Systems genetics has begun to tackle the complexity of insulin resistance by capitalising on computational advances to study high-diversity populations. ‘Diversity Outbred in Australia (DOz)’ is a population of genetically unique mice with profound metabolic heterogeneity. We leveraged this variance to explore skeletal muscle’s contribution to whole-body insulin action through metabolic phenotyping and skeletal muscle proteomics of 215 DOz mice. Linear modelling identified 553 proteins that associated with whole-body insulin sensitivity (Matsuda Index) including regulators of endocytosis and muscle proteostasis. To enrich for causality, we refined this network by focusing on negatively associated, genetically regulated proteins, resulting in a 76-protein fingerprint of insulin resistance. We sought to perturb this network and restore insulin action with small molecules by integrating the Broad Institute Connectivity Map platform and in vitro assays of insulin action using the Prestwick chemical library. These complementary approaches identified the antibiotic thiostrepton as an insulin resistance reversal agent. Subsequent validation in ex vivo insulin-resistant mouse muscle and palmitate-induced insulin-resistant myotubes demonstrated potent insulin action restoration, potentially via upregulation of glycolysis. This work demonstrates the value of a drug-centric framework to validate systems-level analysis by identifying potential therapeutics for insulin resistance. Skeletal muscle is a key determinant of whole-body glycaemic control. Under optimal conditions, insulin secreted from the pancreas initiates a signalling program in muscle and other tissues, culminating in translocation of the insulin-responsive glucose transporter (GLUT4) to the plasma membrane . IncreasOver the past decade, genetically diverse mouse panels have been used to study metabolic diseases . This isThe use of such rodent models for studying complex traits has given rise to the field of systems genetics. System genetics uses global quantification of ‘intermediate phenotypes’, that is, gene transcripts, proteins, and metabolites, to provide mechanistic links between genetic variation and complex traits/diseases . Unlike Here we have utilised DOz mice to interrogate insulin resistance. By combining metabolic phenotyping and skeletal muscle proteomics, we have identified an insulin resistance fingerprint of 76 proteins. We then used CMAP to identify small molecules that give rise to an overlapping transcriptional signature across a number of cell lines, and therefore have the potential to affect insulin action. Strikingly, one of these compounds, the antibiotic thiostrepton, was also identified by us in an independent small-molecule screen for effectors of insulin action in myotubes. Subsequent validation of thiostrepton uncovered profound beneficial effects on insulin resistance in vitro and ex vivo, potentially via modulation of mitochondrial function and glycolysis.DOz mice were metabolically phenotyped by oral glucose tolerance test (GTT) and echoMRI to determine body composition . We inteAs with glycaemic control, muscle proteomes exhibited profound variation, with approximately 200-fold differences in coefficient of variation (CV) across both fractions . InteresTo leverage genetic and metabolic diversity towards uncovering new regulators of insulin action, we constructed linear models comparing insulin sensitivity (Matsuda Index) against protein abundance. Our initial analysis identified 37 mitochondrial and 40 PMF proteins that significantly associated with the Matsuda Index. Many of these appeared to be involved with adiposity rather than insulin action including adiponectin (ADIPOQ), adipsin (CFD), and the mitochondrial β-oxidation proteins ETFA and ETFB . BecauseTo gain further insight into the biology of skeletal muscle glycaemic control, we annotated candidate proteins with PhosphoSite Plus data to test whether any protein of interest had documented roles in metabolic signalling . We obsecis-acting  is not driving the observed differences and therefore the protein-to-phenotype associations are likely causal. Importantly, this assumption can only be made for cis-acting pQTLs. Our analysis yielded a list of 76  proteins that encompassed a wide range of biological processes (MTPN (myotrophin), LGALS1 , PHKB (phosphorylase b kinase), and ADPRHL1 (ADP-ribosylhydrolase Like 1), which may warrant further investigation.Changes in protein levels may be either cause or consequence of changes in insulin sensitivity. In an attempt to select for proteins with a potentially causal relationship, we performed genetic mapping analysis of both the mitochondrial and PMF proteomes . Across s-acting , indicatsistance . We focurocesses . Low mitrocesses  previousrocesses . Using arocesses : MTPN , a readout of insulin action that is defective in insulin resistance. For this we used our established high-sensitivity, high-throughput 96-well plate format screen that is amenable to physiological models of insulin resistance , combineWe performed three separate screens  to captuz-scored the values for each category . Next, we averaged the three in vitro assays z-scores and added it to the CMAP score. This overall score represents how each compound modulates GLUT4 translocation and potentially reverses our insulin resistance fingerprint, relative to the rest of the compound library. Using this final value, we ranked each compound and displayed the top 20 in a heat map  diet feeding . ConsistNext, we attempted to identify the potential mechanisms by which thiostrepton relieved insulin resistance. Canonically, insulin-stimulated GLUT4 translocation is facilitated by a signalling cascade comprising PI3K/Akt and dysfunction in this pathway has been implicated in insulin resistance , althougMany insulin-sensitising agents act via mitochondrial inhibition or uncoupling  and thioWe also assessed glycolysis by way of extracellular acidification rate (ECAR). Like mitochondrial respiration, palmitate suppressed maximal glycolytic capacity; however, unlike respiration this was potently reversed by co-treatment with thiostrepton . To testBy leveraging genetic and phenotypic diversity of DOz mice, we have explored skeletal muscle’s contribution to whole-body insulin action at the molecular level. Our approach was validated by the identification of various ‘positive controls’ at each level of analysis. Firstly, utilising adiposity as a covariate during linear modelling uncovered relationships between whole-body insulin sensitivity and muscle GLUT4 trafficking proteins; secondly, pathway enrichment revealed proteostasis  and endoSystems-based approaches often identify networks as being drivers of disease. Empirical validation of these is difficult due to the complex interactions in biological systems. Here we took a drug-centric approach to validate our findings; this allowed targeting of entire pathways rather than singular nodes. We identified several compounds across both in silico and in vitro analyses which may restore muscle insulin action; indeed, several of these have previously been investigated. Disulfiram, sold under the brand name Antabuse, is used as an alcohol-dependency medication. Two studies have described disulfiram’s ability to reverse diet-induced hepatic insulin resistance and reduce adiposity . Resveracis-pQTLs. The latter was particularly important as we postulated that this would select for proteins that were likely to be causal drivers of insulin resistance. We hypothesised that a protein whose expression is post-translationally regulated in response to insulin resistance would not show a genetic signal and therefore be excluded from our fingerprint. Filtering on this basis improved the overall CMAP score and ultimately identified thiostrepton. One reason for this could be the discordance between mRNA and protein  compared to that required for genetic mapping of complex traits like Matsuda Index  were bred and housed at the Charles Perkins Centre, University of Sydney, NSW, Australia. They were originally established at Geniad, Western Australia, Australia, and then relocated to the University of Sydney. The DOz population comprises 46 breeding pairs and the breeding strategy avoids mating’s between siblings or first cousins. Breeders are selected based on the genotype of the R2d2 locus to limit the meiotic drive favouring the WSB allele on chromosome 2 . Samples were then resuspended in 1.4 ml mitochondrial isolation buffer  and homogenised on ice in a glass Dounce homogeniser. Samples were then twice pelleted by centrifugation, first at 1000 × g × 10 min to remove insoluble debris and second at 10,000 × g × 10 min to extract the crude mitochondrial fraction, both centrifugation steps were performed at 4°C and the supernatant of the second step was collected as the post-mitochondrial fraction (PMF). The mitochondrial pellet was re-solubilised in 1 ml of isolation buffer by repeated pipetting on ice prior to centrifugation (10.000 × g × 10 min at 4°C) and resuspension in 50 µl of isolation buffer. Protein concentration of both mitochondrial and PMF was determined by BCA assay, 10 µg of protein was then prepared as previously described  buffer was added to each sample before incubation for 20 min at 60°C. Once cooled to room temperature, 0.4 mg trypsin and 0.4 mg LysC was added to each sample and incubated overnight (18 hr) at 37°C with gentle agitation. 