Integrated photonics changes the scaling laws of information and communication systems offering architectural choices that combine photonics with electronics to optimize performance, power, footprint, and cost. Application-specific photonic integrated circuits, where particular circuits/chips are designed to optimally perform particular functionalities, require a considerable number of design and fabrication iterations leading to long development times. A different approach inspired by electronic Field Programmable Gate Arrays is the programmable photonic processor, where a common hardware implemented by a two-dimensional photonic waveguide mesh realizes different functionalities through programming. Here, we report the demonstration of such reconfigurable waveguide mesh in silicon. We demonstrate over 20 different functionalities with a simple seven hexagonal cell structure, which can be applied to different fields including communications, chemical and biomedical sensing, signal processing, multiprocessor networks, and quantum information systems. Our work is an important step toward this paradigm. Integrated optical circuits today are typically designed for a few special functionalities and require complex design and development procedures. Here, the authors demonstrate a reconfigurable but simple silicon waveguide mesh with different functionalities. PICs will play a critical role in future 5 G communication systems15, the Internet of Things16 and advanced civil radar systems17, where broadband and upgradable interfaces between the fiber and wireless network segments will be required at the base stations. In chemical and biomedical sensing, PICs enable the concept of photonic lab on a chip19. PICs also enable high-speed20 signal processing operations24, reconfigurable interconnections between processing and memory units in advanced multiprocessor computing systems and data centres27 as well as quantum logic gates30.These features make photonic integration ideal for a wide variety of emerging applications with potential massive impact. In high-speed fiber communications, PICs enable flexible and reconfigurable transceivers and multiplexers supporting all the multiplexing domains 31. In this approach, a particular circuit configuration is designed to optimally perform a particular functionality in terms of propagation losses, power consumption, footprint and number of components. An ASPIC design requires, however, a considerable number of design and fabrication iterations leading to a long time for development32. Furthermore, despite the fact that a reconfigurable processor implementing signal integration, differentiation and Hilbert transformation using an InP ASPIC has been recently reported33, multifunctional ASPICs are difficult to design keeping the same layout.The so-called application-specific photonic integrated circuit (ASPIC) paradigm has been dominant so far in integrated optics35. Several authors38 have reported seminal theoretical work proposing different architectures and design principles based on the cascade of beamsplitters or Mach Zehnder Interferometers (MZIs) that incorporate phase tuning elements, which enable independent control of amplitude and phase of light. These configurations are targeted in particular for multiple input/multiple output feedforward linear optics transformations.An alternative to ASPICs is to consider a general-purpose processor architecture that can be integrated on a photonic chip, featuring single and/or multiple input/output operation and being capable of performing different signal processing tasks by programming of its electronic control signals39. The core concept is to break down complex circuits in a large network of identical two-dimensional (2D) unit cells implemented by means of a MZI waveguide mesh or lattice. Zhuang and co-workers39 have pioneered the field by proposing a programmable optical chip architecture connecting MZI devices in a square-shaped mesh network grid. The distinctive feature of this approach is that it enables both feedforward and feedbackward configurations, selecting the adequate path through the mesh and providing independent tuning of circuit parameters to complex valued coefficients by introducing phase tuning elements in both arms of the MZIs to enable independent control of amplitude and phase of light at coupler outputs40. The structure, fabricated in Si3N4 consisted in two square cells and was employed to demonstrate simple finite (FIR) and infinite (IIR) impulse response filters with single and/or double input/output ports.A more versatile architecture can be obtained by following similar principles as those of the Field Programmable Gate Arrays in electronics41. In particular, hexagonal and triangular-shaped meshes41 feature improved performance in terms of spatial tuning reconfiguration step, reconfiguration performance, switching elements per unit area and losses per spatial resolution41 , coupled resonator waveguides (CROWs), side-coupled integrated spaced sequences of optical resonators (SCISSORs) and ring loaded MZIs to multiple-input/multiple-output linear optic 2 × 2, 3 × 3, and 4 × 4 transformations including Pauli Matrices and a C-NOT gate. The experimental demonstration of this multifunctional integrated waveguide mesh photonic processor core includes both classical FIR and IIR signal processing functions as well as multiport linear optics operations.41. Figure The architecture concept of the software-defined photonic processor is shown in Fig. 42 that given a particular hardware configuration the range of transfer functions that can be synthesized with it is limited. In other words, it is not possible to design a general and well- defined synthesis algorithm accounting for all the possible filter realizations. Therefore, not such synthesis algorithm can be developed for the hexagonal waveguide mesh either. However, the available synthesis methods for the specific hardware configurations that can be emulated using the waveguide mesh can be applied by developing a suitable procedure, which translates the results provided by the synthesis equations into specific parameter values of the MZI that are needed to implement the waveguide coupling points in the emulated layout. We have found that this is possible for all main discrete-time signal processing hardware configurations employed in practice as discussed below, all of which are scalable.The proposed hexagonal waveguide mesh is per se a programmable hardware platform supporting multiple configurations and not a specific layout. This means that it can be programmed to emulate the specific hardware configurations of a wide variety of signal processor architectures, including, among others, traditional feedforward/feedbackward FIR and IIR impulse response filters as well as multiple-input/multiple-output optical linear transformers. It is well known from signal processing theory43 that are directly applicable since the hexagonal waveguide mesh can directly implement both 3 dB-tuneable MZI cascade lattices and transversal filter configurations. For IIR filters, either simple/compound optical ring cavities of ring-loaded 3-dB tuneable MZI cascades are employed. Again, synthesis algorithms have been reported in the literature44 that are directly applicable since the hexagonal waveguide mesh can directly implement either simple or multiple cavity ring filters or ring-loaded 3-dB tuneable MZI cascades.For example, FIR filters are based either on cascades/lattices of 3-dB tuneable MZIs or in transversal filter configurations. For both FIR filter alternatives, synthesis, and recursive scaling algorithms have been developed in the literature36 and by Clements et al.38 for rectangular configurations, which, as mentioned above, need to be adapted to the hexagonal waveguide mesh configuration. We provide these adaptations in the Supplementary Note In the case of multiple-input/multiple-output optical linear transformers, detailed synthesis and recursive scaling algorithms have been reported by Miller for triangular configurations2 and includes 30 MZIs, 60 thermal tuners, 120 pads and features 24 optical input/output ports. The test structure has an additional 8 MZIs (6 + 2), 16 thermal tuners and 32 TBUs. Figure 2.The hexagonal waveguide mesh reconfigurable core is fabricated in silicon on insulator (SOI) and wire-bonded to a chip carrier for experimental demonstration (see Methods). The design process for which the layout is shown in Fig. Full static testing of the device including propagation, bend and insertion losses as well as thermal stability was carried using the test cell (see Methods). We measured the spectral region of operation for the input/output grating coupler devices and found that the optimum performance was in the (1580 ± 15 nm) range rather than the targeted 1550 nm. We obtained full calibration curves for the coupling constants and phase shift versus injected current for all the 30 MZIs in the structure, as well as a detailed characterization of the thermal crosstalk  are 2-input/2-output periodic notch filters that constitute the basic building blocks for lattice and FIR transversal filtersK1 to K4 we tuned the positions of the two zeros provided by the structure and, therefore, reconfigure its transfer function.Since the waveguide mesh has a limited number of cells, we were unable to implement a lattice filter by serially cascading UMZI units. However, we could implement a 3-tap transversal filter by the parallel cascade of UMZI units. The results are shown in Fig. 42. They constitute the basic building blocks for more complex filter designs such as CROWs and SCISSORs. Ring cavities find multiple applications44 including integrators, differentiators and Hilbert transformers33, dispersion compensators45, as well as tuneable radiofrequency phase shifters, and true time delay lines46. By suitably tuning the MZI devices in the 7-cell waveguide mesh, we have been able to implement single ORRs with cavity lengths given by 6, 12, and 18 BULs. Figure K1 and K2, which settle the positions of the zero and the pole42. The IIR filter tunability, which is shown in Fig. π into the ORR round-trip and tune the resonance position along a full spectral period. We compared our experimental results with those predicted by the theory showing an excellent agreement  notch filters, while in the second they can implement both IIR notch and FIR + IIR bandpass filters47, SCISSORS48, and ring-loaded MZIs49 using the former basic building blocks in the 7-cell waveguide mesh and activating more MZI devices to provide additional propagation paths. These are usually 2-input/2-output filters that are characterized by transfer functions with a higher number of zeros and poles. By suitably tuning the coupling constants, one can obtain, for instance, filters with special characteristics in the modulus42 (flat passband) of the phase shift44 (parabolic). The number of current sources allowed us to program 2- and 3-ORR CROWs (6-BUL ORR length), 2-ORR SCISSORs (6-BUL cavity length) and a double ring-loaded MZI.We can build more complex (multicavity) signal processing structures