Group analysis of brain magnetic resonance imaging (MRI) metrics frequently employs generalized additive models (GAM) to remove contributions of confounding factors before identifying cohort specific characteristics. For example, the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) used such an approach to identify effects of alcohol misuse on the developing brain. Here, we hypothesized that considering confounding factors before group analysis removes information relevant for distinguishing adolescents with drinking history from those without. To test this hypothesis, we introduce a machine-learning model that identifies cohort-specific, neuromorphometric patterns by simultaneously training a GAM and generic classifier on macrostructural MRI and microstructural diffusion tensor imaging (DTI) metrics and compare it to more traditional group analysis and machine-learning approaches. Using a baseline NCANDA MR dataset (N = 705), the proposed machine learning approach identified a pattern of eight brain regions unique to adolescents who misuse alcohol. Classifying high-drinking adolescents was more accurate with that pattern than using regions identified with alternative approaches. The findings of the joint model approach thus were (1) impartial to confounding factors; (2) relevant to drinking behaviors; and (3) in concurrence with the alcohol literature. A consensus of cross-sectional and longitudinal magnetic resonance imaging (MRI) studies suggests that cortical gray matter volume declines and the cortical mantle thins3, but white matter volume, microstructural organization, and myelination of fiber tracts increase5, during healthy adolescent development. In this developmentally critical second decade of life, young people commonly engage in risky behaviors, including consumption of alcohol. A recent U.S. survey estimates that 66% of 18-year-olds have drunk alcohol and about 25% report getting drunk6. A rising incidence of binge drinking may put developing youth at particularly high risk for deviations from the normal trajectory of brain development7. Longitudinal studies of heavy relative to minimal drinking during adolescence report acceleration of gray matter volume shrinkage, attenuation of white matter growth8, and decreased fiber integrity9. Similar but subtler developmental changes have been detected in youth who drink regularly, if not heavily10. Despite such reports of quantifiable effects of drinking on normal neurodevelopmental trajectories, weak effects may be difficult to extricate using traditional, hypothesis-driven methods11 and may be enhanced by the use of machine-learning approaches to determine group separating characteristics12.After birth, the human brain undergoes profound change that continues throughout adolescence and into young adulthood15. However, a challenge of many neuroimaging studies is statistical power, particularly given the number of potentially confounding factors16. For example, the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA)17, a landmark longitudinal study supported by the National Institute on Alcohol Abuse and Alcoholism and the National Institutes of Health Big Data to Knowledge initiative, has been collecting MRI and neuropsychological data in adolescents and young adults to (1) expand knowledge about normal brain maturation; (2) document changes following initiation of moderate-to-heavy alcohol consumption; and (3) identify imaging markers that predict early-onset alcohol use disorder (AUD). The number of youth with a notable history of alcohol consumption at baseline was small17. To power this investigation adequately, however, the study also recruited youth with minimal alcohol exposure at baseline that had a high risk for transitioning to the AUD phenotype during the course of the 10-year study.In neuroimaging studies, identifying group differences using classification approaches can be straight forward if the groups are of equal sample size and matched with respect to demographic factors such as age, sex, and ethnicity3. Specific to the NCANDA cohort and its second aim, the study also reported smaller and thinner frontal and temporal cortices for the group initiating moderate-to-heavy alcohol consumption relative to the minimally-drinking group. Matching cohorts, however, is not always successful in revealing significant group differences. For example, analyses of diffusion tensor image (DTI) data from demographically-matched subsets of the NCANDA study did not reveal effects of moderate-to-high drinking on DTI metrics 4. This was surprising given evidence that excessive alcohol consumption in adults disrupts white matter microstructure of select fiber systems21.One popular approach for analyzing unbalanced data sets is to include only subsets of the collected sample matched with respect to basic demographic variables. For example (in support of the first aim of the NCANDA study), age-matched samples selected from another large cohort study, the ‘Pediatric Imaging, Neurocognition, and Genetics’ data set confirmed the longitudinal brain developmental patterns identified in the minimal drinking adolescents of the NCANDA cohort27. Regression approaches, such as the ‘ordinary’ generalized additive model (GAM), remove the effects of confounding factors by first modeling the relationship between the dependent variable  and confounding factors  on a subset of the sample 3, then using that model to remove the effect of confounding factors from each dependent variable so that residuals of the raw metric are used in group analyses. However, GAM often suffers from sensitivity to noise, as demonstrated, for example, by the variance in age associated with peak white matter microstructural maturation4. Robust regression claims to address the sensitivity issue by separately modeling the effects of confound and noise in MRI metrics28. While robust regression has often been used in large neuroimaging studies29, the noise model requires a-priori specification, which can reduce the power of the analysis. For example, a cautious threshold for accounting for noise generally results in a robust GAM but the effects of confounding variables are then estimated on a notably reduced sample size. A small sample generally fails to capture comprehensive effects of confounding factors and the resulting GAM is thus likely inaccurate. We hypothesized that typical sequential use of the GAM to isolate the effects of confounding variables on MR metrics would minimize information relevant for distinguishing groups .An alternative approach to analyzing unbalanced data is to include the entire sample, but to remove the effects of confounding variables before performing group analysisi.e., residual scores of imaging metrics) that is most informative for differentiating minimal from regular drinking youth. We refer to this subset of brain measurements as pattern. To identify a pattern, the classifier’s search for informative brain metrics is constrained to subsets of a certain size, enforced by embedding ‘sparsity constraints’ into the classification model12. To help with an initial understanding of the method used herein, Fig. i.e., not residuals) as the two cohorts are not separated accurately  simultaneously determines optimal parameters 1) of a GAM (for removing the influence of confounding factors) and (2) a logistic classifier (for cohort classification). The classifier identifies a subset of variables  against alternative implementations using the baseline NCANDA imaging data set. The data set consists of structural MRI and microstructural DTI metrics collected in 705 adolescents: 671 that are minimal (no-to-low) drinking and 34 that are regular drinkers. The GAM is defined with respect to age and socioeconomic status, because these two variables are not matched across the two cohorts (and are therefore confounding variables). To apply cross-validation, a popular method to measure accuracy of machine learning approaches, the total data set is divided into subsets  in which the cohorts in each subset are matched with respect to demographic factors other than age and socioeconomic status. Each implementation uses one subset for training. The accuracy of patterns identified during training is then evaluated on the second subset to ensure that solutions are not specific to the first subset. This process is repeated with the second subset used for training and first for testing. The test accuracy of classifiers is summarized with accuracy scores, which include measures for testing the resistance of implementations to confounding factors. Furthermore, we compute p-values representing the statistical significance of accuracy scores and patterns identified by each implementation. Here, we are the first to report progress on the third aim of NCANDA  by presenting patterns of neuromaturation that are impartial to confounding effects (such as age) and correctly classify adolescents who drink regularly.To complete hypothesis testing, we cross-validate our joint algorithm approach . The first data set contained participants infected with the Human Immunodeficiency Virus (HIV) and effected by HIV-Associated Neurocognitive Disorder as well as demographically matched controls. The second data set, which was matched to the first one, contained individuals diagnosed with Mild Cognitive Impairment and a control cohort. In the conference submission, the GAM was used to remove the effect of acquisition differences between the two data sets and the classifier to identify differences between HIV-Associated Neurocognitive Disorder and Mild Cognitive Impairment. The experiment revealed that our joint approach is more accurate than sequential methods in identifying group differences based on data not ideally constructed for classification. Here, we confirm this finding on the NCANDA data set.In a conference paperi.e., age and socioeconomic status) and more accurate than alternative implementations, listed here:No-GAM-Class: performed sparsity constrained classification on raw image scores ; the benchmark for analysis without removing the effects of confounding factors.Seq-GAM-Class: popular sequential approach first parameterized an ordinary GAM and then performed sparsity-constrained classification.Seq-GAMRob-Class: sequentially executed robust regression and sparse classification; the alternative to Seq-GAM-Class that accounted for image