The interferon (IFN) induced anti-viral response is amongst the earliest and most potent of the innate responses to fight viral infection. The induction of the Janus kinase/signal transducer and activation of transcription (JAK/STAT) signalling pathway by IFNs leads to the upregulation of hundreds of interferon stimulated genes (ISGs) for which, many have the ability to rapidly kill viruses within infected cells. During the long course of evolution, viruses have evolved an extraordinary range of strategies to counteract the host immune responses in particular by targeting the JAK/STAT signalling pathway. Understanding how the IFN system is inhibited has provided critical insights into viral virulence and pathogenesis. Moreover, identification of factors encoded by viruses that modulate the JAK/STAT pathway has opened up opportunities to create new anti-viral drugs and rationally attenuated new generation vaccines, particularly for RNA viruses, by reverse genetics. Viruses are obligate parasites that replicate within living cells and use the host’s cellular machinery to do this. During the course of evolution the host has evolved highly sophisticated mechanisms to circumvent infection by viruses and to rapidly shut down their ability to replicate. IFNs are one of the most important weapons in the host’s arsenal to resist infection by viruses and induce an antiviral response through the induction of a vast array of genes in host cells that impede viral replication. Although type 1 IFNs were discovered about 50 years ago, it has only been in the last 15–20 years that it has come to light that many viruses have evolved mechanisms to counteract such a potent host innate response . Almost IFNs are a multifunctional family of cytokines that play a critical role as a first line of defence against viral infection ,6. Type 701) and STAT2 (Tyr690) form a heterodimer that binds IFN regulatory factor IRF9  in the cytoplasm forming the heterotrimeric transcriptional factor complex IFN-stimulated gene factor 3 (ISGF3). ISGF3 translocates to the nucleus where it binds the cis element known as IFN-stimulated response element (ISRE) [IFNs induce the IFN effector response via the JAK/STAT pathway . An excet (ISRE) . STAT1 at (ISRE) . This bit (ISRE) , a role t (ISRE) ,22,23. Tt (ISRE) . Both tht (ISRE) ,26,27,28A major threat to viruses are the type 1 and type 3 IFNs, since they are produced in abundance from virus infected cells and induce a rapid anti-viral response in neighbouring cells in the vicinity of the infection. The literature mostly describes viruses that target the JAK/STAT response induced by type 1 IFNs rather than the type 2 IFNs although many viruses have the ability to modulate both pathways in vitro where the pathways have factors in common such as STAT1. Whether viruses in fact target both pathways in vivo depends on the cells and tissue they infect. As described above, the JAK/STAT pathway that is induced by IFNs is a relatively uncomplicated primordial pathway and the factors encoded involve just a few non-redundant genes. It is perhaps this uncomplicated nature of the JAK/STAT pathway that leaves the host vulnerable to virus manipulation. The mechanisms that viruses use to target the JAK/STAT pathway are numerous. For example, STAT1 and STAT2 are targets for the majority of viruses that manipulate the JAK/STAT pathway and the mechanisms involve ubiquitination and degradation and dephosphorylation. The STATS may be sequestered, delocalised and blocked from phosphorylation by JAKs. In some cases, the virus induces the upregulation of suppressor of cytokine signalling cellular genes (SOCS) that regulate the pathways in question. In addition, many viruses target more than one factor in the IFN signalling pathway and there are many examples in the paramyxoviruses (reviewed in ). RespirIt should be noted that viruses also modulate the IFN response in other ways. Many viruses, in particular large DNA viruses such as poxviruses and herpes viruses, encode numerous factors that target Toll-like receptors and RIG-I-like receptors that are considered to be the main receptors for type 1 IFN induction, and nucleotide-oligomerisation domain-like receptors ,35,36. ISeveral viruses block IFN-α/β and IFN-γ induced signalling by disrupting the function of the IFN receptor by either forming complexes with receptor subunits, reducing receptor expression or by inducing the degradation of receptors. In addition, poxviruses produce secreted IFN-receptor-like decoy factors and herpes simplex virus 1 induces the secretion of an IFN type 1 antagonising protein.Measles virus specifically suppresses the IFN-α induced antiviral state but not IFN-γ induced signalling . Viral iInfluenza A viruses disrupt JAK/STAT signalling in part by reducing the expression of the IFN receptor. The multifunctional non-structural protein 1 (NS1) encoded by influenza A viruses predominantly targets IFN by reducing its production  but in aThe herpes virus Epstein Barr virus