30 µl water and 50 µl 1% TFA in ethyl acetate were added to stop digestion and dissolve any precipitated SDC. Samples were prepared for mass spectrometry analysis by StageTips clean up using SDB-RPS solid-phase extraction material  between 350–1650 according to m/z 350 and 1650 at a resolution of 60,000 and an AGC target of 4e5 with a 50 ms maximum injection time. Ions were fragmented with stepped HCD collision energy at 27.5% and MS2 spectra collected between m/z 300 and 2000 at resolution of 30,000, with an AGC target of 3e5 and the maximum injection time of 54 ms.Proteomic sample analysis was conducted using a Dionex UltiMate 3000 RSLCnano LC coupled to an Exploris Orbitrap mass spectrometer. Then, 2 µl of sample was injected on to an in-house packed 150 µm × 15 cm column  and separated using a gradient elution and with column temperature of 60°C, with Buffer A consisting of 0.1% formic acid in water and Buffer B consisting of 0.1% formic acid in 80% ACN. Samples were loaded to the column at a flow rate 3 µl/min at 3% B for 3 min, before dropping to 1.2 µL/min over 1 min for the gradient elution. The gradient was increased to 32% B over 50 min, then to 60% B over 0.5 min and 98% B over 0.5 min and held for 1.5 min, before returning to a flow rate of 3 µl/min at 3% B. Eluting peptides were ionised by electrospray with a spray voltage of 2.3 kV and a transfer capillary temperature of 300°C. Mass spectra were collected using a DIA method with varying isolation width windows (widths of Proteomics raw data files were searched using DIA-NN using a library-free FASTA search against the reviewed UniProt mouse proteome (downloaded May 2020) with deep learning enabled . The prolm function in R where Matsuda Index = α + proteinX + covariate + ɛ (α = intercept and ɛ = error) using a Gaussian distribution  factor adjustment, a covariate, and a kinship matrix to account for genetic relatedness amongst the DOz animals. PEER factor adjustment was performed using the top 10 calculated PEER factors, as described . This produced a value which indicates how well each compound performs in a given test relative to the rest of the dataset. Then the average of all three GLUT4 assays  was added to the z-scored connectivity score to produce an overall score for each compound. z-score adjustment for each assay and CMAP score was performed as follows:Insulin resistance ‘fingerprint’ proteins were queried in CMAP using the CLUE software platform . The lisZscore is the adjusted value for a given compound, x is the observed score for given compound, mean is the average score across all compounds, and SD is the standard deviation of all compounds.where Overall score for each compound was calculated as follows:GLUT4-HA-L6 myoblasts  were groGLUT4 exocytosis was determined as previously reported . Briefly3H]-2-deoxyglucose at 37°C for 5 min. Cells were then washed 5× with ice-cold PBS and lysed in 1 M NaOH. For non-specific background uptake, one well per condition was pre-treated with cytochalasin B. Counts were determined by PerkinElmer Quantulus GCT Liquid Scintillation Counter . Glucose uptake was expressed relatively to protein concentration as determined by bicinchoninic acid (BCA) assay after subtraction of non-specific uptake.2-Deoxyglucose uptake into GLUT4-HA-L6 cells was performed as previously described with modifications . Cells w2/5% CO2) supplemented with either 10 µM thiostrepton or a DMSO vehicle control. Glucose uptake was assessed by then switching the muscle into KHB supplemented with 0.375 mCi/ml [3H]-2-deoxyglucose, 0.05 mCi/ml [14C]-mannitol, 100 nM insulin, and either thiostrepton (10 µM) or DMSO vehicle control for 20 min at 30°C followed by washing in ice-cold PBS and then snap-frozen in liquid nitrogen. Samples were lysed in 250 mM NaOH at 70°C. Tracer in the muscle tissue lysates was quantified by liquid scintillation counting and [3H]-2-deoxyglucose was corrected for extracellular [14C]-mannitol then normalised to wet weight of the tissue.Ex vivo glucose uptake was performed as previously described . Mice weGlut4-HA-L6 myotubes were incubated overnight in 125 µM palmitate or BSA control prior to treatment with drugs/insulin as indicated. Cells were then washed in ice-cold PBS and lysed by scraping directly into 55°C Laemmli sample buffer with 10% TCEP. Samples were then sonicated for 24 s (3 s on/3 s off) and heated at 65°C for 5 min. SDS-PAGE was performed. Samples were resolved by SDS-PAGE as previously described , transfe2 incubator. Cells were then assayed in XFp Analyzer. The OCR was measured after a 12 min equilibration period followed by 3/0/3 min of mix/wait/read cycles. Following stabilisation of baseline rates, compounds were injected sequentially to reach a final concentration of: 20 mM glucose, oligomycin (5 μg/ml), FCCP (1 μM), and rotenone/antimycin A (5 μM) to assess glucose-dependent respiration , ATP-linked respiration (determined by glucose – oligomycin OCR), maximal respiration , and non-mitochondrial respiration, respectively . Data were normalised against protein concentration and presented as baseline adjusted.Cellular respirometry (oxygen consumption rate [OCR]) was performed using Seahorse XFp miniplates and a Seahorse XF HS Mini Analyzer  as previously described . GLUT4-H fundamental study leverages natural genetic diversity in mice to discover candidate genes for insulin sensitivity, followed by experimental identification of compounds that can modulate insulin sensitivity, and finally initial mechanistic investigation of the mode of action. The generalized approach presented here, - the integration of systems genetics data with drug discovery -, supported by compelling evidence, will be an important guide for others that seek to translate insights from mammalian genetics to drug discovery.This  Masson et al. leveraged the natural genetic diversity presented in a large cohort of the Diversity Outbred in Australia (DOz) mice (n=215) to determine skeletal muscle proteins that were associated with insulin sensitivity. The hits were further filtered by pQTL analysis to construct a proteome fingerprint for insulin resistance. These proteins were then searched against Connectivity Map (CMAP) to identify compounds that could modulate insulin sensitivity. In parallel, many of these compounds were screened experimentally alongside other compounds in the Prestwick library to independently validate some of the compound hits. These two analyses were combined to score for compounds that would potentially reverse insulin resistance. Thiostrepton was identified as the top candidate, and its ability to reverse insulin resistance was validated using assays in L6 myotubes.Below are several comments made on the original version of this study, addressed by the authors in the current version:(1) Please describe the rationale of trypsinizing the tissue prior to mitochondrial isolation.(2) The authors mentioned that the proteomics data were Log2 transformed and median-normalized. Please provide a bit more details on this, including whether the subjects were randomized.(3) In Figure 1D, please give the numbers of mice the authors used for the CV comparisons in each group, whether they were of similar age and sex, and whether the differences in CV values were statistically significant(4) The authors stated in lines 155-157 that proteins negatively associated with Matsuda index were further filtered by presence of their cis-pQTLs. Please provide more explanations to justify this filtering criterion.(5) Please explain why the first half of the paper focused extensively on the authors' discoveries in the mitochondrial proteome, and how proteins involved in mitochondrial processes (such as complex I) were associated with Matsuda Index, but the final fingerprint list of insulin resistance, which contained 76 proteins, only had 7 mitochondrial proteins.