noise.JoiSTR-GAM-Class: the proposed joint model confined to the structural (STR) MRI metrics; the only other implementation indifferent to the confounding factors.JoiDTI-GAM-Class: the proposed joint model confined to microstructural DTI metrics; as with JoiSTR-GAM-Class, this method provided a benchmark for single-image modality analysis.JoiOPT-GAM-Class: a simplified version of our proposed joint model suitable for optimizing group separation, but not indifferent to the effects of confounding factors.Our experiments on the NCANDA data set revealed that our joint approach Joi-GAM-Class (based on MRI and DTI metrics) was indifferent to confounding factors  or the DeLong’s test32i.e., (the probability of the output of one implementation to be generated by another implementation). This significant threshold was considered conservative as the number of implementations compared herein was small4. Unless otherwise stated, significant findings refer to the outcome of the Fisher’s exact test.We measured the accuracy of each implementation using two-fold cross-validation. After training each implementation on the training data to classify minimal and regular drinkers, we measured their accuracies on the testing data by reporting sensitivity, specificity, Area Under the receiver operating characteristic Curve (AUC), and ‘normalized-accuracy’. ‘Normalized-accuracy’ computed the accuracy of an implementation in correctly labeling samples while accounting for differences in sample size between the two cohorts. To ensure the indifference of an implementation to the effects of confounding variables, we also reported ‘matched-accuracy’. To compute ‘matched-accuracy’, we first defined a subset of the test data in which the cohorts were matched with respect to all known demographic scores including age, socioeconomic status, and cohort size and then re-computed the normalized-accuracy with respect to this subset. We set a threshold for labeling an accuracy score as significant at i.e., above the age of 15.4; N = 335) and younger cohort . The two cohorts were matched with respect to all demographic factors  except age. Implementations with p > 0.01 passed the age-test as the effect of age was non-existent or magnitudes weaker than the effects of regular drinking. Thus, desirable implementations that also passed the age-test were considered informative with respect to distinguishing regular drinkers from minimal alcohol exposed adolescents. Critically, all implementations passed the socioeconomic status test, i.e., a replication of the age-test applied to this variable. We thus omit discussion of this test.Desirable implementations were those with significant normalized-accuracy and significant matched-accuracy. For each implementation, indifference to the effects of the confounding variable ‘age’ was calculated using the two-tailed Fisher’s exact test to measure the ability of the relevant classifier to cleanly separate minimal (no-to-low) alcohol-exposed adolescents into an older (Rob-Class) had significant ‘normalized-accuracy’ scores but non-significant ‘matched-accuracy’ scores. Compared to those implementations, the joint methods reported higher AUC, normalized-accuracy, and matched accuracy scores. Although specificity was higher than sensitivity for all implementations, the difference between these was substantially smaller for the joint approaches. The smallest difference was observed for JoiSTR-GAM-Class . JoiSTR-GAM-Class was also informative as it passed the age-test and had significant normalized-accuracy and matched-accuracy scores. Among the joint approaches, the accuracy score was diminished when only DTI metrics were used : this implementation also failed the age-test and did not have a significant matched-accuracy score. JoiOpt-GAM-Class failed the age-test and had the largest difference between normalized-accuracy and matched-accuracy scores (dropped by 12.6%), but it achieved the highest accuracy score (80.8%). Joi-GAM-Class passed the age-test, had a high accuracy score, and the smallest difference between normalized-accuracy (75.9%) and matched-accuracy (77.1%) scores. These accuracy scores were higher than those of the only other informative implementation . Joi-GAM-Class was also the only implementation that was significantly better than No-GAM-Class and Seq-GAM-Class. On a trend level (p < 0.0003), it was also better than Seq-GAMRob-Class.Table i.e., recording the identified pattern and corresponding accuracy for different parameter settings of the implementation. A pattern consists of a small number of MR-derived metrics that the implementation deemed informative for distinguishing the two cohorts. Figure STR-GAM-Class (53 patterns), No-GAM-Class (72 patterns), Seq-GAM-Class (72 patterns), JoiOPT-GAM-Class (72 patterns), JoiDTI-GAM-Class (225 patterns), Joi-GAM-Class (381 patterns), and Seq-GAMRob-Class (853 patterns). Interestingly, Joi-GAM-Class recorded four informative (and dominant) patterns each appearing in at least 50% of the training runs.As part of cross-validating an implementation, training consisted of parameter exploration, i.e., volumes of centrum semiovale and central corpus callosum). The third pattern (54.7%) added DTI metrics fractional anisotropy of anterior corona radiata and posterior thalamic radiation) and the fourth (52.9%) included also axial diffusivity of fornix and volume of cingulate gyrus . Joi-GAM-Class  selected patterns that included structural MRI volumes of the lateral ventricles, centrum semiovale, corpus callosum, and cingulate gyrus and microstructural DTI measures of the fornix, corona radiata, and thalamic radiations. The integrity of each of these regions has been reported to be affected by alcohol misuse in studies using more traditional, hypothesis-driven, morphometric group analysis. When this type of analysis was applied to those eight MRI metrics, only four of them showed significant group differences on the NCANDA data set. We thus conclude that the outcome of machine learning models, such as the one proposed here, requires analyzing MRI metrics as a whole to gain knowledge about the effect of alcohol on individuals.JoiStrong agreement existed among the regions included in the four informative patterns identified by Joi-GAM-Class. While inter-dependencies between the repeated training runs with varying parameter settings of Joi-GAM-Class could account for this finding, this explanation fails to explain the consistency between the informative patterns identified by Joi-GAM-Class and alternative implementations. A more likely explanation for this consistency is the significant impact of regular drinking on the regions identified by Joi-GAM-Class.in vivo neuroimaging26 and postmortem studies33 reporting smaller centrum semiovale volume in heavy alcohol drinking relative to healthy control adults. Smaller than normal white matter volumes could indicate a disruption in adolescent brain development given that white matter continues to grow throughout early adulthood36.The brain regions identified by Joi-GAM-Class are relevant with respect to the Alcohol Use Disorder (AUD) literature. For example, the centrum semiovale, the most frequently appearing region across all patterns, was modestly smaller in the regular than in the minimal drinking group. This finding is consistent with 38. The corpus callosum integrates information and mediates complex behaviors39 and is larger and thicker in adolescents with higher intelligence41 and better problem solving abilities42. The cingulate cortex has been associated with selective attention43, conflict monitoring and decision making in controls44 and alcoholics46. The lateral ventricles are generally enlarged in heavy alcohol consuming adults and serve as a sensitive marker of alcoholic-level drinking48.A number of studies report that the corpus callosum is sensitive to alcohol use disorder15, the current study reports that axial diffusivity of the fornix, fractional anisotropy of the anterior corona radiata and posterior thalamic radiation were high in the regular drinking group. These findings were also reported previously4, albeit at a statistically insignificant level. A recent longitudinal study of detoxified alcohol-dependent male adolescents found evidence of low white matter integrity in the body of the anterior corona radiata15. Microstructural compromise of the fornix, a major fiber bundle connecting limbic structures, has been reported in adult alcoholics49.Joi-GAM-Class also identified regions with altered DTI metrics in the regular drinkers relative to the minimal drinking adolescents. Although low fractional anisotropy and high mean radial diffusivity are often reported in heavy drinking youthi.e., its frequency of appearance in the training runs as specified in Table i.e., only half the scores were significantly different between groups. The importance of a single-region metric was also unrelated to its accuracy in classification, i.e., all single-regional metrics reported low matched-accuracies. These observations were further supported by repeating two-fold cross-validation of the sequential procedure with the classifier (without sparsity constraints) being trained on the 29 regional measurements. These 29 MRI metrics were identified by applying a two-tailed t-test to residual scores of the training dataset and retaining those with i.e., the significance threshold that led to the highest classification accuracy). The resulting normalized-accuracy of the classifier based on these 29 metrics at 67.3% was significantly lower than that of Joi-GAM-Class. Thus, the type of machine learning applied here analyzed all potentially informative metrics as a whole12 to determine those regions impacted by regular alcohol use on the developing adolescent brain. In support of this statement, Joi-GAM-Class received lower accuracy scores than those listed in Table i.e., classified with significant accuracy) individuals with regular drinking habits at baseline.We complete the review of our morphological findings by noting that the importance of single-region metrics , the residual effects of confounding factors can significantly effect the final classification, as observed here, since the sequential approaches (Seq-GAM-Class and Seq-GAMRob-Class) failed the age-test. That the joint implementations JoiDTI-GAM-Class and JoiOPT-GAM-Class also failed the age test is a caution to check the output of regression-based approaches for the effects of confounding factors. The series of stringent statistical tests performed post hoc in this study identified those outputs that were not selected because of contributions of confounding factors. Based on those tests, the only informative patterns were determined by the joint implementations JoiSTR-GAM-Class and Joi-GAM-Class.When parameterizing a GAM independently from classification  than when based on DTI metrics . The higher accuracy scores of Joi-GAM-Class over the single-modality implementations  further highlight the importance of analyzing multiple modalities together.When confining the joint analysis to just one modality, classification achieved higher accuracy when based on structural metrics  met the criteria for minimal (no-to-low) alcohol consumption17 and comprised the control group. The remaining 132 adolescents reported initiating moderate-to-heavy alcohol consumption: female participants consumed four or more drinks  and male participants consumed five or more drinks  on at least one occasion in their lifetime. Of these, 34 subjects met criteria for regular drinking . The total data set on 705 youth  used in this study included demographic information and MRI scans acquired across the five NCANDA collection sites17, two of which used Siemens 3 T Tim Trio scanners (Siemens) and three of which used General Electric 3 T Discovery MR750 scanners (GE). Each participant was described by age, sex, self-reported ethnicity, socio-economic status (based on the highest education achieved by either parent)50, MRI scanner type (GE or Siemens), and supratentorial volume (determined from MR images) (see Table At baselinep > 0.1) on ethnicity , and sex, and MRI scanner type . Age, socio-economic status, and supratentorial volume  were compared using unpaired, two-tailed t-tests53. The two cohorts matched with respect to supratentorial volume but not age and socio-economic status. Most of the regular drinkers were older (18 or older) and had higher socio-economic status than the control group.The two cohorts were matched  by the software platform Scalable Informatics for Biomedical Imaging Studies 54. The Section ‘Data Pre-processing’ of the supplement summarizes the pre-processing steps performed on these data as described by4.Brain imaging metrics used for each individual included 32 MRI derived structural volume scores extracted from the T1- and T2-weighted MRIs12. This method is trained to accurately classify samples by minimizing an energy function that encodes the underlying classification task as finding informative patterns (of MRI metrics) of a certain size. No-GAM-Class directly trained the classifier on the 144 raw imaging metrics of each subject. Training of the sequential approaches Seq-GAM-Class and Seq-GAMRob-Class consisted of first parameterizing a GAM for regressing out the effects of confounding factors  before optimizing the classifier on the residual scores. The GAM used a linear model for capturing the relationship between the image metrics and socio-economic status and a quadratic model for capturing the relationship between the image metrics and age4. Seq-GAM-Class used the least square estimation and Seq-GAMRob-Class used the robust regression 28 to determine the optimal setting of GAM on the minimal drinkers of the training data set. The joint approaches  removed the effects of confounding factors while concurrently optimizing classification accuracy by embedding the GAM model into the energy function of the classifier. While JoiOPT-GAM-Class reported the result with respect to minimizing the energy function, JoiSTR-GAM-Class, JoiDTI-GAM-Class, and Joi-GAM-Class went one step further and extended the energy function so that it accounted for accuracy of the GAM in removing the effects of the confounding factors. Joi-GAM-Class (as well as JoiOPT-GAM-Class) considered all 144 imaging metrics, while JoiSTR-GAM-Class was confined to the 32 macro-structural MRI metrics and JoiDTI-GAM-Class to the 112 micro-structural DTI metrics. The accuracy of each implementation was measured via 2-fold cross-validation described in further detail in the supplemental section on ‘Cross-Validation’.All implementations used here were based on the sparse-logistic classification modelFor the technically inclined reader, the following subsections describe in detail the optimization algorithms used for training the sequential and joint implementations.i.e., minimal drinking cohort) and (2) identify the pattern, i.e., the subset of residual imaging scores most informative for group separation. The pattern was identified by computing the ‘weights’ of a sparse, logistic regression classifier12 that resulted in the highest normalized-accuracy based on the training data.The training of a sequential approach consisted of two steps: (1) determine the optimal setting of the GAM with respect to the ‘control group’  consisted of two cohorts totaling N = 352 subjects with s’ was described by the factor vector ND = 3 subject specific demographic values) and up to is. Training the GAM with respect to the data of the non-drinking cohort was then equivalent to fitting a matrix Φ so that the factor vector of each control subject was a predictor of the corresponding image scores i.e., l2-norm, the optimal fitted matrix D is the set of factor vectors and I is the corresponding set of image scores across all samples.Assuming that the image scores were Gaussian distributed, then determining the optimal N subjects were determined viaInterpreting ω’ encoded the importance of each residual score of rs in distinguishing the two cohorts, and l0-‘norm’ NK < NF of non-zero elements, i.e., the sparsity constraintTraining of the sparse, logistic regression classifier consisted of minimizing a log probability with respect to the weights selecting the subset of informative residual image scores best separating both cohorts. In order to define the log probability, the association of each subjects ‘In other words, parameterizing the sparse, logistic classifier summarizes to determining the optimal parameters 12. The image scores associated with non-zero entries in We determined its solution via penalty decompositionAlternative to the sequential approach, the training of the joint approach consisted of simultaneously determining the optimal values for the variables g to Eq.  and \docmentclass2pt{minimThe previous section parameterized the GAM with respect to the minimal drinkers (controls) so that any significant deviation in the image scores of the second cohort could be directly related to the existing clinical literature. To comply with that model, we confined the second sum of Eq.  to the cL(·) was defined according to Eq. (G(·) according to Eq.  by minimizing Eq.  and was regularized by L  to account for the noise in the image measurements is.Since the penalty function was smooth and convex, Eq.  was solvψ′ by keeping Φ′ and ω′ fixed so that Eq. .In compliance with NIH policy, the data release NCANDA DATA 00010 V5, NCANDA DATA 00011, and NCANDA DATA 00012 V2 that supports the finding of this study is released to the public according to the NCANDA Data Distribution agreement  is available via All procedures performed in this study were in accordance with the Declaration of Helsinki. All participants underwent informed consent processes at the visit with a research associate trained in human subject research protocols. Adult participants or the parents of minor participants provided written informed consent before participation in the study. Minor participants provided assent before participation. The Institutional Review Boards of each NCANDA site approved this study, and each site followed this procedure to obtain voluntary informed consent or assent, depending on the age of the participant.Supplemental Material and Methods


The sterol metabolic network is emerging center stage in inflammation and immunity. Historically, observational clinical studies show that hypocholesterolemia is a common side effect of interferon (IFN) treatment. More recently, comprehensive systems-wide investigations of the macrophage IFN response reveal a direct molecular link between cholesterol metabolism and infection. Upon infection, flux through the sterol metabolic network is acutely moderated by the IFN response at multiple regulatory levels. The precise mechanisms by which IFN regulates the mevalonate-sterol pathway—the spine of the network—are beginning to be unraveled. In this review, we discuss our current understanding of the multifactorial mechanisms by which IFN regulates the sterol pathway. We also consider bidirectional communications resulting in sterol metabolism regulation of immunity. Finally, we deliberate on how this fundamental interaction functions as an integral element of host protective responses to infection and harmful inflammation. Our knowledge of the molecular and functional mechanisms for this coupling has grown dramatically in recent years and it is now accepted that a remodeling of glycololytic, lipid biosynthetic, and associated homeostatic molecular “circuitry” is an integral component of innate and adaptive immune responses –3. In paImmediately after infection, the ligation of cellular pattern-recognition receptors by, for example, dsRNA leads to an induction of NFkB, ATF2/c-jun, and interferon regulatory factor 3 (IRF3), a rapid upregulation of IFNα/β gene expression and secretion of type I IFNs by cells. The autocrine/paracrine binding of IFNα/β or IFN-γ (from activated NK and T cells) to type I or type II IFN receptors, respectively, leads to the activation of JAK/STAT signaling pathways and rapid alterations in the abundance of hundreds of transcripts in the cell. These IFN-stimulated changes reflect an acute re-programing of the cell to resist infection and limit cellular damage. Figure NOS2, OAS2, MX2, and IFITM3 have intensively investigated antiviral or antibacterial effects, IFN downregulated transcripts have received relatively little attention (While interferon (IFN)-stimulated genes (ISG) such as ttention –11. Notattention . This woHere, we first discuss early clinical work showing iatrogenic effects of IFN on sterol metabolism. Next, with an emphasis on molecular oxysterol and miRNA-mediated mechanisms, we consider what is known about how IFN regulates sterol metabolism. Overall, we advance the notion that the mevalonate–sterol pathway is an effector arm of immunity and highlight how this response helps the host limit excessive inflammation and resist infection.via the induction of IFN  or TLR4 ligation results in an IFN-independent inhibition of cholesterol efflux from the cell, little was known at this point about how IFN signaling directly influences cholesterol homeostasis and the physiological purpose this could serve  responsible for a wide array of functions including disruption of the internalization vacuole , Ebola, the Herpesvirus family , Influenza A virus (IAV), rculosis –57. Imporculosis ]. In conrculosis . The reprculosis –62. In t vacuole . Furthery source , 63.It is perhaps unsurprising, given the essential role sterol metabolism plays in their replication, that examples are appearing of pathogens subverting or co-opting the regulation of this system for their own benefit. In 2007, Mackenzie et al. showed that a West Nile virus (WNV) infection of Vero cells induced an upregulation of cholesterol biosynthesis and redistribution of cholesterol resulting in defective IFN-stimulated JAK/STAT signaling. This result was attributed to a disrupted recruitment and activation of the type 1 IFN receptor and IFN signaling proteins and emphasizes the tight integration of IFN and cholesterol regulation in the cell .In summary, evidence increasingly reveals an intimate molecular coupling between IFN signaling and the sterol metabolic network. This underscores the importance of immune-mediated regulation of sterol metabolism as an integral component of the host response to infection.A prototypic outcome of IFN signaling in the cell is the transcriptional activation or suppression of hundreds of genes. Over the past 5 years, significant progress in characterizing which of these genes contribute to regulation of cholesterol in the cell has been made. In this regard, oxysterol and miRNA-mediated mechanisms have risen to prominence and, in the following sections, we will review what is known about the functions of IFN-elicited CH25H/25-HC and miRNA-mediated sterol regulation. We will then conclude by considering mechanisms of cholesterol regulation by the “conventional” ISG Viperin and the IFITM protein family.While cholesterol is a critical component of cell membranes and a precursor of bile acids and steroid hormones, at high concentrations, it may be toxic to the cell. Intracellular cholesterol homeostasis is, therefore, stringently controlled by tightly coupled regulatory mechanisms including influx and efflux, esterification, and storage and biosynthesis . Oxysterin vivo , post-entry via regulation of the sterol biosynthesis pathway. Data presented by Blanc et al. supported an important role for the prenylation side-branch of the sterol biosynthesis pathway, rather than cholesterol, in mediating antiviral effects against CMV (via an inhibition of pathogen [vesicular stomatitis virus (VSV) and (HIV)] entry to the cell. The distinct modes of 25-HC action described likely reflect differences between the cell/virus systems examined. Liu et al. further showed that CH25H−/− mice are more susceptible to MHV-68 infection and the therapeutic administration of 25-HC to humanized mice suppressed HIV-induced T cell depletion  (Interest in the regulation and functions of 25-HC (and its derivatives) has increased dramatically in recent years [reviewed in Ref. ]. In 201elopment . In 2013inst CMV . At the inst CMV , using ainst CMV . Contrarepletion . Togetheepletion . Buildinepletion . Work haepletion –92. Receepletion  showed tepletion  demonstrepletion , 94. In Mycobacterium vanbaalenii  or geranylgeranyl (C20) to conserved amino acid motifs and is key to protein–membrane interactions/intracellular localization of, for example, the Rab proteins. The Rab GTPase superfamily has more than 20 members playing essential roles in vesicle trafficking and protein localization in the cell. Prenylation is the key to this function as it allows attachment of the protein to the lipid bilayer. An ability to inhibit the prenylation and, therefore, the function of Rab GTPases may allow 25-HC to influence a wide range of pathogens that depend on or utilize this family of proteins. For example, Rab11 is key to the recycling endosome pathway in cells and plays a critical role in the assembly of multiple negative strand RNA viruses such as respiratory syncitial virus, Influenza A, and Sendai virus  RNA encoded in introns, exons, and intergenic regions of the mammalian genome and are typically co-expressed with a protein-coding or non-coding primary transcript. miRNA function to regulate gene expression  in Ref. ]. NotablEVL) in the mouse or Ena-Vasp-Like (EVL) gene in the human and co-transcribed with this transcript. Transcription of miR-342 can be induced by all-trans retinoic acid or IFN and suppressed by CpG island methylation upstream of EVL. Processing of the primary transcript results in the production of a pre-miRNA hairpin encoding two functional miRNA—miR-342-3p and miR-342-5p  and can reduce miR-33 abundance in the cell . In this role, miR-342-5p complements and reinforces the antiviral functions of the rapidly induced oxysterol 25-HC on sterol biosynthesis.A summary of the molecular circuitry underlying the regulation of sterol metabolism by IFN is presented in Figure s by IFN . In contin vitro data show that an inhibition of endogenous miR-342-5p can reduce the antiviral effects of exogenous IFN by 40–50%, the relative importance of this miRNA—and also 25-HC—in vivo are not known. A critical next step, therefore, will be the production of single miR-342-5p and combined miR-342-5p/CH25H KO murine models in which the individual and combined functions of the miRNA and oxysterol can be tested in the context of infection.While in vivo testing of therapeutic miR-122 inhibitors that show promise for the treatment of chronic HCV infection . Notably, work to-date supports cell- or tissue-specific expression of IFN-regulated miRNAs. In this regard, a great deal is still unknown about where and when IFN-regulated miRNA are expressed via the sterol metabolic network.While this review has focused on IFN-elicited oxysterol and miRNA-related mechanisms, several “conventional” ISG, integral to the cellular response to infection, also elicit their effects RSAD2) has demonstrated that this protein can inhibit both RNA and DNA viruses (in vitro data show that Viperin inhibits Influenza A budding by disrupting cell membrane lipid raft integrity and increasing membrane fluidity. A key feature of this mechanism is the binding of Viperin to the sterol pathway enzyme farnesyl diphosphate synthase (FDPS)—an enzyme integral to sterol biosynthesis and the processes of farnesylation and geranylgeranylation  proteins were first described some 20 years ago, their antiviral properties remained unknown until 2009 when Brass et al. demonstrated a functional role in cellular resistance to Influenza A, WNV, and Dengue virus . Since 2e et al.  found the et al. . Notablye et al. . Interese et al. . In agrevia the regulation of sterol biosynthesis through SREBP2 and HMGCR in macrophages. A key question arising from work to-date is: how does the sterol metabolic network influence immunity? Studies now show that the molecular coupling between IFN and sterol metabolism is bidirectional. In this regard, the recent work of York et al. (Three decades after Cantell and colleagues speculated that IFN regulates sterol metabolism, the first evidence for a molecular coupling of IFN to the sterol metabolic network was provided by a systems biology investigation of macrophage responses to infection , 13. Givk et al.  is fascik et al. . Similark et al. , 168. InWork to-date emphasizes the complexity of the molecular circuitry governing the regulation of sterol metabolism by IFN and vice versa. Given IFN directly or indirectly regulates the expression of several hundred genes and the inherent complexity of the sterol metabolic network; it is likely that new bidirectional regulatory mechanisms will continue to appear. For example, we have shown that alongside the posttranscriptional and posttranslational effects of miR-342-5p and 25-HC, IFN can also repress SREBF2 transcription. The mechanism for this is unclear, however, epigenetic modifications may play a critical role and this will be important area to pursue in the future.in vitro, in vivo utility in the context of infectious diseases remains inconclusive. The emergence of new pathogens and threat of antibiotic resistance means it is imperative that we develop new methods for treating infectious diseases. While studies have explored oxysterol and miRNA inhibitor regulation of sterol metabolism in a preclinical and clinical context, legitimate concerns have been raised about the pharmacokinetics and potential side-effects of both. For example, the miR-122 inhibitor Miraversin can be effectively delivered in vivo and substantially reduces HCV replication in a Chimpanzee model. Importantly, however, Miraversin administration is typically accompanied by an increase in circulating cholesterol leading to concerns that the cardiovascular health of recipients may be affected. Further, while miR-342-5p regulates sterol biosynthesis and, in doing so, can suppress viral replication it also targets AKT1 and, as a result, can promote inflammation (Given the many recent advances in our understanding of the role sterol metabolism plays in immunity to infection, how can we translate our new knowledge to clinical applications? While changes in systemic cholesterol levels may be of diagnostic value, the therapeutic targeting of host metabolic pathways for anti-infective treatment represents the most exciting application of our knowledge to-date. While statins are a widely utilized, clinically approved, therapy for regulating sterol metabolism and can inhibit a range of pathogens ammation . An impoammation .In conclusion, the sterol metabolic network has now moved center-stage in the context of IFN responses to infection and is increasingly recognized as a fundamentally important immune-metabolomic system holding great promise in the next decades as target for diagnostic and therapeutic intervention.KAR wrote first draft of manuscript. KAR and PG edited and revised final manuscript.The authors declare that this document was written in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MOF particles were characterized by powder X-ray diffraction and fluorescence spectroscopy, showing high crystallinity and intense photoluminescence. Scanning electron microscope images revealed that MOF crystals were mainly in the form of microneedles with a high surface-to-volume ratio, which together with the high porosity of the material enhances its interaction with gas molecules. MOF crystals were soft-imprinted into cellulose acetate (CA) films on quartz at different pressures. Atomic force microscope images of soft-imprinted films showed that MOF crystals were partially embedded into the CA. With this procedure, mechanically stable films were created, with crystals protruding from the CA surface and therefore available for incoming gas molecules. The sensing properties of the films were assessed by exposing them to saturated atmospheres of 2,4-dinitrotoluene, which resulted in a substantial quenching of the fluorescence after few seconds. The soft-imprinted MOF films on CA/quartz exhibit good sensing capabilities for the detection of nitroaromatics, which was attributed to the MOF sensitivity and to the novel and more efficient film processing method based on soft-imprinting.A novel technique for the creation of metal-organic framework (MOF) films based on soft-imprinting and their use as gas sensors was developed. The microporous MOF material [Zn Metal-organic frameworks (MOFs) are crystalline organic-inorganic porous materials comprised of metal atoms or clusters coordinated by organic ligands to form extended coordination networks. The stability, high porosity, and easy à la carte synthesis of these materials are some of their key properties, warranting extensive use in many technological areas . Gas stoOwing to their optical properties and internal channels that provide them with an elevated porosity, microporous luminescent MOFs represent an ideal choice for the fabrication of gas-sensing devices based on fluorescence ,10. ExplIn the search for solid-state sensors, researchers have deposited MOFs onto solid substrates in different ways over the last few years. MOFs cannot be easily dissolved and recrystallized without losing their particular structure, hence some authors report the suspension of the material for its later deposition as dip-coated  or spin-2(bpdc)2(bpee)] (2(bpdc)2(bpee)] for gas sensing by liberating its internal channels from solvent molecules following an improved method. Furthermore, we compare the soft-imprinting technique with the frequently used strategy based on the application of adhesive tape, aiming to improve the existing deposition methods, with a focus on the creation of MOF-based sensors for the detection of nitroaromatic explosives.Here, we use the soft-imprinting technique for the deposition of a microporous MOF onto thin films. Cellulose acetate (CA) films with controlled thickness previously prepared by spin-coating on quartz are used as substrates. This technique allows the replication of nanometric features in thin films , into wh2bpdc and bpee, with the Zn(II) metal ion. As can be seen in the powder X-ray diffraction (PXRD) analysis  and CH2Cl2 (four days) for solvent exchange, followed by a drying period under vacuum at room temperature. Following this protocol, the PXRD analysis revealed that the intended MOF was obtained  with an applied pressure of 2, 4, and 6 bar resulted in MOF particles partially immersed into the CA film, as can be seen in atomic force microscope (AFM) images . In geneTo analyze whether the pressures applied during the soft-imprinting process affected the crystallinity of the MOF, we compared the PXRD patterns of MOF activated (desolvated) at 493 K under vacuum for 5 h with its corresponding films at 2, 4, and 6 bar. In MOF powder was first deposited on glass substrates using either double-sided tape or the adhesive residue left after peeling off the tape. Similar procedures for the creation of MOF films can be found in the literature . We discAs an alternative, soft-imprinted MOF films on CA-coated quartz were used for the detection of DNT. Films prepared at 4 bar were chosen for this purpose. Photoluminescence spectra of activated MOF powder deposited by soft-imprinting showed an intense emission band at 460 nm when excited at 280 nm (2(bpdc)2(bpee)] ] . This qu the MOF . We quane to DNT , inset. e to DNT , which s2(bpdc)2(bpee)] for the detection of nitroaromatics. Besides, the soft-imprinting technique emerges as an ideal tool for the controlled and reproducible processing of MOFs or similar materials onto solid substrates. In our view, these promising results pave the way for an improved method for the fabrication of MOF sensors. To do this, the soft-imprinting technique still needs to be refined by analyzing all variables in the process and by extending the scope of this study to other MOFs and their exposures to other analytes. Besides, efforts are being made to establish a recovery procedure for the reutilization of the sensors. Meanwhile, the potential applications of our films are oriented towards disposable sensors. Last but not least, the system for the exposure to nitroaromatics needs to be perfected. Due to their low volatility, the typical procedure found in the literature merely consists of the insertion of the sensor into a sealed vial containing a saturated headspace generated by the desired compound  were obtained by using a sample holder for solid samples. AFM images were acquired by means of a JPK NanoWizard II AFM functioning in contact mode, connected to a Nikon Eclipse Ti inverted optical microscope. MikroMasch HQ:XSC11/Al BS cantilevers of 0.2 N/m spring constant and 15 kHz resonant frequency were used in air conditions.All solvents and starting materials for synthesis were commercially available and were used as received. Fourier transform infrared spectra (FTIR) were collected using a Shimadzu IRAffinity-1 from 4000–400 cm2(bpdc)2(bpee)] was synthesized using solvothermal conditions as reported by Lan et al. . MOF powder, resulting in the substrates coated with a fine layer of crystals. Afterwards, the films were gently tapped while facing down in order to remove any crystals that were not adhered well to the surface.Glass slides were cut to size  in 4-hydroxy-4-methyl-2-penthanon , was spin-coated at 3000 rpm for 1 min, with the acceleration set at 12,000 rpm/s, onto a quartz substrate. The thickness of the samples was measured using a mechanical profilometer Bruker Dektak XT with a 2.5-mm radio stylus. The applied strength was the minimum possible (around 1 mg), and the length of analysis was 7500 μm. Scratches were performed on the surface of the sample prior to the scan. All measurements were repeated five times and the results averaged. The CA films used in this work had an average thickness of 200 nm, which was determined by the concentration of CA solution (2(bpdc)2(bpee)] in the form of powder was deposited manually on top of the CA film and an identical CA/quartz system was used to encapsulate the MOFs during the soft-imprint process. The films were imprinted by means of a Compact Nanoimprint Tool . During the hot embossing process, the substrates were heated to 413 K with applied pressures of 2, 4, and 6 bar for 1200 s each. The samples were subsequently allowed to cool down to a temperature of 343 K before the pressure was released and the imprinted substrate removed. As a result, two CA/quartz substrates with a similar quantity of partially embedded MOFs were obtained. Given the reduced thickness of the CA film and micron-size of [Zn2(bpdc)2(bpee)], most of the material was exposed, allowing the sensing experiments.First, a solution of CA  was introduced in another vial hermetically sealed and allowed to evaporate for four days at laboratory temperature until headspace saturation. Such long times were chosen due to the low vapor pressure of DNT. Similar procedures for the creation of saturated explosive atmospheres can be found in the literature . The exp2(bpdc)2(bpee)] MOF was successfully prepared by a solvothermal reaction involving the coordination of the ligands H2bpdc and bpee with the Zn(II) metal ion. The powder XRD analysis revealed that a highly porous structure with internal 1D channels was obtained. FTIR showed that DMF molecules were occluded into the MOF channels. These uncoordinated solvent molecules were removed by solvent exchange and a thermal drying procedure under vacuum that proved to be successful according to FTIR spectra, besides being faster than other reported techniques. SEM images showed that MOF particles were arranged mostly as crystalline microneedles, resulting in a high surface area to volume ratio that enhanced the gas molecules’ interaction with the material. MOF particles were successfully soft-imprinted onto CA films at different pressures, resulting in all cases in partially embedded particles, according to AFM images. This arrangement favored both film stability, due to CA holding MOF particles in place, and gas accessibility to the MOF active sites, owing to the particles partially protruding from the CA film. Fluorescence spectra of soft-imprinted MOF films showed a high emission intensity, indicating the suitability of the MOF to be used as a fluorescent probe. Exposure of the soft-imprinted films to DNT resulted in a substantial quenching of the fluorescence after a few seconds. Overall, it can be concluded that [Zn2(bpdc)2(bpee)] soft-imprinted films on CA exhibit good sensing capabilities as candidates for the detection of nitroaromatics, owing to the MOF sensitivity and to the novel and more efficient film processing method based on soft-imprinting.A microporous [Zn


