Rensselaer, NY, United States
Rensselaer, NY, United States

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Plasmids containing the 9-kb mouse villin promoter (pBS-Villin)26, 27 and simian diphtheria toxin receptor (HBEGF (‘DTR’)) with the enhanced green fluorescent protein (pDTR–eGFP) fusion gene28 have been previously described. The pDTR–eGFP was PCR amplified with primers harbouring a 5′ BsiWI site and a 3′ MluI site and cloned into pBS-Villin. The pBS-Villin/DTR–eGFP plasmid was verified by sequencing and the transgene was isolated from the plasmid by restriction enzyme digestion and gel purification. The transgene was microinjected into fertilized eggs from C57BL/6J mice (Jackson Laboratory) and transferred into oviducts of ICR foster mothers as previously described26. Identification of the transgenic mice was performed by PCR amplification using the following primers: 5′-ACTGCTCTCACATGCCTTCT-3′ and 5′-CTTCTTCCCTAGTCCCTTGC-3′. For diphtheria toxin administration, mice were injected intraperitoneally with 2 or 10 ng g−1 diphtheria toxin (EMD Chemicals) and humanely killed 1–24 h later29. Control mice were injected with PBS. For dextran sulphate solution (DSS) (MP Biomedicals) studies, mice were supplemented with 3% DSS in the drinking water for five days. On day three, water bottles were refilled with 3% DSS solution and on day five, replaced with fresh drinking water. Mice were weighed and monitored daily for signs of distress, morbidity or mortality during the course of the experiment until they were killed on day 7. Both male and female mice ages 6–8 weeks were used for all studies. All experiments were approved by the institutional animal care and use committee and carried out in accordance with the ‘Guide for the Care and Use of Laboratory Animals’ (NIH publication 86–23, revised 1985). Before isolating professional phagocytes (‘phagocytes’), VDTR and VDTR negative littermate controls were intraperitoneally injected with PBS (vehicle) or diphtheria toxin (EMD Chemicals) at a low (2 ng g−1) or high (10 ng g−1) dose per body weight. Mice were then killed 1–24 h later and phagocytes were isolated from the SILP as previously described with some modifications30. In brief, the small intestine, including the duodenum, jejunum and ileum, was excised and Peyer’s patches removed. Next, the small intestine was opened longitudinally with surgical scissors and flushed with ice-cold PBS to remove the faecal content. Intestines were then cut into 0.5-cm pieces and transferred into 50-ml conical tubes containing 20 ml of PBS. Samples were then vigorously shaken for 30 s using the vortex genie (Scientific Industries) and passed over 100-μm nylon cell strainers (BD Falcon). Fresh PBS was added to the tissue samples and the shaking and filtering process was repeated a total of eight times. To isolate and remove the intestinal epithelial cell layer, samples were washed with 20 ml of warm PBS containing 3 mM EDTA and passed over cell strainers. This was repeated three times. Flow-through was kept as purified for IECs, whereas whole tissues were further processed to isolate dendritic cell and macrophage subsets. Next, samples were washed with ice-cold PBS followed by RPMI 1640 (Sigma) containing 5% FBS to remove the EDTA. Samples were then re-suspended with RPMI 1640 containing 5% FBS, 1 mg ml−1 collagenase D (Roche), and 1 mg ml−1 DNase I (Roche) and incubated in a 37 °C water bath for 60 min. Samples were shaken every 20 min during this time. At the completion of the incubation, samples were washed with FACS buffer to remove the collagenase and then passed through an 18-gauge needle followed by a 21-gauge needle to create a single-cell suspension. Phagocytes were then enriched from samples by using a 1.065 g ml−1 OptiPrep (Sigma) density gradient according to the manufacturer’s protocol. Following centrifugation, phagocytes were isolated from both low- and mid-density bands and finally re-suspended in FACS buffer for flow cytometric analyses. Mouse spleen was digested in parallel with small intestine samples and used for single-colour compensation controls. All samples were pretreated with Fc block for 10 min at 4 °C followed by fluorescently conjugated antibody labelling at 4 °C for 60 min. The following antibodies were used for these studies: Antibodies from BioLegend including Alexa Fluor 647- or 700-conjugated anti-CD11c (clone N418), PerCP/Cy5.5-conjugated anti-CD24 (clone M1/69), APC/Cy7-conjugated anti-CD45 (clone 30-F11), APC-conjugated anti-CD64 (clone X54-5/7.1), APC-conjugated anti-CD274 (clone 10F.9G2), PerCP/Cy5.5-conjugated anti-F4/80 (clone BM8), Alexa Fluor 700-conjugated anti-Ly-6c (clone HK1.4), and Phycoerythrin (PE) or Brilliant Violet 421-conjugated anti-MHCII I-A/I-E (clone M5/114.15.2); antibodies from eBioscience including FITC-conjugated anti-CD4 (clone RM4-5), PE/Cy7-conjugated anti-CD11b (clone M1/70) and PE-conjugated anti-CD103 (clone 2E7); and TxRed-conjugated anti-CD45 (clone 30-F11) from Invitrogen. Live/Dead Aqua (Life Technologies) was used to discriminate viable cells. Phagocytes isolated from the SILP were surface stained for: APC/Cy7-conjugated anti-CD45, Alexa Fluor 700-conjugated anti-CD11c, Brilliant Violet 421-conjugated anti-MHCII I-A/I-E, PE/Cy7-conjugated anti-CD11b, PE-conjugated anti-CD103, PerCP/Cy5.5-conjugated anti-CD24, and APC-conjugated anti-CD64. The identification of phagocytes from VDTR mice with IEC cargo was determined by the presence of eGFP and this gate was defined on the basis of C57BL/6J and VDTR− littermate controls that were eGFP−. Sample acquisition was performed using the LSRFortessa (BD Biosciences) and data analyses were performed using the FlowJo analytical software (Tree Star). To sort phagocytes with and without apoptotic IEC cargo, the following surface markers were used: APC/Cy7-conjugated anti-CD45, Alexa Fluor 700-conjugated anti-CD11c, Brilliant Violet 421-conjugated anti-MHCII I-A/I-E, PE/Cy7-conjugated anti-CD11b, PE-conjugated anti-CD103, PerCP/Cy5.5-conjugated anti-CD24, and APC-conjugated anti-CD64. The identification of phagocytes from VDTR mice with IEC cargo was determined by the presence of eGFP and this gate was defined on the basis of C57BL/6J and VDTR− littermate controls that were eGFP−. Sorted populations were live, CD45+MHCII+CD11c+ phagocytes that were either eGFP− or eGFP+ including (i) CD103+CD11b−CD24+CD64− (hereafter CD103), (ii) CD103+CD11b+ CD24−CD64+ (hereafter CD103 CD11b), and (iii) CD103−CD11b+CD24− CD64+ (hereafter CD11b) for a total of six populations. Owing to the four-sample sort-maximum of the instrument, the three eGFP+ populations were collected first and then fresh collection tubes were added for the three eGFP− populations. Cells were sorted directly into 0.5 ml TRIzol LS reagent (Life Technologies) for microarray processing (see below). Each sort was performed at 4 h following diphtheria toxin administration and consisted of 3–4 pooled VDTR mice. The following are the cell yield ranges for each subset: 1,000–5,000 eGFP+CD103+; 3,000–9,000 eGFP+CD103+CD11b+; 10,000–40,000 eGFP+CD11b+; 4,500–10,000 eGFP−CD103+; 40,000–80,000 eGFP−CD103+CD11b+; and 30,000–100,000 eGFP−CD11b+. FACS was conducted on the FACSAria IIu SORP (BD Biosciences). The following are the RNA yield ranges for each subset: 200–2,400 pg eGFP+CD103+; 200–3,000 pg eGFP+CD103+CD11b+; 600–3,000 pg eGFP+CD11b+; 200–4,600 pg eGFP−CD103+; 600–5000 pg eGFP−CD103+CD11b+; and 450–4,000 pg eGFP−CD11b+. The purity and identity of each subset was validated as indicated in Extended Data Fig. 5 and according to markers as previously reported31. For analysis of IEC engulfment by CD11c+ phagocytes, single-cell suspensions were prepared as described for flow cytometric analyses and acquired using the IS 100 Imaging flow cytometer (Amnis Corp). Phagocytes with eGFP+ cargo were identified as those that contained single nuclei and were CD45+, CD11c+ and MHCII+. Data were analysed using IDEAS software (Amnis Corp) and spectrally compensated using a compensation matrix generated from the following single-colour controls; FITC-conjugated CD4, PE-conjugated MHCII, Alexa Fluor647-conjugated CD11c, TxRed-conjugated CD45, and Hoechst stain. Total RNA was isolated from mouse small intestine using RNeasy mini-kit (Qiagen) and quantified by a spectrophotometer. Reverse transcription was performed with Superscript III (Invitrogen) and cDNA was synthesized using the Mastercycler ep (Eppendorf). Real-time quantitative RT–PCR was conducted in duplicate on a ViiA 7 Real-time PCR System (Life Technologies) using TaqMan quantitative PCR Master Mix at a concentration of 1× (Applied Biosystems) or SYBR Green Real-Time PCR Master Mixes for the eGFP and HBEGF (‘DTR’) transgenes. Samples were normalized to β-actin and relative expression was determined by 2-ΔΔC method. Forward (FW) and reverse (RV) primers for SYBR Green include: All probe sequences are in the format: 5′ FAM-sequence-BHQ-1 3′ and together with forward (FW) and reverse (RV) primer pairs were synthesized by Biosearch Technologies. 5′-AGCCACCCCCACTCCTAAGAGGAGG-3′ Actb probe, 5′-GAAGTCCCTCACCCTCCCAA-3′ Actb FW, 5′-GGCATGGACGCGACCA-3′ Actb RV; 5′-AAATCGGTGATCCAGGGATTGTTCCA-3′ Acadsb probe, 5′-CCTCTGGTTTCCTCTATGGATGA-3′, Acadsb FW, 5′-TCCCTCCATATTGTGCTTCAAC-3′ Acadsb RV; 5′-CGGGACAGGGCAACTCTTGCAA-3′ Aldh1a2 probe, 5′-GCTTGCAGACTTGGTGGAA-3′ Aldh1a2 FW, 5′-GCTTGCAGGAATGGCTTACC-3′ Aldh1a2 RV; 5′-CCCACTTTCCTTGTGGTACTCTGGAC-3′ Alox5ap probe, 5′-CAACCAGAACTGCGTAGATGC-3′ Alox5ap FW, 5′-GAAGGCGGCAGGGACTTG-3′ Alox5ap RV; 5′-TGCCTTTAGTGGCCTCATTGTTCC-3′ Atrn probe, 5′-GGACTCAATCTACGCACCTCTGAT-3′ Atrn FW, 5′-GCCGTCTCATTGCCATCTCTT-3′ Atrn RV; 5′-TTGGCATCAATCTGAGCTGTTGGTG-3′ Axl probe, 5′-GCCCATCAACTTCGGAAGAAAG-3′ Axl FW, 5′-CCTCTGGCACCTGTGATATTCC-3′ Axl RV; 5′-AGTGAAGGAGTTCTTCTGGACCTCAA-3′ Ccl22 probe, 5′-CACCCTCTGCCATCACGTT-3′ Ccl22 FW, 5′-ATCTCGGTTCTTGACGGTTATCA-3′ Ccl22 RV; 5′-CCACTGCTCATGGATATGTTGAACAATAGAGACC-3′ Ccr2 probe, 5′-AGGGTCACAGGATTAGGAAGGTT-3′ Ccr2 FW, 5′-CGTTCTGGGCACCTGATTTAA-3′ Ccr2 RV; 5′-CAGTGCCCAAGTGGAGGCCTTGATC-3′ Ccr7 probe, 5′-CACGCTGAGATGCTCACTGG-3′ Ccr7 FW, 5′-ATCTGGGCCACTTGGATGG-3′ Ccr7 RV; 5′-AGATTCGCTGTCACCAGCACAGACA-3′ Cd40 probe, 5′-TCTCAGCCCAGTGGAACA-3′ Cd40 FW, 5′-CGGTGCCCTCCTTCTTAACC-3′ Cd40 RV; 5′-CGAATCACGCTGAAAGTCAATGCCC-3′ Cd274 probe, 5′-CGGTGGTGCGGACTACAAG-3′ Cd274 FW, 5′-CCCTCGGCCTGACATATTAGTTC-3′ Cd274 RV; 5′-TTCCCAGGGCTTGAGGCTCCC-3′ Cd300a probe, 5′-GGCCACCGTGAACATGACTA-3′ Cd300a FW, 5′-GCAGGAGAGCTAACACAGACAAC-3′ Cd300a RV; 5′-ATGGAAAATGGGTGGCGTCTAACCCA-3′ Cfh probe, 5′-CCGAACACTTGGCACTATTGTAA-3′ Cfh FW, 5′-CTCCGGGATGCCCACAAG-3′ Cfh RV; 5′-CCCTGAACAACCAACAGATGACACTGG-3′ Elf3 probe, 5′-GGCACTGAAGACTTGGTGTTG-3′ Elf3 FW, 5′-CCCTGAACAACCAACAGATGACACTGG-3′ Elf3 RV; 5′-AGCTGACAGATACACTCCAAGCGGA-3′ Fos probe, 5′-AGTGCCGGAATCGGAGGA-3′ Fos FW, 5′-TGCAACGCAGACTTCTCATC-3′ Fos RV; 5′-CTGCTCCTGCTGGCTTCCGAGT-3′ Gas6 probe, 5′-CTGGGCACTGCGCTTCTG-3′ Gas6 FW, 5′-CGCAACAGCACAGTGTGA-3′ Gas6 RV; 5′-TCTTATGCAGACTGTGTCCTGGCA-3′ Ido1 probe, 5′-GGGCCTGCCTCCTATTCTG-3′ Ido1 FW, 5′-CCCACCAGGAAATGAGAACAGA-3′ Ido1 RV; 5′-TCACAAGCAGAGCACAAGCCTGTC-3′ Il1b probe, 5′-AAAGACGGCACACCCACCCTGC-3′ Il1b FW, 5′-TGTCCTGACCACTGTTGTTTCCCAG-3′ Il1b RV; 5′-TCTGCAAGAGACTTCCATCCAGTTGCCT-3′ Il6 probe, 5′-CCAGAAACCGCTATGAAGTTCC-3′ Il6 FW, 5′-TCACCAGCATCAGTCCCAAG-3′ Il6 RV; 5′-TTCAAACAAAGGACCAGCTGGACA-3′ Il10 probe, 5′-TCAGCCAGGTGAAGACTTTC-3′ Il10 FW, 5′-GGCAACCCAAGTAACCCTTA-3′ Il10 RV; 5′-TAACTGGGATCCAGGCACGCC-3′ Ly75 probe, 5′-GTCAGACTTCAGGCCACTCAA-3′ Ly75 FW, 5′-TGACCCACCAATCACAGGT-3′ Ly75 RV; 5′-TCCCTTACTTTATTAAGCAGCCTGAGAGTG-3′ Mertk probe, 5′-TGATCCCATATACGTGGAAGTTCA-3′ Mertk FW, 5′-CCTGGCAGGTGAGGTTGAAG-3′ Mertk RV; 5′-TTTGCGTCTGACTGCCGAGACTC-3′ Muc2 probe, 5′-CCTGGCCTCTGTGATTACAAC-3′ Muc2 FW, 5′-GGTGCACAGCAAATTCCTTGTAG-3′ Muc2 RV; 5′-TCGCAACCAGATCGGAGATGTGG-3′ Nlrc5 probe, 5′-CCAGAACTCAGGAAATTTGACTTGA-3′ Nlrc5 FW, 5′-TTTGGCAAGATGGCAGCTAA-3′ Nlrc5 RV; 5′-CTGCTGCCTCACTTCTAGCTTCTGC-3′ Nlrp3 probe, 5′-GTTGCCTGTTCTTCCAGACT-3′ Nlrp3 FW, 5′-GGCTCCGGTTGGTGCTTAG-3′ Nlrp3 RV; 5′-TAGGCTGCTTTGGGAATGGCACC-3′ Oasl1 probe, 5′-CGCGTGCTCAAGGTACTCAAG-3′ Oasl1 FW, 5′-GACCAGCTCCACGTCTGTAG-3′ Oasl1 RV; 5′-TTGTGATGACTACATGGTCACACTCTTC-3′ Plac8 probe, 5′-GAACCCGATACGGCATTCCT-3′ Plac8 FW, 5′-TCTTGCCATCCAGCTCCTTAG-3′ Plac8 RV; 5′-ACCAACACATCGGAGCTGCGGA-3′ Relb probe, 5′-GAGCCTGTCTACGACAAGAAGTC-3′ Relb FW, 5′-GCCCGCTCTCCTTGTTGATTC-3′ Relb RV; 5′-AGTTATGCACGAGTGCGAGCTGT-3′ Spred1 probe, 5′-CGGCGACTTCTGACAACGATA-3′ Spred1 FW, 5′-GGTAGCCATCCACCACTTGAG-3′ Spred1 RV; 5′-AGAGGTCACCCGCGTGCTAATGGTG-3′ Tgfb1 probe, 5′-CCCGAAGCGGACTACTATGC-3′ Tgfb1 FW, 5′-ATAGATGGCGTTGTTGCGGT-3′ Tgfb1 RV; 5′-CTCTGCCTGCATCCAATCACTCTCA-3′ Timd4 probe, 5′-GGTCCGCCTTCACTACAGAATC-3′ Timd4 FW, 5′-GGCCTGAGTACGGCTATGTC-3′ Timd4 RV; 5′-TGGGCTTTCCGAATTCACTGGAGC-3′ Tnf probe, 5′-ATGCACCACCATCAAGGACTCAA-3′ Tnf FW, 5′-ACCACTCTCCCTTTGCAGAACTC-3′ Tnf RV; 5′-TCAACTGGTGTCGTGAAGTCAGGA-3′ Tnfaip3 probe, 5′-TCCCTGGAAAGCCAGAAGAAG-3′ Tnfaip3 FW, 5′-GAGGCAGTTTCCATCACCATTG-3′ Tnfaip3 RV; 5′-TCCGGAGCTACTTCAAGCAAGGC-3′ Vil1 probe, 5′-GGCAACGAGAGCGAGACTT-3′ Vil1 FW, 5′-CGCTGGACATCACAGGAGTT-3′ Vil1 RW. A total of five sorting experiments with a pool of 3–4 mice were performed for the cDNA microarrays. Following cell sorting into TRIzol LS reagent, samples were shipped on dry ice to the Center for Functional Genomics and the Microarray & HT Sequencing Core Facility at the University at Albany (Rensselaer). A sample clean-up step was performed using RNeasy columns (Qiagen) that included DNase treatment. The isolated RNA was checked for quality using NanoDrop (Thermo Scientific) and Bioanalyzer (Agilent), following which 1 ng of total RNA was processed using WT-Ovation Pico RNA Amplification System (NuGEN). A total of three biological replicates were used for the microarray. When required, RNA was pooled from additional sorts to achieve the 1 ng of total RNA needed for the amplification system. The following are the sort experiments used for each sample: (2, 2 and 5, 2 and 5) eGFP+CD103+; (2, 3, 2 and 5) eGFP+CD103+CD11b+; (2, 4, 5) eGFP+CD11b+; (3, 2 and 4 and 5, 2 and 4 and 5) eGFP−CD103+; (2, 3, 4) eGFP−CD103+CD11b+; and (2, 3, 5) eGFP−CD11b+. RNA was reverse-transcribed and sense-target cDNAs were targeted using the standard NuGEN protocol and hybridized to Affymetrix mouse Gene 2.0 ST arrays. These arrays were then washed, stained on a FS 450 station, and scanned on a GeneChip 3000 7G scanner using Affymetric GeneChip Command Console Software (AGCC). The Affymetrix microarray data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO)32 and are accessible with the GEO series accession number GSE85682. Fold changes and statistical significance were identified as those genes that were differentially expressed between eGFP+ and eGFP− subsets by at least 1.2 fold (ANOVA (Benjamini–Hochberg false discovery rate correction Q < 0.05) and Tukey’s HSD post-hoc test (P < 0.05; -1.2> fold >1.2) and determined using R software (version 3.2.0). Hierarchical clustering of differentially expressed genes meeting the aforementioned criteria were Z-scored and plotted with heatmap.2 (gplots version 2.17.0, CRAN/R). Principal component analyses of the 1,534 genes (ANOVA (Benjamini–Hochberg false discovery rate correction Q < 0.05); 4.8% of total) with the most variable expression in each CD11c+ subset with and without eGFP cargo were generated using R software which are freely available online. Small and large intestine were dissected and fixed in 10% formalin (Fisher Scientific) for 24 h and then processed for paraffin embedding. Tissue blocks were then cut into 5-mm sections, de-paraffinized by xylene immersion, and hydrated by serial immersion in 100%, 90%, 80%, 70% ethanol and PBS. Antigen retrieval was performed by heating samples in a pressure cooker (Cuisinart) in citrate buffer solution (10 mM citric acid monohydrate, 0.05% Tween 20 and PBS). Sections were then washed twice in PBS, blocked for 30 min in blocking buffer (10% BSA, 0.3% Triton X-100 (Sigma) and TBS), and prepared for labelling. TdT-mediated dUTP nick end labelling (TUNEL) was performed using the in situ cell-death detection kit, TMR red (Roche), per the manufacturer’s instructions, stained with DAPI, and mounted using Fluoromount-G (Southern Biotech). For cleaved caspase-3 (Cell Signaling), samples were labelled for 60 min at room temperature, stained with DAPI, and mounted using Fluoromount-G. For paraffin images, eGFP signal was not present owing to sample quenching following paraffin embedding and processing. Small and large intestine were dissected and fixed overnight in 1.6% paraformaldehyde (Thermo Scientific) containing 20% sucrose at 4 °C. Samples were then placed in OCT (Tissue-Tek) and snap-frozen over dry ice. Tissue sections of 8-mm thickness were cut, air-dried and blocked using blocking solution. Tissues were then labelled using an Alexa Fluor 594-conjugated phalloidin (Invitrogen) or a primary mouse anti-mouse pan-cytokeratin antibody (clone PCK-26) (Abcam) for 60 min in a humidified atmosphere followed by a secondary goat anti-mouse Alexa Fluor 594 (Thermo Fisher Scientific) for 30 min, then stained with DAPI, and mounted using Fluoromount-G. For fluorescent in situ hybridization, small intestine and large intestine were dissected and prepared as described for frozen sections33. Following tissue blocking, sections were incubated with 0.45 pmol μl−1 eubacterial oligonucleotide probe (AminoC6 + Alexa Fluor 594) 5′-GCTGCCTCCCGTAGGAGT-3′; (Operon)33 in a pre-chilled hybridization buffer (Sigma) overnight at 4 °C. Sections were counterstained with DAPI and mounted with Fluoromount-G. To label small intestine tissues, the whole-mount histology protocol was modified from previously described methods34. In brief, small intestine samples were excised, opened longitudinally, and washed in ice-cold PBS. Samples were then cut to 1 cm in length and placed in 6-ml polypropylene tubes (BD Biosciences). Next, samples were incubated with Fc block at 10 μg ml−1 in 200 μl of 2% paraformaldehyde with 1% FBS, 0.3% Triton X-100 in PBS for 3 h at 4 °C with gentle rocking. After blocking and fixing, samples were put into new polypropylene tubes and labelled using 3 μg ml−1 of the following antibodies: PE-conjugated anti-CD11c (clone N418) (eBioscience), APC-conjugated anti-CD31 (clone 390) (eBioscience) and anti-cleaved caspase-3 at 1:100. All labelling was conducted in the dark at 4 °C with gentle rocking for 3 h. Finally, samples were washed for 30 min in the dark at 4 °C with fresh PBS and mounted for imaging. Conventional microscopy was performed using the Eclipse Ni-E motorized upright microscope (Nikon) and images were acquired from paraffin, frozen, and whole mount tissue sections using a Nikon DS-Qi1 Mc camera. Cell quantification was calculated using NIS Elements imaging software (Nikon) and the object count application including intensity of stain thresholds and area restriction filters. Confocal microscopy was performed at the Microscope CORE at the Icahn School of Medicine at Mount Sinai using the Leica SP5 DM upright microscope and Leica LAS AF software. Naive mouse splenic CD4+ T cells were isolated by sorting with MACS CD4+ beads (Miltenyi Biotech) according to the manufacturer’s instructions and then by FACS using the FACSAria IIu SORP. T cells were sorted on the basis of the following criteria: live, CD45+CD3+CD4+ CD25−CD44−/lowCD62L+/high. Surface antibodies for sorting included: APC/Cy7-conjugated anti-CD45, eFluor 450-conjugated anti-CD3 (clone 145-2c11), PE-conjugated anti-CD4, APC-conjugated anti-CD25 (clone PC61.5), FITC-conjugated CD62L (clone MEL-14), and Alexa 700-conjugated anti-CD44 (clone IM7) (all eBiosciences). 1 × 105 T cells were then cultured with 1 × 104 eGFP+ or eGFP− CD103 dendritic cells sorted from the MLN which were identified as: live, CD45+MHCIIhiCD11c+, eGFP+ or eGFP−, CD103+CD11b− using the aforementioned antibodies for flow cytometry. These cells were cultured in round-bottom 96-well plates (Falcon) with complete IMDM (Gibco) supplemented with 10% FBS, 100 μg ml−1 penicillin, 100 μg ml−1 streptomycin, 2 mM l-glutamine, 10 mM HEPES and 1 nM sodium pyruvate for 5 days. Additionally, all cultures were supplemented with 1 μg ml−1 of soluble anti-CD3 (clone 2C11) as well as 5 ng ml−1 of recombinant human anti-IL-2 (Pepro Tech) on days 2 and 4. A total of 2 ng ml−1 of recombinant human anti-TGFβ1 (clone 1D11 R&D systems) was added to culture wells where indicated on days 1 and 4. On day 5, cells were first surface stained with FITC-conjugated anti-CD25, PE/Cy7-conjugated anti-CD4, Alexa Fluor 700-conjugated anti-CD3, and APC/Cy7-conjugated anti-CD45, followed by fixation and permeabilization (using the concentrate and diluent provided by eBioscience), and finally intracellular staining for eFluor 450-conjugated anti-FOXP3 (clone FJK-16 s), PE-conjugated anti-RORγ(t) (clone B2D), PerCP-eFluor 710-conjugated anti-GATA-3 (clone TWAJ), and APC-conjugated anti-T-bet (clone eBio4B10) (all eBioscience). Data are presented as mean ± s.e.m. Statistical significances were determined by a one-way ANOVA with Dunnett’s and Newman–Keuls post-tests or unpaired two-tailed t-test with Welch correction where specified. ***P < 0.001, **P < 0.01, *P < 0.05. NS, not statistically significant (P > 0.05). No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. The Affymetrix microarray data have been deposited in the NCBI Gene Expression Omnibus (GEO) under GEO series accession number GSE85682.


News Article | February 15, 2017
Site: news.mit.edu

Many scientists are pursuing ways to treat disease by delivering DNA or RNA that can turn a gene on or off. However, a major obstacle to progress in this field has been finding ways to safely deliver that genetic material to the correct cells. Encapsulating strands of RNA or DNA in tiny particles is one promising approach. To help speed up the development of such drug-delivery vehicles, a team of researchers from MIT, Georgia Tech, and the University of Florida has now devised a way to rapidly test different nanoparticles to see where they go in the body. “Drug delivery is a really substantial hurdle that needs to be overcome,” says James Dahlman, a former MIT graduate student who is now an assistant professor at Georgia Tech and the study’s lead author. “Regardless of their biological mechanisms of action, all genetic therapies need safe and specific drug delivery to the tissue you want to target.” This approach, described in the Proceedings of the National Academy of Sciences the week of Feb. 6, could help scientists target genetic therapies to precise locations in the body. “It could be used to identify a nanoparticle that goes to a certain place, and with that information we could then develop the nanoparticle with a specific payload in mind,” says Daniel Anderson, an associate professor in MIT’s Department of Chemical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science (IMES). The paper’s senior authors are Anderson; Robert Langer, the David H. Koch Institute Professor at MIT and a member of the Koch Institute; and Eric Wang, a professor at the University of Florida. Other authors are graduate student Kevin Kauffman, recent MIT graduates Yiping Xing and Chloe Dlott, MIT undergraduate Taylor Shaw, and Koch Institute technical assistant Faryal Mir. Finding a reliable way to deliver DNA to target cells could help scientists realize the potential of gene therapy — a method of treating diseases such as cystic fibrosis or hemophilia by delivering new genes that replace missing or defective versions. Another promising approach for new therapies is RNA interference, which can be used to turn off overactive genes by blocking them with short strands of RNA known as siRNA. Delivering these types of genetic material into body cells has proven difficult, however, because the body has evolved many defense mechanisms against foreign genetic material such as viruses. To help evade these defenses, Anderson’s lab has developed nanoparticles, including many made from fatty molecules called lipids, that protect genetic material and carry it to a particular destination. Many of these particles tend to accumulate in the liver, in part because the liver is responsible for filtering blood, but it has been more difficult to find particles that target other organs. “We’ve gotten good at delivering nanoparticles into certain tissues but not all of them,” Anderson says. “We also haven’t really figured out how the particles’ chemistries influence targeting to different destinations.” To identify promising candidates, Anderson’s lab generates libraries of thousands of particles, by varying traits such as their size and chemical composition. Researchers then test the particles by placing them on a particular cell type, grown in a lab dish, to see if the particles can get into the cells. The best candidates are then tested in animals. However, this is a slow process and limits the number of particles that can be tried. “The problem we have is we can make a lot more nanoparticles than we can test,” Anderson says. To overcome that hurdle, the researchers decided to add “barcodes,” consisting of a DNA sequence of about 60 nucleotides, to each type of particle. After injecting the particles into an animal, the researchers can retrieve the DNA barcodes from different tissues and then sequence the barcodes to see which particles ended up where. “What it allows us to do is test many different nanoparticles at once inside a single animal,” Dahlman says. The researchers first tested particles that had been previously shown to target the lungs and the liver, and confirmed that they did go where expected. Then, the researchers screened 30 different lipid nanoparticles that varied in one key trait — the structure of a component known as polyethylene glycol (PEG), a polymer often added to drugs to increase their longevity in the bloodstream. Lipid nanoparticles can also vary in their size and other aspects of their chemical composition. Each of the particles was also tagged with one of 30 DNA barcodes. By sequencing barcodes that ended up in different parts of the body, the researchers were able to identify particles that targeted the heart, brain, uterus, muscle, kidney, and pancreas, in addition to liver and lung. In future studies, they plan to investigate what makes different particles zero in on different tissues. The researchers also performed further tests on one of the particles, which targets the liver, and found that it could successfully deliver siRNA that turns off the gene for a blood clotting factor. Victor Koteliansky, director of the Skoltech Center for Functional Genomics, described the technique as an “innovative” way to speed up the process of identifying promising nanoparticles to deliver RNA and DNA. “Finding a good particle is a very rare event, so you need to screen a lot of particles. This approach is faster and can give you a deeper understanding of where particles will go in the body,” says Kotelianksy, who was not involved in the research. This type of screen could also be used to test other kinds of nanoparticles such as those made from polymers. “We’re really hoping that other labs across the country and across the world will try our system to see if it works for them,” Dahlman says. The research was funded by an MIT Presidential Fellowship, a National Defense Science and Engineering Graduate Fellowship, a National Science Foundation Graduate Research Fellowship, the MIT Undergraduate Research Opportunities Program, the Koch Institute Frontier Research Program through the Kathy and Curt Marble Cancer Research Fund, and the National Institutes of Health.


News Article | February 10, 2017
Site: www.biosciencetechnology.com

Many scientists are pursuing ways to treat disease by delivering DNA or RNA that can turn a gene on or off. However, a major obstacle to progress in this field has been finding ways to safely deliver that genetic material to the correct cells. Encapsulating strands of RNA or DNA in tiny particles is one promising approach. To help speed up the development of such drug-delivery vehicles, a team of researchers from MIT, Georgia Tech, and the University of Florida has now devised a way to rapidly test different nanoparticles to see where they go in the body. “Drug delivery is a really substantial hurdle that needs to be overcome,” said James Dahlman, a former MIT graduate student who is now an assistant professor at Georgia Tech and the study’s lead author. “Regardless of their biological mechanisms of action, all genetic therapies need safe and specific drug delivery to the tissue you want to target.” This approach, described in the Proceedings of the National Academy of Sciences the week of Feb. 6, could help scientists target genetic therapies to precise locations in the body. “It could be used to identify a nanoparticle that goes to a certain place, and with that information we could then develop the nanoparticle with a specific payload in mind,” said Daniel Anderson, an associate professor in MIT’s Department of Chemical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science (IMES). The paper’s senior authors are Anderson; Robert Langer, the David H. Koch Institute Professor at MIT and a member of the Koch Institute; and Eric Wang, a professor at the University of Florida. Other authors are graduate student Kevin Kauffman, recent MIT graduates Yiping Xing and Chloe Dlott, MIT undergraduate Taylor Shaw, and Koch Institute technical assistant Faryal Mir. Finding a reliable way to deliver DNA to target cells could help scientists realize the potential of gene therapy — a method of treating diseases such as cystic fibrosis or hemophilia by delivering new genes that replace missing or defective versions. Another promising approach for new therapies is RNA interference, which can be used to turn off overactive genes by blocking them with short strands of RNA known as siRNA. Delivering these types of genetic material into body cells has proven difficult, however, because the body has evolved many defense mechanisms against foreign genetic material such as viruses. To help evade these defenses, Anderson’s lab has developed nanoparticles, including many made from fatty molecules called lipids, that protect genetic material and carry it to a particular destination. Many of these particles tend to accumulate in the liver, in part because the liver is responsible for filtering blood, but it has been more difficult to find particles that target other organs. “We’ve gotten good at delivering nanoparticles into certain tissues but not all of them,” Anderson said. “We also haven’t really figured out how the particles’ chemistries influence targeting to different destinations.” To identify promising candidates, Anderson’s lab generates libraries of thousands of particles, by varying traits such as their size and chemical composition. Researchers then test the particles by placing them on a particular cell type, grown in a lab dish, to see if the particles can get into the cells. The best candidates are then tested in animals. However, this is a slow process and limits the number of particles that can be tried. “The problem we have is we can make a lot more nanoparticles than we can test,” Anderson said. To overcome that hurdle, the researchers decided to add “barcodes,” consisting of a DNA sequence of about 60 nucleotides, to each type of particle. After injecting the particles into an animal, the researchers can retrieve the DNA barcodes from different tissues and then sequence the barcodes to see which particles ended up where. “What it allows us to do is test many different nanoparticles at once inside a single animal,” Dahlman said. The researchers first tested particles that had been previously shown to target the lungs and the liver, and confirmed that they did go where expected. Then, the researchers screened 30 different lipid nanoparticles that varied in one key trait — the structure of a component known as polyethylene glycol (PEG), a polymer often added to drugs to increase their longevity in the bloodstream. Lipid nanoparticles can also vary in their size and other aspects of their chemical composition. Each of the particles was also tagged with one of 30 DNA barcodes. By sequencing barcodes that ended up in different parts of the body, the researchers were able to identify particles that targeted the heart, brain, uterus, muscle, kidney, and pancreas, in addition to liver and lung. In future studies, they plan to investigate what makes different particles zero in on different tissues. The researchers also performed further tests on one of the particles, which targets the liver, and found that it could successfully deliver siRNA that turns off the gene for a blood clotting factor. Victor Koteliansky, director of the Skoltech Center for Functional Genomics, described the technique as an “innovative” way to speed up the process of identifying promising nanoparticles to deliver RNA and DNA. “Finding a good particle is a very rare event, so you need to screen a lot of particles. This approach is faster and can give you a deeper understanding of where particles will go in the body,” said Kotelianksy, who was not involved in the research. This type of screen could also be used to test other kinds of nanoparticles such as those made from polymers. “We’re really hoping that other labs across the country and across the world will try our system to see if it works for them,” Dahlman said. The research was funded by an MIT Presidential Fellowship, a National Defense Science and Engineering Graduate Fellowship, a National Science Foundation Graduate Research Fellowship, the MIT Undergraduate Research Opportunities Program, the Koch Institute Frontier Research Program through the Kathy and Curt Marble Cancer Research Fund, and the National Institutes of Health.


News Article | February 15, 2017
Site: phys.org

Encapsulating strands of RNA or DNA in tiny particles is one promising approach. To help speed up the development of such drug-delivery vehicles, a team of researchers from MIT, Georgia Tech, and the University of Florida has now devised a way to rapidly test different nanoparticles to see where they go in the body. "Drug delivery is a really substantial hurdle that needs to be overcome," says James Dahlman, a former MIT graduate student who is now an assistant professor at Georgia Tech and the study's lead author. "Regardless of their biological mechanisms of action, all genetic therapies need safe and specific drug delivery to the tissue you want to target." This approach, described in the Proceedings of the National Academy of Sciences the week of Feb. 6, could help scientists target genetic therapies to precise locations in the body. "It could be used to identify a nanoparticle that goes to a certain place, and with that information we could then develop the nanoparticle with a specific payload in mind," says Daniel Anderson, an associate professor in MIT's Department of Chemical Engineering and a member of MIT's Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science (IMES). The paper's senior authors are Anderson; Robert Langer, the David H. Koch Institute Professor at MIT and a member of the Koch Institute; and Eric Wang, a professor at the University of Florida. Other authors are graduate student Kevin Kauffman, recent MIT graduates Yiping Xing and Chloe Dlott, MIT undergraduate Taylor Shaw, and Koch Institute technical assistant Faryal Mir. Finding a reliable way to deliver DNA to target cells could help scientists realize the potential of gene therapy—a method of treating diseases such as cystic fibrosis or hemophilia by delivering new genes that replace missing or defective versions. Another promising approach for new therapies is RNA interference, which can be used to turn off overactive genes by blocking them with short strands of RNA known as siRNA. Delivering these types of genetic material into body cells has proven difficult, however, because the body has evolved many defense mechanisms against foreign genetic material such as viruses. To help evade these defenses, Anderson's lab has developed nanoparticles, including many made from fatty molecules called lipids, that protect genetic material and carry it to a particular destination. Many of these particles tend to accumulate in the liver, in part because the liver is responsible for filtering blood, but it has been more difficult to find particles that target other organs. "We've gotten good at delivering nanoparticles into certain tissues but not all of them," Anderson says. "We also haven't really figured out how the particles' chemistries influence targeting to different destinations." To identify promising candidates, Anderson's lab generates libraries of thousands of particles, by varying traits such as their size and chemical composition. Researchers then test the particles by placing them on a particular cell type, grown in a lab dish, to see if the particles can get into the cells. The best candidates are then tested in animals. However, this is a slow process and limits