(6) The authors found that thiostrepton-induced insulin resistance reversal effects were not through insulin signalling. Please list the proteins in the fingerprint list that led to identification of thiostrepton on CMAP, and discuss whether you think that thiostrepton directly or indirectly acts on these protein targets. In the present study, Masson et al. provide an elegant and profound demonstration of utilization of systems genetics data to fuel discovery of actionable therapeutics. The strengths of the study are many: generation of a novel skeletal muscle genetics proteomic dataset which is paired with measures of glucose metabolism in mice, systematic utilization of these data to yield potential therapeutic molecules which target insulin resistance, cross-referencing library screens from connectivity map with an independent validation platform for muscle glucose uptake and preclinical data supporting a new mechanism for thiostrepton in alleviating muscle insulin resistance. Future studies evaluating similar integrations of omics data from genetic diversity with compound screens, as well as detailed characterization of mechanisms such as thiostrepton on muscle fibers will further inform some remaining questions. In general, the thorough nature of this study not only provides strong support for the conclusions made but additionally offers a new framework for analysis of systems-based data. I had made several comments on the prior submission, all of which have been fully addressed and incorporated. The following is the authors’ response to the original reviews.Reviewer #1 (Public Review):(1) What's the rationale of trypsinizing the tissue prior to mitochondrial isolation? This is not standard for subsequent proteomics analysis. This step will inevitably cause protein loss, especially for the post mitochondrial fractions (PMF). Treating samples with 0.01ug/uL trypsin for 37oC 30 min is sufficient to partially digest a substantial portion of the proteome. If samples from different subjects were not of the same weight, then this partial digestion step may introduce artificial variability as variable proportions of proteins from different subjects would be lost during this step. In addition, the mitochondrial protein enrichment in the mito fraction, despite statistically significant, does not look striking . As a comparison, Williams et al., MCP 2018 seem to have obtained high mitochondrial protein content in the mito fraction without trpsinizing the frozen quadriceps using a similar SWATH-MS-based approach.Trypsinisation of the tissue prior to mitochondrial isolation is based on previous work and a Nature Protocol  which isolated mitochondria for skeletal muscle. The rationale is that it aids in mechanical homogenisation from highly fibrous tissues such as quadriceps muscle by digesting extracellular matrix proteins. The trypsin/protein ratio used to aid in this process is at least 400 times lower than the amount of trypsin used for formal proteomic tryptic digestion. Three pieces of evidence suggest this step has negligible effect on downstream proteomic analysis. First, because the trypsinisation buffer is detergent free, trypsin will only affect extracellular or exposed membrane proteins. Filtering our PMF dataset for proteins with ‘extracellular matrix’ gene ontology identifies at least 90 unique extracellular matrix proteins indicating good retention of proteins susceptible to partial digestion. Second, the trypsin dose used is 50 times lower than the concentration used for passaging cultured cells, which retain viability after trypsinisation. Third, and contrary to the point raised by the reviewer, we observe less missingness in PMF samples compared to mitochondrial samples. We thank the reviewer for bringing the Williams et al. 2018 MCP paper to our attention. We note that mitochondrial enrichment between the two papers is comparable (~2- fold). To improve clarity line 408 now reads: “Whole quadriceps muscle samples were prepared as previously described with modification . First, tissue was snap frozen with liquid nitrogen…” and line 95 reads: “Mitochondrial proteins were defined based on their presence in MitoCarta 3.0 (24) and consistent with previous work (25) were approximately two-fold enriched in the mitochondrial fraction relative to the PMF .”(2) The authors mentioned that the proteomics data were Log2 transformed and median- normalized. Would it be possible to provide a bit more details on this? Were the subjects randomized?Samples were randomised prior to sample processing and mass spectrometry analysis. Because of possible variation in total protein content, it is critical to normalise protein intensities between samples. Median normalisation adjusts the samples so that they have the same median, thereby accounting for technical variation. Log2 normalisation helps to achieve normal distributions, critical for many downstream statistical tests. Line 471 now reads: “…to achieve normal distributions and account for technical variation in total protein.”(3) In Figure 1D, what were the numbers of mice the authors used for the CV comparisons in each group? Were they of similar age and sex? Were the differences in CV values statistically significant?The mitochondrial and PMF proteomes originated from the same quadriceps sample from the same mouse, and thus the age and sex are the same across both proteomes. After quality control, we had mitochondrial proteomes for 194 mice and PMF proteomes for 215 mice. The overall CV in the mitochondrial fraction was significantly greater than in the PMF, however whether the source of this variation is biological, or the result of mitochondrial isolation is unclear and as such we have avoided making a statement within the body of the manuscript. We have now more clearly described the nature of the samples in the revised manuscript and added sample sizes to figure 1F.(4) The authors stated in lines 155-157 that proteins negatively associated with the Matsuda index were further filtered by presence of their cis-pQTLs. Perhaps more explanations would be needed to justify this filtering criterion? Having a cis-pQTL would mean the protein abundance variation is explained by the variation in its coding gene, this however conceptually would not be relevant to its association with the Matsuda index. With the data that the authors have in hand, would it not be natural to align the Matsuda index QTL with the pQTLs (cis and trans if available), and/or to perform mediation analysis to examine causal relationships with statistical significance?https://qtlviewer.jax.org/) has demonstrated that cis-pQTL have markedly higher LOD scores than trans-pQTLs, and our own unpublished work suggests that trans-pQTLs do not reproduce well between datasets. The reviewer rightfully suggests aligning protein QTL with those for Matsuda. This is our long-term goal but to identify genome wide significant peaks associated with altered Matsuda will require many more mice than studied here.The rationale for filtering by cis-pQTL was not to study the genetics of either Matsuda or associated proteins but rather to identify proteins that were more likely to be causally associated with Matsuda Index as opposed to adaptively associated. To clarify this line 165 now reads: “Filtering based on cis-pQTL presence was based on the rationale that if genetic variation can explain protein abundance differences between mice, then we can be confident that phenotype (Matsuda Index) is not driving the observed differences and therefore the protein-phenotype associations are likely causal. Importantly, this assumption can only be made for cis-acting pQTLs.” Previous work by Matthew et al. (see (5) It seems a bit odd that the first half of the paper focused extensively on the authors' discoveries in the mitochondrial proteome, and how proteins involved in mitochondrial processes (such as complex I) were associated with Matsuda Index, but the final fingerprint list of insulin resistance, which contained 76 proteins, only had 7 mitochondrial proteins. Was this because many mitochondrial proteins were filtered out due to no cis-pQTL presenting?There are three reasons our fingerprint is lacking mitochondrial proteins: (1) there are more non-mitochondrial than mitochondrial proteins in the muscle proteome; (2) we focussed on negatively associated proteins, and as demonstrated in figure 2c, the mitochondrial proteome is enriched for positively associated proteins; (3) as implied by the reviewer, we filtered for pQTL presence, further reducing the number of mitochondrial proteins in our fingerprint. To improve clarity, line 170 now reads: “Low mitochondrial representation in the fingerprint is the result of selecting negatively associating proteins, and as seen  previously, the mitochondrial proteome is enriched for positive contributors to insulin resistance.”