, written, photographed and designed by Otterbein University\u27s ESL and international students. The magazine serves to give them a safe space in which to use their voice to share their cultures, experiences and lives. If you are interested in submitting to The Howl, please email your writing or photography to [email protected]://digitalcommons.otterbein.edu/the_howl/1001/thumbnail.jp

Multiple Routes to Oncogenesis Are Promoted by the Human Papillomavirus-Host Protein Network.

We have mapped a global network of virus-host protein interactions by purification of the complete set of human papillomavirus (HPV) proteins in multiple cell lines followed by mass spectrometry analysis. Integration of this map with tumor genome atlases shows that the virus targets human proteins frequently mutated in HPV- but not HPV+ cancers, providing a unique opportunity to identify novel oncogenic events phenocopied by HPV infection. For example, we find that the NRF2 transcriptional pathway, which protects against oxidative stress, is activated by interaction of the NRF2 regulator KEAP1 with the viral protein E1. We also demonstrate that the L2 HPV protein physically interacts with the RNF20/40 histone ubiquitination complex and promotes tumor cell invasion in an RNF20/40-dependent manner. This combined proteomic and genetic approach provides a systematic means to study the cellular mechanisms hijacked by virally induced cancers.Significance: In this study, we created a protein-protein interaction network between HPV and human proteins. An integrative analysis of this network and 800 tumor mutation profiles identifies multiple oncogenesis pathways promoted by HPV interactions that phenocopy recurrent mutations in cancer, yielding an expanded definition of HPV oncogenic roles. Cancer Discov; 8(11); 1474-89. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 1333

CRL4^(AMBRA1) targets Elongin C for ubiquitination and degradation to modulate CRL5 signaling

Multi‐subunit cullin‐RING ligases (CRLs) are the largest family of ubiquitin E3 ligases in humans. CRL activity is tightly regulated to prevent unintended substrate degradation or autocatalytic degradation of CRL subunits. Using a proteomics strategy, we discovered that CRL4^(AMBRA1) (CRL substrate receptor denoted in superscript) targets Elongin C (ELOC), the essential adapter protein of CRL5 complexes, for polyubiquitination and degradation. We showed that the ubiquitin ligase function of CRL4^(AMBRA1) is required to disrupt the assembly and attenuate the ligase activity of human CRL5^(SOCS3) and HIV‐1 CRL5^(VIF) complexes as AMBRA1 depletion leads to hyperactivation of both CRL5 complexes. Moreover, CRL4^(AMBRA1) modulates interleukin‐6/STAT3 signaling and HIV‐1 infectivity that are regulated by CRL5^(SOCS3) and CRL5^(VIF), respectively. Thus, by discovering a substrate of CRL4^(AMBRA1), ELOC, the shared adapter of CRL5 ubiquitin ligases, we uncovered a novel CRL cross‐regulation pathway

CRL4^(AMBRA1) targets Elongin C for ubiquitination and degradation to modulate CRL5 signaling

Multi‐subunit cullin‐RING ligases (CRLs) are the largest family of ubiquitin E3 ligases in humans. CRL activity is tightly regulated to prevent unintended substrate degradation or autocatalytic degradation of CRL subunits. Using a proteomics strategy, we discovered that CRL4^(AMBRA1) (CRL substrate receptor denoted in superscript) targets Elongin C (ELOC), the essential adapter protein of CRL5 complexes, for polyubiquitination and degradation. We showed that the ubiquitin ligase function of CRL4^(AMBRA1) is required to disrupt the assembly and attenuate the ligase activity of human CRL5^(SOCS3) and HIV‐1 CRL5^(VIF) complexes as AMBRA1 depletion leads to hyperactivation of both CRL5 complexes. Moreover, CRL4^(AMBRA1) modulates interleukin‐6/STAT3 signaling and HIV‐1 infectivity that are regulated by CRL5^(SOCS3) and CRL5^(VIF), respectively. Thus, by discovering a substrate of CRL4^(AMBRA1), ELOC, the shared adapter of CRL5 ubiquitin ligases, we uncovered a novel CRL cross‐regulation pathway

Happiness around the world: A combined etic-emic approach across 63 countries.

What does it mean to be happy? The vast majority of cross-cultural studies on happiness have employed a Western-origin, or "WEIRD" measure of happiness that conceptualizes it as a self-centered (or "independent"), high-arousal emotion. However, research from Eastern cultures, particularly Japan, conceptualizes happiness as including an interpersonal aspect emphasizing harmony and connectedness to others. Following a combined emic-etic approach (Cheung, van de Vijver & Leong, 2011), we assessed the cross-cultural applicability of a measure of independent happiness developed in the US (Subjective Happiness Scale; Lyubomirsky & Lepper, 1999) and a measure of interdependent happiness developed in Japan (Interdependent Happiness Scale; Hitokoto & Uchida, 2015), with data from 63 countries representing 7 sociocultural regions. Results indicate that the schema of independent happiness was more coherent in more WEIRD countries. In contrast, the coherence of interdependent happiness was unrelated to a country's "WEIRD-ness." Reliabilities of both happiness measures were lowest in African and Middle Eastern countries, suggesting these two conceptualizations of happiness may not be globally comprehensive. Overall, while the two measures had many similar correlates and properties, the self-focused concept of independent happiness is "WEIRD-er" than interdependent happiness, suggesting cross-cultural researchers should attend to both conceptualizations

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E. Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported by a French National Research Agency ‘Investissements d’Avenir’ programme grant (ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research Training Program Scholarship. The Raipur Group is thankful to: (1) the University Grants Commission, New Delhi, India for the research grants received through its SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science; and (2) the Center for Translational Chronobiology at the School of Studies in Life Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was supported by grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported by the National Science Foundation Graduate Research Fellowship (R010138018). We acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E. Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova); S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R. C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T. Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera); J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B. Todorova (University of Vienna, Austria). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution