Structural Studies on ι-Carrageenan Derived Oligosaccharides and Its Application

ABSTRACT Mild hydrochloric acid hydrolysis of ι-carrageenan from Eucheuma spinosum yielded two oligosaccharides of sulfated tetrasaccharide structure. These were characterized by Fourier Transform Infrared Spectroscopy (FT-IR), Nuclear Magnetic Resonance (NMR) and Electrospray Ionization Mass Spectrometry (ESIMS). Both oligosaccharides have structure of β-D-galactopyranose(Galp)4S-(1→4)-α-D-AnGalp2S-(1→3)-β-D-galactopyranose Galp)4S-(1→4)-α-D-AnGalp2S-(1→3) . Application of the resulting oligosaccharides on protein delivery system in terms of encapsulation efficiency was performed

Characterization of the physicochemical, thermal and rheological properties of cashew kernel starch

The study aimed to characterize physicochemical, thermal, and rheological properties of cashew nut starch (CNS) and then compare the obtained results with the properties of potato and corn starches. CNS showed higher gelatinization temperatures (112.29 °C) than those noted for potato and maize starches (78.44–94.65 °C). In addition, CNS had higher peak viscosity (19.03 mPa·s) than high amylose corn starch. The static shear rheological test indicated that the CNS followed a pseudoplastic behavior. In addition, CNS sample showed a thixotropic patter, which was less pronounced than that observed for potato starch, but higher than the value reported for high amylose corn starch. These results demonstrated that the shear resistance of CNS was lower than high amylose corn starch, but higher than potato starch. The storage and loss modulus (G' and G“, respectively) of the CNS were higher than those reported for the rest of samples. In this line, elastic properties were predominant in CNS sample. In conclusion, results from this study provided insight into physicochemical and structural properties of cashew nut starch, which could represent a preliminary step for its future application in food processing.Fil: Chen, Nan. Shandong Academy of Agricultural Sciences; China. Jilin Agricultural University; ChinaFil: Wang, Qing. Shandong Academy of Agricultural Sciences; ChinaFil: Wang, Mu Xuan. Shandong Academy of Agricultural Sciences; ChinaFil: Li, Ning yang. Shandong Agricultural University; ChinaFil: Briones, Annabelle V.. Dost Complex; FilipinasFil: Cassani, Lucía Victoria. Universidad de Vigo; España. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Prieto, M. A.. Universidad de Vigo; EspañaFil: Carandang, Maricar B.. Dost Complex; FilipinasFil: Liu, Chao. Ministry Of Agriculture Of The People's Republic Of China; ChinaFil: Gu, Chun Mei. Jilin Agricultural University; ChinaFil: Sun, Jin Yue. Ministry Of Agriculture Of The People's Republic Of China; Chin

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

BackgroundWe previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in similar to 80% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1x10(-4)) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1x10(-4)). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4x10(-3)), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7x10(-8)). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68x10(-5)).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old