www.nature.com/scientificreports

OPEN Publisher Correction: Systemically‑delivered biodegradable PLGA alters gut microbiota and induces transcriptomic reprogramming in the liver in an obesity mouse model Alice Chaplin, Huiyun Gao, Courteney Asase, Palanivel Rengasamy, Bongsoo Park, Danielle Skander, Gürkan Bebek, Sanjay Rajagopalan & Andrei Maiseyeu

Correction to: Scientifc Reports https://doi.org/10.1038/s4159​ 8-020-69745​ -x​ , published online 14 August 2020

Tis Article contains errors in Figures 2, 3, 4 and 6 where the legends are incorrectly shown in black and grey. Te correct Figures 2, 3, 4 and 6 with coloured legends are shown below as Figures 1, 2, 3, and 4 respectively.

Scientific Reports | (2020) 10:16010 | https://doi.org/10.1038/s41598-020-72446-0 1 Vol.:(0123456789) www.nature.com/scientificreports/

2 weeks a Diet-induced obesity model b 40 Start of IV injections PBS C57BL/6 Control 35 5 weeks 30 PLGA 1 mg/ml Control PLGA 25 PLGA Body weight, g High-fat diet (45% kcal from fat) 20 02468 Control Week c d PLGA e f 2.0 0.3 600 p=0.051 200 L g 1.5 150 0.2 400 1.0 100 weight, g 0.1 200 Adipose tissue 50

0.5 , ng/mL Glucose, mg/d Cecum weight, 0.0 0.0 0 0 eWAT BAT 0306090 120150 Minutes

Figure 1. Efect of PLGA on body weight and glucose metabolism in diet-induced obese mice. (a) C57BL/6 mice were fed a high-fat diet for 5 weeks and were then injected IV with either PLGA nanoparticles or PBS six times during two weeks; (b) Body weight throughout experiment and (c) adipose tissue and (d) cecum weight at the end of the study were not signifcantly altered by treatment; (e) IPGTT before euthanasia revealed that PLGA nanoparticle-injected animals presented signifcantly better glucose clearance at 60 min (n = 10 mice/ group); (f) Fasting insulin was not diferent between groups. n = 10 mice/group, independent t-test, p < 0.05.

Scientific Reports | (2020) 10:16010 | https://doi.org/10.1038/s41598-020-72446-0 2 Vol:.(1234567890) www.nature.com/scientificreports/

Vehicle a p=0.032 b 5 6 200 ) 2.5x10 PLGA r: .-813 5 p <0.0001 2x10 5 4 150 1.5x10 5 1x10 2 100 4 <0.0001 5x10 p Insulin, ng/m L Lactate, nmole/µl 2DG uptake (RLU 0 50 0 3456 6 h24 h Lactate, nmole/µl c Vehicle PLGA d 2500 20 p=0.089 2 h24 h 2000 Veh PLGA Lac Veh PLGA 15 Insulin + - + - + - + - + - + - 1500 10 p=0.013 pAKT S473 1000 pan AKT

Lactate (µM) Lactate (µM) 5 500 βACTIN 0 0 2 h24 h 2 h24 h Supernatant Lysate

Figure 2. PLGA alters intracellular glucose uptake and lowers lactate levels. (a) Administration of PLGA nanoparticles for 2 weeks signifcantly lowered plasma lactate levels (t-value: 2.435; degrees of freedom: 11.528) (n = 10 mice/group). Furthermore, plasma lactate was negatively correlated with plasma insulin levels; (b) 2-deoxy-D-( +)-glucose (2DG) uptake in L6 myotube cells was signifcantly lower afer 24 h treatment with PLGA (1 mg/mL) (t-value: 8.359; degrees of freedom: 13.698) (n = 12 replicates/group); (c) Lactate concentrations in supernatants and lysates of L6 myotubes were signifcantly higher in cell lysates afer 24 h treatment with PLGA (1 mg/mL) (t-value: − 2.942; degrees of freedom: 11.476) (n = 12 replicates/group); (d) Insulin signaling experiments in L6 myotubes treated with 1 mg/mL PLGA, 50 mM lactate (Lac) or vehicle control (veh) showed no diference in levels of phosphorylated AKT in response to 100 nM insulin for 5 min. Representative images of phosphorylation on AKT residue ­Ser473 and loading controls, pan AKT and βACTIN are shown. Uncropped blot images are presented in supplementary fles. Independent t-test was used when comparing two groups and correlation was determined by Spearman’s rank correlation analysis, p < 0.05.