The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.The online version of this article (10.1007/s10545-018-0140-0) contains supplementary material, which is available to authorized users. The human genome consists of ~25,000 protein coding genes and most proteins carry out cellular functions by interacting with other small molecules or macromolecules. In the network resulting from all these interactions—the human interactome—the cellular components, such as proteins, RNA, or metabolites, serve as nodes and their interactions are commonly depicted as edges Schadt . Inborn Proteins are the drivers of cellular function and the entirety of protein-protein interactions forms a large sub-network within the human interactome  in 15 disease groups (www.ssiem.org/resources/IEC.asp). By using the emerging tools of network medicine, we aimed to investigate whether the entity of IEM is indeed organized as disease modules in the interactome of protein interactions and how these relate to each other. We approached IEM on a proteome scale and established an IEM-specific interactome (IEMi), which comprised 298 of 427 IEM-related proteins. To further improve the clinical relevance of the strategy we investigated relations of the IEMi to other non-IEM diseases, integrated database information such as functional annotations from the gene ontology, phenotypic features, and links to drugs or biologically active compounds.However, network medicine approaches have not been used to systematically study IEM  of the IEMi was 0.32; thus, larger than for the human interactome . The diameter is the largest distance between any two pairs of nodes in a network. The diameter was 22 for the IEMi and 13 for the human interactome. The largest connected component of the IEMi  was larger than the random expectation (52.4), whereas the network-based mean shortest distance of IEM-associated proteins was smaller  than the random expectation (1.81) . Proteins associated with other disease groups either contributed to heterogeneous clusters central to the largest connected component or they were distributed over remote areas of the network.The ree Fig. b. The anlargest connected component and mean shortest distances for all disease groups . Disease groups 6 (disorders of the metabolism of sterols) and 15 (disorders in the metabolism of xenobiotics) were excluded from the analysis due to low numbers of associated proteins or lack of interactions. The IEM disease groups were compared to sets of nodes with the respective numbers of nodes that represented the general characteristics of the human interactome. We computed z-scores for each comparison and observed that 13 disease groups established significant disease modules . The largest disease modules were identified for lysosomal disorders (25 nodes), peroxisomal disorders (22 nodes), and disorders of energy metabolism (18 nodes), where the latter formed a highly interlinked motif. Three medium-sized modules arising from the disease groups 1, 5, and 8 were less interlinked. The remaining modules either were not interlinked (disease group 7) or were very small (disease groups 12–14).To test the disease modules hypothesis for each of the IEM disease groups following the SSIEM classification, we calculated the network-based measures ups Fig. a. The anNext, we aimed to investigate the overlap between the IEM disease groups based on protein interactions Fig. b and foui.e., many nodes belonging to the same disease group are organized within the specific module , established few interactions to other disease groups. In the case of disease groups showing a low degree of self-organization, i.e., only a small share of the nodes is organized within the specific module, the degree of interconnection with other disease groups increased . Taken together, we identified disease groups with a high degree of interaction-mediated overlap to other IEM, whereas others constituted more distinct entities based on their interaction behavior. Disease group 3 (disorders of fatty acid and ketone body metabolism) represented a special case. Here we observed a compact specific disease module where half of the proteins belonging to the disease group contributed to the module. In addition to this rather high degree of self-organization strong interactions were established with disease groups 1 (disorders of amino acid and peptide metabolism), 4 (disorders of energy metabolism), and 11  and weak interactions were established with disease groups 2, 7, 9, and 12.Disease groups with a high degree of self-organization, largest connected component, 2362), the average degree increased to 33 as compared to 4 for the IEMi and the diameter of the eIEMi decreased to 8 , 48.01; p-value, 1.81E-90), cytosol , nucleolus , and cytoplasm . Membrane proteins were underrepresented  , apoptosis signaling pathway , and for the two diseases Parkinson disease  and Huntington disease   encodes for adenine nucleotide translocase type 1 (ANT1) that interacts with mitochondrial peptidyl-prolyl cis-trans isomerase F (PPIF) in the IEM drug target network. Mutations in SLC25A4 are associated with different mitochondrial disorders including phenotypes of mitochondrial myopathy, exercise intolerance, cardiomyopathy, and progressive external ophtalmoplegia  the metabolites pertained by the genetic variation such as disorders of carbohydrate metabolism; (ii) the affected biological pathways such as disorders of glycosylation; (iii) the cellular localization of the IEM-associated proteins such as lysosomal disorders. A considerable number of IEM have been discovered over the past 100 years and particularly the more frequent among them are well studied. However, also due to the recent advances in genomics, many new, partly very rare IEM have been discovered, for which satisfying knowledge about diagnostics, care, and treatment is often not available.Besides their heterogeneity, IEM share that their associated proteins function in metabolism. The latter is a huge functional unit that, as all extended functional pathways, is organized in biological networks. Network-based studies gave rise to the disease module hypothesis . Therefore, the lack of associations between certain IEM-disease groups has to be evaluated carefully considering this incompleteness.We used publicly available protein-protein interaction data between IEM-associated proteins and showed that IEM-associated proteins tend to locate to the same neighborhood within the human interactome. We termed the resulting sub-network IEM interactome (IEMi). The identification of IEM-specific disease modules revealed that the IEMi indeed contained allocations that correspond to the IEM-disease groups. Certain disease modules were highly interlinked to each other, on the other hand, we identified disease modules in the IEMi that consisted of proteins from many different disease groups. Notably, some IEM-associated proteins were not part of the IEMi and based on our network medicine approach did not show any overlap with other IEM. Therefore, the IEMi may offer a platform to systematically explore not only the molecular complexity of a particular disorder, but also the relationships among apparently distinct pathophenotypes of different IEM disease groups. In our study, we used a high-quality and comprehensive human interactome that has been curated recently  explored the overlap between IEM and the human interactome through integration of database information on biological pathways, phenotypic features, and drugs or other active compounds. The graphical workup provided in this study exemplified network medicine approaches for IEM, offered resources in the supplementary material, and may be useful for the investigation of shared molecular functions, disease mechanisms, and the evaluation of existing or novel therapeutic concepts for IEM.The transfer of knowledge within single disease groups, between related IEM disease modules or between IEM and non-IEM diseases will help to elucidate the molecular consequences associated with new disease genes, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to expand the druggable space.http://www.ssiem.org/resources/IEC.asp on March 15, 2015. Mapping of IEM diseases to associated disease genes was done in a web-based manner using the OMIM API (https://www.omim.org/help/api). This resulted in 427 IEM-related disease genes each assigned to the respective SSIEM disease group . For the construction of the IEMi, we extracted only protein interactions that directly linked IEM-related proteins resulting in 708 IEM-to-IEM protein interactions between 298 different proteins. To expand the IEMi we included protein interactions of first order proteins. These are non-IEM proteins that directly interact with IEM-associated disease proteins. The eIEMi contains IEM-to-IEM, protein interactions to first order proteins and protein interactions between first order proteins, in total 39,916 links. The 1994 first order proteins are listed in Suppl. Table We used protein-protein interaction data from a previously curated high-quality interactome  and stand(VRandom) indicate the mean value and standard deviation of the random expectation.Z-scores for the observed values V of largest connected component size and mean shortest distances were calculated as follows:http://pantherdb.org, Panther Annotation version 12 released 10 July, 2017, Reference List Homo sapiens). We excluded metabolic pathways for the heat map presentation of enriched pathways. A full list of results is provided in Suppl. Table Enrichment analysis was performed using the web-interface provided by PANTHER . In more detail, we used the approved Target Drug-UniProt sheet to retrieve only approved drug target information for our proteins. We mapped UniProt IDs to Entrez Gene IDs and found 634 approved drugs including small molecules and biotech drugs targeting 316 non-IEM binding proteins.We downloaded drug target information from the DrugBank database as of July 16, 2017 Suppl. Table 2(XLSX 156 kb)Suppl. Table 3(XLSX 18 kb)Suppl. Table 4(XLSX 52 kb)Suppl. Table 5(XLSX 46 kb)Suppl. Fig. 1(GIF 137 kb)High resolution image (TIFF 1324 kb)