encodes the protein BZLF1 that decreases the ability of IFN-γ to activate a number of genes including IRF-1, p48 (IRF9), and MHC class II expression, however it also inhibits phosphorylation of STAT1 and decreases the expression of the IFN-γ receptor . In addiM-T7 gene from the myxoma virus genome showed that it was a critical virulence factor for the development of myxomatosis in rabbits [A strategy that a small number of viruses have evolved to counteract the biological activity of IFNs is to intercept these cytokines before they bind to their cognate receptors. In general the viral encoded molecules mimic the extracellular binding domain of host cytokine receptors and are secreted from virus-infected cells. Poxviruses, with their large genomes, appear to have captured genes from their host species by unknown mechanisms. Myxoma virus is a highly infectious disease of rabbits that causes swelling of the mucous membranes and inflammation and is usually fatal. Myxoma virus encodes a protein, T7, that is an IFN-γ like receptor and specifically binds rabbit IFN-γ and was the first example of this strategy used against the interferon family ,43. More rabbits ,45. In a rabbits .A number of orthopoxviruses also express a type I IFN binding protein. Vaccinia virus encodes a secreted protein B18R that has significant regions of homology with the alpha subunits of the mouse, human and bovine type I IFN receptors . B18R haHerpes Simplex Virus 1 has evolved a mechanism similar to that of orthopoxviruses to inhibit the type I IFN response . The expThe Janus kinase family includes JAK1, JAK2, JAK3 and Tyrosine kinase (Tyk2). Tyk2 and JAK1 are major targets of both RNA and DNA viruses.Flaviviruses are transmitted to humans by mosquitoes and ticks and cause millions of infections annually. West Nile virus, Japanese encephalitis virus, dengue virus, yellow fever virus and tick-borne encephalitis virus are associated with morbidity and mortality worldwide. All flaviviruses studied to date inhibit host innate responses to infection by inhibiting type 1 IFN mediated JAK/STAT signal transduction and there are a number of viral encoded factors involved ,52,53,54Pneumovirinae), that is the causative agent of lower respiratory tract infections, bronchiolitis and pneumonia during (early) childhood impairs both IFN-α/β and IFN-γ signalling in primary murine alveolar macrophages and in human macrophages (THP-1 cells) by two distinct mechanisms [Respiratory syncytial virus  proteins SOCS1 and SOCS3. SOCS are normally induced by cytokine stimulation and they serve to interfere with signalling, not only from the inducing cytokine in a classic “negative-feedback” loop, but also regulate signalling downstream of other cytokines . All SOCThe first report that demonstrated a viral protein was capable of inducing the expression of SOCSs molecules came from a study of hepatitis C virus. Hepatitis C is a major cause of chronic hepatitis, end-stage cirrhosis and hepatocellular carcinoma. Eighty per cent of patients newly infected with hepatitis C virus develop chronic infection and treatment with IFN-α, has only achieved moderate results . StudiesOther viruses have been shown to modulate the JAK/STAT pathway by upregulating SOCS1. Herpes simplex virus 1 is a large double-stranded DNA that infects epithelial cells and establishes a latent infection in the sensory ganglia that can become reactivated . Herpes Iridoviridae and was isolated from mandarin fish in which the virus is highly infectious and pathogenic. Sequence and bioinformatic analyses of the virus genome demonstrated that open reading frame 103R encodes a predicted viral SOCS with high homology to SOCS1 but lacks a SOCS box domain. The novel viral SOCS protein was shown to interact with the JAK1 protein and to inhibit tyrosine kinase activity in vitro [A viral encoded SOCS has been reported for a virus that infects fish but no such genes have been reported for mammalian viruses . Infectiin vitro . Of evolin vitro , illustrThe majority of viruses that impair the JAK/STAT signalling pathway have evolved mechanisms to target STAT1 and STAT2. The mechanisms used are diverse and include viral phosphatases, retention of suppression of STATS in the cytoplasm, inhibition of STAT phosphorylation, degradation of STATS through the proteasome and blocking of nuclear import.701 is critical for activation and viruses have evolved proteins to specifically block this process. Blocking STAT1 phosphorylation prevents either homodimerisation in the case of IFN-γ induced signalling to form GAF, or the formation of a trimeric complex of ISGF3 with STAT2 and IRF9 in IFN-α/β induced signalling. STAT1 may be a common target for some viruses since it is required for both type 1 IFN as well as IFN-γ induced signalling.A number of viruses inhibit phosphorylation of STAT1 or STAT2 or both either directly or indirectly as described above by interaction with JAKs or altering the levels of JAKs. In general, this requires new viral gene