(6) The authors found that thiostrepton-induced insulin resistance reversal effects were not through insulin signalling. It activated glycolysis but the mechanism of action was not clear. What are the proteins in the fingerprint list that led to identification of thiostrepton on CMAP?Is thiostrepton able to bind or change the expression of these proteins? Since thiostrepton was identified by searching the insulin resistance fingerprint protein list against CMAP, it would be rational to think that it exerts the biological effects by directly or indirectly acting on these protein targets.This is indeed the implication of our data. Because of the timescales involved it is unlikely that thiostrepton is changing fingerprint protein levels but could be binding to and inhibiting them. Searching the CMAP thiostrepton signature reveals ARHGDIB and NAGK as the fingerprint proteins with the most positive and negative fold-changes respectively perhaps suggesting they play a role in thiostrepton’s mechanism of action. Experiments are underway to test this hypothesis however these are beyond the scope of the current paper.Reviewer #2 (Public Review):Line 105: The observation that variance in respiratory proteins is stable while lipid pathways is variable is quite interesting. Is this due to lower overall levels of lipid metabolism enzymes .The relationship between coefficient of variation (CV) and relative abundance of proteins is important to consider. To address this, we have now also performed GSEA on proteins ranked from high to low relative abundance. These comparisons have been added to supplementary figure 1 and line 110 now reads: “As a control experiment, we also performed enrichment analysis on proteins ranked by LFQ relative abundance. High CV pathways (enriched for high CV proteins) tended to be lower in relative abundance (enriched for low relative abundance proteins) . However, many high variability pathways, lipid metabolism for example, were not enriched in either direction based on relative abundance suggesting differences in relative abundance do not fully explain pathway variability differences.”Line 154: the 664 associations are impressive and potentially informative. It would be valuable to know which of these co-map to the same locus - either to distinguish linkage in a 2mb window or identify any cis-proteins which directly exert effects in trans-To assess this, we have analysed pQTL position relative to gene position to generate a ‘hotspot’ plot. We have also generated a histogram of this pQTL density (in a 2 Mbp window) and added these figures to figure 3. We did not detect any obvious pQTL hotspots, and the distribution of pQTLs across the genome appears fairly uniform. Line 159 now reads: “These were distributed across the genome and were predominately cis acting ...”Line 194: Cross-platform validation of the CMAP fingerprint results is an admirable set of validations. It might be good to know general parameters like how many compounds were shared/unique for each platform. Also the concordance between ranking scores for significant and shared compounds.The Connectivity Map (CMap) query included 5163 compounds, the Prestwick library included 1120, and the overlap was 420. We have added these comparisons to supplementary figure 2. Supplementary figure 2 now also contains a comparison of CMap scores between overlapping compounds (found in CMap and the Prestwick library) against all significant compounds identified by CMap (supplementary figure 2b). Interestingly, compounds present in both platforms scored higher on average, suggesting the Prestwick library captures a significant proportion of highly scoring CMap candidates. Line 206 now reads: “In total, 420 compounds were found across both platforms, and these consensus compounds captured a significant proportion of highly scoring CMap compounds .”Line 319: Another consideration in the molecular fingerprint is how unique these are for muscle. While studies evaluating gene expression have shown that many cis-eQTLs are shared across tissues, to my knowledge, this hasn't been performed systematically for pQTLs. Therefore, consider adding a point to the discussion pointing out that some of the proteins might be conserved pQTLs whereas others which would be more relevant here present unique druggable targets in muscle.To examine tissue specificity, we determined whether our skeletal muscle fingerprint proteins were detected and contained a pQTL in two metabolically important tissues, liver and adipose. Despite detecting almost all the fingerprint proteins in both adipose and liver tissue, they were depleted for pQTL compared to skeletal muscle. These data have now been added to figure 3c. Line 172 now reads: “To assess the tissue specificity of our fingerprint we searched for the same proteins in metabolically important adipose and liver tissues. Despite detecting 94% and 82% of muscle fingerprint proteins across each tissue respectively, both adipose and liver were depleted for pQTL presence  suggesting that regulation of our fingerprint protein abundance is specific to skeletal muscle.”Line 332: These are fascinating observations. 1, that in general insulin signaling and ampk were not themselves shown as top-ranked enrichments with matsuda and that this was sufficient to alter glucose metabolism without changes in these pathways. While further characterization of this signaling mechanism is beyond the scope of this study, it would be good to speculate as to additional signaling pathways that are relevant beyond ROS (ex. CNYP2 and others)We have now added further discussion to the manuscript to address this point., Line 347 now reads: “Aside from glycolysis, other pathways may be involved in enhancing insulin sensitivity. For example, the negatively associated protein ARHGDIA  is a potent negative regulator of insulin sensitivity, and our fingerprint of insulin resistance contained its homologue ARHGDIB. Both ARHGDIA and ARHGDIB have been reported to inhibit the insulin action regulator RAC1 thus lowering GLUT4 translocation and glucose uptake. Further investigations may uncover a role for thiostrepton in modulating the RAC1 signalling pathway via ARHGDIB.”Line: 314: Remove the statement: "While this approach is less powerful than QTL co- localisation for identifying causal drivers,", as I don't believe that this has been demonstrated. Clearly, the authors provide a sufficient framework to pinpoint causality and produce an actionable set of proteins.We have edited line 314, which now reads: “Moreover, our approach has the major advantage that it requires far fewer mice to obtain meaningful outcomes (222 mice in this study) compared to that required for genetic mapping of complex traits like Matsuda Index.”Line 346: I would highlight one more appeal of the approach adopted by the authors. Given that these compound libraries were prioritized from patterns of diverse genetics, these observations are inherently more-likely to operate robustly across target backgrounds.This point is further supported by our thiostrepton results in both C57BL6/j and BXH9 mice. Line 317 now reads: “Furthermore, because we have used genetically diverse datasets (DOz mice and multiple cell lines in Connectivity Map) our findings are likely robust across diverse target backgrounds.”Line 434: I might have missed but can't seem to find where the muscle data are available to researchers. Given the importance and novelty of these studies, it will be important to provide some way to access the proteomic data.These data are now available via the ProteomeXchange Consortium. Line 465 now reads: “The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (104) partner repository with the dataset identifier PXD042277.”1. Frezza C, Cipolat S, Scorrano L. Organelle isolation: functional mitochondria from mouse liver, muscle and cultured filroblasts. Nat Protoc. 2007;2(2):287-95.2. Acin-Perez R, Benador IY, Petcherski A, Veliova M, Benavides GA, Lagarrigue S, et al. A novel approach to measure mitochondrial respiration in frozen biological samples. The EMBO Journal. 2020;39(13):e104073.3. Chick JM, Munger SC, Simecek P, Huttlin EL, Choi K, Gatti DM, et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature. 2016;534(7608):500- 5.4. Gatti DM, Svenson KL, Shabalin A, Wu L-Y, Valdar W, Simecek P, et al. Quantitative Trait Locus Mapping Methods for Diversity Outbred Mice. G3 Genes|Genomes|Genetics. 2014;4(9):1623-33.


Critically ill patients in intensive care units experience profound alterations of their gut microbiota that have been linked to a high risk of hospital-acquired  infections and adverse outcomes through unclear mechanisms. Abundant mouse and limited human data suggest that the gut microbiota can contribute to maintenance of systemic immune homeostasis, and that intestinal dysbiosis may lead to defects in immune defense against infections. Here we use integrated systems-level analyses of fecal microbiota dynamics in rectal swabs and single-cell profiling of systemic immune and inflammatory responses in a prospective longitudinal cohort study of critically ill patients to show that the gut microbiota and systemic immunity function as an integrated metasystem, where intestinal dysbiosis is coupled to impaired host defense and increased frequency of nosocomial infections. Longitudinal microbiota analysis by 16s rRNA gene sequencing of rectal swabs and single-cell profiling of blood using mass cytometry revealed that microbiota and immune dynamics during acute critical illness were highly interconnected and dominated by Enterobacteriaceae enrichment, dysregulated myeloid cell responses and amplified systemic inflammation, with a lesser impact on adaptive mechanisms of host defense. Intestinal Enterobacteriaceae enrichment was coupled with impaired innate antimicrobial effector responses, including hypofunctional and immature neutrophils and was associated with an increased risk of infections by various bacterial and fungal pathogens. Collectively, our findings suggest that dysbiosis of an interconnected metasystem between the gut microbiota and systemic immune response may drive impaired host defense and susceptibility to nosocomial infections in critical illness. Dysbiosis