Mycobacterium tuberculosis (Mtb) inside macrophage are largely unknown. In this work, we present an inter-system scale atlas of the gene expression signatures, their interactions and higher order gene functions of macrophage-Mtb environment at the time of infection. We have carried out large-scale meta-analysis of previously published gene expression microarray studies andhave identified a ranked list of differentially expressed genes and their higher order functions in intracellular Mtb as well as the infected macrophage. Comparative analysis of gene expression signatures of intracellular Mtb with the in vitro dormant Mtb at different hypoxic and oxidative stress conditions led to the identification of the large number of Mtb functional groups, namely operons, regulons and pathways that were common and unique to the intracellular environment and dormancy state. Some of the functions that are specific to intracellular Mtb are cholesterol degradation and biosynthesis of immunomodulatory phenolic compounds. The molecular signatures we have identified to be involved in adaptation to different stress conditions in macrophage environment may be critical for designing therapeutic interventions against tuberculosis. And, our approach may be broadly applicable for investigating other host-pathogen interactions.Molecular signatures and their interactions behind the successful establishment of infection of  Mycobacterium tuberculosis (Mtb) is the causative factor for tuberculosis (TB). Besides HIV, Mtb is the leading cause of death worldwide3. After successful infection into its host, Mtb survives as an intracellular pathogen, localized inside the macrophages and dendritic cells5. The phenomenon of infection of a healthy macrophage by Mtb can be divided into three inter-related stages such as triggering inflammatory response from macrophage upon infection; activation of cell mediated immunity and followed by formation of granulomas, and finally the reactivation of Mtb due to various environmental and genetic factors6. Mtb is known to survive for many years under the adverse intracellular environment of macrophages by down regulating the metabolic pathways and switching to non-replicative state8. It was observed that to survive in the adverse conditions, Mtb not only alters its gene expression profiles but also it alters the gene expression profiles of the host9.Mtb as well as macrophages during the stable infection19. In addition to these studies, there are also few gene expression microarray studies conducted to identify adaptive mechanisms of Mtb to adverse conditions in macrophages by using in-vitro models such as nutrient starvation, hypoxia, and oxidative stress21. Furthermore, Meta-analysis of gene expression microarray data from independent and related datasets was also performed in several studies by which the random errors created in experiments were nullified and also the right estimate of differential expression was obtained23. Moreover, by performing the meta-analysis, it was also found that there is an increase in the reliability and generalization of results25. In this work, we performed simultaneous meta-analysis of gene expression microarray data from infected host cell (macrophage) as well as pathogen (Mtb) to identify the ranked list of gene expression signatures induced in host-pathogen environment.Previously, many independent microarray studies were conducted to identify differentially expressed genes in Furthermore, functional over-representation analysis (FOA) of different gene groups namely complexes, pathways, regulons and gene ontology terms among the gene expression signatures was also performed which led us to identify the system-wide functions in Mtb-macrophage environment. Inclusion of operons for FOA of Mtb gene expression signatures identified many unknown pathways/complexes and adaptive mechanisms with in the macrophage environment.Mtb-macrophage environment. Inclusion of operons for FOA of Mtb gene expression signatures identified many unknown pathways/complexes and adaptive mechanisms within the macrophage environment.Furthermore, functional over-representation analysis (FOA) of different gene groups namely complexes, pathways, regulons and gene ontology terms among the gene expression signatures was also performed which led us to identify the system-wide functions in http://www.ncbi.nlm.nih.gov/geo/) and ArrayExpress (http://www.ebi.ac.uk/microarray-as/ae/). To remove the redundancy in the gene expression data, we have included the datasets from ArrayExpress database, if they are not found in GEO database. To facilitate the data comparison across the different platforms, gene expression microarray features were mapped to Entrez Gene IDS. Mapping information from gene names, refseq IDs, Ensembl IDs, Clone IDS, GenBank IDS to Entrez Gene IDS were obtained from various platform annotation files and also from various databases such as NCBI, BioMart, NIA array28.Gene expression microarray data sets were downloaded from two major public repository databases Gene Expression Omnibus (GEO) 32. Then we took the weighted sum of the Z-scores and divided by the square root of sum of the weights to generate the average Z-score. Here the weights are equivalent to number of replicates in the gene expression microarray experiments. Finally the average Z-score was converted to p-value by using CDF33.Meta-analysis of gene expression microarrays was carried out using our previously described method35. PHISTO is a Pathogen Host Interaction Search Tool which contains a total of 23,633 pathogen host PPI data including 9318 interactions between bacteria and human. HPIDB is a Host Pathogen Interaction Database that currently contains 23.485 interactions between 66 host and 541 pathogen species. In addition, we curated a total of 53 experimentally verified host-pathogen protein-protein interaction information from 12 pathogens including Mtb from literature. The genome sequences of Mtb and other bacteria, which have experimentally verified interactions, were downloaded from NCBI ftp site (ftp://ftp.ncbi.nlm.nih.gov/genomes/). Then, we identified orthologs of Mtb in ~1500 sequenced genomes of bacterial species, which have experimentally verified host-pathogen interactions. Finally, the experimentally verified interactions were transferred to Mtb on the basis of orthology relationship. Totally we identified 2808 host-pathogen interactions between two sets of proteins 564 from Human and 1725 from Mtb.Experimentally verified Host-Pathogen interaction data from different bacterial species were retrieved from PHISTO and HPIDB databases37. Since the MSigDB does not maintain any functional gene groups for Mtb, we have curated the various functional gene groups such as complexes, pathways and operons etc., for Mtb from the published literature38.Annotated functions of human, i.e., pathways, motifs, gene ontology (GO) and immunological signatures were collected from Molecular Signature Database (MSigDB)39. For each gene in a gene group, we have converted p-value of differential expression into Z-score using, inverse normal Cumulative Distribution Function (CDF). The FOA score of each gene group is the sum of the Z-scores divided by square root of number of genes. In order to identify the overrepresented target genes of various transcription regulators (regulon), we have taken sum of the absolute value of target genes Z-scores, then the total was divided by square root of number of genes. Finally the q-value was converted to P-value by using CDF. To calibrate the gene group (containing n genes) FOA score against the background distribution, we randomly sampled the n number of Z-scores and calculated the random FOA score. We repeated this process over 100,000 times and mean and standard deviation of the sampling distribution was used for correction of original score.Functional over-representation analysis (FOA) was carried out using our previously described method16. Therefore, we performed the meta-analysis of gene expression changes in both Mtb and human macrophages during stable infection. In addition, we carried out Functional over-representation analysis (FOA) to identify higher order functions of induced or suppressed gene expression signatures both in Mtb as well as in the infected macrophages by considering functional groups of genes such as operons, regulons, pathways, complexes and biological process. Inclusion of diverse functional gene groups for the analysis ensured functional annotation for most of the genes. We extensively discuss the relevance of higher order functions of the genes in relation to the mechanisms adopted by the pathogen for its adaptation as well as the host defence.Since both the host and the pathogen reciprocally influence the gene expression profiles of each other, it is pertinent to study the gene expression profiling of the pathogen as well as the host. This would provide insight into the strategies employed by the pathogen to survive in host environment and the host response to contest against the virulence of the pathogenMtb and the other from the macrophages during the course of infection. Differentially expressed genes of Mtb were identified by comparing the gene expression data of Mtb which resides inside macrophage after infection  with the Mtb before infection (in vitro Mtb cultures) using RankProd29. Similarly, we have also identified differentially expressed genes of macrophages by comparing gene expression data of the infected macrophages with the uninfected macrophages  also have above average Z-score in the meta-analytic ranked list, indicating that top ranked genes in the meta-analytic list are highly important for defence against Mtb. Similarly, some of the genes reported to be important for survival of Mtb in different physiological environments were also have above average Z-scores  supply model and another one is Wayne growth (WG) model, in which the oxygen supply is gradually depleted41. In order to identify the nature of environment created by macrophage towards Mtb infection, we compared the meta-analytic ranked list of gene expression signatures induced inside macrophages with a large number of previously published gene expression signatures generated using in-vitro models of diverse environmental conditions at various time points 42. The gene expression signatures induced under conditions of constant O2 (0.2%) for 4hrs, Wayne growth for 8 days, 5 mM H2O2 for 40 mins and 0.5 mM DETA (Diethylenetriamine) NO for 24hrs best correlates with the meta-analytic ranked list (intracellular), indicating the likely environmental conditions Mtb exposed in macrophage environment  in the ranked list of differentially expressed genes across the different conditions to get the list of the transcription regulators, which are critical for adaptation of Mtb to macrophage environment. Our analysis shows that the target genes of the persistence regulator, Rv0348 (MosR) were consistently differentially regulated across the different conditions , which code for Suf-like proteins, which are part of SUF machinery, an exclusive Fe-S cluster assembly existing in Mtb47. The Pps1 protein is orthologous to SufB which is essential and conserved component of iron sulphur cluster assembly (Fe-S cluster) complex of prokaryotes. It was reported that as Fe-S clusters serve as cofactors of proteins which are involved in multiple cellular mechanisms48.Over-representation analysis shows that the entire operon encoding for the components of NADH dehydrogenase complex-I were significantly down-regulated across the different conditions of intracellular, hypoxic and oxidative stress Fig.  