ZNF542P), the signature gene with changes most correlated with statin-induced change in cellular cholesterol ester, an in vitro marker of statin response. ZNF542P knock-down in a human hepatoma cell line increased intracellular cholesterol ester levels upon simvastatin treatment. Together, these findings imply a role for ZNF542P in LDLC response to simvastatin and, importantly, highlight the potential significance of noncoding RNAs as a contributing factor to variation in drug response.Statins are the most commonly prescribed cardiovascular disease drug, but their inter-individual efficacy varies considerably. Genetic factors uncovered to date have only explained a small proportion of variation in low-density lipoprotein cholesterol (LDLC) lowering. To identify novel markers and determinants of statin response, we used whole transcriptome sequence data collected from simvastatin and control incubated lymphoblastoid cell lines (LCLs) established from participants of the Cholesterol and Pharmacogenetics (CAP) simvastatin clinical trial. We looked for genes whose statin-induced expression changes were most different between LCLs derived from individuals with high versus low plasma LDLC statin response during the CAP trial. We created a classification model of 82 “signature” gene expression changes that distinguished high versus low LDLC statin response. One of the most differentially changing genes was zinc finger protein 542 pseudogene ( Genome-wide association studies (GWAS) have identified a modest number of SNPs associated with LDLC statin response in or near genes such as APOE, LPA, ABCG2, SORT1, and SLCO1B16. However, these variants together account for only about 5% of the variance in LDLC statin response6.Statins, the most widely prescribed drugs for reducing cardiovascular disease risk, exhibit wide inter-patient variability in their efficacy with regard to the magnitude of plasma low-density lipoprotein cholesterol (LDLC) reduction10. For example, using microarray expression data from immortalized lymphoblastoid cell lines (LCLs) derived from participants of the Cholesterol and Pharmacogenetics (CAP) 6 week 40 mg/day simvastatin clinical trial1, we recently identified a panel of 100 signature genes whose endogenous expression levels differentiated the high vs. low statin LDLC responders7. With a radial-basis support vector machine (SVM), we found that these signature genes explained 12.3% of the variance in statin-mediated LDLC change, which is the largest proportion of variance in statin efficacy explained by molecular biomarkers to date. Similarly, Geeleher et al. used expression data from untreated tumor cell lines to predict patients’ chemotherapeutic response, demonstrating that that their approach outperformed other existing biomarkers  in several clinical datasets11.Recognizing that GWAS have only had limited success in pharmacogenomics, we and others have performed transcriptomic profiling to cumulatively investigate gene expression variation to predict complex drug efficacyin vitro simvastatin versus control incubated cell lines established from 104 European American and 53 African American ancestry CAP participants. RNA-seq can detect thousands of transcripts that are not traditionally present on expression arrays, such as long non-coding RNAs. In addition, unlike our earlier analysis, which was limited to endogenous expression variation from European American ancestry cell lines alone, in this study we attempted to maximize signal by testing for differences in the change in gene expression after simvastatin treatment using RNA-seq data from the tails of the European American LDL response distribution, and testing for cross-ancestry replication in the tails of the African American LCL response distribution. Here we report identification of 82 “signature” genes whose statin-induced expression changes differed between the tails of the LDLC response distribution. From this analysis, zinc finger protein 542 pseudogene (ZNF542P) emerged as a novel candidate gene implicated in LDL simvastatin response.Given the success of RNA expression profiling of patient-derived cell lines for the identification of novel drug response genes, here we sought to extend our findings from the CAP LCL expression array studies by using whole transcriptome sequencing (RNA-seq) data from Our goals were to: (i) to identify genes whose simvastatin-induced change in expression levels differed between LCLs derived from high and low LDLC simvastatin responders , and 25 each of high and low responders from the European American population . To minimize the effects of these confounders, we used a principal components (PCs) based correction method , where each PC served as a proxy for unknown sources of variation in the dataTo evaluate the effects of PC correction, we obtained the first 20 PCs from the 5, 10 and 15 PC corrected datasets, and we tested for correlations with several potential non-genetic covariates .Next, we sought to further refine the model by testing if varying the number of signature genes affected the prediction performance. Using the 15 PC corrected data, we again tested the prediction performance of the European American signature genes in the African American subset, testing varying numbers of the most differentially changing genes. From this analysis, we found that a dataset including the top 80 most differentially changing genes outperformed (AUC = 0.86) other gene sets with smaller or larger numbers of genes Fig. . To furtS3in vitro simvastatin exposure reduced cellular cholesterol ester levels in LCLs from the high responders, whereas there was either no change or slightly increased cellular cholesterol ester in LCLs from the low responders . Not unexpectedly, when change in plasma LDLC was treated as a continuous phenotype, ZNF542P expression change adjusted for 15 PCs in LCLs was also correlated with change in LDLC in the plasma during the CAP clinical trial . A similar trend was observed with change in plasma total cholesterol . No significant associations were observed with change in plasma triglycerides or HDLC (P > 0.3).Next, we tested if simvastatin-induced expression differences of any the 82 signature genes were associated with variation in simvastatin-induced change in cellular cholesterol ester. From this analysis, we found that ZNF542P was among the signature genes whose expression in response to simvastatin showed the greatest difference between the European American high and low responders. This was due to reduced expression levels in the low responders, with no effect in the high responders  or uptake (LDLR) in either simvastatin or control treated conditions, Supplementary Figure Since 15. Since statins are not uniformly effective in preventing cardiovascular disease14, and the incidence of adverse effects such as myopathy and new-onset diabetes may be more prevalent than originally thought18, identification of predictors of statin efficacy has become increasingly more important from a public health perspective. To obtain a more comprehensive understanding of genetic markers contributing to variation in simvastatin LDLC response, we applied a radial-basis SVM classification model to whole transcriptome sequence data and identified a set of 82 signature genes whose simvastatin-induced change in expression levels could be used to differentiate between individuals with “high” vs. “low” LDLC response to statin treatment. ZNF542P emerged as an interesting novel candidate gene based on two factors: (1) it was one of the signature genes whose expression levels were most differentially changed between the high and low LDLC simvastatin responders, and (2) its simvastatin-induced expression difference was highly correlated to change in cellular cholesterol ester content.In 2013 the American Heart Association and American College of Cardiology released expanded guidelines for statin prescription, dramatically increasing the number of potential statin usersZNF542P in simvastatin-exposed LCLs were significantly associated with both changes in cellular cholesterol ester content and with in vivo statin-induced changes in plasma LDLC and total cholesterol in the individuals from whom the LCLs were derived. Notably, these relationships were consistent with each other, as greater simvastatin-induced reductions in ZNF542P were correlated with smaller reductions in both cellular cholesterol ester content and plasma LDL cholesterol. Furthermore, these results are consistent with our findings that ZNF542P knock-down increased cholesterol ester levels upon simvastatin treatment. Cholesterol ester is a storage form of cholesterol that is found primarily within lipid droplets. The lack of effect of ZNF542P knock-down on cellular unesterified cholesterol content is not surprising given that this form of cholesterol is located primarily in cell membranes and is subject to tight homeostatic regulation.Here, we report that changes in expression of the putative pseudogene ZNF542P. Pseudogenes were once thought to be inactive gene sequences, evolutionary remnants from past gene duplication events. However, more recently, several reports have found that long non-coding RNAs (lncRNAs) transcribed from pseudogenes have functional effects on a variety of cellular processes, and they often regulate their protein-coding counterparts19. For example, PTENpg1, a PTEN pseudogene, regulates PTEN transcript levels by preventing miRNAs from targeting the PTEN transcript20. Flanked by ZSCAN5A (zinc finger and SCAN domain containing 5A) and ZNF582 (zinc finger protein 582), ZNF542P lies within a cluster of several ZNF genes that have been implicated in gene transcription. However, further study is necessary to determine the mechanism by which ZNF542P impacts intracellular cholesterol ester in the context of simvastatin treatment.To date, there have been no reports regarding the function of ZNF542P as a novel modulator of LDLC response to simvastatin treatment, other genes identified within the set of 82 signature genes also likely contribute to response. For example, the signature gene set contains solute carrier organic anion transporter family member 2B1 (SLCO2B1), a known statin transporter21. A SNP in SLCO2B1 has been implicated in simvastatin pharmacokinetics22. Thus, the relationship between the signature gene set expression changes and statin response may be driven by both pharmacodynamic and pharmacokinetic mechanisms.Although our findings implicate ZNF542P specifically emerging as a novel candidate gene implicated in cholesterol metabolism and simvastatin response. A recent comprehensive survey of the human transcriptome identified over 91,000 expressed genes, of which 68% are classified as lncRNAs23. Our findings highlight the need to further our understanding of the roles of these genes in mediating cellular processes well as determinants of drug response.In summary, we here identify a set of 82 signature genes whose simvastatin-induced change in expression levels distinguish high versus low LDLC statin responders in both European American and African American populations. This gene set contains 13 noncoding RNAs, with 