Scientific Reports | (2020) 10:16010 | https://doi.org/10.1038/s41598-020-72446-0 3 Vol.:(0123456789) www.nature.com/scientificreports/

ab Axis 2 (19.21%) 7 c 0.6 p=0.001 Control PLGA 6 0.4 5

distanc e 0.2 4

3 Unweighted UniFra 0.0

Shannon diversity index ControlPLGA ControlPLGA Axis 3 (7.54%) Axis 1 (58.43%)

c

% Class

% Phylum 100 100 Verrucomicrobia Verrucomicrobiae 80 Bacteroidetes 80 Clostridia 60 Firmicutes 60 Erysipelotrichi Proteobacteria Alphaproteobacteria 40 40 Bacteria (unclassified) Bacteria (unclassified) 20 Actinobacteria 20 Coriobacteriia 0 Tenericutes 0 Bacteroidia Relative frequency, Relative frequency, ControlPLGA ControlPLGA Betaproteobacteria %

Order % Family 100 Verrucomicrobiales 100 Verrucomicrobiaceae 80 Clostridiales 80 S24-7 Bacteroidales 60 60 Bacteria (unclassified) Burkholderiales Lachnospiraceae 40 Bacteria (unclassified) 40 Bacteroidaceae 20 Erysipelotrichales 20 Rikenellaceae Coriobacteriales Ruminococcaceae 0 Bifidobacteriales 0 Relative frequency, Alcaligenaceae ControlPLGA Relative frequency, ControlPLGA Oceanospirillales Erysipelotrichaceae

% Genus Bifidobacteriaceae 100 Akkermansia Clostridiaceae 80 Bacteria (unclassified) 60 Clostridium Oscillospira Allobaculum 40 Bacteroides Ruminococcus 20 Sutterella Coprococcus 0 Ruminococcus Bifidobacterium

Relative frequency, ControlPLGA

Figure 3. Bacterial diversity measurements show that gut microbiota diversity was afected by treatment. (a) Alpha diversity was determined using the Shannon diversity index on raw OTU abundance afer fltering out contaminants (not signifcant, p = 0.286). However, when comparing phylogenetic tree information between groups using the unweighted unique fraction (UniFrac) distance measurement, there was a signifcant diference regarding gut microbiota diversity (pairwise Permanova) (n = 10/group); (b) Gut microbiota composition similarity among groups was represented using a principal coordinate ordination, based on weighted UniFrac distances, where points are individual samples; (c) Stacked column graphs show the relative frequency of bacterial species in control and PLGA mice in the gut microbiota of cecum feces, analyzed using Qiime2 Naive Bayes classifer using Greengenes (v13.5). Statistics: n = 10 mice/group, pairwise Kruskal–Wallis test (when comparing diversity indices), p < 0.05.

Scientific Reports | (2020) 10:16010 | https://doi.org/10.1038/s41598-020-72446-0 4 Vol:.(1234567890) www.nature.com/scientificreports/

a PCA plot b Volcano plot c Control C1 8 .6 PLGA Color key & N2 histogram ) 0. 40 15 6 p=0.003 100 p=0.006 10 .2 Count

80 05 −1 0 1 N4 Row Z−Score 0. 00 60

PC2 (25.81% C3 p=0.004

−log10 (FDR) Mt1 Atf5 FPK M 40 p=0.005 Gm45774

−0.2 p=0.002 Apcs C4 p=0.003 Orm2 Serpina7 20 Lyz2 Lcn2

N5 02 4 Saa1 −0.4 Saa2 0 Fam166a −0.4 0.0 0.2 −6 −4 −2 0 246 Hspa1b −0.2 0.4 o 8 1 Cish rc mn Ccl6 PC1 (31.04%) log2(FC) dc17 Lyz2 g d6 Isyna1 Ma L C Syvn Marco Wf Gm45836 Wfdc17 Ptgis Nupr1 d Gm45190 Molecular Function Biological Process Clec4a3 Mrvi1 Mthfd2 G −coupled receptor binding inflammatory response Nr4a1 Prtn3 Crybb3 Gm17041 receptor ligand activity response to bacterium Cpa2 Sycn Tr y5 receptor regulator activity cell chemotaxis Gm12300 Count Reg2 Count Junos serine−type inhibitor activity 2 response to inorganic substance 3 Gm2602 4 Hspa1a 3 Gm15611 4 5 Gm15270 endopeptidase inhibitor activity myeloid leukocyte differentiation 6 Styx 5 7 Adgb Cacng7 peptidase inhibitor activity p.adjust acute inflammatory response 8 A830073O21Rik Capn11 0.01 p.adjust Sytl2 endopeptidase regulator activity 0.02 cellular response to cadmium ion 0.010 Bhmt−ps1 0.03 0.015 Timp1 0.04 Plat 0.020 Gm31266 chemoattractant activity response to cadmium ion 0.025 F730311O21Rik Gm17655 chemokine activity acute−phase response C3 C4 C1 N5 N2 N4 chemokine receptor binding detoxification