Supplem, indicatons Fig. . The ppshspX-Rv2030c-pfkB-Rv2028 was reported to be essential for the Mtb during persistent infection in the host and also for its survival during low iron, low pH and hypoxic condition51. The protein encoded by hspX gene of the operon is known to be highly up-regulated during the latent infection and reported to play an important role in anaerobic respiration of Mtb during stress conditions. The other genes of the operon are found to be involved in carbohydrate metabolism (pfkB) and nucleotide metabolism (Rv2030c)51.The operon hsaABCD and cyp125-fadE28-fadE29-Rv3542c-Rv3541c-ltp2 were highly conserved across the members of Mtb complex53. They participate in cholesterol degradation pathway and play an important role in intracellular survival of Mtb by providing cholesterol as an alternative carbon source59. Another two operons pks1-pks15-fad22-fad29 and fadD26-ppsABCDE-drrABC were involved in synthesis of phthiodiolone dimycocerosates (DIMs) and immuno-modulatory phenolic glycolipids, which have a role in immuno-modulation62. Some of the genes  in aforementioned operon were known to encode for a drug efflux pump, which confers resistance to many antibiotics63. The genes with in the operon  encode for alpha-ketoglutarate-dependent dioxygenase , which catalyses oxygenation of hydrocarbons64, could be having role in cholesterol degradation. The genes involved in cholesterol utilization and other genes  with unknown function were highly expressed in intracellular environment in comparison to the other conditions.The two operons mbtB-mbtC-mbtD) and meromycolic acids (accD6-kasB-kasA-acpB-fabD) were highly expressed in presence of NO and H2O2 indicating their role in oxidative stress defence. On the other hand the genes with in the operons (devR-devS-Rv3134c and hspX-Rv2030c-pfkB-Rv2028c) were highly expressed during hypoxic conditions. Furthermore the genes with in the operons (furA-katG-Rv1907c and ahpC-ahpD) were expressed across intracellular, hypoxic, H2O2 induced oxidative stress conditions. In contrast to the previous reports, co-expression of katG and furA indicates that furA may not be a repressor of katG under these conditions65.Comparative analysis of operons with the genes induced across different conditions has identified several functions that are common and unique to each of the environment. Pathways involved in biosynthesis of iron-sulfur cluster, mycobactins . It was documented that MAP adopts a different strategy to escape from the host defence. MAP activates the mitogen activated protein kinase pathway, which finally results in up-regulation of IL-10 and repression of other inflammatory cytokines. Further the up-regulated IL-10 also inhibits the apoptosis in macrophages78. According to our findings and the supporting evidence, it may be assumed that the up-regulation of IL-10, an anti-inflammatory pathway of macrophage may aid in the growth and virulence of Mtb and may also be considered as one of the immune-modulatory strategies adopted my Mtb to escape from host defence mechanisms for its survival.IL-10 family of cytokines has a total of 6 IL’s. In contrast to the rest of the IL-10 family of cytokines, IL-10 acts as an anti-inflammatory cytokine and is also capable of inhibiting the synthesis of pro-inflammatory cytokines such as IL-2, TNF-α, IL-3, IFN-α which indirectly leads to the survival of 79. Apoptosis of this kind, where there is engagement of death receptors is the sign of innate immune response of macrophages against Mtb79. IL22 soluble receptor signalling pathway was reported to inhibit the growth of Mtb by enhancing the expression of intracellular signalling molecule, calgranulin A. In addition, it was reported to be involved in tissue modulation, induction of proliferative and anti-apoptotic pathways and production of anti-microbial substances80.The pathway ‘induction of apoptosis through the engagement of death receptors’ has a role in apoptosis of infected macrophages via engagement of variety of death receptors such as Fas, TNFR, DR3, DR4 and DR5Mtb infection81. TNF- α shows its immune response in variety of ways such as macrophage activation, induction of cytokines, chemokines and induction of apoptosis and also restriction of bacterial growth in infected macrophage85. TNFR2 signalling pathway is mainly responsible for apoptosis, acetylation/deacetlyation of RelA and is responsible for innate immune response against Mtb. Overall the over-representation analysis in Biocarta pathway shows that all the pathways related to recognition of molecular patterns of Mtb are responsible for triggering inflammation and apoptosis, are up-regulated.NF-kB pathway, inflammatory cytokine and TNF-α are responsible for inflammatory response towards Mtb infection  which in turn recognize the pathogen associated molecular patterns (PAMPS). The recognition of PAMPS by PRRs leads to the activation of transcription factors, which are involved in providing innate immunity86. In the case of mycobacterial infections, mycobacterial cell wall-associated components such as lipoarabinomannan are recognized by TLR1 and TLR2. Similarly there are several cell wall associated components which may be recognized during the time of mycobacterial infection as reported earlier89. In NOD-like receptor signalling pathway, the NOD like receptors belonging to NLR family and they may play key roles in innate immunity and inflammation90. They recognize the peptidoglycan fragments of the pathogens escaped from endosomal compartments leading to the activation of cytokine production and ultimately resulting in programmed cell death92.Over-representation of analysis of KEGG pathways show that the genes involved in cytokine-cytokine receptor interaction, toll-like receptor signalling, NOD-like receptor signalling, RIG-I like signalling, chemokine signalling and apoptosis signalling were highly up-regulated in the macrophages during ion Fig. . After i94. These have been reported to be involved in the intracellular detection of all pathogens including Mtb95. Chemokine signalling pathway plays an important role in migration of leukocytes to the site of infection and orchestrates the host defence mechanism96.Apart from the NOD like receptors, there are also few other proteins called as cytosolic RNA helicases  that were also highly up-regulated. These cytosolic RNA helicases (RIG-I-like receptors) are reported to recognize the antigens from viral pathogens and induce the innate immune response against them by triggering the signalling pathways which produce the type I interferon and inflammatory cytokines99. In addition, the pathways in involved in branched chain amino-acid degradation were down-regulated, unlike in intracellular Mtb, in which the biosynthetic pathways of branched chain amino-acids were up-regulated as mentioned previously.The genes involved in apoptotic pathways were highly up-regulated Fig. . The celAnalysis of Reactome pathways show that there are two kinds of the pathways that were up-regulated during infection: pathways pertaining to immune system activation and pathways responsible for cell survival and growth. The former category consists of cytokine signalling related genes, interferon signalling, interleukins signalling and adaptive immunity. The second category consisting of the pathways related to cell cycle, DNA replication and G1/S transition pathways were also observed to be highly up-regulated Fig. . In the 100. This induction of apoptosis further leads to the killing of Mtb. The other highly overrepresented proteins in the cellular localization data set are mainly related to the intracellular proteins associated with kinetochore  were observed to be up-regulated during infection. In macrophages, the mycobacterial antigen ESAT-6 evokes the ER stress, which in turn mediates the process of apoptosisore Fig.  (Supplemore Fig. . In contMtb and macrophages during the stable infection. We observed a strong correlation of gene expression signatures between the intracellular bacteria with in-vitro models of dormancy conditions, which reflects the likely environmental conditions in existing in macrophages at the time of infection. Moreover from this analysis, it is clear that Mtb effectively fights against the oxidative, starvation and hypoxic conditions presented by the host as defence against infection. Analysis of different functional signatures associated with different stress conditions provides the valuable information related to the strategies adapted by Mtb inside the macrophage for its survival and persistence. For instance such as, the mechanisms adapted by Mtb to encounter the oxidative stress induced by intracellular H2O2, through catalase-peroxidase and alkyl hydro peroxide reductase, similarly, recruitment of iron-sulfur clusters, mycobactins and meromycolic acids to encounter the oxidative stress induced by NO shows the strategies adapted by Mtb to survive in infected macrophage. Further, from our analysis it is observed that, Mtb effectively tackles the harsh environmental conditions of macrophage by intracellular environment by up-regulation of cholesterol utilization pathways and bio-synthesis of immuno-modulatory phenolic glycolipids. Further study of these pathways and other genes with unknown function would establish their role and mechanisms of action to counter each of the stress conditions posed by macrophage environment. In addition possible pathways of Mtb that target and exploit mitochondria should be identified. This study clearly inferred that, when therapeutic strategies to combat dormant Mtb should be designed, the multiple pathways involved in adaptation to different stress conditions and pathways that target mitochondrial functions should be taken into consideration.In this report by performing the meta-analysis of publicly available gene expression datasets and analysis of higher order functions and interactions, we have derived comprehensive view about cross talk between Supplementary data 1Supplementary data 2Supplementary data 3Supplementary data 4Supplementary data 5