like CROWs42. A similar concept is employed in the SCISSOR structure . The phase shifter flattens the spectral region in between two consecutive notches and provides two slightly parabolic phase shifts of opposed concavity in that region, which correspond to two linear group delay regions of opposed slopes. Within this region, the structure can be employed as a tuneable dispersion compensator or as a true time delay line44. The TBU marked with an asterisk is an example of how TBUs can be configured in order to extract non-ideal leaking due to optical crosstalk from the circuit. Anyway, no deteriorated performance was observed for a measured optical crosstalk around 40 dB. Figure 49.Figure N × N unitary matrix U50–52. These include, among others, switching and broadcasting, mode combiners and splitters, and quantum logic gates. We programmed the 7-cell waveguide mesh to demonstrate several 3 × 3 and 4 × 4 linear transformations. These are relevant examples of signal processing tasks that are needed in different applications and the results are shown in Fig. 53  featuring a 1 pm wavelength resolution was connected to the input grating coupler of the test cell and scanned to provide a wavelength range characterization. The test cell output grating coupler was connected to an optical spectrum analyzer (ANDO AQ6217C). Measurements included: differential path length to characterize propagation losses, cascaded bends structures to characterize bend losses and two different cascaded and coupled MMI structures to characterize MMI insertion losses and bandwidth. For electro-optical characterization of the TBUs in the 7-cell waveguide mesh, we employed current sources of different resolution and quality: 3 Keihtley2401, 13 Thorlabs LDC8010 and 2 TECMA 72-2535. For the outer perimeter TBUs, the process consisted in injecting optical power into one of the ports of the TBU and sweeping the electrical current bias applied to one of the two heaters. This process was carried out for the 76 thermal tuners present on each of the two characterized PCBs. Altogether with resistance and output optical power, we obtained as a result the normalized coupling constant calibration curves of each TBU. Through this method, we also extracted the phase shift induced by each heater (See Supplementary Note 1D featurAll data are available from the corresponding author upon reasonable request.Supplementary InformationPeer Review File


Chronic psychosocial stress is a well-established risk factor for neuropsychiatric diseases. Abnormalities in brain activity have been demonstrated in patients with stress-related disorders. Global brain activation patterns during chronic stress exposure are less well understood but may have strong modifying effects on specific brain circuits and thereby influence development of stress-related pathologies. We determined neural activation induced by chronic social defeat stress, a mouse model of psychosocial stress. To assess chronic activation with an unbiased brain-wide focus we used manganese-enhanced magnetic resonance imaging (MEMRI) and immunohistochemical staining of ∆FOSB, a transcription factor induced by repeated neural activity. One week after 10-day social defeat we observed significantly more activation in several brain regions known to regulate depressive and anxiety-like behaviour, including the prefrontal cortex, bed nucleus of stria terminalis, ventral hippocampus and periaqueductal grey in stressed compared to control mice. We further established that the correlation of ∆FOSB positive cells between specific brain regions was altered following chronic social defeat. Chronic activation of these neural circuits may relate to persistent brain activity changes occurring during chronic psychosocial stress exposure, with potential relevance for the development of anxiety and depression in humans. This term refers to a broad range of experiences, including lack of normative care, abuse, and problems in peer relationships such as bullying5. Stress-related mental disorders like major depressive disorder (MDD) and anxiety disorders are associated with altered activation of specific brain regions. For example, neuroimaging has revealed that resting-state activity is reduced in the prefrontal cortex (PFC)6 and increased in the hippocampus7 while task-related reactivity is decreased in the hippocampus8 and nucleus accumbens9 of MDD patients. Interpretation of the human imaging findings is difficult as activation patterns in tasks carried out during brain imaging may differ from those occurring during naturalistic chronic stress exposure. Because patients enrolled in these studies also tend to have an extensive history of symptoms and medication use, it is challenging to reveal direct consequences of chronic psychosocial stress itself. In contrast, animal models allow investigation of specific stressors in a controlled manner to establish the mechanisms connecting chronic psychosocial stress exposure, brain activation, and subsequent emotional and social behaviour.Psychosocial stress increases the risk for both somatic and psychiatric diseases and worsens their prognosis11. These behavioural phenotypes last for several weeks post-stress10, and can be reversed by chronic treatment with selective serotonin re-uptake inhibitors or tricyclic antidepressants12, or a single dose of ketamine13. Thus, CSDS is an excellent mouse model to determine psychosocial stress-induced chronically occurring neural activation patterns, which may underlie the behavioural outcomes of stress exposure.Chronic social defeat stress (CSDS) is a rodent model of psychosocial stress, which produces many behavioural features similar to the symptoms of human anxiety and mood disorders, such as social aversion, anhedonia (decreased sucrose-preference) and increased self-administration of drugs of abuse2+ ions into excitable cells through voltage-gated Ca2+ channels during depolarization, which increases T1-weighted signal intensity and thereby provides a marker of activated neurons15. Due to the slow efflux of Mn2+ from the brain, MEMRI allows functional brain imaging in anesthetized animals that have previously undergone pharmacological or behavioural testing17. In addition, Mn2+ ions are transported along axons, released from the terminals into the synaptic cleft, and taken up by the postsynaptic neuron19, providing a global measure of network activation.We hypothesized that CSDS induces neural activation in an extensive network of brain regions, including those previously implicated in acute stress and stress-related psychiatric disorders. We employed a combination of two methods for assessing neural activation in mice: manganese-enhanced magnetic resonance imaging (MEMRI) and ∆FOSB immunohistochemistry. MEMRI is based on the influx of paramagnetic Mn20. Since the estimated half-life of ΔFOSB is 8 days it is a useful marker for repeatedly activated neurons22.ΔFOSB is a truncated hyperstable splice variant of the transcription factor FOSB. While the parent isoform FOSB is rapidly induced and degraded, the ∆FOSB form accumulates through repeated stimuli during a course of a chronic treatment, with each acute stimulus inducing a low level of ΔFOSB, and the repeated stress exposure causing the gradual increase in its total levelsIn contrast to MEMRI that enables brain-wide mapping of neural activation and activity-dependent connectivity, ∆FOSB immunohistochemistry detects nuclear activation with high cellular resolution. By combining these methods, we compared CSDS-exposed and control mice one week after cessation of stress to determine brain regions activated due to chronic psychosocial stress exposure.2 was infused from an implanted osmotic minipump  and forceps minor (fmi), a white matter tract. More caudally, the nucleus accumbens (Acb), caudate putamen (CPu), and lateral septum (LS) exhibited increased activation. Both the striatal and septal clusters were also activated by chronic stress in more caudal sections. In the basal forebrain, ventral parts of the bed nucleus of the stria terminalis (BNST) and preoptic area (LPO) were more activated in stressed compared to the control mice. The preoptic activation continued caudally as a distinct activation cluster encompassing the lateral hypothalamus (LH) with ventrolateral thalamic (VL) connections. We detected stronger MEMRI signal in the midbrain, including the ventral tegmental area (VTA), substantia nigra (SN), periaqueductal grey (PAG), superior colliculus (SuG), ventral hippocampus (vHP), dentate gyrus (DG), and reticular formation (Rt) in stressed compared to the control mice. Cortical activation was found in the visual cortex (V). In the hindbrain, social defeat was associated with increased signal intensity in the pontine (Pn) and vestibular nuclei (Ve), with connectivity with the cerebellum (Cb).We exposed mice to the 10-day CSDS protocol, during which MnClt- or U-tests depending on normality of distributions  of the number of ∆FOSB positive cells between each brain region, separately for defeated and control mice  in both groups with expression in the DG and AcbC, while in defeated mice we also observed correlation of both regions with the CPu and VL , and the vHP and DG. MEMRI revealed stress-induced activation at the orbital cortex and fmi, an anterior portion of the corpus callosum, possibly reflecting the spread of Mn2+ transport in addition to local activity19. Therefore, strong MEMRI signal can also be found in regions with low ∆FOSB expression levels but dense axonal tracts passing through them, as seen in VL, LH and midbrain regions. For example, the LH receives strong inputs from the extended amygdala, striatum, septum, and hippocampal formation, which had higher numbers of ∆FOSB positive cells in stressed compared to control mice. These regions also send reciprocal projections to the hypothalamus. In addition, the LH is connected with the midbrain and brainstem26 which had widespread MEMRI signal enhancement in stressed mice. These regions play an important role in somatomotor, sensory, and autonomic control mechanisms that have been implicated in stress and anxiety27.Although both ∆FOSB expression and MEMRI mapping are expected to reveal differences in brain activation induced by chronic psychosocial stress, the observed regional pattern of activation was not completely overlapping. These findings are not contradictory, but rather reflect methodological differences, and represent complementary measures of repeated brain activation. While ∆FOSB immunohistochemistry allows measurement of high resolution neuronal activation, MEMRI can also reveal long-range connectivity in activated pathways due to anterograde axonal Mn21, the number of ∆FOSB positive cells we observe one week after 10-day CSDS likely reflects repeated activation that occurred during defeat and/or the time between CSDS and dissection. The half-life of Mn2+ in the brain is longer than that of ∆FOSB, and therefore brain activation measured by MEMRI one week after social defeat may partly reflect acute stress effects that occurred during the first days of social defeat. However, we have previously shown that activation induced by a week of alcohol exposure either largely subsided or exhibited an altered pattern following a week-long abstinence from alcohol, suggesting that the changes seen during abstinence could not be ascribed to acute alcohol exposure alone17. Similarly, the MEMRI signal measured one week after CSDS could reflect neuronal activation related to long-term physiological and behavioral changes observed in defeated animals10.Neither MEMRI nor ∆FOSB mapping allows us to determine the exact time of neuronal activation giving rise to the observed differences between stressed and control animals. However, with the 8-day half-life of ∆FOSB28. However, we only observed repeated activation of a subset of these regions during chronic psychosocial stress in mice. These differences to previous publications are most likely due to varying nature of stress and the interval between the stress exposure and measurement of brain activation. Similarly to our findings, the dorsal BNST, CPu, LSV and DG were previously found to be activated already 24 h following CSDS in mice29, and the PFC, LH and DG were activated less than 24 h after chronic variable stress in rats30. No differences were observed in ∆FOSB expression 20 days post-defeat between the control and defeated mice using Western blot detection of FOSB isoforms in bulk tissue samples including the whole PFC and whole hippocampus31. Also the Acb, basolateral amygdala (BLA) and dorsal raphe nuclei were found to be activated 24 h following stress29, but not in our study with a longer post-defeat delay. These regions could therefore be involved more significantly during early stress exposure, as ∆FOSB produced in the early days but not later days of stress exposure may have decayed by the time we measured its expression. The regions displaying activation in our study may mediate the effects of chronic psychosocial stress in the long term and are potential sites of alterations that may also contribute to the development of behavioural outcomes of stress.The regions we selected for immunohistochemical analysis have been implicated in the processing of social and emotional behaviour in mice and humans33, electrical recordings in monkeys34 and staining for activity-induced proteins in rodents36. Although immunohistochemical data analysis reveals such functional connections, directionality of the connections remains elusive. Some regions are even known to be connected by parallel tracts with functionally opposed consequences on activation37. Furthermore, regions that engage in correlated activation are not necessarily physically connected, but rather part of the same functional network. Despite these limitations, we aimed at addressing functional connectivity for establishing how chronic psychosocial stress is processed by specific brain networks.When two brain regions jointly participate in a task, their activity becomes correlated as measured by functional neuroimaging in humans38, but its activation in CSDS has not been extensively studied. BNST neurons mediate anxiety-like behaviour and regulate endocrine functions following acute stress exposure39. Some of its subregions have opposing roles in anxiety-like behaviour: the anterodorsal nuclei produce anxiogenic responses, while the ventral nuclei mediate anxiolysis37. The dorsal segment shows increased activation 24 h following 10-day CSDS29. We found that the anterodorsal BNST is repeatedly activated in stress-exposed mice, and that in the stressed mice the BNST ∆FOSB expression correlation with other brain regions, including the DG, LH, VL, CPu and PrL, was altered. The BNST has afferent and efferent connections with several regions, including the amygdala, LH, PAG and the PFC42, and neural activity of BNST could partly contribute to increased MEMRI signal in these areas through axonal transport. However, out of these established connections we only observed significant correlation of BNST activation with the LH and the PrL following CSDS. The observed correlation between the BNST and DG is interesting because no direct structural connections are reported for these regions in mice43, suggesting that their functional connectivity may be mediated by one or more other regions. Therefore, while structural and functional connections of the BNST are wide-reaching, only some of these connections may be involved in the response to chronic psychosocial stress. In particular, our data suggest that networks connecting the BNST, PFC, LH and DG are activated during chronic stress, and may mediate previously unprobed long-term adaptive changes after stress.The BNST is implicated in risk assessment and anxiety-like behaviour in mice8, but more active at rest7 compared to healthy controls, while the PFC exhibits decreased activity at rest6. Both regions are activated in humans during an acutely stressful task45. We observed repeated activation of both regions and a significant correlation of ∆FOSB expression between them in mice exposed to CSDS. In mice these regions are connected by a monosynaptic tract46, in addition to several indirect connective loops47. Temporally synchronized firing between these regions occurs during anxiety-provoking tasks48, and inhibiting activity along this pathway optogenetically reduces anxiety-like behaviour49. Interestingly, in humans the tract connecting the hippocampal region to the PFC, the uncinate fasciculus, shows decreased white matter integrity by diffusion tensor imaging in adults with a history of childhood maltreatment50. We observed that the fmi, another major white matter tract of the PFC, was more active in defeated than control mice as measured by MEMRI, suggesting that this connection also becomes recruited during chronic stress. White matter alterations have also been found in this tract in mice exposed to social isolation51 and in MDD patients52. Thus, chronic psychosocial stress in mice, and acute social stress in humans, both increase activity in the circuit involving the PFC and vHP.The vHP and PFC both regulate emotionality and depressive states in humans. In patients with MDD the vHP is less active during a memory task53, and it is an important mediator of defensive behavioural responses via inputs from the amygdala54. Similarly to the BNST, only a subset of the regions with direct synaptic connectivity with the PAG showed significantly correlated activation in the stressed mice, including the PFC, AcbC, and VL. Anatomical connectivity of these regions with PAG may also lead to the increased Mn2+ accumulation we observed. The emergence of correlation between low PAG ∆FOSB expression and other regions suggests that the observed correlations were not artefacts arising from an increased number of ∆FOSB expressing cells .ad libitum access to standard pellet food and water.We used 21 male mice , aged 5 weeks on arrival. During a 7-day acclimatization period they were group-housed in Makrolon type III cages. The housing facility was temperature- and humidity-controlled with a 12 h light/dark cycle (light phase: 6:00–18:00). Mice had Aggressor mice used for the chronic social defeat stress (CSDS) procedure (see below) were male Clr:CD1 (Charles River) mice aged between 13 and 26 weeks. Following a 7-day acclimatization period in single-housed individually ventilated cages we screened them for appropriate levels of aggression by assessing their propensity for attacking an intruder mouse (no sooner than 10 seconds or later than 90 seconds after introduction to a C57BL/6NCrl test mouse). Twenty-four hours before the start of CSDS each selected aggressor mouse was placed alone into a cage to be used for CSDS.2. The mice were allowed to recover from the surgery for one week, after which they underwent the 10-day CSDS procedure or the control housing procedure. Manganese was infused constitutively during CSDS. After the end of CSDS, all mice were placed in single-housed cages until the MRI scan and dissections. We wanted to mainly assess chronic brain activation without bias from acute activation occurring at the beginning of the social defeat and therefore set the post-defeat delay to six days.The experimental procedures are illustrated in Fig. 2 (0.5 µl/h) were implanted as described previously55. MnCl2 was dissolved into Tris-buffered saline (pH 7.4) and administered with osmotic minipumps during the 16-day infusion, corresponding to a total MnCl2 dose of 160 mg/kg/week. The concentration of MnCl2 in the pumps was adjusted to the body weight of the animals. Before the surgery, the pumps were primed overnight in a 37 °C saline solution. Animals were anesthetized with 5% isoflurane and the pumps were implanted subcutaneously on the dorsum, slightly caudal to the scapulae. For post-surgical analgesia, animals received one subcutaneous injection of carprofen (5 mg/kg) immediately after implantation.Subcutaneous osmotic minipumps  delivering 200 µl of MnClN = 11) were exposed to CSDS56. In short, the mouse was placed into a cage containing an aggressor mouse. The mice were observed for a maximum of 10 min, after which they were separated by a perforated plexiglass wall, exposing the experimental mouse to sensory cues from the aggressor mouse without risk of physical contact. If during the initial observation phase the aggressor mouse exhibited excessive physical aggression  the mice were separated before the 10-minute time had elapsed. The average contact time throughout the experiment was 6.43 min. The sensory contact phase lasted for 24 h, after which the experimental mouse was placed into the cage of a novel aggressor mouse and the 10-min physical contact + 24 h sensory contact cycle was repeated. This procedure was repeated for 10 consecutive days. Control mice (N = 10) were housed in similar cages but with another control mouse as a neighbour and no physical contact, switching cage-mates daily. The social aversion test was performed 24 h after the last session of social defeat for both defeated and control mice56. Briefly, mice were placed in an arena containing a perforated cylinder for 150 s. The mice were placed back in the home cage, and the cylinder replaced with one containing a novel CD-1 mouse. The mouse was then returned to the arena for 150 s. The social preference score  did not differ (p = 0.256) between the groups due to small sample size and large variation between animals.Following recovery from implantation mice allocated to the defeated group (3 voxel resolution). Values for the automatically adjusted receiver gain were collected to verify that the observed group differences were not caused by changes in receiver gain. Total scanning time, including acquisition of scout images, was approximately 60 min per animal.Magnetic resonance imaging was performed with a 4.7 T scanner  using a 38 mm linear volume coil for transmit and receive. Mice were transferred from the vivarium to the scanner room, immediately anaesthetized with 5% isoflurane in oxygen (0.8–1 L/min) and positioned on a custom-made holder with a stabilizing tooth bar and a nose cone. Accurate positioning was confirmed with the help of scout images and all inaccuracies were corrected by adjusting the position and angles of the field. During scanning anaesthesia was maintained with 2–3% isoflurane and the body temperature was kept constant at 37 °C with a heating pad. The scanning order was counterbalanced according to the experimental groups. T1-weighted images were acquired using a three-dimensional rapid acquisition-relaxation enhanced (RARE) pulse sequence , the overall shape of the brain and ventricle location, and defined grey matter structures (e.g. caudate putamen). The observed increased MEMRI signal in stressed animals is likely not attributable to alterations in Ca2+ channel function as we did not observe any gene coding for Ca2+ channel components (GO:0005891) to be differentially expressed between stressed and control animals in RNA sequencing data from the medial PFC or vHP (data not shown).All MRI images were converted to Analyze format, scaled by a factor of 10 and spatially preprocessed with SPM8 software running under MATLAB (version R2015). All T1-weighted images were spatially normalized to a custom-made mouse brain template. Bias correction was performed to eliminate field inhomogeneity of the magnetic field. For creating statistical parametric maps of differential brain activation between experimental groups, the groups were compared by voxel-wise independent two-tailed Following the MRI scan mice were injected with a lethal dose of pentobarbital  without breaks in anaesthesia. Mice were transcardially perfused with 4% paraformaldehyde (PFA) in PBS. We postfixed the brains in 4% PFA for 2–24 h, cryoprotected them in 20% sucrose for 24 h, froze them in 2-methyl butane, and stored them in −80 °C. We then cryosectioned the brains with a Leica CM3050 cryostat . Sections were stored in −20 °C submerged in cryoprotectant. One defeated animal died during MRI acquisition and was not further analysed.All immunohistochemical, microscopy and quantification procedures were carried out blind to the condition of each animal. Five animals  were not stained due to issues with fixation. Tissue sections were stained in batches with balanced numbers of mice belonging to each experimental group. The sections were rinsed in TBS and endogenous peroxidase activity was quenched with hydrogen peroxide. We blocked non-specific binding with 10% normal goat serum (NGS) with 0.5% Tween-20 detergent (Sigma Aldrich) in TBS. Primary antibody incubation with a polyclonal rabbit anti-FOSB antibody  in TBS with 10% NGS + 0.5% Tween-20 was carried out for 18 h in ambient room temperature. Biotinylated affinity-purified goat anti-rabbit  diluted in TBS with 1.5% NGS was used as the secondary antibody and incubated for 2 h in room temperature. The sections were incubated in an avidin-biotin solution prepared in PBS , rinsed in 0.1 M PB and staining detected with 3,3′-diaminobenzidine . Sections were dehydrated and stored at +4 °C.21 and is thus not expected to contribute to our findings. As the mice were kept anaesthetized throughout the scanning, which lasted approximately 60 min, and the perfusion was done immediately after the scan, acute stress effects incurred during transport from the vivarium to the scanning room are expected to be minimal. Due to the counterbalanced order of scanning, any minor acutely induced full-length FOSB did not impact our results. The primary antibody was tested on male C57BL/6NCrl mice receiving injections of saline or cocaine for 6 consecutive days (15 mg/kg daily), and naïve controls (N = 2/group). Cocaine is known to induce both full-length and ∆FOSB in the caudoputamen after acute exposure, but only ∆FOSB following chronic exposure59. Mice receiving cocaine had significantly more ∆FOSB positive cells than both saline and naïve control mice , using a colour deconvolution plugin with a vector for DAB-staining built in to this version of the software. Thresholded images were analysed for particles larger than 15 µm2 in size and quantified with automated measurement.Stained sections were imaged with a Pannoramic FLASH II digital scanner using a 0.8 NA objective at a resolution of 0.24 µm/pixel  at the Institute of Biotechnology (University of Helsinki). We captured regions of interest images from the digital scans at 20x magnification using the Pannoramic Viewer software version 1.15.3 (3DHistech) based on anatomical landmarks indexed in the Mouse Brain Atlas60, was included due to its expected lack of involvement in CSDS. Exclusion criteria for single cell count data points was a deviation of ≥3 times the interquartile range below or above the median of the group. If half or more data points collected from one animal within one brain region were outliers based on this criterion, the whole animal was excluded from analyses concerning that region. Another criterion for excluding an animal from any specific test was insufficient fixation  was used for all analyses. The Benjamini Hochberg procedure was used for correcting multiple comparisons for false discoveries61.All All data are available from the corresponding authors upon request.Supplementary FiguresSupplementary Table S1


Fluid resuscitation during cardiac surgery is common with significant variability in clinical practice. Our goal was to investigate current practice patterns of fluid volume expansion in patients undergoing cardiac surgeries in the USA.t tests, to evaluate differences among respondent groups.We conducted a cross-sectional online survey of 124 cardiothoracic surgeons, cardiovascular anesthesiologists, and perfusionists. Survey questions were designed to assess clinical decision-making patterns of intravenous (IV) fluid utilization in cardiovascular surgery for five types of patients who need volume expansion: (1) patients undergoing cardiopulmonary bypass (CPB) without bleeding, (2) patients undergoing CPB with bleeding, (3) patients undergoing acute normovolemic hemodilution (ANH), (4) patients requiring extracorporeal membrane oxygenation (ECMO) or use of a ventricular assist device (VAD), and (5) patients undergoing either off-pump coronary artery bypass graft (OPCABG) surgery or transcatheter aortic valve replacement (TAVR). First-choice fluid used in fluid boluses for these five patient types was requested. Descriptive statistics were performed using Kruskal-Wallis test and follow-up tests, including The most commonly preferred indicators of volume status were blood pressure, urine output, cardiac output, central venous pressure, and heart rate. The first choice of fluid for patients needing volume expansion during CPB without bleeding was crystalloids, whereas 5% albumin was the most preferred first choice of fluid for bleeding patients. For volume expansion during ECMO or VAD, the respondents were equally likely to prefer 5% albumin or crystalloids as a first choice of IV fluid, with 5% albumin being the most frequently used adjunct fluid to crystalloids. Surgeons, as a group, more often chose starches as an adjunct fluid to crystalloids for patients needing volume expansion during CPB without bleeding. Surgeons were also more likely to use 25% albumin as an adjunct fluid than were anesthesiologists. While most perfusionists reported using crystalloids to prime the CPB circuit, one third preferred a mixture of 25% albumin and crystalloids. Less interstitial edema and more sustained volume expansion were considered the most important colloid traits in volume expansion.Fluid utilization practice patterns in the USA varied depending on patient characteristics and clinical specialties of health care professionals.The online version of this article (10.1186/s13741-017-0071-6) contains supplementary material, which is available to authorized users. Cardiac surgeries are commonly performed procedures that almost universally require fluid resuscitation during the intraoperative and perioperative period  involved in cardiovascular surgeries in the USA was developed to (a) examine the use of different fluid types for resuscitation  in patients undergoing cardiovascular surgery, (b) determine whether certain patient characteristics and/or practice settings influence the type of fluid utilized for resuscitation, (c) determine whether the fluid selected for resuscitation varies by clinical specialties of the treating HCPs, and (d) determine the fluids used to prime the CPB circuit in patients undergoing on-pump procedures.Volume expansion during CPB when not experiencing significant blood lossVolume expansion in the presence of blood loss during CPB when blood transfusion is not indicated (adequate hemoglobin [Hb])Volume maintenance during acute normovolemic hemodilution Volume expansion while patients were supported with extracorporeal membrane oxygenation (ECMO) or a ventricular assist device (VAD)Intraoperative volume expansion for off-pump coronary artery bypass graft (OPCABG) surgery or transcatheter aortic valve replacement (TAVR)This study was cross-sectional and collected survey data from 124 cardiothoracic surgeons, cardiovascular anesthesiologists, and perfusionists to investigate the patterns of fluid utilization in cardiovascular surgery. The online survey was conducted November 4 through 17, 2015, with an average survey completion time of 9 min. The 38-item self-administered questionnaire , third-generation HES or HES 130 , and blood-derived blood products other than albumin. The participants rated the frequency with which they used various fluid types for volume expansion using a 5-point scale . Participants were also asked to indicate the bolus volumes (mL) of the crystalloids and of colloids that they typically use for volume expansion. Participants rated the importance of certain colloid characteristics  and non-oncotic properties of albumin  on the patient treatment decision-making process using a 5-point scale . Four of the 38 questions addressed pump priming preferences for CPB circuits and were asked of perfusionists only.A total of 124 participants were recruited from the e-Rewards Medical panel. e-Rewards Medical is a leading provider of market research services to the professional health care community. The panel consists of HCPs who have opted to become members of the panel and were paid for their time. Email invitations for participation in this study were sent from e-Rewards to the non-probability sample of its panelists, meaning this sample set was not a random selection of all physicians. Of note, the panelists remained anonymous to the investigators in this study. The email invitation provided a general description of the survey topic  and a link by which to access the online survey. Each invitation contained a unique identifier that prevented any one respondent from taking the survey more than once. To qualify for participation, respondents had to specialize in cardiac surgery, adult cardiovascular anesthesiology, or be a perfusionist; had been in practice for at least 2 years since residency or training in the USA but not in the states of Minnesota, Vermont, West Virginia, Massachusetts, nor the District of Columbia as these states prohibit or limit compensation to physicians; and had performed or were involved in at least four cardiac bypass surgeries