Smad7 enables STAT3 activation and promotes pluripotency independent of TGF-β signaling

TGF-β and related growth factors critically regulate cell potency and functions. Smad7 is induced by TGF-βs and inhibits the physiological functions of TGF-β signaling. This study describes an unexpected finding that Smad7 promotes self-renewal of embryonic stem cells (ESCs) in a manner independent of its inhibition on TGF-β signaling. Instead, Smad7 acts to induce activation of transcription factor signal transducers and activators of transcription 3 (STAT3) in ESCs. Smad7 activates STAT3 through its direct binding to the cytokine receptor upstream of STAT3 activation. In agreement with the role of STAT3 in maintaining ESC pluripotency, Smad7 promotes ESC self-renewal and induced pluripotent stem cell reprogramming. This finding illustrates a regulatory mechanism for Smad7 in maintaining pluripotency, and likely in cancer and inflammation

Curated genome annotation of Oryza sativa ssp. japonica and comparative genome analysis with Arabidopsis thaliana

We present here the annotation of the complete genome of rice Oryza sativa L. ssp. japonica cultivar Nipponbare. All functional annotations for proteins and non-protein-coding RNA (npRNA) candidates were manually curated. Functions were identified or inferred in 19,969 (70%) of the proteins, and 131 possible npRNAs (including 58 antisense transcripts) were found. Almost 5000 annotated protein-coding genes were found to be disrupted in insertional mutant lines, which will accelerate future experimental validation of the annotations. The rice loci were determined by using cDNA sequences obtained from rice and other representative cereals. Our conservative estimate based on these loci and an extrapolation suggested that the gene number of rice is ~32,000, which is smaller than previous estimates. We conducted comparative analyses between rice and Arabidopsis thaliana and found that both genomes possessed several lineage-specific genes, which might account for the observed differences between these species, while they had similar sets of predicted functional domains among the protein sequences. A system to control translational efficiency seems to be conserved across large evolutionary distances. Moreover, the evolutionary process of protein-coding genes was examined. Our results suggest that natural selection may have played a role for duplicated genes in both species, so that duplication was suppressed or favored in a manner that depended on the function of a gene

TRAIL Receptor Signaling Regulation of Chemosensitivity In Vivo but Not In Vitro

Background: Signaling by Tumor Necrosis Factor-Related Apoptosis Inducing Ligand (TRAIL) and Fas ligand (FasL) has been proposed to contribute to the chemosensitivity of tumor cells treated with various other anti-cancer agents. However, the importance of these effects and whether there are differences in vitro and in vivo is unclear. Methodology/Principal Findings: To assess the relative contribution of death receptor pathways to this sensitivity and to determine whether these effects are intrinsic to the tumor cells, we compared the chemosensitivity of isogenic BJAB human lymphoma cells where Fas and TRAIL receptors or just TRAIL receptors were inhibited using mutants of the adaptor protein FADD or by altering the expression of the homeobox transcription factor Six1. Inhibition of TRAIL receptors did not affect in vitro tumor cell killing by various anti-cancer agents indicating that chemosensitivity is not significantly affected by the tumor cell-intrinsic activation of death receptor signaling. However, selective inhibition of TRAIL receptor signaling caused reduced tumor regression and clearance in vivo when tested in a NOD/SCID mouse model. Conclusions: These data show that TRAIL receptor signaling in tumor cells can determine chemosensitivity in vivo but not in vitro and thus imply that TRAIL resistance makes tumors less susceptible to conventional cytotoxic anti-cancer drugs a

Poxvirus Protein N1L Targets the I-κB Kinase Complex, Inhibits Signaling to NF-κB by the Tumor Necrosis Factor Superfamily of Receptors, and Inhibits NF-κB and IRF3 Signaling by Toll-like Receptors