1. Plasma lipid measures were quantified twice before statin treatment, and after 4 and 6 weeks on-treatment, with delta LDL calculated as the difference of the average pre-treatment vs. the average post-treatment values. All experimental protocols were approved by the institutional review boards at Children’s Hospital Oakland Research Institute and at UCLA and UCSF, where the clinical trial was performed. Lymphoblastoid cell lines (LCLs) were generated from each study participant as previously described24. Informed consent for participation in the statin clinical trial and the use of cell lines was obtained from each study subject. All methods were performed in accordance with the relevant guidelines and regulations.The Cholesterol and Pharmacogenetics (CAP) clinical trial was comprised of 944 participants (609 self-identified whites and 335 self-identified blacks) treated with 40 mg/day simvastatin for 6 weeks 10. Indexed, strand-specific, paired-end Illumina sequencing libraries were prepared by LabCorp  as previously described10. Sequences were aligned using TopHat225 and adjusted for library size and variance stabilized using DESeq226 as previously described10. Quality control checks were performed as previously described10, except that 7 samples in experimental batch 1 were included here but excluded previously.LCL lines from CAP participants were exposed to 2 µM activated simvastatin  or control buffer for 24 hours and total RNA was extracted as previously described13. Among the PCs of a covariance matrix between samples sorted by the proportion of explained variation in the original matrix, up to 25 PCs were selected such that adding another PC would explain less than 0.5% of the variation. PCs 1 through 25 were progressively regressed out, and the residuals from each regression were quantile normalized and used as the change in expression level of each gene.Differential RNA-seq expression was determined by subtracting the control-treated variance stabilized data from the statin-treated variance stabilized data and the resulting expression changes for each gene were quantile normalized and adjusted for experiment batch 1 using regression. To adjust expression differences for unmeasured confounders, principal component analysis (PCA) was adopted as previously describedt-statistics as in Kim et al.7. Starting from a relative difference d(i) which is defined asi in the high (H) and low (L) responder groups, respectively, and s(i) is the standard deviation of repeated measurements, we introduced varying s0 values in the denominator to stabilize the variance of d(i) irrespective of the gene expression level using an empirical Bayes approach. A more detailed description on this method has been previously reported7.To identify signature genes from 25 each of high and low European American responders, we used empirical Bayes moderated 27. For the SVM, the radial basis kernel was chosen due to its superior performance in the cross-validation results. The prediction performance was evaluated by ROC curve analysis and quantitated by AUC using the ROCR package in R28.Radial-basis SVMs were used for training and predicting 12 high and 14 low African American responders in the SVM classification models. The performance of the models was evaluated by randomly splitting the data into 10 sets, with 9 assigned as the training set and the tenth as the testing set. The model was trained using the training set and applied to the testing set for prediction. This process was repeated 5000 times and the prediction power of the model was estimated based on the 5000 testing sets. The SVM function in the R package (kernlab) was used to implement the models with default parameter settingsin vivo clinical phenotypes for correlation analyses, delta log measures were calculated as the log  minus the log . The distribution of the plasma LDLC change adjusted for age, race and smoking status is shown in Supplementary Figure To measure statin-induced changes of in vitro cholesterol measurements, lipids were extracted from the CAP LCLs with hexane and isopropyl alcohol , and dried under nitrogen. Intracellular total cholesterol and free cholesterol levels were quantified using the Amplex Red Cholesterol Assay Kit (Life Technologies) following manufacturer’s instructions, and normalized to total cellular protein content. For measurement of cholesterol esters, extracted lipids were incubated with esterase up to 2hrs, and cholesterol ester was calculated as difference of the total minus free cholesterol. The change in cholesterol ester was calculated as the delta log of the cholesterol ester in the statin minus control treated cells.For in vivo and in vitro phenotypes were tested for association with the ZNF542P expression changes using Spearman rank correlation in R.All ZNF542P (n258919) or a non-targeting control  using the siPORT transfection reagent as previously described29. After 24 hrs, cell culture media was replaced with media supplemented with either 2 μM activated simvastatin or control buffer. Simvastatin was kindly provided by Merck. RNA was extracted using Qiashredders (Qiagen) and the PureLink RNA Mini Kit (Life Technologies), and cDNA was synthesized using the cDNA Archive Kit (Life Technologies). ZNF542P values were quantified by a TaqMan assay  and normalized to CLTPM as a loading control. HMGCR, HMGCS, MVK and LDLR transcript levels were quantified as previously described30. All qPCR reactions were performed in triplicate. Intracellular total cholesterol, free cholesterol, and cholesterol ester were quantified as above. All cultures were verified to be mycoplasma free using the MycoSensor qPCR Assay Kit (Agilent).Huh7 cells grown in MEM with 10% FBS were reverse transfected with a Silence Select siRNA (Life Technologies) targeting ZNF542P knock-down on levels of cellular cholesterol and transcripts. For ANOVA p < 0.05, statistically significantly differences between treatment conditions were identified using Tukey’s multiple comparisons test with adjusted p-values reported.One-way ANOVA was used to identify statistically significant effects of Supplementary Information


The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class. It is known that resistance against protease inhibitors is associated with a wider active site, but results from our large scale molecular dynamics simulations combined with statistical tests and network analysis further show, for the first time, that there are regions of local expansions and compactions associated with high levels of resistance conserved across eight different protease inhibitors visible in their complexed form within closed receptor conformations. The observed conserved expansion sites may provide an alternative drug-targeting site. Further, the method developed here is novel, supplementary to methods of variation analysis at sequence level, and should be applicable in analysing the structural consequences of mutations in other contexts using molecular ensembles. As the viral genome continues to accumulate mutations under the selective pressures of therapy2, surviving viral populations inevitably become less sensitive to one or more drugs over time. HIV reservoirs and its existence as a quasispecies3 means that an ARV should ideally inhibit a pool of slightly different conformations of receptor targets. The decreasing efficacy of drug binding over time means that patients may have to switch to more difficult treatment regimens with the possibility of experiencing more severe side-effects if no better-tolerated alternative exists. At the same time, ARVs are a finite resource which should be used with proper timing failing which resistance develops sooner. In order to design more robust ARVs and/or improve onto existing resistance prediction methods, additional knowledge of the motions associated with resistance may be helpful. However this is not straight-forward, as patterns of resistance mutations in HIV are complex4, and may require special consideration in order to extract deeply-engrained behaviour. In this manuscript, we focus on HIV protease, which is a crucial enzyme for viral maturation, and is a well-established HIV drug target6. There are minor differences as to how various functional segments of HIV protease are defined in literature (possibly due to its high variability) for which we show some of the structural features in Fig. 7, which under normal circumstances process the viral polyproteins Gag and Gag-Pol8. Multi-drug resistance within members of the PI class is not uncommon due to their long period of use and their three-dimensional and electrostatic similarities10.Antiretroviral (ARV) drug resistance still persists despite recent improvements in antiretroviral therapy11. However, drug resistance mechanisms in HIV are not fully-understood12. Previous research has evaluated various computational modelling approaches over the years in order to predict or understand drug resistance mechanisms in HIV protease, ranging from the use of delaunay triangulations from static protease structures13, binding energies from molecular docking15 to elastic network modelling applied to coarse-grained models using a uniform spring stiffness16, 100 fs molecular dynamics (MD)17 and many more, as reviewed by Cao and co-workers18. Here we adopt a structural approach using MD applied to 100 highly-resistant and 100 hyper-susceptible HIV sequences against eight docked protease inhibitors using the labelled sequence data available from the Stanford HIVdb19. We focus on the majority subtype (B), with the sequences containing rare residues removed, as described in our previous work20. 