per month. Anesthesiologists who specialized in pediatrics were excluded because fluid management for pediatrics is different than for adults due to vast differences in the pathophysiology of their circulatory system. To ensure a minimum number of completed surveys were received from each group, subquotas were set for each clinical specialty: 50 surgeons, 50 anesthesiologists, and 50 perfusionists.This research project involved obtaining the opinions of physicians and perfusionists about their choices for the use of various fluids for volume expansion in five different hypothetical patient situations. No patient data were obtained, and no questions were asked of the participants that would help in identifying them. All participant data were de-identified. Hence, this study was exempt from requiring institutional review board approval under United States Code of Federal Regulations Title 45 Part 46.101(b)(2) by Copernicus Group Independent Review Board (CGIRB). The study did receive a formal Letter of Exemption from the CGIRB.T tests were used to evaluate differences across ratio variables . Statistical significance was assessed at the alpha level of less than 0.05. Descriptive analyses were performed using SPSS (Version 23.0).Most questions were based on 5-point scales and provided ordinal data which, by definition, are not normally distributed. As such, descriptive statistics were performed using a Kruskal-Wallis test to evaluate differences among the respondent groups on the ordinal measures. Follow-up tests were conducted to evaluate differences within and between patient scenarios. Of the 124 HCPs who completed the survey, 52 (41.9%) were anesthesiologists, 47 (37.9%) were surgeons, and 25 (20.2%) were perfusionists (Table P < .001), pulse pressure variation , and stroke volume variation .The five most commonly used indicators of volume status were blood pressure (77%), urine output (76%), cardiac output (74%), central venous pressure (73%), and heart rate 61%) . Higher percentages of surgeons than anesthesiologists chose HES and blood-derived products other than albumin, while no perfusionists chose any of those fluid types. The most frequently used adjunct fluid to crystalloids was 5% albumin  fluid for a patient needing volume expansion during CPB when not experiencing significant blood loss (scenario 1) was crystalloids, followed by 5% albumin and 25% albumin, respectively , while no HCP chose HES 130. When the first fluid choice was 5% albumin, the most frequently chosen adjunct fluid was crystalloids , HCPs chose 5% albumin most frequently as the first choice of IV fluid Fig. . CrystalP < .05) for volume maintenance during ANH. Again, 5% albumin was the most frequently used adjunct fluid to crystalloids  was crystalloids, followed by 5% albumin and then 25% albumin Fig. . AnestheP < .05), more surgeons preferred 25% albumin than anesthesiologists . Only surgeons utilized HES fluids for this scenario. When the first fluid choice was 5% albumin, crystalloid was the most frequently chosen adjunct fluid , HCPs preferred 5% albumin and crystalloids equally as the first choice of IV fluid . Crystalloid fluid was the most preferred first choice of IV fluid, followed by 5% albumin and then 25% albumin Fig. , and 5% The most important colloid traits that influenced the decision to use colloids for volume expansion seemed to be “less interstitial edema” and “more sustained volume expansion” Fig. . Most peThis cross-sectional study provided insights on the patterns of fluid utilization in cardiovascular surgery from a survey of 52 cardiovascular anesthesiologists, 47 cardiothoracic surgeons, and 25 perfusionists in the USA. This survey examined the fluids chosen for volume resuscitation by these 124 HCPs to treat five different hypothetical patient scenarios. The 25 perfusionists were surveyed to determine the solutions that they utilized to prime the CPB circuit. There were remarkable variability in clinical practice and a lack of consensus about the use of various fluid types in cardiovascular surgery patients.In this survey, the most commonly preferred indicators of volume status were blood pressure, urine output, cardiac output, central venous pressure, and heart rate. Anesthesiologists preferred transesophageal echocardiography, pulse pressure variation, and stroke volume variation as indicators of volume status significantly more frequently than surgeons. Different fluid types were chosen as the first choice of IV fluids depending on the clinical context of the patients. For example, crystalloid fluid was the predominant first choice for patients needing volume expansion during CPB without bleeding (scenario 1), for fluid maintenance during ANH (scenario 3), and for intraoperative volume expansion during OPCABG or TAVR (scenario 5). On the other hand, 5% albumin was the primary fluid choice for patients needing volume expansion in the presence of blood loss during CPB not requiring transfusion (scenario 2) or during ECMO or VAD (scenario 4). When choosing colloids for volume expansion, HCPs felt that “less interstitial edema” and “more sustained volume expansion” were more important colloid traits than “faster volume expansion,” “better respiratory function,” and “less weight gain.” “Other blood-derived products” were chosen infrequently (less than 10%) as the first choice for fluid resuscitation by both university- and non-university-affiliated physicians; the only situation in which it was chosen by more than 10% of the respondents was for patients on ECMO and VAD.In addition, there were differences in practice patterns among the clinical specialties of the treating HCPs. Significantly larger numbers of surgeons, relative to anesthesiologists, preferred 25% albumin for patients who need volume expansion in the presence of blood loss (scenario 2), for fluid maintenance during ANH (scenario 3), and for volume expansion during EMCO or VAD (scenario 4). Sixty percent of the perfusionists in this study preferred to use only crystalloids for priming the CPB circuit, while the other 40% used a mixture of albumin and crystalloids. The data from this study demonstrate that volume repletion practices vary dramatically and clinical data do not allow for consensus recommendations. While there are no large, randomized trials available to inform HCPs of definitive protocols for optimal volume resuscitation during cardiovascular surgeries, it is well accepted that fluid overload increases the risk of major complications after CABG . HES should be given with caution or possibly avoided altogether due to warnings from the FDA . In addition, perfusionists predominately preferred crystalloids to prime the CPB circuit; one third of the perfusionists preferred 25% albumin mixed with crystalloids for priming.Additional file 1:Albumin Surgical Utilization Survey. (DOCX 415 kb)Additional file 2: Figure S1.a . CPB, cardiopulmonary bypass; HES, hydroxyethyl starch. aResponses to the following question: How often do you use each of the following as an adjunct to your first choice in a patient not experiencing significant blood loss when volume expansion is indicated during cardiovascular surgery with CPB? (JPEG 168 kb)Frequency of adjunct fluid use for patients needing volume expansion during CPB when not experiencing significant blood loss when the first choice is crystalloidsAdditional file 3: Figure S2.a when first fluid choice is 5% albumina (n = 52) and b when first fluid choice is crystalloidsb (n = 39). CPB cardiopulmonary bypass; Hb, hemoglobin; HES, hydroxyethyl starch. aResponses to the following question: How often do you use each of the following as an adjunct to your first choice in a patient not experiencing significant blood loss when volume expansion is indicated during cardiovascular surgery with CPB? bResponses to the following question: How often do you use each of the following as an adjunct to your first choice in a patient for volume expansion in the presence of blood loss when blood transfusion is not indicated (adequate Hb) during cardiovascular surgery with CPB? (ZIP 186 kb)Frequency of adjunct fluid use for patients needing volume expansion in the presence of blood loss during CPB when blood transfusion is not indicated (scenario 2) Additional file 4: Figure S3.a . HES, hydroxyethyl starch. aResponses to the following question: How often do you use the following as an adjunct to your first choice for a patient for volume maintenance during acute normovolemic hemodilution (autologous blood collection)? (JPEG 167 kb)Frequency of adjunct fluid use for patients needing volume maintenance during acute normovolemic hemodilution when first fluid choice is crystalloidsAdditional file 5: Figure S4.a when first fluid choice is albumin 5%a (n = 33b) and b when first fluid choice is crystalloidsa (n = 31b). ECMO, extracorporeal membrane oxygenation; HES, hydroxyethyl starch; VAD, ventricular assist device. aResponses to the following question: How often do you use the following as an adjunct to your first choice for a patient who needs volume expansion during ECMO or VAD? bNo statistical tests were performed due to small sample size. (ZIP 169 kb)Frequency of adjunct fluid use for expansion during ECMO or VAD (scenario 4) Additional file 6: Figure S5.a . HES, hydroxyethyl starch; OPCAB, off-pump coronary artery bypass surgery; TAVR, transcatheter aortic valve replacement. aResponses to the following question: How often do you use the following as an adjunct to your first choice for a patient who needs intraoperative volume expansion for OPCAB or TAVR? (JPEG 167 kb)Frequency of adjunct fluid use for intraoperative volume expansion for OPCABG or TAVR when first fluid choice is crystalloids