the number of particles that can be tried. "The problem we have is we can make a lot more nanoparticles than we can test," Anderson says. To overcome that hurdle, the researchers decided to add "barcodes," consisting of a DNA sequence of about 60 nucleotides, to each type of particle. After injecting the particles into an animal, the researchers can retrieve the DNA barcodes from different tissues and then sequence the barcodes to see which particles ended up where. "What it allows us to do is test many different nanoparticles at once inside a single animal," Dahlman says. The researchers first tested particles that had been previously shown to target the lungs and the liver, and confirmed that they did go where expected. Then, the researchers screened 30 different lipid nanoparticles that varied in one key trait—the structure of a component known as polyethylene glycol (PEG), a polymer often added to drugs to increase their longevity in the bloodstream. Lipid nanoparticles can also vary in their size and other aspects of their chemical composition. Each of the particles was also tagged with one of 30 DNA barcodes. By sequencing barcodes that ended up in different parts of the body, the researchers were able to identify particles that targeted the heart, brain, uterus, muscle, kidney, and pancreas, in addition to liver and lung. In future studies, they plan to investigate what makes different particles zero in on different tissues. The researchers also performed further tests on one of the particles, which targets the liver, and found that it could successfully deliver siRNA that turns off the gene for a blood clotting factor. Victor Koteliansky, director of the Skoltech Center for Functional Genomics, described the technique as an "innovative" way to speed up the process of identifying promising nanoparticles to deliver RNA and DNA. "Finding a good particle is a very rare event, so you need to screen a lot of particles. This approach is faster and can give you a deeper understanding of where particles will go in the body," says Kotelianksy, who was not involved in the research. This type of screen could also be used to test other kinds of nanoparticles such as those made from polymers. "We're really hoping that other labs across the country and across the world will try our system to see if it works for them," Dahlman says. Explore further: DNA 'barcoding' allows rapid testing of nanoparticles for therapeutic delivery More information: Barcoded nanoparticles for high throughput in vivo discovery of targeted therapeutics, PNAS, www.pnas.org/cgi/doi/10.1073/pnas.1620874114


Oliver J.C.,Purdue University | Linger R.S.,University of Charleston | Chittur S.V.,Purdue University | Chittur S.V.,Center for Functional Genomics | Davisson V.J.,Purdue University
Biochemistry | Year: 2013

Glutamine amidotransferases catalyze the amination of a wide range of molecules using the amide nitrogen of glutamine. The family provides numerous examples for study of multi-active-site regulation and interdomain communication in proteins. Guanosine 5′-monophosphate synthetase (GMPS) is one of three glutamine amidotransferases in de novo purine biosynthesis and is responsible for the last step in the guanosine branch of the pathway, the amination of xanthosine 5′-monophosphate (XMP). In several amidotransferases, the intramolecular path of ammonia from glutamine to substrate is understood; however, the crystal structure of GMPS only hinted at the details of such transfer. Rapid kinetics studies provide insight into the mechanism of the substrate-induced changes in this complex enzyme. Rapid mixing of GMPS with substrates also manifests absorbance changes that report on the kinetics of formation of a reactive intermediate as well as steps in the process of rapid transfer of ammonia to this intermediate. Isolation and use of the adenylylated nucleotide intermediate allowed the study of the amido transfer reaction distinct from the ATP-dependent reaction. Changes in intrinsic tryptophan fluorescence upon mixing of enzyme with XMP suggest a conformational change upon substrate binding, likely the ordering of a highly conserved loop in addition to global domain motions. In the GMPS reaction, all forward rates before product release appear to be faster than steady-state turnover, implying that release is likely rate-limiting. These studies establish the functional role of a substrate-induced conformational change in the GMPS catalytic cycle and provide a kinetic context for the formation of an ammonia channel linking the distinct active sites. © 2013 American Chemical Society.


Jain R.,University at Albany | Doyle F.,University at Albany | George A.D.,University at Albany | Kuentzel M.,Center for Functional Genomics | And 3 more authors.
Methods in Molecular Biology | Year: 2010

Microarrays are extensively used to evaluate the effects of compounds on gene expression in the cells. Most of the studies so far have analyzed the transcriptome of the cell. The basic assumption of this approach is that the changes in gene expression occur at the level of transcription of a gene. However, changes often occur at the posttranscriptional level and are not reflected in the analysis of whole transcriptome. We have pioneered the development of "ribonomic profiling" as a high-throughput method to study posttranscriptional regulation of gene expression in the cell. This method is also often referred to as RIP-CHIP. In this chapter, we describe how to use the RIP-CHIP technology to assess the posttranscriptional changes occurring in the cell in response to treatment with a drug. © 2010 Humana Press, a part of Springer Science+Business Media, LLC.


Grant
Agency: Cordis | Branch: FP7 | Program: CSA-SA | Phase: REGPOT-2007-1-01 | Award Amount: 535.10K | Year: 2008

Over the past decade significant resources have been committed to development of capacities for genomic research in Croatia. These efforts have lead to formation of the Centre for Functional Genomics of the Faculty of Medicine and University Hospital, University of Zagreb. Within the national confines, Centre for Functional Genomics formed strong links with other Croatian groups interested in genomic research, as well as with recognised centres of excellence in European Union (in Scotland, Portugal and Germany), and also with the Harvard University (USA). Initially formed through common research interests, these collaborations resulted in award of several research grants from the Croatian Ministry of Science, as well as international grants with EU partners (from The British Council, Royal Society UK, The Wellcome Trust, Medical Research Council UK, National Institutes of Health USA and European Union). This proposal therefore seeks to strengthen and reinforce Centre for Functional Genomics of the Zagreb Medical School and its existing links with Croatian and EU centres. The timing in Croatia is now very favourable for building on recent successes that include employment of promising researchers in the field of genomics, securing substantial competitive international research funding and publication of research results in high-impact journals. Support from the European Commission to this initiative would secure further development of genomic advances in the Croatian biomedical scientific community, integration of existing capacities within the country and bolstering the links with international centres of excellence to ensure the sustainability of existing programmes. These activities could also have potential impact on local economy, social environment and health. This would result in creation of a highly competitive group of scientists from the EU convergence regions that could get involved in further applications within the EU FP7 program.

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