Transient genome-wide interactions of the master transcription factor NLP7 initiate a rapid nitrogen-response cascade

Dynamic reprogramming of gene regulatory networks (GRNs) enables organisms to rapidly respond to environmental perturbation. However, the underlying transient interactions between transcription factors (TFs) and genome-wide targets typically elude biochemical detection. Here, we capture both stable and transient TF-target interactions genome-wide within minutes after controlled TF nuclear import using time-series chromatin immunoprecipitation (ChIP-seq) and/or DNA adenine methyltransferase identification (DamID-seq). The transient TF-target interactions captured uncover the early mode-of-action of NIN-LIKE PROTEIN 7 (NLP7), a master regulator of the nitrogen signaling pathway in plants. These transient NLP7 targets captured in root cells using temporal TF perturbation account for 50% of NLP7-regulated genes not detectably bound by NLP7 in planta. Rapid and transient NLP7 binding activates early nitrogen response TFs, which we validate to amplify the NLP7-initiated transcriptional cascade. Our approaches to capture transient TF-target interactions genome-wide can be applied to validate dynamic GRN models for any pathway or organism of interest. Conventional methods cannot reveal transient transcription factors (TFs) and targets interactions. Here, Alvarez et al. capture both stable and transient TF-target interactions by time-series ChIP-seq and/or DamID-seq in a cell-based TF perturbation system and show NLP7 as a master TF to initiate a rapid nitrogen-response cascade

Sortase-Mediated Site-Specific Conjugation and 89Zr-Radiolabeling of Designed Ankyrin Repeat Proteins for PET

Designed ankyrin repeat proteins (DARPins) are genetically engineered proteins that exhibit high specificity and affinity toward specific targets. Here, the G3-DARPin, which binds the HER2/neu receptor, was site-specifically modified with enzymatic methods and 89Zr-radiolabeled for applications in positron emission tomography (PET). Sortase A transpeptidation was used to install a desferrioxamine B (DFO) chelate bearing a reactive triglycine group to the C-terminal sortase tag of the G3-DARPin, and 89Zr-radiolabeling produced a novel 89ZrDFO-G3-DARPin radiotracer that can detect HER2/neu-positive tumors. The triglycine probe, DFO-Gly3 (1), was synthesized in 29% overall yield. After sortase A transpeptidation and purification from the nonfunctionalized protein component, the DFO-G3-DARPin product was radiolabeled to give 89ZrDFO-G3-DARPin. Binding specificity was assessed in HER2/neu-expressing BT-474 and SK-OV-3 cellular assays. The pharmacokinetics, tumor uptake, and specificity of 89ZrDFO-G3-DARPin were measured in vivo by PET imaging and confirmed by final time point (24 h) biodistribution experiments in female athymic nude mice bearing BT-474 xenografts. Sortase A transpeptidation afforded the site-specific and stoichiometrically precise functionalization of DFO-G3-DARPin with one chelate per protein. The modified DFO-G3-DARPin was purified from the nonfunctionalized DARPin by using Ni-NTA affinity chromatography. 89ZrDFO-G3-DARPin was obtained with a radiochemical purity of >95% measured by radio-size-exclusion chromatography. BT-474 tumor uptake at 24 h postadministration reached 4.41 ± 0.67 %ID/g (n = 3) with an approximate ∼70% reduction in tumor-associated activity in the blocking group (1.26 ± 0.29 %ID/g; 24 h postadministration, n = 5, P-value of <0.001). Overall, the site-specific, enzyme-mediated functionalization and characterization of 89ZrDFO-G3-DARPin in HER2/neu positive BT-474 xenografts demonstrate that DARPins are an attractive platform for generating a new class of protein-based radiotracers for PET. The specific uptake and retention of 89ZrDFO-G3-DARPin in tumors and clearance from most background tissues produced PET images with high tumor-to-background contrast. Keywords: 89Zr; DARPin; HER2/neu; positron emission tomography; site-specific bioconjugation; sortase

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.