Microbial remediation of oil polluted habitats remains one of the foremost methods for restoration of petroleum hydrocarbon contaminated environments. The development of effective bioremediation strategies however, require an extensive understanding of the resident microbiome of these habitats. Recent developments such as high-throughput sequencing has greatly facilitated the advancement of microbial ecological studies in oil polluted habitats. However, effective interpretation of biological characteristics from these large datasets remain a considerable challenge. In this study, we have implemented recently developed bioinformatic tools for analyzing 65 16S rRNA datasets from 12 diverse hydrocarbon polluted habitats to decipher metagenomic characteristics of the resident bacterial communities. Using metagenomes predicted from 16S rRNA gene sequences through PICRUSt, we have comprehensively described phylogenetic and functional compositions of these habitats and additionally inferred a multitude of metagenomic features including 255 taxa and 414 functional modules which can be used as biomarkers for effective distinction between the 12 oil polluted sites. Additionally, we show that significantly over-represented taxa often contribute to either or both, hydrocarbon degradation and additional important functions. Our findings reveal significant differences between hydrocarbon contaminated sites and establishes the importance of endemic factors in addition to petroleum hydrocarbons as driving factors for sculpting hydrocarbon contaminated bacteriomes. These findings, along with others, established bioremediation as a process mediated by a consortium of microbes rather than a few. Thus, characterization of microbial communities of oil contaminated environments could potentially provide guidelines for effective remediation and restoration of such environments.Anthropogenic activities and agents leading to contamination of the environment is one of the major issues that developing and developed industrial societies face today. Petroleum hydrocarbons are the most widespread of these anthropogenic agents and frequently contaminate aquatic and terrestrial ecosystems through releases of hydrocarbon during production, operational use, and transportation. The development, effectiveness and availability of technologies and strategies pose a significant challenge for the remediation, rehabilitation and restoration of these contaminated environments. Many of the technologies developed and in use for the restoration of oil contaminated environments exploit the potential of biological systems, in particular microbial systems, to use these toxic compounds as substrates for growth. Hence, much of the research conducted on bioremediation has concentrated on the capabilities of a single or couple of microbes exhibiting robust and effective growth using petroleum hydrocarbons. However, in the environment, bioremediation is often a complex process involving co-metabolism, cross-induction, inhibition and non-interaction among microbes4. The advent of high throughput massively-parallel sequencing methods has however, allowed us to investigate the entire complement of organisms inhabiting a certain environment. These next-generation sequencing methods (NGS) include a variety of procedures to holistically study any biological system such as amplicon sequencing (for variant identification and phylogenetic surveys), whole genome shotgun sequencing (for single organism genome and metagenomes) and RNA-Seq . These powerful methods have ushered in rapid advances in bioinformatic approaches leading to development of software capable of handling huge amounts of data and offering meaningful biological interpretations of the same. Although a technological breakthrough in modern science, several NGS methods such as metagenomic and transcriptomic/metatranscriptomic sequencing are still expensive and hence, most studies on ecological processes like bioremediation report marker surveys as 16S rRNA gene amplicon sequencing when dealing with multiple samples. Thus, in general, most of these studies concentrated on interpretations from microbial community composition but inferred poorly regarding functional and metabolic properties of the same.Until recently, it was only possible to study a handful of microorganisms of interest isolated from source materials , given the restrictions of the composition of culture media which cannot reflect and mimic the dynamic nutrient fluxes of the source environment. Indeed, only 1% of microorganisms were found to be cultivable using a set of media from the highly characterized soil rhizosphere6. These tools have obvious applications for the analysis of huge amounts of microbial genomic/amplicon/transcriptomic data collected from other sources such as soil, water and so on. Some particularly interesting computational tools allow us to explain the complex mutual interactions and heterogeneity inherent in microbial communities through network-based correlation analyses7, prediction of metagenomic biomarkers8 and prediction of metagenomes from 16S rRNA data10. While metagenomic shotgun sequencing has certain advantages over 16S rRNA gene surveys for studying microbial communities, it has some major disadvantages. These are: (i) Metagenomic shotgun sequencing can be 5–15 fold more expensive than 16S rRNA gene sequencing, which can be a limiting factor for microbial ecology research in smaller laboratories, (ii) Analysis of metagenomic shotgun sequencing datasets is manifold computationally more intensive than 16S rRNA gene analysis and requires much more advanced and powerful hardware, further escalating the operational costs, (iii) Metagenomic shotgun sequencing provides a much lower taxonomic resolution than 16S rRNA data11 which can lead to loss of the rare biosphere, large number of sequences being binned as unclassified and loss of information on lower level taxa  which can be projected as potential biomarkers in certain cases, and (iv) Unlike analysis of 16S rRNA gene sequences no consensus exists on the analysis of metagenomes and bioinformatic tools use diverse strategies for taxonomic and functional classification, relying on a variety of reference databases which bias the results depending on the approach taken. The availability of bioinformatic tools for prediction of functions i.e. metagenomes, from 16S rRNA gene sequences is therefore particularly attractive to microbial ecologists as it allows them to study the metabolomes of complex microbial communities with reasonable precision and confidence at a high taxonomic resolution while being able to construct robust hypotheses for further work at a much lower operational cost compared to metagenomic sequencing.Recently, with the implementation of the Human Microbiome Project (HMP), bioinformatic advancements have been furthered through the development of powerful new computational tools for effective interpretation and visualization of taxonomic and functional composition of microbial communitiesIt is well understood that depending on the environment, the method of bioremediation will vary. Essential information required for the development of bioremediation technologies include the response of microbes to petroleum hydrocarbons and their dynamics with the immediate environment. Unfortunately, despite the large amount of work done on microbial community composition across a myriad of oil contaminated environments, mainly through 16S rRNA amplicon sequencing, no attempt has been made to find differential metagenomic signatures among these studies. In the present study, we have aimed to investigate the taxonomic and functional characteristics of diverse oil contaminated environments using recent bioinformatics tools through an evolving pipeline to process metagenomic data. In this bioinformatic pipeline we have employed tools that allow the analysis and interpretation of high-resolution taxonomic data generated from 16S rRNA gene surveys along with metagenome prediction tools that allow investigation of the functional dynamics of these microbial communities. We used 61 publicly available 16S rRNA datasets and 4 from this study as inputs for our analysis. Consequently, metagenomic level characteristics of bacterial composition and metabolic potential were comprehensively deduced for 12 petroleum hydrocarbon contaminated habitats. We also inferred an array of differentially abundant taxonomic and functional features which may be used as biomarkers for successful distinction of different oil contaminated habitats as well as for monitoring of bioremediation efforts in the same. Additionally, correlation between enriched taxa and functional orthologs was also evaluated along with estimation of metagenomic contributions by various taxa to hydrocarbonoclastic capabilities. Furthermore, a network of bacterial interaction patterns was inferred to deduce complex co-occurrence and co-exclusion relationships in these environments. We found that phylogenetic and functional composition of oil contaminated bacteriomes were significantly different to each other and greatly influenced by immediate environmental factors along with petroleum hydrocarbon contamination. Besides providing a robust bioinformatic pipeline for microbial ecology studies in the future, our investigation provides novel and valuable insights into the differential nature of various oil polluted habitats and hopefully improves upon previous understanding of these environments.13. Oil contaminated soil samples representing Indian oil refineries (I) were collected from Noonmati Oil Refinery in Guwahati and Barhola oilfields, both in Assam, India. Soil samples were collected in both sites from the surface (0–10 cm) and beneath (20–40 cm)  Table . All the15 was used to estimate abundances of bacterial taxa in the 16S rRNA datasets collected from diverse oil contaminated habitats. Filtered high quality sequences obtained from the quality control step were aligned to the mothur implementation of the SILVA database and trimmed for the alignment region. Chimeric sequences were then removed from the datasets using the mothur implementation of Uchime16. Filtered sequences were then taxonomically classified using the May 2013 release of the Greengenes database17 and contaminating archaeal, eukaryal, mitochondrial and chloroplast sequences or sequences classified as unknown were removed from further analysis. Finally, OTUs were predicted from these high-quality sequences. OTUs were again mapped to the sequence taxonomy file generated previously in mothur and converted to number of sequences to generate comparative taxonomy data for the datasets. We also assessed the compositional similarity between the soil samples from different sites. For doing this, we compared the pairwise taxonomic abundances from each site against each other and within the datasets as well, using Bray-Curtis measure for estimation of beta diversity18. The permutation-based multivariate analysis of variance (PERMANOVA) was used to test the homogeneity of taxonomic dispersion across samples along with concomitant estimation of 2D stress. Computation of Bray-Curtis distances and PERMANOVA tests were carried out in PAST v3.1119. The resulting Bray-Curtis similarity distance matrix was used as input for ordination of the oil contaminated samples through non-metric multidimensional scaling (NMDS) in PAST v3.1119.mothur9. OTU data generated in mothur for all 16S rRNA datasets was used to prepare BIOM20 files formatted as input for PICRUSt v1.1.09 with the make.biom script available in mothur. PICRUSt requires OTU abundances mapped to Greengenes OTU IDs as input for prediction of corresponding metagenomes. PICRUSt databases for 16S rRNA gene copy number normalization and KEGG ortholog prediction were updated using publicly available information listed in Integrated Microbial Genomes (IMG)21 as on 4th April, 2016, according to the instructions provided for the Genome Prediction Tutorial for PICRUSt  using default settings. The update involved the inclusion of 16S rRNA gene copy number information and KEGG ortholog (KO) annotation data as per KEGG v77.122 for ~34,000 bacterial and archaeal genomes available in IMG. 16S rRNA gene copy numbers for 16S rRNA datasets were normalized using the normalize_by_copy_number.py script. Metagenomes were predicted from the copy number normalized 16S rRNA data in PICRUSt using the predict_metagenomes.py script against the updated and PICRUSt-formatted, characterized-protein, functional database of KEGG Orthology. Contributions