3D structures of the 200 protease sequences are built using homology modelling21 with a common drug-bound template for each target. Ligand docking with the eight ARVs, namely atazanavir (ATV), darunavir (DRV), fosamprenavir (FPV), indinavir (IDV), lopinavir (LPV), nelfinavir (NFV), saquinavir (SQV) and tipranavir (TPV) then gives 1,600 3D structures of drug-bound protease complexes. FPV is the amprenavir prodrug which is released in its active form upon hydrolysis22.In resource-available settings drug efficacy can be inferred by monitoring viral load or CD4 cell counts, yet when available, knowledge of genotypic information can improve the choice of therapy to be used23 and drug-binding alters their dynamics24 so each case requires its own MD run. Allowing for replications, a total of 3,200 MD runs are performed. Each run is about 2 ns, so that in total the MD simulations amount to about 6,400 ns. Such a design was necessary in order to cover as much resistance-related complexity from as large a number of independent observations such that highly-conserved patterns would emerge within the noise from protein dynamics in order to minimize bias while maintaining biological variance, for a reasonable amount of CPU hours. The observation of conserved resistance-related dynamics across this high number of independent short simulations of PI-bound receptor complexes shows that highly drug-resistant sequences may be structurally-detectable in a short amount of simulated time. Considerable amounts of conformational sampling are typically required to observe motions that are of large amplitude26 or rare27. Same applies for increasing the accuracy of binding free-energy estimations (for instance between a ligand and a receptor), which comes with increased computational costs29. We circumvent these issues in this context, by describing two short and specific motions that are detectable very early in all-atom dynamics simulations of the retroviral protease, using the idea of preferential attachment applied to local residue motion. This concept stems from the tendency of initially highly-connected nodes to attract new connections, leading to a global behaviour referred to as being scale-free, in non-random graph topologies30. We use this idea to reinforce the detection of significantly different  pairwise residues distances derived from statistical tests of averages on the premise that a residue will most likely be at a given distance within an ensemble if there is larger number of other residue pairs supporting the observed difference.Proteins are in constant motionin silico methods used to simulate MD of drug-receptor complexes are partially stochastic33 - we therefore mitigate chance events by calculating statistical properties of each ensemble and applying the network degree centrality (connectivity) measure. Networks are an intuitive way of representing relational data using nodes and edges34, with much of the underlying ideas having emerged from insights made by representing social networks as graphs35. Network analysis has thus evolved into an ideal tool for examining inherently complex biological contexts such as single nucleotide variation analysis37, protein-protein interactions39, gene co-expression data40, intra-protein networks44 and allosteric modulation analysis45. A tool that uses network analysis over MD simulations is given in44. Network graphs are composed of nodes and edges, where each node represents a particular object while an edge is drawn between any node pair to represent a shared property. Edges can either be directed, in which case a relationship does not entail reciprocation or conversely be undirected whereby connections are mutual. Additionally, edges can be weighted or binary. While the former preserves information continuity, the latter only denotes the presence or absence of a connection34. Undirected binary edges are used in our case to independently represent a significantly larger or a smaller distance on separate graphs. In our analysis, we used the degree centrality, which is simply the number of neighbours adjacent to a given node. Adjacencies can be represented as a square matrix. The degree centrality then is the row (or column) sum from the matrix.The Cα atoms, we remarkably find structural features of HIV protease that differ between susceptible and resistant sequences, with further conservation occurring across all 8 ARVs. The results can form the basis for more robust ARV design and better prediction of drug resistance. Further, the combination of molecular dynamics, network centrality and statistical analyses used here provides an alternative way of analysing the effects of non-synonymous mutations, and should be applicable to other diseases whereby protein variants can be independently simulated as two ensembles of variants, ideally comprising over 30 samples in each case, to represent cases and controls to benefit from normalizing properties of the central limit theorem46 for the t-test. Given our performance at predicting conserved motion using this method, such an approach could be very insightful if similar motions were to be picked up in non-B subtypes of varying residue composition as our method highlights the most definitively distinct dominant motions prevailing between drug resistance and susceptibility within the confines of their ensembles. Schematics illustrating the experimental workflows are shown in Fig. By calculating degree centrality within graphs generated from statistically-inferred edges between Cα root mean square deviation (RMSD) values were first computed to exclude any error in periodic boundary corrections. A condensed representation of the mean and the standard deviations of RMSD values for each ARV is depicted in Supplementary Fig. Rg) across drug ensembles, as shown in Fig. Cα angles from receptors  reduced dimer stability in L24I, I50V and F53L mutants9 and (3) single or co-operative distal mutations49. Additional research on multi-drug resistant HIV protease further described an expanded active site pocket51 and cavity expansion due to atomic volume for the HIV protease mutants V82A and I84V5. As Rg measures compaction by calculating the RMSD of each atomic centre-of-mass with respect to that of the whole molecule, we generally expected that higher values would be associated with resistance ensembles. However, our simulations showed no global tendency towards a less compact (larger Rg) series of conformations in the resistant ensemble compared to the drug-susceptible state, as shown in Fig. Rg values in their resistant ensemble. The reverse is actually observed in FPV and IDV. No appreciable shifts were observed for ATV, LPV and even less for TPV. Of notable interest are the slight shifts in the means of the Rg distributions across all drug ensembles, which are suspected to either be inherited from the template used for modelling or the docked drugs themselves, which could be propagating a different set of local receptor-ligand signals towards various parts of the receptor. Similar trends were observed upon replication  impaired hydrophobic sliding shown in the G48T/L89M double mutant with saquinavirWe hypothesize that compactions may instead be observable at a residue level and that these might be masked by more chaotic motions happening globally. Therefore we further investigated each protein-ligand complex by increasing experimental sensitivity by calculating time-averaged one-tailed t-tests from pairwise residue distances obtained from aggregations of independent MD simulations before summarizing and analysing the results with network analysis.52. The five top-ranked residue positions are subsequently prioritized on the basis of their connectivities showing statistically-significant larger or smaller distances across the ensembles.Results from Bonferroni-moderated t-tests are transformed into a network graph as explained in the Materials and Methods section for the distances, and are represented as normalized degree centrality plots onto which several architectural features of HIV protease are mapped using the coloring scheme from Fig. 53 are shown in bold black and red fonts respectively. From our simulations, a similarly conserved behavior is not immediately apparent from the degree centralities of those residues with larger distances in the susceptible ensemble, but can be seen from their mapping onto protease 3D structures. A lateral widening involving the elbow region and/ or the 10’s loop of the fulcrum is generally observed across all PIs. Part of this motion is described by Hornak and co-workers54 as events leading to flap opening, involving a concerted downward motion of the cantilever, fulcrum and flap elbow with an upward motion of the catalytic aspartate from the floor of the binding cavity. Further, we observed that residues of the receptor cavity do not appear amongst any of the top-ranked residues for each drug and ensemble, and behave in a quite opposite manner, with very low degree centralities. Occasional spikes did manifest themselves for some cavity residues, but these can be ignored as they may be chance events that would not be ranked similarly upon replication of the experiment, as seen in Supplementary Fig. It is remarkable to note that despite the stochasticity of different sections of the experiment compounded with protein variations, our results show features common to all drug complexes. The base of the cantilever (very close to the 60’s loop) is drawn closer to the catalytic core for all drug complexes Figs  and 5 inIn Fig. For FPV Figs  and 5c, IDV Figs  and 5d dResistance in LPV Figs  and 5e wIn the case of NFV Figs  and 5f, SQV Figs  and 5g dIn the case of TPV Figs  and 5h, Cα atoms. Absolute conservation was observed at residue position 84 in only one of the replicates in Fig. We further investigated receptor backbone movement by comparing angle distributions occurring at protein In conclusion, HIV protease inhibitors are used to delay the symptoms associated with late stages of the infection, however resistance is unrelenting due to the virus’s resilience to the current drug designs. Drug resistance patterns are complex. Nevertheless, our large scale simulations show that despite various DRMs and additional variations, lateral expansion and fulcrum compaction are conserved in the drug resistance state, both within and between different types of PIs. The observation of conserved lateral expansion provides additional support for investigating alternative drug-targeting sites rather than the active site, as done in55. The results may be hinting at (1) conserved mechanistic ripple effects emanating from certain similarities in PI drug design, which possibly hints at how crucial these preliminary early movements are in leading to a less-favorable drug positioning within the active site, or (2) a well-conserved pair of local motions associated with drug resistance lying underneath the complexity of DRM patterns.Analysis of the backbone motions hinted that there is no single angular trajectory leading to resistance, even for the same sequence. Knowledge of characteristic motions around similar energy wells may be an interesting and inexpensive route for supplementing extant drug resistance prediction approaches in HIV subtype B. Given phenotypically-labelled protease sequences from other subtypes, a similar experimental design may prove to be quite useful in extracting conserved local motions. Additionally, this approach could theoretically extend to proteases harbouring indel mutations by selecting homologous residues after simulation, shedding more light on sequences that are more divergent from the consensus B subtype.34 which may arise when pairwise distances are very close to, but not bound by, the chosen cut-off distance. To our knowledge, this method is novel, although elastic network models were used to determine the functional effects of variants in other proteins56. While the Anisotropic (ANM) and Gaussian Network Models (GNM) are based on the application of Hooke’s potential on a single structure with a uniform spring constant, our method is based on the more thorough Newtonian mechanical simulation employing an all-atom forcefield to analyse a large number of independent observations. We expect that our method will be highly useful in other cases for analysing protein structural variations. For instance, one could use a subset of validated antimalarial drug targets from artemisinin-resistant variants and another batch of sequences for artemisinin-susceptible variants and extract subtle motions hidden within the protein dynamics.We have used MD simulations coupled with network centrality measures