expression. Phosphorylation of STAT1 at TyrP gene (phosphoprotein) by RNA editing that results in frame-shifts [P gene including V, C and P and a protein named W, D or I. The V proteins are generally considered to be the principle IFN antagonists, however, the P, W and C proteins also play diverse roles in antagonising IFN signalling as described below. The V protein has multiple functions in disrupting signalling, including direct binding to STAT1 and STAT2 to prevent phosphorylation by way of their N-terminal and C-terminal regions. This mechanism is found for mumps virus, Hendra virus, Nipah virus and the swine virus Rinderpest virus. Rinderpest virus and Nipah virus also block STAT1 phosphorylation by way of their W and P proteins, respectively. In addition Sendai virus inhibits IFN-induced tyrosine phosphorylation of STATs by its C proteins [C gene abolishes the ability to block the IFN-α/β response [The paramyxovirus family includes the human viruses measles virus, mumps virus, parainfluenza viruses, Hendra virus and Nipah virus. Hendra virus and Nipah virus are zoonoses and cause up to 70% mortality in humans. The paramyxoviruses disrupt STAT signalling by various mechanisms  inclproteins ,83,84,85response ,85. In aresponse   ). DendriMarburg virus is a member of the filovirus family of negative strand RNA viruses and causes highly lethal haemorrhagic fever in humans and in non-human primates. Marburg virus encodes a matrix protein VP40 that inhibits phosphorylation of STAT1 and STAT2 .A small number of viruses use a strategy to inhibit IFN signalling by dephosphorylating activated STATs. Poxviruses encode a phosphatase that has roles in morphogenesis, as well as dephosphorylating STATs while other viruses induce cellular phosphatases to inhibit IFN signalling.701 as well as Ser727 when the signalling pathway is activated by IFN-γ [701 and not Ser727 in human cells, suggesting a possible evolutionary divergence of this virion associated enzyme. The viral phosphatase is a novel mechanism and has not been found in other viruses outside the Poxviridae family.The orthopoxvirus, vaccinia virus, blocks IFN-γ signal transduction by a phosphatase (VH1) that is incorporated into the viral particle and is released upon entry of the virus into cells and uncoating of viral cores . Signifiby IFN-γ . Since iby IFN-γ . VH1-likby IFN-γ ,96. The by IFN-γ , has theby IFN-γ . InteresHuman cytomegalovirus is a ubiquitous β-herpesvirus that can persist in the host by its ability to enter latency . Human cParamyxoviridae family, including Nipah virus and Sendai virus, evade IFN-α and IFN-γ induced intracellular responses by preventing STAT activation and nuclear accumulation by forming high molecular weight complexes [701 is enhanced, although STAT1 does not form an active transcriptional complex. The C protein blocks signalling by interfering with the domain arrangement of the STAT1 dimer, leading to the accumulation of phosphorylated STAT1 in the cytoplasm and the formation of high molecular weight complexes.Several members of the omplexes ,102. Theomplexes . In SendParamyxoviridae family, adenovirus, human cytomegalovirus, dengue virus and Zika virus, have evolved specific proteins that directly suppress IFN signalling by reducing the concentration of cellular STAT proteins [Members of the proteins ,104,105.Rubulavirus genus of the subfamily Paramyxovirinae have acquired the ability to specifically target either STAT1 or STAT2 for degradation using their accessory V protein encoded by the P gene [Avulavirus genus of the subfamily Paramyxovirus targets STAT1 for degradation [Almost all members of e P gene ,108,109.e P gene  and degrradation .Respiratory syncytial virus has evolved several strategies to inhibit IFN-α/β signalling that involve inhibiting Tyk2 phosphorylation (refer to Section 3) and modulating STAT1-CBP protein–protein interactions and transcriptional complex assembly ,105  . VHS is UL41 . UL41 isUL41 ,131. SinUL41 ,132. Herar mRNAs .MicroRNA (miRNA) has recently been implicated in viral immune evasion. Hepatitis C virus infection is one of the main causes of liver disease and a prophylactic anti-hepatitis C virus vaccine is unavailable. In chronic hepatitis C virus infection both the innate and adaptive immune signalling mechanisms are adversely affected . The miRThe critical role of the IFN induced JAK/STAT signalling pathway in anti-viral immunity, is highlighted by the ever-growing number of viruses that have evolved strategies to combat its induction. Moreover, such studies have shown a strong correlation between the ability of a virus to inhibit IFN induced signalling and virulence ,140. OthAlphavirus, family Togaviridae include a number of human pathogens with a worldwide distribution and are transmitted through mosquito vectors. Sindbis virus, the prototypic alphavirus, causes arthralgia in humans. Infection of mice with Sindbis virus causes encephalomylitis and has provided an excellent model for this viral-induced