of the gut microbiome in critically ill patients, coupled with impaired innate immune responses, may lower the barrier to acquiring infectious pathogens in the hospital setting. Susceptibility to severe infections in critical illness has been linked to widespread impairment of innate and adaptive immunity and a breakdown of host defense mechanisms, as well as breaching of physical barriers by medical devices 6. In addition, nosocomial infections in the ICU are often caused by pathogens that are rarely associated with invasive disease in healthy individuals, consistent with a state of severely impaired host defense1; however, the mechanisms that induce and propagate immune dysfunction in critical illness are poorly understood.Critically ill patients requiring life-support interventions in intensive care units (ICUs) suffer very high rates of hospital-acquired  infections (20–50%) that contribute a markedly elevated risk of mortality9. Critically ill patients harbor profound dysbiosis of their gut microbiota14 and this dysbiosis observed in ICU and other hospitalized patients has been associated with an increased risk of adverse outcomes, including infections through unclear mechanisms16. Colonization and overgrowth of pathobiont microbes may lead to translocation into the bloodstream as a direct mechanism linking gut dysbiosis to infections18; however, additional mechanisms likely contribute to the high rates of infections across multiple body sites in ICU patients that are caused by diverse pathogens beyond typical gut pathobionts1. In particular, alterations to the gut microbiota may render the host susceptible to infections through pathological crosstalk with the immune system, leading to impaired host defense.Evidence from both mice and humans has revealed an important role for gut microbes in the maintenance of immune homeostasis and protective host defense in the gut as well as extra-intestinal, systemic compartmentsIn this study, we tested the hypothesis that susceptibility to nosocomial infections in critical illness is driven by pathological microbiota–immune interactions, in which gut microbiota dysbiosis triggers impaired systemic immunity and host defense. Using integrated systems-level analysis of gut microbiota dynamics and systemic immune function in 51 critically ill adults, we propose that the gut microbiota and systemic immune response behave as an integrated microbiota–immune metasystem, wherein dysbiosis characterized by progressive enrichment of Enterobacteriaceae in the gut microbiota leads to dysregulated innate immunity and impaired host defense and increased susceptibility to bacterial and fungal nosocomial infections.We conducted a prospective, longitudinal, integrated multi-omics analysis of the fecal microbiota, systemic cellular immune and inflammatory responses in 51 critically ill adults in medical, surgical, trauma and neurological ICUs in Calgary  and emergence of pathobiont taxa (Enterococcaceae and Enterobacteriaceae) ICU Fig. . Proteobion Fig. . Enterobion Fig.  and penaion Fig. . Increasion Fig. . Communiion Fig. . In contion Fig. , which a26) revealed that patients with progressive Enterobacteriaceae enrichment had both progressive increase in total bacterial density and total Enterobacteriaceae quantity, indicating that enrichment was mediated by Enterobacteriaceae expansion rather than contraction of other taxa  had a significantly increased risk of nosocomial infection or death compared to patients with a high Shannon diversity (>3.59) on admission  cells  including a population resembling recently characterized dysfunctional CD123+ neutrophils (cluster N4)29, with reduction of mature  and aged  neutrophil populations  and decreased activated interferon-γ+ NK cells (cluster NK2)  and regulatory T (Treg) cell  clusters  revealed that patients with progressive Enterobacteriaceae enrichment (doubling or more of Enterobacteriaceae relative abundance during the first week in ICU) displayed cellular immune responses that differed significantly compared to those without progressive enrichment Fig. , even afNext, we investigated whether Enterobacteriaceae-associated metasystem dysbiosis was characterized by defects in specific immune defense programs that may contribute to the elevated risk of bacterial and fungal nosocomial infections. Dimensionality reduction analysis revealed that the adaptive immune cell compartment in patients with progressive Enterobacteriaceae enrichment was not significantly different from those without Fig. . Aside f34. Strong correlations were observed in ICU patients between Enterobacteriaceae relative abundance and increased immature neutrophils  and decreased mature neutrophils   were found to have reduced quantities of circulating NET markers in their plasma (both cell-free DNA and myeloperoxidase (MPO)–DNA complexes) compared to those without enrichment  Fig. . DespiteN9) Fig. . Consistent Fig. . OverallHere we show that the intestinal microbiota and systemic immune response of acute critical illness are functionally integrated as a dynamic metasystem. Dysbiosis of this metasystem in critical illness is associated with progressive Enterobacteriaceae expansion in the gut microbiota, dysregulated innate immunity and increased incidence of bacterial and fungal nosocomial infections.39. In critically ill humans, we observed that gut microbiota dysbiosis was coupled predominantly with innate immune dysregulation, with less impact on the landscape of adaptive immunity. In particular, the dominant response of neutrophils to microbiota alterations during the early stages of critical illness may reflect their rapid turnover and greater capacity for acute functional plasticity compared to the more protracted time course of adaptive immune responses. As our study focused on the first week of critical illness, it is possible that intestinal dysbiosis may drive adaptive immune dysregulation later in the course of illness, as previous studies have documented widespread lymphocyte apoptosis and functional exhaustion that persists in some patients and contributes to ongoing organ dysfunction and opportunistic nosocomial infections6. The impact of metasystem dysbiosis on long-term imprinting of both innate and adaptive immunity remains to be explored, but a previous retrospective study of hospitalized patients suggested that intestinal dysbiosis may be associated with a prolonged increased risk of infections and recurrent sepsis that lasts for months (or longer)40.Evidence from mouse models has shown that gut microbes regulate various mechanisms of systemic immunity and that germ-free and antibiotic-conditioned mice display impaired defense against a variety of bacterial, fungal and viral infections34. In mice, intestinal dysbiosis or germ-free status leads to suppressed myelopoiesis, impaired neutrophil maturation and defective antimicrobial effector functions41. Consistent with this, we observed a profound shift in neutrophil responses in patients with intestinal dysbiosis and progressive Enterobacteriaceae enrichment, marked by immature and hypofunctional neutrophil responses, including decreased NETs. This mechanism of microbiota–immune metasystem dysbiosis involving a core program of innate host defense is consistent with the elevated risk of systemic infections caused by diverse bacterial and fungal pathogens across multiple organs.Gut microbes have been implicated in systemic neutrophil homeostasis through regulation of their maturation, aging, modulation of effector functions, as well as granulopoiesis in the bone marrow20. Notably, this was observed in patients with both subacute pre-ICU illnesses (such as sepsis) as well as more acute presentations  and was present despite our stringent exclusion criteria that limited pre-ICU hospitalization or antibiotic exposure. Following admission to ICU, we observed progressive and dynamic changes to the microbiota during the acute phase of critical illness that was characterized by expansion of Enterobacteriaceae and reduction in anaerobic fermenters, consistent with evidence implicating anaerobic fermenters in colonization resistance against Enterobacteriaceae42. Notably, among the multi-taxa changes observed during acute critical illness, only progressive Enterobacteriaceae enrichment (but not reduced anaerobic fermenters such as Ruminococcaceae or Lachnospiraceae) was associated with impaired host defense and risk of nosocomial infections, suggesting that Enterobacteriaceae exerts a unique influence on the microbiota–immune metasystem during critical illness. A recent study of ICU patients found that expansion of Enterobacteriaceae in the gut in the setting of anti-anaerobic antibiotic administration was also coupled with an increased risk of nosocomial infections20. These findings raise the possibility that interventions such as antibiotics may propagate a vicious cycle of microbiota–immune metasystem dysbiosis through suppression of gut anaerobe-mediated colonization resistance and expansion of Enterobacteriaceae. Of note, intestinal Enterobacteriaceae expansion has been observed in other hospitalized non-ICU patient populations who are at risk of nosocomial infections and therefore our findings may have implications beyond critical illness.The ecological pressures driving microbiota dysbiosis in critical illness are likely multifactorial, including host intrinsic factors  as well as host-extrinsic factors that are inherent to critical illness , invasive devices and ICU environment). Of note, we and others have found that abnormalities of community composition, diversity, as well as expansion of pathobionts such as Enterobacteriaceae are present from the time of ICU admission43. The association between microbiota dysbiosis and nosocomial infections has led to clinical trials of microbiota-modifying therapies in critically ill patients, but studies have been hindered by a limited understanding of the mechanisms linking dysbiosis with infections, resulting in untargeted approaches such as probiotics and digestive tract decontamination that have yielded modest or negligible benefits46, as well as possible harm in this vulnerable patient population49. Our findings reveal that pathological microbiota alterations in critical illness may render the host more susceptible to infections via induction of immune dysfunction, suggesting that microbiota therapeutics in the ICU should be targeted at correcting the drivers of microbiota–immune metasystem dysbiosis. We have identified intestinal Enterobacteriaceae expansion as a marker of metasystem dysbiosis in critical illness and therefore precision editing of intestinal Enterobacteriaceae colonization50 may represent a strategy to reduce infections and adverse outcomes by fortifying systemic immune defenses.Last, our findings may have important therapeutic implications for prevention and treatment of infections in the ICU. Nosocomial infections remain a leading cause of adverse outcomes in critical illness including mortality, prolonged hospitalization and resource utilizationThis study has a number of limitations, including a single-center design, limited sample size and inherent heterogeneity of critically ill patients (including diverse comorbidities and treatments). While our prospective and longitudinal analysis enabled the identification of notable associations with temporal directionality between microbiota–immune metasystem dysbiosis and subsequent nosocomial infections, we cannot definitively show causality. In addition, our interrogation of systemic immunity was limited to the bloodstream and microbiota analysis was limited to the rectal compartment, due to safety and ethical considerations of obtaining invasive tissue biopsies or endoscopic sampling of the proximal GI tract in critically ill patients. Furthermore, 16s sequencing and mass cytometry analysis provide detailed, albeit incomplete resolution of microbiota dysbiosis and immune cell heterogeneity. Therefore, future studies using expanded immune analysis and deeper microbiota sequencing may uncover additional mechanisms of immune dysfunction involving neutrophils and other antimicrobial effector cells and microbial alterations of the gut or other sites  that contribute to metasystem dysbiosis and impaired host defense in critical illness.44): adult (>18 years of age) with an index admission to ICU, requiring mechanical ventilation, who was expected to require continuous mechanical ventilation for >72 h as judged by the treating ICU specialist. Patients were excluded if they had a pre-existing immunocompromised state , had been hospitalized >48 h before ICU admission in the previous 3 months, had received systemic antimicrobial therapy in the previous 3 months, had inflammatory bowel disease or active GI malignancy, previous surgery leaving a discontinuous GI tract, pregnancy, goals of care that excluded life-support interventions or moribund patients not expected to survive >72 h. At the onset of the COVID-19 pandemic, the study team added SARS-CoV-2 infection as an exclusion criterion and therefore no patients with COVID-19 were included in this study.This study was approved by the conjoint health research ethics board of the University of Calgary and Alberta Health Services (REB18-1294). Written informed consent was obtained from all study participants or appropriate surrogate decision maker for patients who were unable to provide consent due to incapacitating illness. Enrollment occurred between 23 July 2019 and 20 July 2021, with substantial delays and disruptions in enrollment due to the COVID-19 pandemic between March 2020 and April 2021. Patients admitted to the medical, surgical, neurological and trauma ICUs at the Foothills Medical Center in Calgary were screened for the following inclusion criteria (adapted from elsewheren = 51) and again from survivors who remained in the ICU on day 3 (n = 45) and day 7 (n = 18) following admission. For reference comparisons, rectal swabs and blood samples were also collected from healthy volunteers (n = 18) for use as controls. As a discovery-based study of microbiota–immune interactions, there were no previously published effect size estimates to facilitate an a priori sample size calculation, therefore we enrolled a cohort size that was comparable to other recently published multi-omic studies in critically ill patients55, as well as human microbiota–immune omics studies58. The number of patient samples included in each analysis is indicated in figure legends and varies slightly due to rare instances where the quality or quantity of individual samples were unsuitable for certain assays and could not be re-collected. Patient demographic and clinical data were collected at the time of admission and are displayed in Table 6 l−1 or <3.0 × 106 l−1, as previously described44 and all cases of VAP met the Center for Disease Control and Prevention National Healthcare Safety Network (CDC NHSN) case definition of probable or definite VAP59. Nosocomial BSIs met the CDC NHSN definition of BSI/central-line-associated BSI, UTIs were identified based on Infectious Disease Society of America clinical practice guideline criteria for the diagnosis of catheter-associated urinary tract infections and Clostridium difficile infection was based on the presence of new diarrhea and positive stool testing for C. difficile toxin61. The sites of nosocomial infection and clinical microbiology data are presented in Table Rectal swabs and blood samples were collected from prospectively enrolled patients on the day of ICU admission n = 5 and agai62. PCR products were cleaned and size selected using Nucleomag beads (Macherey Nagel) following manufacturer’s instructions. Individual sample libraries were normalized using a SequalPrep Normalization Plate (Invitrogen), after which samples were pooled to create the final library. Quality control of the pooled next-generation sequencing library was performed using an Agilent Technologies 2200 TapeStation and Qubit dsDNA analyzer. The pooled 16S V4 amplicon library was sequenced using an Illumina MiSeq platform to produce 2 × 250-bp paired-end reads.All experimental analyses were performed at the University of Calgary. Rectal swabs were collected and stored in sterile tubes at −80 °C. DNA was isolated using the DNeasy PowerSoil (QIAGEN) following the manufacturer’s protocol. Negative control swabs were processed identically and run through the study protocol as controls. PCR amplification of the 16S V4 region was performed using previously described dual indexed primers with sample barcodes and sequencing adaptors and PCR conditions63. ASVs taxonomically assigned to Cyanobacteria, mitochondria or chloroplast were removed. Publicly available datasets of healthy volunteers  from Dadkhah et al.22 were processed on raw FastQ files as described above.De-multiplexed Illumina MiSeq paired-end reads (FASTQ) were processed in R v.4.1.2 following the DADA2 pipeline v.1.14. Forward and reverse reads were truncated to 230 bp and 210 bp, respectively or to the first base with a quality score Q < 2. Reads containing any ambiguous (N) nucleotides or reads containing more than two errors were removed. Samples with fewer than 1,000 reads following trimming and filtering steps were discarded from further analysis. Taxonomy of unique amplicon sequence variants (ASVs) was assigned in DADA2 by the RDP Classifier using the SILVA v.138.1 database. ASVs and sample data were combined using the Phyloseq package v.1.38.0 for further downstream analysis. Potential contaminants were identified and removed by the Decontam package v.1.14.0 based on the distributed frequency of ASVs and the DNA concentration of individual samples64 v.1.1.0 and shows Spearman correlations >0.2 of the most abundant (upper quartile 25%) bacterial families in all samples. Data are reported as per the STORMS (Strengthening the Organization and Reporting of Microbiome Studies) guidelines  was calculated on the Bray–Curtis dissimilarity measure by PERMANOVA using the adonis function in Vegan v.2.6 and three-dimensional visualization was performed by plotting the first three dimensions of the Bray–Curtis dissimilarity ordination