to identify common structural features in drug-resistant mutations of HIV protease. As opposed to the conventional ways of constructing residue contact networks using distance cut-offs, we used statistical tests, thus mitigating the known effect of edge discontinuity19. These were reconstituted and filtered as explained in20. After ranking the sequences based on decreasing average distance for each of the 8 PIs, 100 highly-resistant and 100 hyper-susceptible sequences were short-listed, using cut-offs defined in57. These two classes of sequences are henceforth referred as to the resistant and the susceptible ensembles respectively. Sequences are provided in Supplementary Dataset 58 was used for dataset storage and manipulation. Seaborn 0.7.1 and matplotlib 2.1.059 were used for plotting.HIV subtype B protease sequence variants labelled with fold drug resistance ratios were obtained from the Stanford HIVdb unfiltered dataset60, 3EL961, 2HS162, 2AVO63, 2O4S64, 3EL561, 2NMZ65 and 3SPK66). Very slow refinement was used, with a random seed set at −10000, while model quality was assessed using z-DOPE scores. As a preparation for molecular docking, template crystal structures were systematically preprocessed to only retain high-occupancy side-chain rotamers. The last rotamers for each concerned residue were kept in cases of equal occupancies. Interfacial water was retained from each template crystal structure by choosing any water molecule shared between the ligand and receptor flaps (ILE50 from chains A and B), at an intersecting distance of 3 Å, except for the case of TPV, which does not require such for stability.Modeller (version 9.16) was used to model each of the protein sequences in their closed receptor conformation. The main criteria for choosing the templates, in order of selection were (1) presence of the PI complexed within the a closed conformation receptor active and (2) high resolution of the crystal structures. These two characteristics were determined to be important in giving a good starting point for observing comparable changes within short dynamics simulations. High resolution (<1.55 Å) crystal structure templates were thus retrieved for each of the 8 available PIs from the HIVdb dataset  was picked from a saquinavir atom from template 2NMZ subsequently used as reference to align the totality of the homology models using ProDy67. Receptors were protonated to pH7 using PDB2PQR (version 2.1.0)68 using the PROPKA method before merging non-polar hydrogen atoms and assigning Gasteiger partial charges using the prepare_receptor4.py tool from AutoDockTools (ADT)69, whilst having interfacial water present. Ligands were fully-protonated using ADT’s prepare_ligand4.py tool. A grid box size of dimensions 20 × 26 × 20 Å3 and an exhaustiveness value of 16 was chosen for ligand docking at the designated grid center.Flexible ligand docking was performed using AutoDock Vina (version 1.1.2)70 after full protonation using VEGA (version 3.1.1)71. All 8 × 200 complexes were prepared for molecular dynamics using GROMACS (version 2016.1)72. The AMBER03 forcefield was used with a short-range non-bonded interaction cut-off distance of 1.2 nm. Long-range electrostatics were handled using the smooth Particle Mesh Ewald algorithm. Energy-minimization was performed using the method of steepest descent after neutralizing charges using 0.15 M sodium chloride in SPC-modeled water within a triclinic periodic box. A 50 ps temperature equilibration (at 310 K) was followed by 50 ps of pressure equilibration (1 atm) with time steps of 2 fs and finally a 2 ns production MD was performed at the same temperature, pressure and time step. All MD runs were distributed over a 2400-core queue with 24 cores per job using GNU Parallel (version 20160422)73, managed by the PBS Professional scheduler over the lengau cluster (Centre for High Performance Computing (CHPC)).The previously-protonated receptors were used, while parameters for the docked ligand poses were determined using ACPYPERg values were calculated to have an overview of the levels of compaction observed in the resistance ensemble compared to susceptible ensemble for each drug investigated. Thereafter, local analyses were performed: (a) Welsch t-tests were evaluated over pairwise residue distances across the ensembles. To do so, pairwise Cβ (and Cα for glycine) atom distances from each trajectory were time-averaged within each ensemble. For each drug, each pairwise residue distance was aggregated into separate two-dimensional arrays - one for each ensemble. The t-tests were then performed between each analogous array at a 99% confidence level. (b) Similarly, the time-averaged angles between Cα residue triplets were computed for each complex within an ensemble and compared against the analogous array of time-averaged angles in the other ensemble using t-tests. Only those angles corresponding to the negative logarithm (base 10) p-value being above 2.5 standard deviations were retained for either of the larger or smaller angles in the resistance ensembles. Bonferroni correction was applied in both approaches to correct for multiple testing and reduce chances of false positives. Finally, the angles found to be significant for each drug were clustered by average linkage from the matrix of pairwise Euclidean distances. The MDTraj library (version 1.9.1)74 was used in Python 3.5 for trajectory distance and angle calculations. Numpy 1.13.3 and scipy 1.0.075 were used for general computations and statistical tests respectively.After generating MD trajectories, the proteins were centered and rotations/ translations were removed using the trjconv command in GROMACS. RMSD values were first evaluated to detect any failure in correcting periodic boundary conditions. These plots identified an initial period of fluctuation spanning the first 100 ps, which were dropped from any subsequent analysis. Cβ (or glycine Cα) atoms. Edges were obtained from significant p-values obtained from independent t-tests performed on arrays of time-averaged pairwise residue distances. In other words, each time-averaged pairwise distance 〈Dij〉 for a given protein concatenated to those of other proteins within the ensemble. Each array of 〈Dij〉 values is then compared to its corresponding position in the other ensemble of 76. Edge mappings onto protein structures were generated using the NGLview library (version 1.0)77.Network graphs were built from nodes corresponding to Supplementary FileSupplementary Data 1-6


Trichuris suis ova (TSO) in ileal Crohn’s disease failed to demonstrate a clinical benefit compared to placebo after 12 weeks. Nonetheless, it has recently been shown that the spontaneous small intestinal inflammatory changes in -/-Nod2 (Nucleotide-binding oligomerization domain 2) mice could be substantially ameliorated when these mice were colonized by Trichuris muris. Those and complementary epidemiologic findings in humans lead to the hypothesis that helminths may be advantageous only in patients carrying defective NOD2 variants. Thus, 207 participants of the TSO trial were retrospectively genotyped for six functional NOD2 genetic variants to evaluate whether the treatment outcome differed in patients carrying NOD2 variants. We observed no significant association of the NOD2 variants or their haplotypes with clinical outcome after TSO treatment.A recent randomized study of whipworm  Trichuris trichiura can be inversely correlated with the manifestation of immune-related conditions like Crohn’s disease. Yet, epidemiologic influences are difficult to entangle and possible mechanisms are still poorly understood.Inflammatory bowel disease is most common in the Western world where helminthic colonization is rare. Along the lines of the so-called hygiene hypothesis, the presence of helminths including whipworms like Trichuris suis ova (TSO) both in Crohn’s disease and ulcerative colitis . In addition, the authors considered that the prevention of disease relapse might have been a more suitable endpoint. Yet, it has to be acknowledged that the interplay between host immune function and ncreased , which mTrichuris species (in this case Trichuris muris) has been demonstrated in colonic epithelial cells in -/-Nod2 mice. In the absence of Nod2, dendritic cell recruitment is impaired, which leads to an increased worm burden at day 21 alongside lower numbers of T cells in mesenteric lymph nodes (A role for the cytosolic pattern recognition receptor Nod2 (Nucleotide-binding oligomerization domain 2) in the host response to ph nodes .-/- mice harbor several small intestinal abnormalities including increased proportion of IFN-γ expressing intraepithelial lymphocytes, goblet cell defects, and altered antimicrobial peptide expression. Nod2-/- mice had a significantly different microbiome and the small intestinal changes depend on expansion of the Gram-negative anaerobic coccobacillus Bacteroides vulgatus. Accordingly, Nod2 prevents inflammation by restricting the pathological expansion of this important commensal bacterium . Genotyping of genomic DNA for 2104C > T (rs2066844), 2722G > C (rs2066845), and 3016_3017insC (rs2066847) was performed using TaqMan technology , as described previously . GenotypP-values (likelihood ratio test).Statistical analyses were performed with R-3.4.1 and additional packages SNPassoc 1.9-2 and haplo.stats 1.7.7 . The priP-value ≤ 0.05. Given P-values were not adjusted for multiple testing.Haplotype estimation was based on an expectation–maximization (EM) algorithm (R package haplo.stats). The most frequent haplotype was defined as the “base” haplotype and haplotypes with a frequency below 5% were summarized in the “rare” haplotype. A generalized linear model of the haplo.stats package incorporating treatment as covariate without interaction term was used. Haplotypes were coded in the additive model. All tests were two-sided, NOD2 variants are already known to have an increased susceptibility to ileal Crohn’s disease .We considered the most investigated functional variants Arg702Trp (rs2066844), Gly908Arg (rs2066845) and the frameshift variant Leu1007Profs (rs2066847) that leads to a truncated version of NOD2. In addition, three further coding variants were included in the analysis: the rare variant Ser431Leu (rs104895431) that does not activate NFκB in response to muramyl-dipeptide , and theNOD2 is a known susceptibility locus for Crohn’s disease.The minor allele frequencies of the variants rs2066844, rs2066845, and rs2066847 in our Crohn’s disease cohort where higher compared to the general population, similarly as described in the literature . The comTable 1).The influence of the treatment or the genetic variants on the primary endpoint of remission [defined as Crohn’s disease activity index (CDAI) < 150 points after 12 weeks] or the secondary endpoint of response (defined as ≥100 points drop in CDAI after 12 weeks compared to baseline) were evaluated with a multiple logistic regression analysis. No consistent significant association of both outcome measures with the six NOD2 variants or the treatment was observed: remission was significantly associated with the presence of rs5743291, but