0.075 0.1 0.125 0.15 0.12 0.16 0.20 0.24 GeneRatio GeneRatio

e Gene Ontology Biological Process Upregulated Gene Ontology Cellular Component Downregulated FDR <0.005 FDR <0.00003 FDR <0.003 FDR <0.0003 platelet degranulation (GO:0002576) alpha- catabolic process (GO:1901606) secretory lumen (GO:0034774) mitochondrion (GO:0005739) regulated exocytosis (GO:0045055) mitochondrial transport (GO:0006839) lumen (GO:0031093) mitochondrial matrix (GO:0005759) degranulation (GO:0043312) bile acid biosynthetic process (GO:0006699) cytoplasmic vesicle lumen (GO:0060205) mitochondrial inner membrane (GO:0005743) neutrophil activation involved in immune response (GO:0002283) aspartate family amino acid catabolic process (GO:0009068) platelet alpha granule (GO:0031091) peroxisomal part (GO:0044439) neutrophil mediated immunity (GO:0002446) cristae formation (GO:0042407) endocytic vesicle lumen (GO:0071682) integral component of mitochondrial membrane (GO:0032592) response to unfolded protein (GO:0006986) dicarboxylic acid metabolic process (GO:0043648) endoplasmic reticulum lumen (GO:0005788) mitochondrial outer membrane (GO:0005741) cholesterol biosynthetic process (GO:0006695) fatty acid beta-oxidation (GO:0006635) ficolin-1-rich granule lumen (GO:1904813) microbody lumen (GO:0031907) positive regulation of cellular biosynthetic process (GO:0031328) bile acid metabolic process (GO:0008206) vacuolar lumen (GO:0005775) peroxisomal matrix (GO:0005782) sterol biosynthetic process (GO:0016126) mitochondrion organization (GO:0007005) ficolin-1-rich granule (GO:0101002) integral component of mitochondrial outer membrane (GO:0031307) regulation of glucokinase activity (GO:0033131) respiratory electron transport chain (GO:0022904) endocytic vesicle (GO:0030139) intrinsic component of mitochondrial outer membrane (GO:0031306) IRE1-mediated unfolded protein response (GO:0036498) 2-oxoglutarate metabolic process (GO:0006103) azurophil granule (GO:0042582) mitochondrial proton-transporting ATP synthase complex (GO:0005753) 024681012 0246810 02468101214 0510 15 20 25 30 – log10P - log10P - log10P - log10P

Figure 4. RNA-seq transcriptome analysis identifes metabolism, enzymatic degradation and cellular stress pathways in liver. (a) Principal component analysis (PCA) of whole-transcriptome RNA-seq read counts in liver. Dotted ellipses indicate the 95% confdence interval of samples that fall into two distinct groups (PLGA nanoparticle-treated and control). Axis percentages indicate variance in the data contribution (n = 3/ group); (b) Volcano plot indicating the genes in liver with signifcantly increased (red dots) or decreased (blue dots) expression in PLGA treated group compared to control. Te x-axis shows log2 fold-changes (FC) in the expression and the y-axis the log 10 false discovery rate (FDR) of a given gene being diferentially expressed. Selected most signifcantly regulated genes are plotted in the bar graph as gene vs. fragments per kilobase of transcript per million mapped reads (FPKM): Wfdc17: activated macrophage/microglia WAP domain protein (t-value: − 7.225; degrees of confdence: 3.414); Lyz2: C-2 (t-value: − 4.166; degrees of confdence: 6); Marco: macrophage receptor with collagenous structure (t-value: − 5.416; degrees of confdence: 6); Lgmn: legumain (t-value: − 7.369; degrees of confdence: 3.283); CD68: cluster of diferentiation 68 (t-value: − 4.777; degrees of confdence: 6); Syvn1: Synoviolin 1 (t-value: − 4.286; degrees of confdence: 6); (n = 4 mice/group, independent t-test, p < 0.05); (c) Heatmap of hierarchical clustering indicates diferentially expressed genes (columns) in liver from individual control (C3, C4, C1) and PLGA nanoparticle-treated animals (N5, N2, N4) samples (n = 3/group); (d) Gene ontology (GO) analysis presented as a scattergram of overrepresented GO terms in molecular function and biological process categories; (e) Additional GO analysis using more stringent FDR fltering (as indicated above the plot) demonstrated upregulation of various cell exocytosis and secretion pathways and downregulation of oxidative metabolism (mitochondrial function) pathways in liver.

Open Access Tis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat​iveco​mmons​.org/licen​ses/by/4.0/.

© Te Author(s) 2020

Scientific Reports | (2020) 10:16010 | https://doi.org/10.1038/s41598-020-72446-0 5 Vol.:(0123456789)