using the plotly package v.4.10.0. Spearman correlation matrices were performed between the 15 most abundant bacterial families present in at least 10% of samples using the rcorr function in the Hmisc package v.4.7 and visualized using the ggplot2 package v.3.3.6 in R. Taxonomy plots showing relative abundances were plotted using ggplot2 package microbiomeutilities v.1.00.16 and the Phyloseq packages v.1.38.0 in R. Penalized ridge regression analysis (glmnet R package v.4.1-4) was used to determine the importance of the 15 most abundant bacterial families toward specified microbiome outcomes (change in Shannon diversity or change in Enterobacteriaceae relative abundance between ICU days 1 and 3) using a threefold cross validation repeated ten times through the R package caret v.6.0-93. Results were visualized using the vip package v.0.3.2 in R. Community stability was determined using the codyn R package on data from days 1 and 3 of ICU admission. Differential abundance analysis was performed using ANCOM-II on relative abundances, with patients fit as a random effect to account for repeat measures using the microbiomeMarker R package v.1.0.2. Network analysis was performed on bacterial families using the NetCoMi R package65 and a standard curve was generated from Escherichia coli strain Xen14 DNA (PerkinElmer). The PCR reaction was performed using the PowerUp SYBR Green kit (Thermo Fisher) on a StepOnePlus Real-Time PCR System (Thermo Fisher). Cycle conditions were as follows: 50 °C 2 min, 95 °C 10 min, 95 °C for 15 s (40 cycles) and 60 °C for 1 min. Determination of Enterobacteriaceae abundance was performed as previously reported by Chanderraj et al.20 by multiplying the absolute bacterial density by Enterobacteriaceae relative abundance determined by 16s rRNA gene amplicon sequencing.Bacterial density measurements were performed by qPCR using a universal 16s rRNA gene primer set  as previously described58. Samples were thawed at room temperature and red blood cell lysis was performed using PROT1 RBC lysis buffer (SmartTube) and white blood cells were washed in cell staining medium (PBS with 1% BSA) followed by labeling with a custom metal-conjugated antibody panel  at a ratio of 1:1.4 and stored at −80 °C to enable batched analysis of patient samples as described66. Next, corrected FCS files were imported into Cytobank (Cytobank) for manual gating on CD45+ single-cell events and major cell populations  were calculated on absolute cell counts and controlled for clinical covariables that were significantly associated with immune cell composition . Cell-free DNA levels were quantified using the Quanti-iT PicoGreen kit (Invitrogen) according to the manufacturer’s instructions and quantification of MPO–DNA complexes in plasma was performed as previously described68. Briefly, microbiota taxonomic data were aggregated to the family level and filtered to a cutoff of 25% prevalence, after which the count data was transformed by center log ratio (clr). Single-cell mass cytometry count data and inflammatory mediator concentrations were log transformed before dataset integration. The resulting MOFA factors were then compared between healthy volunteers and ICU patients to determine latent factors that explain variation between these populations. Modeling of the combined datasets in MOFA was performed using the default parameters with model fitting identifying the top ten MOFA factors that explained the largest amount of variation between samples. Next, MOFA factors showing contributions from all meta-systems datasets  that explained at least 5% of variance were compared between healthy volunteers and ICU patients. Within each MOFA factor, individual features weights  were compared between healthy volunteers and ICU patients.To perform an integrated multi-omics analysis of the fecal microbiota, cellular immune composition and inflammatory mediator landscape in blood, we employed an unsupervised factor analysis approach in R with MOFA v.1.4.0, as previously describedP < 0.1) and significant values were visualized using the circlize package v.0.4.15 in R. Heat maps depicting the Spearman correlation coefficients between the 15 most abundant bacterial families  and immune components (cell counts and inflammatory mediator concentrations) were generated using the rcorr function in the Hmisc package v.4.7 and visualized using the pheatmap package v.1.0.12 in R.Connectivity between microbiota, cellular immune landscape and inflammatory mediators was determined using Chord diagram analysis. A Spearman correlation coefficient was calculated for pairings of all microbial taxa , immune cell subset counts and inflammatory mediatory concentrations (FDR-adjusted for multiple comparisons with Microbiota α-diversity metrics (Shannon index and Chao1), as well as relative abundance data of individual bacterial families were analyzed between healthy volunteers and ICU patient days using a Kruskal–Wallis test with a post hoc Tukey test. Analysis of paired measurements from ICU patients across days 1, 3 and 7 of admission was performed using a linear mixed-effects model to account for repeated measures and variable dropout across sampling time points using the lmerTest package v.3.1.3 in R . A post hoc Tukey test was performed on the model for comparison between days in ICU using the emmeans package v.1.7.4 in R. Microbiota β-diversity was calculated on the Bray–Curtis dissimilarity by PERMANOVA using the adonis function in the Vegan package v.2.6, with pairwise ANOVA comparisons between healthy volunteers and ICU patient days using the EcolUtils package v.0.1 in R. To determine the associations between microbiota β-diversity and patients demographic and clinical covariables , multivariable permutational ANOVA was performed in Vegan v.2.6. in R with permutations blocked by patients to account for repeated measures across sampling time points, with results shown in Extended Data Table Statistical analyses of NMDS of immune cell population abundances and inflammatory mediators between patients with and without progressive Enterobacteriaceae enrichment were performed using multivariable PERMANOVA in Vegan v.2.6. To account for repeated measures for multi-time-point comparisons, permutations were blocked by patients. To determine the associations between demographic and clinical covariables and control for their effects on immune cell composition and inflammatory mediator landscapes, both categorical variables  and continuous variables  were included in the models and are shown in Supplementary Tables To investigate associations between the outcome of progressive Enterobacteriaceae enrichment in ICU patients and demographic/clinical covariables known to influence microbiota and/or immune composition, we performed both univariable and multivariable analyses, with full model results shown in Extended Data Table The 30-d nosocomial infection-free survival data were visualized using a Kaplan–Meier curve and analyzed using a log-rank (Mantel–Cox) test. Maximally selected rank statistics were utilized to define cutoff values for microbiota variables  to identify maximal separation of patients based on 30-d nosocomial infection-free survival using the surv_cutpoint function in the survminer R package v.0.4.9. To avoid bias from exclusion of zero values, relative abundance values of zero were replaced with a value of 0.00001 . Odds ratios were calculated and analyzed using a two-sided Fisher’s exact test.P values was performed to account for multiple comparisons.All statistical analyses were performed in R or GraphPad Prism v.9.3.1. Where applicable, FDR adjustment of An analysis of all key data with study participants stratified by biological sex is provided in Supplementary Fig. Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41591-023-02243-5.Supplementary InformationSupplementary Table 1 and Supplementary Figs. 1–7.Reporting SummarySupplementary TableSupplementary Tables 2–17.Supplementary Table 18STORMS checklist.