this could not be ascertained for response, which is clinically implausible. The effect of this variant was independent of the treatment group. No significant interaction effect between the variants and any treatment group was evident . The haplotype 4 differs from the “base” haplotype solely in rs5743291 and shows the same inconsistent association to the binary clinical endpoints as rs5743291 in the single variant analysis described above. Considering the study endpoints as continuous variables  did not reveal any additional significant associations after correction for multiple testing.We further computed haplotypes in the cohort from the unphased genotype data. A pre-dominating haplotype (“base” haplotype) was found in 54.8% of cases, and additional four haplotypes showed a frequency of 8% or more , and from 346 to 60 points (7500 TSO). The latter patient simultaneously carried two further variants, rs2066842 and the frameshift variant rs2066847. Although this might be due to pure coincidence, the response to TSO treatment in the individual carrying three functional variants is noteworthy.NOD2 variants seem not to have a clinically meaningful impact on the outcome of the TSO trial, which can be explained by various reasons.Yet in general, T. suis E/S antigen specific IgG showed dose-dependent effects  Guideline for Good Clinical Practice and was approved by independent ethic committees for each study center. All subjects gave written informed consent for original clinical trial and all patients included in this additional genetic analysis have signed a separate, optional informed consent.SJ drafted the manuscript, organized and acquired data, took part in statistical analysis, interpreted data and was clinical investigator in original TSU trial. ES coordinated the genetic analysis, interpreted the data and critically revised the manuscript. SW and RT are biostatisticians who performed the statistical analysis and critically revised the manuscript. JS was the lead investigator of the original clinical trial and critically revised the manuscript. NM provided reagents and provided comments on drafts. EFS was investigator in the original clinical trial and critically revised the manuscript. MS contributed to conception and design of the study, provided funding and critically revised the manuscript. JW was principal investigator in the clinical trial, contributed to conception and design of the study, provided funding and critically revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bacterial communities are dominated by Alpha-, Beta-, Gammaproteobacteria, Actinobacteria and Firmicutes, whereas Euryarchaeota is the major archaeal group. Strong correlation among the abundance of autotrophic and heterotrophic taxa is noted. Bacteria known for nitrite, sulfur and hydrogen oxidation represent the autotrophs. Fermentative, nitrate/sulfate reducing and methane metabolising microorganisms represent the heterotrophs. Lack of shared operational taxonomic units and distinct clustering of major taxa indicate possible community isolation. Shotgun metagenomics corroborate that chemolithoautotrophic assimilation of carbon coupled with fermentation and anaerobic respiration drive this deep biosphere. This first report on the geomicrobiology of the subsurface of Deccan traps provides an unprecedented opportunity to understand microbial composition and function in the terrestrial, igneous rock-hosted, deep biosphere.Scientific deep drilling at Koyna, western India provides a unique opportunity to explore microbial life within deep biosphere hosted by ~65 Myr old Deccan basalt and Archaean granitic basement. Characteristic low organic carbon content, mafic/felsic nature but distinct trend in sulfate and nitrate concentrations demarcates the basaltic and granitic zones as distinct ecological habitats. Quantitative PCR indicates a depth independent distribution of microorganisms predominated by bacteria. Abundance of  Recent assessments confirm that in spite of considerable physical as well as chemical constraints, the oligotrophic, dark biosphere underneath the continental crustal system  harbour 2–19% of Earth’s total biomass represented by bacteria, archaea and fungi4. In contrast to areas of abundant resources, deep biosphere within the crustal systems represents an extraordinary environment, as many of the standard forces of ecosystem  are either much reduced or not applicable5. With mostly unexplored physiological and biochemical details, these microorganisms generally lack culturable phylogenetic neighbours. Interestingly, it remains highly enigmatic that how these vast communities survive in environment that provide only marginal energy for growth, division and minimal metabolic function for cell sustenance6.Microbial life that resides within the deep continental subsurface represents one of the largest and most diverse biospheres on this planet7. It has been established that in contrast to sedimentary rocks, crystalline igneous rocks are typically low in organic matter, porosity and interconnectivity, and have been subjected to high temperature and/or pressure in the geologic past or at present9. Microbe mediated mineral precipitation may lead to loss of porosity, although secondary increase in porosity at greater depths via pressure dissolution of minerals may also occur9. Endolithic microorganisms in such extreme habitats typically occupy the fractures through available interconnections, with nutrients made available either from the rock itself and/or through transportation (via available interconnections) in the form of dissolved gases, solutes, or colloids10. While the lack of photosynthetically derived organic carbon led ‘autotrophy to outstrip heterotrophy’9, geogenic supply of CO2 and CH4 (as carbon source), H2, CH4, NH4+ and HS− (as electron donor) and Fe3+, Mn4+ and CO2 (as electron acceptors) have potential role in providing minimal nutrient and energy supply11. Hydrogen-driven subsurface lithoautotrophic microbial ecosystems (SLiME) wherein microbial populations can thrive on low concentrations of H2 and CO2 were initially considered to fuel the subsurface environment15. Although the dominance of such lithotrophic metabolism has been reported from several terrestrial subsurface including Columbia River basalt12, Fennoscandian shield16 and Witwatersrand Basin17, the role of heterotrophic organisms, capable of utilizing the metabolic products of autotrophs in carbon assimilation and driving the overall community function is highlighted in later studies21. Overall, a combination of metabolic processes encompassing both autotrophy (including chemolithoautotrophy) and heterotrophy is implicated in deep terrestrial ecosystems21.During the past several decades, our knowledge on geomicrobiology of deep terrestrial subsurface has been broadened significantly27, subsurface crustal environments with high temperature basalts28, subsurface gabbros and mantle type rock30 and crustal fluids31. Most of these studies reveal the dominance of bacterial members affiliated to Proteobacteria , Actinobacteria, Firmicutes, Bacteroidetes and Chloroflexi known for sulfate reduction, nitrate reduction, fermentation, and chemolithotrophic metabolism34. Members of archaea, though present in much lower abundance mostly belong to the Miscellaneous Crenarchaeota group (MCG), Methanosarcinales, and Methanobacteriales taxa. Compared to oceanic system, terrestrial subsurface crustal environments within igneous rocks  are microbiologically less explored. A few additional studies have investigated the terrestrial subsurface igneous rocks directly36, while most such studies in the terrestrial realm remain restricted to groundwater samples recovered from basaltic/granitic aquifers38. In contrast to our understanding of structure and function of deep subsurface microbial community through all these studies, the microbiome of Deccan subsurface remains elusive. In this work, we try to demonstrate the nature and function of microbial communities within the deep terrestrial subsurface of Deccan traps using the rock cores recovered through scientific deep drilling40. Exploration of deep biosphere of granitic-basaltic crustal system is highly imperative to answer the fundamental questions on microbial life within deep, dark, oligotrophic crust, their mode of interaction and role in Earth’s biogeochemical processes, origin of life on Earth and even on extra-planetary locations . Compared to a reasonable level of understanding of deep life within marine subsurface, geomicrobiology of subterranean igneous rocks at greater depths remains almost elusive.Endolithic microbial populations in deep igneous provinces have been investigated in basalt hosted oceanic crusts40. The Deccan traps represents a continental flood basalt province covering an area of over 0.5 million km2 of lava flows (~65 Ma) with a total thickness of over 2000 m near the eruptive centre in western India44. This thick pile of Deccan basalts rests on ~2.5 Ga Archean crystalline basement45. The Koyna-Warna region has gained further importance owing to the reservoir triggered seismicity ever since the impoundment of the Koyna dam in 196239. Exploratory core drilling has been carried out at multiple sites in the Koyna seismogenic zone to ascertain the subsurface geology and structure in the region and carry out seismological investigations. The boreholes penetrated the entire thickness of Deccan traps (412–1251 m) and passed through a few hundred meters of the underlying granitic basement46. Study of drill cores also reveal that the transition of Deccan basalts to granitic basement rock occurs over a relatively short span of few tens of centimetres to few meters51. In these sections, rocks appear to be intermixed with both granitic and basaltic compositions.Scientific deep drilling in the Koyna-Warna region of Deccan traps, western India provides a unique opportunity to investigate microbial life within deep terrestrial igneous rocks In the present study, rock cores obtained from three exploratory boreholes in the Koyna-Warna region are used to decipher microbial community structure and function. Samples are collected from three horizons, i.e., Deccan basalt (BS) (~65 Ma), the underlying granitic basement rock (GR) (~2500 Ma), and the short section between the two formations representing the weathered surface of granitic basement affected by first lava flows . To the best of our knowledge, this is the first report describing subsurface microbiology of the late Cretaceous basaltic lava flows over Archean granitic basement.3−, PO43−, SO42−, Mg, Ca, Fe, K, SiO2, Al2O3 and MgO. BS samples are distinctive based on slightly acidic to neutral pH (pH range 6.4–7.4), relatively higher total organic carbon (TOC) (25.2–48.1 mg/kg), total inorganic carbon (TIC) (93.2–101.2 mg/kg), CaO (14–16% w/w), TiO2 (3–5% w/w) and Fe2O3 (32–36% w/w). Due to the weathered nature of the transition zone, concentrations of a few parameters  do not fit well with the trend observed for granites and basalts. Variations in oxide concentrations with respect to depth in basalts and granites are consistent with the fact