of various taxa to different KOs were computed with the script metagenome_contributions.py and visualized with the script plot_metagenome_contributions.R (https://groups.google.com/forum/#!topic/picrust-users/Hq9_G23J9W4) and ggplot223 in R (http://www.R-project.org). Predicted metagenomes were then used as inputs in HUMAnN224 for metabolic reconstruction of oil contaminated habitats using KEGG Pathways and/or KEGG modules. KEGG ortholog (KO) information derived from PICRUSt was used by MinPath25 implemented in HUMAnN2 to infer coverage and relative abundances of KEGG modules, which are manually defined tight, functional units. KEGG Pathways and KEGG modules (KEGG v77.1) data for HUMAnN2 were updated according to publicly available information in IMG21 and KEGG22. Coverages of a subset of KEGG modules were represented through heat maps generated in STAMP26.Metagenomes were predicted from 16S rRNA data using PICRUSt8 to identify differentially abundant features that can be used as potential metagenomic biomarkers. For this analysis, the alpha parameter significance threshold for the Krushkal-Wallis (KW) test implemented among classes in LEfSe was set to 0.01 and the logarithmic LDA score cut-off was set to 2.0, due to the relatively small sample size under consideration. All analysis carried out through LEfSe was performed through the Galaxy server27. Estimated biomarkers were represented using circular cladograms generated through the standalone graphical tool GraPhlAn v0.9528.We furthered our study through detection of taxonomic clades, KEGG orthologs and KEGG modules that are significantly over/under-represented  in the individual oil contaminated environments through statistical analyses carried out on the inferred relative abundances. To this end, the procedure of linear discriminant analysis (LDA) effect size was employed through LEfSe v1.0psych29. Correlations were only computed for oil polluted sites represented by at least 6 samples. A subset of the resultant correlation network was visualized using the interactive platform, Cytoscape v3.4.030.Additionally, to estimate the relationship between taxonomic and functional enrichments in each oil polluted environment, we carried out tests of correlation between abundances for KEGG orthologs (metagenomic gene families) and taxonomic clades using a non-parametric test of Spearman’s rank correlation. Detection of significant relationships, defined as a correlation >0.7 with a p-value < 0.001 and reaching a Benjamini-Hochberg false discovery rate < 0.01 was carried out through the function corr.test implemented in the R package, 7, a tool capable of computing significant correlations from compositional data while correcting for the effects of the same, was used to detect significant co-occurrence and co-exclusion patterns. SparCC was run on absolute count OTU tables generated by mothur for each sample, using the command sparcc with default settings except a single non-default parameter of permutations = 10,000. OTU associations having an absolute SparCC correlation value above 0.6 with p-values < 0.01 were considered statistically significant and incorporated into subsequent network construction. The final network of significant SparCC correlations was built in Cytoscape 3.4.030. The nodes in the reconstructed networks represent OTUs participating in robust, statistically significant relationships (both positive and negative), which are in turn portrayed by edges i.e. connections between the nodes.Bacterial interactions in oil contaminated environments was investigated in the present study through non-random bacterial co-occurrence and co-exclusion relationships within individual soil sites. Only polluted sites consisting of more than 4 samples were subjected to deductions of bacterial interactions. mothur implementation of the Sparse Correlations for Compositional data algorithm (SparCC)16S rRNA amplicon sequencing data generated in this study was deposited in the NCBI Sequence Read Archive (SRA) under accession numbers SRR3168574-SRR3168577. The amplicon sequence data is bundled under NCBI BioProject number PRJNA306989.Proteobacteria dominated the bacterial community composition at the phylum level with mean relative abundances ranging from 20–77% across samples  in I and DWH samples and Actinomycetales dominated A samples with an average relative abundance of 24%  was found in greater abundance in DWH samples, Ellin329 (15%) abundance was highly elevated in Taiga upper active layer (Tu), and Burkholderiales (25%), Pseudomonadales (27%), Rhizobiales (22%) were enriched in the OSC  for A samples, Alteromonadaceae (14%), Xanthomonadaceae (20%) for DWH samples, and Moraxellaceae (26%) for OSC samples  (Supplementary Table p = 0.2–0.9) (Supplementary Table p = 0.054–0.09)  (Supplementary Table p = 0.0262–0.0316)  was then used by LEfSe to detect metagenomic biomarkers. In all, LEfSe detected 255 differentially abundant taxa including 66 families, 47 genera and 11 species level biomarkers across all habitats Table  and S5. For understanding the metabolic potential of oil polluted environments and identifying differentially abundant functional features, metagenomes were predicted by PICRUSt using 16S rRNA gene amplicon data analyzed in mothur. Predicted proteins were classified as KEGG orthologs (KOs) resulting in the identification of 7020 KOs across all samples. Metabolic reconstruction of metagenomes predicted by PICRUSt was carried out in HUMAnN2, which detected 585 KEGG modules across all samples. Among these functional modules, 19 functional modules were present across all samples at a coverage of >90% and were identified as core modules Table . Most ofIn addition to differently covered functional modules, 414 KEGG modules were detected to be differentially abundant in at least one of the 12 contaminated environments Fig.  and S6. ex-silico studies.Overall, all the sites were found to harbor a variety of differentially abundant modules dedicated to the transport of saccharides, polyols, peptides, metallic cations, vitamins, amino acids, mineral ions, organic ions, lipids and phosphate, underlining the large genetic investment of resident bacteria in the processing of environmental information specific to the said site Fig.  and S6. et al.36 . The results indicated strong and significant associations between a number taxonomic clades and metagenomic gene families predicted by PICRUSt  and psb28-2 (K08904), found to be differentially abundant in C samples exhibited strong positive association with an over-represented cyanobacterial order, Oscillatoriophycideae   were differentially abundant in C samples and also significantly positively correlated to known polyaromatic hydrocarbon degrader and taxonomic biomarker Mycobacterium 35 , cebE-G (K10240-42), chvE (K10546), gguA-B (K10547-48), and gluA-D (K10005-08) in arctic samples  , bbsH (K07546) and pcaL (K14727) were significantly correlated to class Actinobacteria in a positive manner in the same samples   and 2-oxopent-4-enoate/cis-2-oxohex-4-enoate hydratase , an enzyme implicated in oligosaccharide metabolism  , flagellar synthesis/chemotaxis  and aminobenzoate metabolism gene regulation    coding for alkane-1-monooxygenase   and benzoate degradation   . Furthermore, several two-component systems (TCS) showed strong positive association with Acinetobacter and Enterobacteriaceae in the OSC samples. Acinetobacter was positively correlated with the enrichment of RstA/RstB stress response TCS , while Enterobacteriaceae showed affirmative relationships with the aerobic stress response sensor kinase ArcB (K07648) and nitrate/nitrite response regulator NarP (K07685)   than bacteria from phylum Proteobacteria (i = 23), even though the mean relative abundance of Proteobacteria was much higher than Actinobacteria  (01) Fig. . Among t01) Fig. . Interesria Fig.  across aria Fig. . Thus, oria Fig. . Additioles Fig. . Furtherles Fig. . Converseae Fig. . Similarbia Fig. .Figure 5Cupriavidus shared a significantly negative correlation with Herminiimonas in OSC samples  and D-galacturonate (M00631)  technologies has revolutionized investigative approaches into microbial processes. This has led to re-exploration of well-known microbial processes as the nitrogen cycleld Table , includiTo our knowledge this is the only study that has congregated existing 16S rRNA gene NGS data generated during experiments on hydrocarbon pollution in different habitats around the world to deduce possible biomarkers and associated bacterial characteristics and interactions. The bioinformatics pipeline we designed to analyze this data employed PICRUSt, which is a recently developed tool that uses 16S rRNA data to predict metagenomes along with LEfSe which predicts potential biomarkers and HUMAnN2 for metabolic reconstruction of PICRUSt predicted metagenomes. It is to be noted however, that KEGG orthologs and KEGG module databases for PICRUSt and HUMAnN2 were meticulously updated (previously PICRUSt KEGG databases included KEGG orthologs only up to K15039 and HUMAnN had a KEGG module database represented only up to M00378) to include currently available definitions of KEGG functional modules and represent the metabolic terrain of environmental habitats in totality, especially with respect to hydrocarbon degradation . Prediction of metagenomes for petroleum hydrocarbon contaminated habitats will therefore be incomplete without the use of the database developed in this study and may constitute a gross misrepresentation of the said environments.ex silico work is advised in the latter case. However, for obvious reasons, conclusions can be drawn with much more confidence from 16S rRNA gene datasets that have already been published and reviewed and when using an updated prediction database, (iii) Although the process is robust and has been proven to be much more than a hypotheses building exercise in a number of studies24, conclusions drawn from such bioinformatic pipelines require ex silico confirmation, similar to any other kind of sequencing based experiment.It must be noted that although promising, studying of environmental systems and processes through prediction of metagenomes from 16S rRNA data is bound by certain limitations. The main disadvantages of this method include: (i) The requirement of an updated database for prediction of metagenomes from 16S rRNA data and consequent estimation of metabolic pathways present. An obsolete database will lead to identification of a partial metagenome and huge loss of information leading to a steep drop in the quality of conclusions drawn. To elaborate in brief, the metagenome prediction relies on sequenced genomes for creation of a prediction database and the availability of sequenced genomes can therefore dictate the quality of the prediction database created. Thus, non-updated prediction tools will only be able to predict a more complete metagenome for highly studied environments like the human gut as opposed to scarcely studied habitats such as hypersaline mats. Fortunately, focus on environmental research and large depositions of environmental sequences in public databases in