There is mounting evidence that the development of Alzheimer’s disease (AD) interacts extensively with immunological processes in the brain and extends beyond the neuronal compartment. Accumulation of misfolded proteins can activate an innate immune response that releases inflammatory mediators and increases the severity and course of the disease. It is widely known that type-I interferon-driven neuroinflammation in the central nervous system (CNS) accelerates the development of numerous acute and chronic CNS diseases. It is becoming better understood how the cyclic GMP–AMP synthase (cGAS) and its adaptor protein Stimulator of Interferon Genes (STING) triggers type-I IFN-mediated neuroinflammation. We discuss the principal elements of the cGAS–STING signaling pathway and the mechanisms underlying the association between cGAS–STING activity and various AD pathologies. The current understanding of beneficial and harmful cGAS–STING activity in AD and the current treatment pathways being explored will be discussed in this review. The cGAS–STING regulation offers a novel therapeutic opportunity to modulate inflammation in the CNS because it is an upstream regulator of type-I IFNs A key component of the immune defense system in many species is the ability to recognize foreign DNA. The foreign  or self-DNA are sensed by several nucleic acid sensors, which elicit an immune response by activating several signaling cascades. Some of the well-studied DNA sensors include Toll-like receptor 9 (TLR9) and cyclic guanosine monophosphate (GMP)–adenosine monophosphate (AMP) synthase (cGAS), which is absent in melanoma 2 (AIM2) . NormallIn comparison to the classical pattern-recognition receptors (PRRs) associated with pathogen-specific patterns, the cGAS system is ineffective in differentiating self-DNA from foreign DNA ,5. HenceThe cyclic GMP–ATP Synthase (cGAS) is a ~522 amino acid-long protein that contains a basic domain of approximately 160 unstructured base pairs at the N terminal and a globular ~360 amino acids at the C-terminal domain containing a nucleotidyltransferase  core domThe cGAMP is sensed by the endoplasmic reticulum (ER) membrane protein STING, which undergoes dimerization on binding to cGAMP. The activated STING translocates from the ER to the golgi compartment, and this trafficking is facilitated predominantly by coatomer protein complex II (COPII) vesicles . InteresMounting evidence indicates that autophagy plays an important role in regulating cGAS–STING signaling to maintain immune homeostasis. Several lines of evidence indicate that several ATG proteins are involved. Specifically, the binding of cGAS with Beclin-1 (an autophagy protein) promotes the autophagic degradation of both cytosolic DNA and cGAS itself . FurtherSimilar to cGAS, STING induces autophagy. It does so through both canonical autophagy, involving the ULK complex and TBK1, and non-canonical autophagy, involving selective autophagy components (PI3P effector WIPI2 and the ATG5-12-16L1 complex) ,19,20. FFurthermore, the LC-3-interacting regions of STING interact with LC3 to induce ATG5-dependent non-canonical autophagy. The LC3 lipidation induced by STING depends on WIP12 and ATG5 but not on ULK and VPS34–Beclin kinase complexes—the most important for autophagic signaling. Additionally, autophagy can degrade STING and prevent immune damage by involving TBK1 and p62 .Importantly, autophagy induction by STING prevents viral replication/propagation and serves as a major host defense mechanism. Additionally, STING autophagy machinery prevents tumor cell overgrowth by engaging the cells in autophagic cell death . ConversThe other important downstream signaling of cGAS–STING activation includes mediating cellular senescence and cell death. cGAS is an important inducer of the senescence-associated secretory phenotype (SASP) in response to DNA damage, particularly through the production of type 1 IFNs . In an iThe primary mechanisms by which cGAS activation occurs are either due to dysfunctional DNA compartmentalization or its metabolism . The difSecondly, the loss of mitochondrial membrane integrity due to mitochondrial dysfunction also allows mtDNA to leak into the cytosol  has been linked to the activation of cGAS–STING. Under normal conditions, the released extracellular DNA is digested by deoxyribonuclease 1 (DNase1) and its homolog DNase1L3 in serum or degraded by DNase2 in the phagolysosomal compartment when engulfed by macrophages . HoweverCell-intrinsic genomic DNA: Recent studies indicate that cGAS is present in the nucleus; however, several inhibitory mechanisms restrict its catalytic activity on chromatin. For instance, the presence of barrier-to-autointegration factor (BAF) displaces cGAS from genomic DNA , therebyDNA damage and subsequent cell-cycle progression through mitosis lead to the formation of micronuclei, which act as a repository for cGAS . These mOne of the key pathophysiological mechanisms implicated in AD is mitochondrial dysfunction, characterized by mitochondrial damage and dysfunctional mitophagy caused by cellular stress due to the accumulation of toxic protein aggregates such as amyloid-β or hyperphosphorylated tau ,58,59. TTnfa, C1q, Il1a, and C3) and decreased Aβ42 fractions in cortical tissues of 5xFAD mice [A study by Xie et al., demonstrated increased levels of cytosolic mtDNA in a 5xFAD mouse brain compared to aged-matched wild-type mice. Furthermore, increased cGAS–dsDNA interactions were demonstrated in human AD brains and the 5xFAD mouse model of AD. Increased phosphorylation of STING, TBK1, p-65 and IRF3, along with an increased type I IFN response, were noted in the prefrontal cortex of a human with AD and in aged wild-type mice samples. Genetic deletion of the cGAS gene in 5xFAD mice protected against cognitive impairment and ameliorated Aβ pathology and neuroinflammation. Furthermore, cGAS deficiency in microglia inhibited a neurotoxic A1 astrocytic phenotype, thus alleviating oligomeric amyloid-β peptide-induced neurotoxicity. Pharmacological inhibition of the cGAS–STING pathway using H-151, a STING inhibitor, effectively decreased protein phosphorylation , neuroinflammatory genes  is a cell surface receptor on the microglia, which plays a crucial role in the phagocytosis of various substrates such as Aβ, apoptotic neurons and bacteria ,83,84. HTnf and Isg54. Finally, PQPB1 knockout mice injected with extracellular tau monomer showed less cognitive impairment. These results indicated that PQBP1 acted as an intracellular receptor for tau and activated the cGAS–STING pathway, specifically in the microglial cells [There are only limited studies that have evaluated the association between tau pathology and the cGAS–STING pathway. A recent study by Jin et al. showed that the tau protein interacts with Polyglutamine-binding protein 1 (PQBP1) and induces a neuroinflammatory response by activating the cGAS–STING pathway in microglial cells. Microglia-specific depletion of PQBP1 in primary cultures and PQPB1 knockout mice showed that PQPB1 is essential for sensing tau to induce nuclear translocation of NFκB and IRF3 (downstream transcription factors of the cGAS–STING pathway), increased transcription of inflammatory genes such as al cells . The abnal cells , and thial cells . More stOverstimulation of the cGAS–STING pathway and its associated neuroinflammation is thought to be associated with the development or progression of various neurodegenerative diseases, including AD. Hence, therapeutics inhibiting this pathway could attenuate disease progression in AD and other neurodegenerative diseases. The goal of developing agents that will inhibit the cGAS–STING pathway is possibly to protect neurons by alleviating chronic inflammation due to mitochondrial stress and autophagic dysregulation. On the other hand, inhibition of this pathway could increase susceptibility to infection that is secondary to the suppression of an innate immune response. Furthermore, the promotion of cancer can occur by limiting cellular senescence in cells with genomic instability/DNA damage. Although there are currently no cGAS–STING inhibitors currently approved for AD, studies using cell culture models and animal studies indicate that certain compounds are beneficial for attenuating the pathologies of AD .Before evaluating STING-based therapies in neuroinflammatory diseases like AD, several considerations must be addressed. First, there is not yet a consensus on which types of brain cell express cGAS and STING. Several studies have demonstrated conflicting evidence regarding the expression of cGAS and STING in different cell types ,95,96. FSecond, considering the natural history of the disease (AD), the time course of cGAS and STING activation varies at different time points, and the subsequent window for treatment should be thoroughly characterized. In the context of AD, the main aim is to prevent inflammation and neuronal death. However, it is likely that there are critical times when cGAS–STING activation might be beneficial. Hence, a better understanding of the time of activation of cGAS or STING during the disease process will aid in providing therapeutic benefits.Third, variability between species can hamper drug discovery and development because an unexpected species-specific role for STING as a receptor needs consideration. For instance, considerable molecular differences are noted between murine STING and human STING, and some agonists for murine STING are ineffective in activating human STING . A studyThe dysregulation of the innate immune cGAS–STING pathway and the associated neuroinflammation contribute to neurodegenerative diseases such as AD. Recent studies on cGAS–STING signaling in AD have increased our understanding of the role of this pathway in mediating neuroinflammation and neurodegeneration. Since the activation of cGAS–STING is context dependent in AD, the beneficial and detrimental effects need to be carefully evaluated while targeting this pathway. Furthermore, additional in-depth research needs to be performed to determine the temporal pattern of cGAS–STING activation at various time points in the course of AD pathogenesis to target this pathway for therapeutic benefit.