that granites are felsic whereas basalts are mafic in nature. Principal component analysis (PCA) based on all the measured parameters shows that the samples from the three different zones  are geochemically distinctive  is generally low whereas phosphate is below detection limit except for samples obtained from the deeper horizon .Thirteen rock core samples covering BS, TZ and GR horizons are collected from three bore holes. Core samples from different depths portray geochemical characteristics of the subsurface system Fig.  and S2. ive Fig. . AnalysiH′ 9.26–11.62) with exception of PV2 (3.94) and PV8 (4.87). The Simpson index, which measures the species dominance in a community, lies in a narrow range of 0.98–0.99 for all the samples except PV2 (0.52) and PV8 (0.62). Lower values of Shannon and Simpson indices for samples PV2 and PV8 signify lower diversity in the community when compared to other samples.Sequence information and diversity indices are summarized in Table 5 and 4.01 × 106 with an average of 1.61 × 106. Highest bacterial abundance is observed in P2, a sample of weathered granite from 1254 mbsl (meters below surface level). Archaeal 16S rRNA gene copies per gram of rock vary between 1.88 × 102 and 9.41 × 104, with an average of 2.29 × 104. Assuming an average of 4.2 and 1.7 16S rRNA gene copy per genome of bacteria and archaea respectively48, our estimate suggests microbial cell abundance ranging from 8.65 × 104 to 1.01 × 106 per gram of rock . With respect to distribution of estimated bacterial and archaeal cells, higher relative abundance  of archaeal cells are observed in samples from the transition zone followed by the basaltic horizon . Samples from granitic horizon exhibit on average 1.32% (SD = 1.38%) archaea of the total estimated number of cells. The observed abundance of archaea in samples from TZ and BS zone samples corroborates with higher level of total organic carbon and near constant level of total inorganic carbon in these samples compared to the GR horizon. Abundance of two major biomarker genes (dsrB involved in sulfate reduction and mcrA involved in methanogenesis) of sulfur and methane metabolism is studied by quantitative PCR across the different rock samples. Presence of dsrB gene is noted in all but one sample with copy numbers varying from 7.6 × 102 to 2.7 × 105 per gram of rock with an average of 7.7 × 104 (SD = 6.88 × 104). Gene fragment of mcrA is observed in 10 out of 13 samples and its copy number per gram of rock varies from 4.1 × 101 to 2.5 × 103 with an average of 7.7 × 102 (SD = 7.54 × 102). Interestingly, the overall abundances of both the genes are higher by at least one order of magnitude in BS samples when compared with those from TZ and GR.The abundances of bacterial and archaeal 16S rRNA gene copies are determined by quantitative PCR based approach to delineate vertical distribution of microbes  followed by Actinobacteria (8–73%) and Firmicutes (2–19%)  than the GR  and TZ  cores. On the other hand, average abundance of Alpha- and Beta-Proteobacteria are higher in the BS cores  than that of GR  and TZ . Out of 12 assigned archaeal classes, Thermoplasmata exhibits highest average abundance followed by Methanobacteria, Methanomicrobia and Halobacteria.On the basis of cumulative abundance, Acinetobacter followed by Corynebacterium, Pseudomonas and Staphylococcus represent the most abundantly detected genera. Other bacterial genera which are detected in significant amount across all the samples are Enhydrobacter, Halomonas, Micromonospora, Rhizobium, Agromyces, Bacillus, Sphingomonas, Shewanella and Deinococcus. On the other hand, at genus level, Thermoplasma, Methanobacterium and Methanosaeta are found to be the most abundant archaeal genera.Taxonomic affiliation at the lowest level (genus) is possible only for 89% of total classified reads (91% of the OTUs). viz., Anaerolineae, Methanomicrobia, Deltaproteobacteria, Spirochaetes, Methanobacteria, Blastocatellia, Cyanobacteria and Nitrospira show strong positive correlation among one another. Thermophila, Chloroplast, Cytophagia, Holophagae, Epsilonproteobacteria, Rubrobacteria and Acidobacteria subgroup 6 form the second distinct clade. Deinococci, Bacilli and Flavobacteria also have strong correlation among each other. Relative abundances of Acidimicrobiia, JG30-KF-CM66, Phycisphaerae, Solibacteres, KD4–96, Clostridia, Parcubacteria_uncult_bac, Chloroflexia, Planctomycetacia, Fusobacteriia, Bacteroidia, Thermomicrobia, Coriobacteriia and Acidobacteria are well correlated mutually. Relatively more abundant classes like Gammaproteobacteria, Actinobacteria, Beta- and Alphaproteobacteria show poor correlation among one another.Spearman correlation is used to identify the populations (at class level) whose abundances are strongly correlated Fig. . A numbeAlpha- and Betaproteobacteria and lesser abundance of Gammaproteobacteria, Actinobacteria, Chloroflexi and Euryarchaeota. GR microbiome in this reference show elevated dominance of Actinobacteria compared to its BS and TZ counterparts. SIMPER analysis is used to determine the microbial taxa (class level), responsible for the differences observed between the BS, TZ and GR microbiomes  and environmental parameters have been established. Distance based redundancy analysis (db-RDA) separate the samples into two distinct abundance-weighted categories. Regardless of the borehole sites, subsurface rock microbiomes partition into (i) BS zone communities and (ii) TZ-GR zone communities Fig. . Averagees Table . AdditioActinobacteria , Alphaproteobacteria  and Betaproteobacteria , but lower abundance of Bacilli and Gammaproteobacteria . TZ microbiome core community is more enriched. Actinobacteria, Alphaproteobacteria and Bacilli are mainly present with elevated abundance. However, lower abundance of Gammaproteobacteria, and unique presence of Clostridia, Bacteroidia, Deltaproteobacteria, and Thermoleophilia are noted. Marked difference is noted with respect to Betaproteobacteria in BS and GR horizons. Compared to BS, wherein Betaproteobacteria is solely represented by Burkholderiales, at least three more highly relevant orders, viz., Hydrogenophilales, Methylophilales and Nitrosomonadales are present in GR (as Betaproteobacteria). Rank abundance histogram for these shared OTUs across the three different rock microbiomes mostly indicates uneven distributions .Microbial co-occurrence network analysis is performed based on family level data obtained across the three horizons. Network consists of different sub-networks which are composed of both positive and negative connections on the basis of Spearman correlation. The main network consisting of 82 families has two distinct hotspots (or regions where large number of connections are observed among nodes) Fig. . One of viz. rbcL, acsB, accC and ACLY, methane production (mcrA), methane utilization (pmoA) and different fermentative processes  have been detected , 3-hydroxypropionate cycle (accC) and reverse TCA cycle (ACLY) are observed in all the samples with varying abundance whereas the gene responsible for CO2 fixation in Wood–Ljungdahl pathway (acsB) is observed in U7 (BS) and U11 (GR) only. Gene involved in methane production, mcrA is detected in all the samples whereas gene involved in methane utilization, pmoA is detected in BS and TZ zone samples only. Overall abundance of affiliated reads to mcr and pmo are considerably lower than the reads affiliated to marker genes of different carbon fixation pathways. 3-hydroxypropionate pathway is found to be the most dominant carbon fixation pathway in the Deccan subsurface  is found. Presence of both cytoplasmic (narABDG) and periplasmic (napABC) nitrate reductases and all other enzymes that perform steps in the complete denitrification process suggests that dissimilatory nitrogen-transforming metabolisms are common  is observed in all the metagenomes whereas genes aiding in the process of nitrification (amoA and hao) are observed in U7 (BS) and U6 (TZ). Sulfur cycle is one of the important processes that take place in the subsurface environment where microorganisms are a significant contributor. Presence of genes taking part in dissimilatory sulfur reduction (dsr and apsAB), assimilatory sulfur reduction  and sulfur oxidation  are detected in the subsurface metagenomes  is also observed in low and varied abundances across the three samples.Metagenomic inventories of three representative rock microbiomes from BS, TZ and GR zones are studied through shotgun metagenomics approach. Metagenomic sequencing information and assembly statistics are provided in Supplementary Table ted Fig. . Key genace Fig. . Presencmon Fig. . Furthermes Fig. . Howevermes Fig. . Presenc49. Geochemical attributes of the subsurface rock samples suggest distinct partitioning of the three major crustal provinces  deep underneath the Deccan traps. During last five decades, the Koyna-Warna region has experienced recurrent seismicity attributable to the changing water levels in the Koyna reservoir, potentially causing structural deformation and associated fault and fracture zones in the adjoining regions52. The samples from all three zones used in this study portray characteristic geochemical signatures with respect to minerals present, alkalinity, and low organic carbon along with distinct pattern of abundance for oxides. Nevertheless, variations as evident in a few samples could be attributed to geological phenomena. The variations are mostly in terms of relative abundance of oxides, in particular, higher weight percentage of Fe2O3 and lower weight percentage of SiO2 than the standard basalts and granites53. Further investigation on rock mineralogy by our laboratory (data not shown) indicates that apart from the characteristic minerals of igneous rocks 54, some samples contain metasomatic minerals . Presence of these metasomatic minerals indicates secondary alterations and possible movement of fluids through different faults and fractures. These results are consistent with a previous study which depicts structural deformations and several instances of water movement through different fractures and faults associated with seismicity in the Koyna-Warna region51.The subsurface rock samples from the Koyna-Warna region in western India span a substantial geological timescale, varying from ~65 Ma  to Archaean (basement granitoids) and including a small zone of weathered basement rocks that experienced the first rounds of lava flows during the emplacement of the Deccan traps50 and moderate-to-high temperature and pressure 55. In comparison to several other subsurface igneous provinces, the BS, TZ and GR samples of the present study show significantly low (over one order of magnitude) organic carbon (TOC)58. Deep subsurface igneous provinces are often devoid of photosynthetically derived organic carbon. Nevertheless, the geogenic carbon , electron donors  and electron acceptors  can act as essential metabolic resources and facilitate microbial life59. From the overall geochemical data, we could elucidate the nutritional status of this igneous realm which suggests its potential to support microorganisms that prefer