Poxviruses encode proteins that suppress host immune responses, including secreted decoy receptors for pro-inflammatory cytokines such as interleukin-1 (IL-1) and the vaccinia virus proteins A46R and A52R that inhibit intracellular signaling by members of the IL-1 receptor (IL-1R) and Toll-like receptor (TLR) family. In vivo, the TLRs mediate the innate immune response by serving as pathogen recognition receptors, whose oligomerized intracellular Toll/IL-1 receptor (TIR) domains can initiate innate immune signaling. A family of TIR domain-containing adapter molecules transduces signals from engaged receptors that ultimately activate NF-kappaB and/or interferon regulatory factor 3 (IRF3) to induce pro-inflammatory cytokines. Data base searches detected a significant similarity between the N1L protein of vaccinia virus and A52R, a poxvirus inhibitor of TIR signaling. Compared with other poxvirus virulence factors, the poxvirus N1L protein strongly affects virulence in vivo; however, the precise target of N1L was previously unknown. Here we show that N1L suppresses NF-kappaB activation following engagement of Toll/IL-1 receptors, tumor necrosis factor receptors, and lymphotoxin receptors. N1L inhibited receptor-, adapter-, TRAF-, and IKK-alpha and IKK-beta-dependent signaling to NF-kappaB. N1L associated with several components of the multisubunit I-kappaB kinase complex, most strongly associating with the kinase, TANK-binding kinase 1 (TBK1). Together these findings are consistent with the hypothesis that N1L disrupts signaling to NF-kappaB by Toll/IL-1Rs and TNF superfamily receptors by targeting the IKK complex for inhibition. Furthermore, N1L inhibited IRF3 signaling, which is also regulated by TBK1. These studies define a role for N1L as an immunomodulator of innate immunity by targeting components of NF-kappaB and IRF3 signaling pathways

Vitamin D Binding Protein-Macrophage Activating Factor Directly Inhibits Proliferation, Migration, and uPAR Expression of Prostate Cancer Cells

Background: Vitamin D binding protein-macrophage activating factor (DBP-maf) is a potent inhibitor of tumor growth. Its activity, however, has been attributed to indirect mechanisms such as boosting the immune response by activating macrophages and inhibiting the blood vessel growth necessary for the growth of tumors. Methods and Findings: In this study we show for the first time that DBP-maf exhibits a direct and potent effect on prostate tumor cells in the absence of macrophages. DBP-maf demonstrated inhibitory activity in proliferation studies of both LNCaP and PC3 prostate cancer cell lines as well as metastatic clones of these cells. Flow cytometry studies with annexin V and propidium iodide showed that this inhibitory activity is not due to apoptosis or cell death. DBP-maf also had the ability to inhibit migration of prostate cancer cells in vitro. Finally, DBP-maf was shown to cause a reduction in urokinase plasminogen activator receptor (uPAR) expression in prostate tumor cells. There is evidence that activation of this receptor correlates with tumor metastasis. Conclusions: These studies show strong inhibitory activity of DBP-maf on prostate tumor cells independent of it

The silicon isotope composition of Ethmodiscus rexlaminated diatom mats from the tropical West Pacific: Implications for silicate cycling during the Last Glacial Maximum

The cause of massive blooms of Ethmodiscus rex laminated diatom mats (LDMs) in the eastern Philippine Sea (EPS) during the Last Glacial Maximum (LGM) remains uncertain. In order to better understand the mechanism of formation of E. rex LDMs from the perspective of dissolved silicon (DSi) utilization, we determined the silicon isotopic composition of single E. rex diatom frustules (δ30SiE. rex) from two sediment cores in the Parece Vela Basin of the EPS. In the study cores, δ30SiE. rex varies from −1.23‰ to −0.83‰ (average −1.04‰), a range that is atypical of marine diatom δ30Si and that corresponds to the lower limit of reported diatom δ30Si values of any age. A binary mixing model (upwelled silicon versus eolian silicon) accounting for silicon isotopic fractionation during DSi uptake by diatoms was constructed. The binary mixing model demonstrates that E. rex dominantly utilized DSi from eolian sources (i.e., Asian dust) with only minor contributions from upwelled seawater sources (i.e., advected from Subantarctic Mode Water, Antarctic Intermediate Water, or North Pacific Intermediate Water). E. rex utilized only ~24% of available DSi, indicating that surface waters of the EPS were eutrophic with respect to silicon during the LGM. Our results suggest that giant diatoms did not always use a buoyancy strategy to obtain nutrients from the deep nutrient pool, thus revising previously proposed models for the formation of E. rex LDMs

Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics

Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100