recent years has largely bridged this divide. However, caution must be taken considering the type of habitats being analyzed, (ii) Due to the inherently predictive nature of the process, some caution must be exercised while interpreting results. This can be done by concentrating primarily on identification of patterns rather than on single points of conclusion i.e. relying on several proteins or pathways belonging to the same metabolic grouping to derive a conclusion rather than on the presence of one. Additional et al.54 for the marine sediment samples. Our analysis of the marine sediment samples identified a highly dominant Gammaproteobacterial genus, HB2-32-21 (Greengenes OTU ID 248394) belonging to the family Alteromonadaceae    Fig.  and S65551) Fig.  and S656PICRUSt was used to predict metagenomes from 16S rRNA data and KEGG metabolic modules were detected using HUMAnN2 in order to elaborate the functional landscape of each oil polluted environment. We identified 19 core modules which were present across all habitats with a coverage of >90%. Most of these are involved in processes central to survival of bacteria in the environment. Furthermore, to identify preferential genetic investments among resident bacteria at each habitat, differentially abundant KOs and KEGG modules were detected through LEfSe. Consequently, we analyzed over-represented KOs and KEGG modules across all habitats to identify broad metabolic signatures that may be indicative of important areas of genetic expenditure, especially outside hydrocarbon degradation.57, and therefore its prevalence in a marine oil polluted environment. Similarly, availability and possible use of carbon sources besides hydrocarbons was apparent in the C samples. The differential presence of a complete complement of D-xylose transport system (M00215) and a putative aldouronate transport system (M00603) along with the over-representation of KEGG module M00014 (Glucuronate pathway), strongly indicated that besides petroleum hydrocarbons, plant wastes may be available as possible sources of energy for resident soil bacteria at the China oil refineries site  were detected to be differentially abundant in the C samples along with bacterial proteasomes (M00342)  and a transport system for arginine/ornithine (M00235) were detected to be differentially abundant for the DWH samples Fig.  and S6. ite Fig.  and S6. 42) Fig.  and S6. des gene coding for a desaturase that helps control the saturation state of membrane lipids at low temperatures59 was detected to be differentially abundant in the arctic samples  was detected to be differentially abundant in OSC samples along with a number of modules dedicated to transfer of sulfur compounds  indicating a conceivably large genetic investment in scavenging and metabolism of sulfur compounds in this site , implicated in regulation of the les Fig.  and S6. les Fig.  and S6. ite Fig.  and S6. ite Fig.  and S6. ite Fig.  and S6. les Fig.  and S6. les Fig.  and S6. 12, where deeper regions tend to be highly anaerobic. Additionally, modules for copper processing (M00762) and copper tolerance sensor (M00452) were detected in OSTPd  was over-represented in OSTPu, along with methane assimilation modules M00344-45 and M00608 detected to be differentially abundant in OSTPu and OSTPm respectively  Fig.  and S6. 65 Fig.  and S6. 65 Fig.  and S6.34 and metal tolerance (M00499) were detected as differentially abundant  along with transport systems for sugars like fructose (M00273) in M samples, indicate the possible availability of simple sugars as carbon sources besides hydrocarbons , a limiting nutrient for mangroves4, a limiant Fig.  and S6. ons Fig.  and S6 aose Fig.  and S6.67. Our results thus indicate that for all habitats, genetic composition of the bacteriome is representative of the immediate environment especially in terms of substrate usage, nutrient availability, energy metabolism, biosynthesis of compounds, and survival strategies including quorum sensing, chemotaxis, and stress response. Our findings reveal pathways differentially important in these oil polluted environments, especially those not related to hydrocarbon degradation and can therefore be used for differentiation between habitats of interest. Further empirical studies will however be required to strengthen these observations and pinpoint functional biomarkers absolutely exclusive to oil polluted environments in specific biomes.All functional modules for degradation of aromatic hydrocarbons were detected to be differentially abundant in OSC, DWH, taiga and OSTP (in that order) samples . Furthermore, another taxonomic biomarker identified for DWH samples, Colwelliaceae, was closely associated to the anaerobic C4-dicarboxylate transporter DcuB (see Results), which is responsible for transport of molecules as fumarate, succinate and malate69. This is important, as it may help the facultatively aerobic Colwelliaceae to degrade alkanes anaerobically by addition of fumarates in marine sediments67. Similarly, Mycobacterium was detected as a biomarker for C samples   and psb28-2 (K08904) through an over-represented cyanobacterial order for C samples, Oscillatoriophycideae , alpha-glucosides (M00201), glutamate  and multiple sugars  .21) Fig. , which cMethylobacterium was associated with genes involved in degradation of furfural and other hydrocarbons   and anaerobic bacteria   Fig. . These olum Fig. . Methyliles Fig. .et al.12, we encountered a significantly high proportion of anaerobic taxa in the OSTP samples, among which Anaerolinaceae, Syntrophaceae, Desulfobulbaceae, Peptococcaceae, Geobacteraceae, Syntrophorhabdaceae and the thermophilic Caldiserica76 were detected as biomarkers  that was identified in OSTP samples  and Rhodocyclaceae (K04100-01)  Table  and S5 f83. Interestingly, not a large proportion of taxonomic biomarkers were observed to be represented in these significant correlations. This can possibly happen due to separation of niches due to various environmental and even temporal factors. For example, in the bacterial association network for DWH samples, biomarker Colwelliaceae was detected to participate in a significantly positive relationship with Desulfobulbaceae, a strictly anaerobic sulfate utilizing bacterial family  may be driven by factors other than only oil pollution in oil contaminated sites with apparently benign taxa being involved in such interactions. This indicates that biomarkers and correlation networks must be studied in tandem to deduce meaningful conclusions.We also investigated significant bacterial associations in oil polluted sites to decipher important co-occurrence and co-exclusion relationships. Our results showed that greater co-occurrence exists between phylotypes sharing an evolutionary lineage while more co-exclusions were observed between phylotypes from different ancestries. This observation has also been previously reported in microbial correlation studies in the environmentily Fig. . The exi53. In the study on mangrove oil pollution and detection of microbial proxies by dos Santos et al.53, Marinobacter, belonging to family Alteromonadaceae, was identified as a possible biomarker for oil pollution in mangroves. However, in our study when compared to other sites, Alteromonadaceae was detected to be differentially abundant in the DWH samples and Marinobacter was not identified as over-represented in any of the oil polluted sites (Table Our results therefore show that, detected biomarkers may contribute differently to strictly hydrocarbonoclastic properties when compared across sites, but their close association with most differentially abundant KOs and as an extension, several over-represented functional pathways for each site underlines their significance in these oil contaminated sites. We find that although many of the taxonomic biomarkers contribute to hydrocarbonoclastic capabilities, some do not and can therefore contribute to other possibly important functions. These observations not only elucidate important taxa contributing functions more specific and essential to each site, but also shows that niches related to functions other than hydrocarbon degradation may significantly influence bacteriome structure in oil polluted sites, possibly more in sites with understandably lower degrees of contamination. This indicates clearly that while hydrocarbonoclastic capabilities may be a driving force for continued survival in these sites, other immediate factors including availability of different organic and inorganic compounds and environmental stress can heavily influence evolution of the bacteriome also. Thus, we see that a combination of oil degradation capabilities and environmental factors shape the landscape for bacterial petroleum degradation. As an extension, it therefore becomes imperative to examine oil bioremediation processes, especially aimed at empirical identification of biomarkers, in totality with due comparison to similar studies and not in isolation as it may lead to misleading conclusions. This is well illustrated in some previous studies that have focused on predicting microbial markers or proxies for oil pollution in certain environmentses Table  and S5.Meta-omics approaches such as meta-genomics, transcriptomics or proteomics, integrated with an in-depth analysis of large and exhaustive datasets using state-of-the-art bioinformatic tools bound in efficient cohesion offer enhanced and possibly novel interpretations of microbial and trophic associations occurring in the environment. In this study, we have implemented an atypical, evolving computational pipeline, that employs contemporary bioinformatic contrivances to explore and decipher characteristics of bacterial response to oil contamination in diverse environments from 16S rRNA sequence datasets. Our study showed that significant taxonomic and functional differences exist between geographically and/or spatially isolated oil polluted sites and that oil pollution is not the sole driving factor in determination of the metagenomic fabric at these sites, even if maybe the most predominant one. We have successfully demonstrated that several important taxonomic clades and functional modules detected for these habitats are often involved in extra-hydrocarbonoclastic activities, thus underlining the importance of these apparently peripheral niches related to endemic environmental responses in the survival of oil contaminated ecosystems. In the process, we inferred robust taxonomic and functional biomarkers along with competitive and cooperative interactions among bacteria at diverse oil contaminated sites, that are representative of an entire oil polluted habitat and not only its hydrocarbonoclastic capabilities. To our knowledge, this is the first population genomics study carried out on petroleum hydrocarbon polluted habitats. The present study contributes novel insights into the complex ecological dynamics of oil polluted bacteriomes besides providing relevant analytical and visualization methods for studying the relation between soil biodiversity and ecosystem function from environmental 16S rRNA phylogenetic survey data.Supplementary Data