chemolithotrophic/chemolithoautotrophic lifestyle. The chemolithotrophic/chemolithoautotrophic mode could be linked to heterotrophic metabolism through metabolic products of lithotrophic organisms, thus driving the community’s overall function.Microbial life within the nearly impermeable, highly oligotrophic basalts and granitic bedrock is presumed to be constrained by prevailing physical and chemical factors. The igneous rocks in the deep subsurface have very low porosity (<0.2 ± 0.2% for granitoids)5 cells per gram of rock. Bacteria outnumber archaea in all samples. Estimation of cell numbers from qPCR based gene copies is done considering an average 16S gene copy per genome and this estimate may be prone to bias27. Additional caveat may be due to primer bias during 16S rRNA gene amplicon preparation. Nevertheless, the 16S rRNA gene amplicon data, qPCR based analysis and whole metagenome based data corroborate among each other with respect to microbial community composition. Cell number observed in our study is consistent with the “rocky deep biosphere trend”9. Current estimated cell number in various deep subsurface igneous provinces range between 104 and 108 cells per gram of rock64. Lack of correlation of cell number with depth within deep subsurface of Deccan traps is consistent with previous reports on several other terrestrial deep subsurface studies65. Relative consistency of cell numbers across depth in Deccan traps could be attributed to a common set of metabolic or ecophysiological constraints to the inhabiting cells. Considering the geologic age of formations and very low porosity (<0.2 ± 0.2%) of the rocks50, the origin of the observed subsurface basaltic and granitic crustal communities remains enigmatic. The only plausible explanation is dispersion of cells through water movement in various fractures and faults developed due to tectonic perturbations. A recent study suggests movement of water through fissures and fractures in the Deccan subsurface of Koyna seismic zone which is evident from secondary mineralization in basalts and basement granitoids51.Our qPCR results on 16S rRNA gene abundance indicate the presence of nearly 1037. Sequencing depth attained in our work is significant leading to the detection of more OTUs and diversity. In addition to this methodological aspect (sequencing depth), previous studies have linked higher diversity in igneous rocks with the rock age and thus its alteration state. Younger basalts are reported to host less diverse communities while older basalts (>20000 years) generally display richer communities adapted to local alterations of the rocks24.16S rRNA gene based community composition corroborates with our qPCR based estimation suggesting a more diverse bacterial population when compared to archaea. Considerably higher diversity of bacteria as observed in this study is in the same order of magnitude or even higher than those reported from other deep terrestrial habitats from a similar depth rangeGamma- and Alpha/Betaproteobacteria, Actinobacteria, Chloroflexi, Firmicutes, Nitrospirae, Deltaporteobacteria, and Epsilonproteobacteria69. The global occurrence of most of these taxa in almost all basaltic-granitic environments could possibly be considered as an indication for igneous rock hosted core group27. Members of Alpha- and Betaproteobacteria are relatively more abundant in BS horizon. Most of these proteobacterial taxa are known to be of diverse metabolic types especially H2/Fe2+/S oxidizing autotrophs; C1 compound-, hydrocarbon- metabolizing, microaerophilic, fermentative and strict anaerobic organotrophs70. Actinobacteria, though found in all three zones, is relatively more abundant in GR. Among the actinobacterial classes, presence of obligate acidophilic Acidimicrobiales members commonly found in Fe-rich ecosystem (including sites with volcanic rocks) and with Fe2+ oxidizing or Fe3+ reducing abilities justifies their niche specific colonization within the deep subsurface of Deccan traps71. A small proportion  of the communities is represented by Cyanobacteria. Despite that the ‘rocks at this site have not seen sunlight’ for several thousands to millions of years, presence of Cyanobacteria in the deep subsurface of Deccan traps environment corroborates well with several earlier deep biosphere studies, indicating their potential role in supporting sulfate reducing bacteria by providing dark fermentation derived H275. Cyanobacteria are considered to be ubiquitous in aquatic environments and it is possible that a cyanobacterial bloom at or near surface was trapped in the aquifer long time ago and/or the water might have intruded into the deeper layers of rock strata through faults/fractures. Whether these phototrophs survived because of their ability to switch to a fermentative lifestyle76 or they are less prone to degradation cannot be determined with the data in hand and remains an open question to answer through further research.Phylum level composition of microbial communities of deep Deccan subsurface corroborates with recent surveys on microbial life within igneous rocks  indicating predominance of Anaerolinea, Cyanobacteria, Epsilonproteobacteria), lithotrophic nutrition with oxidation of inorganic nitrogen (NO2−), sulfur (S0) and iron (Fe2+)  and organotrophic nutrition with fermentative, denitrifying and sulfate reducing activities  can be noted84. Presence of methanogenic archaea  along with Deltaproteobacteria is a clear indication of interspecies metabolite transfer through syntrophic activities. Close metabolic tie-up between autotrophic and heterotrophic members of the communities is previously reported from several oligotrophic deep subsurface ecosystems77. Although present in low abundance, correlation among Nitrospira, Spirochaetes, Chloroflexi, Cyanobacteria and Epsilonproteobacteria indicates that they could be the key players in driving the redox reactions and carbon flux through oxidation of inorganic substrates  and dark fixation of carbon in the nutrient deprived, mineral rich, deep, dark Deccan subsurface84. Assimilated carbon and its fermentation products  drive other lithotrophic and heterotrophic metabolism including anaerobic nitrate/sulfate reduction and methanogenesis. Lower level of correlation among more abundant proteobacterial groups as evident from our study has been previously noted in the Fennoscandian shield, and could support the hypothesis that close interrelations among these taxa are possibly more relevant on or near surface rather than in deep terrestrial subsurface16.Correlation among the microbial classes based on their abundance highlights the possible interplay among the groups. On the basis of known metabolic properties of these interrelated taxa, we hypothesize a possible metabolic roadmap followed by the Deccan deep subsurface microbiome which allows utilization of available resources and provides survival benefits. Close association of several taxa previously known from diverse deep, oligotrophic igneous subsurface and capable of autotrophic carbon fixation  corroborates well with the previous reports of a basalt specific microbiome86. Based on these results, we propose that although taxonomically similar organisms have occupied different igneous rocks at varied depths, environment specific taxon recruitment based on local availability of electron donors, acceptors and carbon sources, and limited opportunities of migration led to the distinct taxonomic assemblages. Our db-RDA data clearly indicate such partitioning of interrelated guilds and these distinct microbial assemblages of GR and BS horizons are controlled by iron oxide, total organic carbon, nitrate, nitrite and sulfate. The BS microbiome guilds could potentially be regulated by higher nitrate but relatively lower nitrite and indiscriminately present (2 out of 6 samples) low sulfate levels. GR microbiome on the other hand is strongly affected by sulfate. Since we could not measure hydrogen and sulphide from the rock samples, their potential in influencing the microbial assemblages could not be ascertained in this analysis. Differential abundance of genes related to dissimilatory sulfate reduction (dsrB) and methanogenesis (mcrA) within BS and GR horizons possibly imply the disparity in recruitment of such processes within the subsurface provinces. These results suggest that microbial assemblages are affected by different geochemical parameters across the subsurface horizons of Deccan traps.It is interesting to note that a high percentage of OTUs are unique to BS, TZ and GR microbiomes (as sampled from various depths) and only a handful of OTUs (15/107721) covering 2.8% of the total sequences are shared among all the 13 samples. This possibly indicates community isolation between different horizons . This particular observation presents a sharp contrast to the high degree of overlap in OTU distribution pattern in deeply buried oceanic crust and sedimentary habitat aboveviz., heterotrophic, anaerobic/microaerophilic fermentation, nitrate and sulfate reduction92. Nitrate and sulfate reducers of this group may be fuelled by hydrogen generated by the fermentative members. Members of the second hotspot are mainly anaerobes/facultative anaerobes, syntrophs, autotrophs, nitrate reducers, hydrogen- and iron oxidizers known to withstand different environmental stresses97. This hotspot represents microbial groups which are generally observed in extreme and oligotrophic environments and further highlight the link between autotrophic-fermentative/organotrophic-syntrophic organisms that could drive communities through anoxygenic carbon fixation, hydrogen metabolism, and nitrate/sulfate reduction. Nitrospiracea, Cyanobacteria and the other members of micro-network 1 play important roles in subsurface nitrogen and carbon cycle101, while Thermoplasmataceae and Terrestrial Miscellaneous Group archaea are known thermophiles, previously observed in hot springs and other igneous provinces102.Microbial co-occurrence analysis indicates the presence of distinct and well defined networks across the three horizons, which corroborates the correlation based observation. Families affiliated to the first hotspot have previously been reported to be involved in diverse, yet deep biosphere relevant metabolic processes, 20. Nevertheless, whole genome metagenome based studies, supported by 16S rRNA gene based analysis allow us to gain a better understanding. The metagenomic inventory supports our observation that there is a syntrophic relationship between autotrophic and heterotrophic organisms which could exchange fixed carbons, CO2, H2, etc. for their sustenance. Though there are presence of both denitrification and dissimilatory nitrate and sulfate reduction pathways for harvesting energy in the Deccan subsurface, nitrate appears to be the preferred energy source. Reduced sulfur, nitrite, iron and hydrogen could be potent electron donors to fuel the community metabolism. This inference is in line with previous reports on thermodynamics of microbial processes within deep biosphere as well as their metagenomic analyses103. The presence of different genes related to assimilatory as well as dissimilatory nitrogen metabolism indicates that there is likely a dynamic nitrogen cycling occurring within the Deccan subsurface. Overall abundance of nirB in all the samples (ammonia assimilation) is higher when compared to denitrification genes . Results from previous studies indicate that ATP synthesis in denitrification is far lower than that expected from the free energy changes and even lower than in nitrate ammonification104. This justifies the fact that ammonification could be more common pathway when compared to denitrification in the Deccan subsurface not only for generating more ATP but also the product of ammonification (NH3) is more bioavailable than the product of denitrification, i.e., N2. Relative abundances of genes related to carbon cycle give a sense that CO2 could be the driving force in subsurface igneous provinces of Deccan traps. 3-hydroxypropionate pathway (3-HP) is found to be the most abundant carbon fixation pathway. In addition to CO2 fixation, this pathway enables synthesis of necessary metabolic intermediates and fatty acids from HCO3− 105. Ample presence of HCO3− in the igneous rocks including the Deccan traps, which contains highest HCO3− concentration among other similar rock types and basaltic watersheds from around the world106, justifies the fact that 3-HP could be an important and common carbon fixation pathway. High abundance of HCO3− in the Deccan traps selectively allows both chemoautotrophic and chemoorganotrophic organisms harbouring accC to thrive in the Deccan subsurface. Similar result with ubiquity of accC gene is obtained in deep Fennoscandian bed rock fluid samples18.Microbial life in oligotrophic deep terrestrial subsurface hosted by igneous rocks are highly constrained by metabolic resources. Due to the extremely low cell turnover rates (hundreds to thousands of years), it is difficult to describe microbial processes by direct measurementsaccC activities) are used as potent substrates in the nutrient deprived subsurface provinces of Deccan traps. Low levels of organic molecules may be utilized by chemoorganotrophs for gaining energy or may be assimilated as a carbon source. CO2 is a useful fuel for the autotrophs which further produce nutrients for other heterotrophic community members. The coupling of redox transformation of sulfur and nitrogen substrates to carbon flux is clearly revealed from metagenomic data. Previous reports suggest that though organotrophic denitrification is more spontaneous than lithotrophic denitrification in presence of reduced sulfur , presence of acetate may accelerate the process of denitrification and sulfur oxidation simultaneously108. Higher abundance of acetyl CoA synthase and acetate kinase among other genes related to fermentation in all the three metagenomes gives us a sense that the presence of acetate is prevalent in the Deccan subsurface. Though metagenomic binning would yield better results and provide improved understanding of the coupled metabolic processes, from the present scenario it can be hypothesized that sulfur oxidation coupled with denitrification in presence of acetate may be a common process in Deccan subsurface, considering the subsurface anaerobic environment of Deccan traps. Similar results have been reported in a 1.34 km deep fault zone of Witwatersrand Basin, South Africa17. Overall metagenomic data are in accordance with the taxonomic outcomes where close association of chemolithoautotrophs and heterotrophs are evident.In view of the geochemical environment, taxonomic composition and metagenomic inventory, we infer that autotrophic carbon fixation, sulfur/hydrogen oxidation, fermentation and nitrification-denitrification are likely to be the key reactions driving the subsurface ecosystem linking the carbon, nitrogen and sulfur cycles. Products of fermentation and other organic molecules  used during drilling according to the protocol of Nyyssönen et al.16. Only interior pieces of rock were selected for microbiological study to avoid potential drilling mud contamination as recommended elsewhere109. Samples were collected following aseptic techniques and stored in sterile containers at 4 °C for shipment. In laboratory, the samples were stored at −20 °C to limit the chance of microbial contamination, till further analysis.Subsurface core samples were collected from different depths of three exploratory drill holes  in the Koyna-Warna region of Deccan traps, Maharashtra, India during July 2014 [Panchgani  and Ukhalu ] and May 2015 [Phansavale ]  and sub-coring of the rock was done to get rock samples devoid of any possible contamination that might have occurred during drilling. At first, the uneven ends of the cores were removed using granite cutter fitted with sterilized cutting blade. Subsequently, with either a mechanical drilling instrument fitted with sterilized drill bit or with sterilized chisel and hammer, rock powder was obtained from the core interior part. The rock powder thus obtained was stored in sterilized DNA free containers at −20 °C for further geochemical and microbial analysis.−, NO2−, SO42−, NO3−,and PO43−) analysis by using Dionex ICS-2100 (Thermo Scientific). Total organic carbon, total inorganic carbon and total carbon were measured on OI analytical TOC analyzer. Before analysing TOC, rock powders were fumigated with 1 N HCl for 48 hours under fume hood. Quantification of the elemental oxides was done using PANalytical Epsilon3 XRF instrument. All the measurements for major elements, anions, carbon content and oxides were carried out in triplicates and the standard error was <1% from the mean for all the analyses. Mineralogical analysis of the powdered rocks was conducted using PANalytical XRD X’pert Powder and on the basis of 2θ and d-spacing, mineral composition was evaluated using X’pert HighScore Plus software. Alkalinity of the rocks was measured using USEPA method 310.2. pH measurement was done by incubating 1 g of rock powder in 0.1 M CaCl2 at 1:10 ratio (w/v), and highly sensitive probes (Orion) fitted with an Orion multimeter  were used110.Rock powder was sieved through 2 mm mesh. Acid digestion of the rock powders was carried out in a microwave digester (Milestone SK12) followed by the measurement of major elements by using ICP-MS . Rock powders were ultra-sonicated in presence of water for major anions (ClMetagenomic DNA was extracted from 13 samples using MoBio power soil DNA isolation Kit (MoBio) following the manufacturer protocol. Total DNA from reagent control was also extracted using the same procedure (as mentioned before) and were used subsequently to check any possible contamination. Quality of the extracted metagenomic DNA and its concentration was measured using NanoDrop 2000 spectrophotometer, followed by fluorometric quantitation using Qubit (Thermo-Fisher Scientific).dsrB and mcrA) were also quantified using qPCR-based technique to estimate the sulfate reducing and methanogenic populations. Details of qPCR primers used in the study are provided in Supplementary Table 2 to 1010 of plasmid DNA containing the 16S rRNA gene of Achromobacter sp. MTCC 12117. Genes encoding archaeal 16S rRNA, dsrB, and mcrA were PCR amplified from the metagenome, cloned in TA cloning vector and plasmid DNAs for each with copy numbers 102 to 1010 were used as standards for quantitation purpose.The bacterial and archaeal populations in a rock sample were quantified by estimating the copy number of bacterial and archaeal specific 16S rRNA gene. Copy numbers of two functional genes  and control OTUs  were removed and alpha diversity parameters were calculated as mentioned before. Average abundance of each phylum was calculated considering values obtained for samples from each horizon.Raw data obtained from Ion S5 were preprocessed and quality filtered using the Quantitative Insights Into Microbial Ecology (QIIME 1.9.1)114 on the basis of geochemical parameters. Geochemical parameters were normalized on the basis of feature scaling before PCA was plotted. Statistical significance of similarity of geochemical parameters across different horizons was evaluated on Euclidean matrix in PAST software using analysis of similarities (ANOSIM). Average abundances of each phylum across all the samples were calculated and compared. Standard deviations (SD) were calculated during average calculations and mentioned in brackets. Classes having average abundance of 0.1% across all the samples were selected for correlation analysis and correlation heatmap was constructed on the basis of Spearman correlation using METAGENassist115. Distance based redundancy analysis (db-RDA) of Bray-Curtis distance on the basis of all the microbial classes present across 11 samples, and 10 different physicochemical parameters was performed using Vegan package in R statistical software116. Similarity percentage (SIMPER) analysis was also done using PAST software for 11 samples to understand the microbial classes responsible for the grouping of different rock types. Two samples, PV2 and PV8 were not considered for db-RDA and SIMPER analysis due to over-representation of a single OTU in these samples. OTU overlap among the samples from similar rock type and among different rock types were elucidated using InteractiVenn117 and Venny 2.1118.Principal component analysis (PCA) was performed using PAST software119. Based on pairwise Spearman correlations with correlation values >0.75 and <−0.75 were used for construction of co-occurrence network using Cytoscape 3.4.0120.Co-occurrence network analysis was performed considering the microbial families detected in the samples. Microbial families having greater than 0.1% average abundance across all the samples was used for the network construction. Correlation among different families was calculated using otu.association command in mothur121. Quality checking of the trimmed paired end reads was conducted using FastQC (Version 0.11.7). Trimmed sequences were assembled using metaSPAdes assembler123 using default parameters. Quast 4.3 was used for evaluations of metagenomic assemblies and different assembly statistics124. K-mer of 55 was chosen (on the basis of longest contig) for annotation using Genomes OnLine Database (GOLD) v.6125. KEGG based annotation was selected for further analysis and metabolic pathway reconstruction was done. The annotated data are available under IMG GOLD Analysis Project ID Ga0232666, Ga0232668 and Ga0232669.Metagenomes of three representative samples from basaltic (U7), transition (U6) and granitic zones (U11) were considered for whole metagenome sequencing. After multiple DNA extraction, concentrations of DNA were determined by Quant-iT Picogreen dsDNA assay. DNA samples were fragmented to ~250 base pairs (bp) using a Covaris S220 Focused-ultrasonicator  and paired end (2 × 151 bp) sequencing was performed at Marine Biological laboratory, Woods Hole, USA using Illumina NextSeq500 platform under Deep Carbon Observatory (DCO), Census of Deep Life program. Details of metagenomic library preparation and sequencing are mentioned in the supplementary information. The raw sequences were adapter trimmed using cutadapt 1.9.1Supplementary Information