disease [Members of the genus  disease ,156,157. disease ,158,159. disease . In cont disease  concludeA similar approach to that taken with Sinbis virus has been used to identify a mutation in the NS5 protein of Kunjin virus that results in its attenuation. Kunjin virus is a naturally attenuated subtype of West Nile virus that is endemic in Australia but only rarely causes clinical disease in humans . MoleculThe paramyxovirus measles virus is a leading cause of death among children worldwide. The measles virus P gene encodes three proteins, the phosphoprotein (P) and accessory proteins V and C generated by RNA editing . These proteins have roles in replication and transcription and interfere with innate immunity by controlling IFN responses ,161. P iRespiratory syncytial virus is a leading cause of respiratory tract illness in infants and children worldwide and is a priority for vaccine development ,166. The50 of wt influenza virus strain PR8. Trials have been performed in elderly human subjects where the NS1 open reading frame has been fully deleted by reverse genetics [Influenza viruses cause significant clinical disease annually and historically there has been an increased risk of more serious infection in the elderly . The gragenetics . DespiteDuring tumour development it has often been reported that diminished IFN responsiveness co-evolves as a frequent genetic defect and that tumours evade their immune control by mutations frequently affecting genes involved in JAK/STAT signalling ,185,186.V gene (V protein) that antagonises IFN signalling and V gene deficient mutants grow to high titres in many tumour cells but fail to grow in normal cells [V gene deficient mutant virus injected into the tumour replicated and spread through the tumour and effectively cleared the tumour burden [Newcastle disease virus is an avian paramyxovirus that is inherently oncolytic and tumour selective. Its tumour selectivity is thought to be due to the defective IFN response in tumour cells ,188,189,al cells . Moreover burden .Sinbis virus causes mild symptoms in humans  and has STAT2 gene. As above for the child with a mutation in IFNAR2, individuals in the family with STAT2 deficiency remained generally healthy with no obvious defects in adaptive immunity. Findings of this nature have suggested that the systemic route of vaccine administration evades tissue-specific innate mechanisms that might otherwise complement the lack of IFN-α/β such as mucosal IFN-γ [Safety issues have recently come to light in the use of live attenuated viral vaccines where individuals have deficiencies in genes that encode factors involved in JAK/STAT signalling. A child with fatal encephalitis following inoculation with live attenuated measles, mumps and rubella (MMR) vaccine was shown to have a homozygous mutation in the IFN-α/β receptor (IFNAR2) that rendered cells from this individual unresponsive to type 1 IFN . Surprisal IFN-γ ,202.DNA-damage DNA-binding protein (DDB)I-dependent ubiquitin-ligase complexes to catalyse ubiquitin conjugation of STAT2 for degradation [M27, deaths were recorded at 26 days post-infection for the wt virus and 42 days post-infection for the M27 deletion mutant virus [Attenuation of live virus by deletion of IFN antagonist genes has raised safety issues in animal studies. Cytomegalovirus encodes the protein pM27 that selectively antagonises STAT2. pM27 exploits radation . Infectint virus .Reducing the risk of live attenuated viruses by a combination of attenuation strategies could achieve safer vaccines and reduce the risk of revertants. Codon-pair deoptimisation (CPD) is a highly effective approach to attenuate viruses . This inIn the last 20 years, there has been much discovered as to how viruses modulate IFN signalling and in many cases specific viral proteins involved have been identified. Almost all viruses have evolved strategies to combat the effects of type 1 and type 3 IFNs by antagonising the IFN signalling pathway. IFN is one of the most potent innate responses to prevent viral replication during the early stages of infection and it is clear that some of the most potent determinants of virulence are those viral encoded factors that antagonise the JAK/STAT signalling pathway. This knowledge is being exploited towards producing new generation highly attenuated recombinant vaccines that afford protective immunity. The genetic manipulations of several viruses have shown that targeting elements that block IFN signalling has major effects on viral virulence and studies on measles virus have also shown that it is possible to produce a highly attenuated virus that elicits a potent protective immune response. In the past, the means to attenuate viruses has relied on a hit-or-miss approach using conventional methods such as cell culture passaging. Nowadays, there is the potential to attenuate RNA viruses, in particular, by specific gene mutation of virulence determinants, codon-pair deoptimisation and the introduction of temperature sensitive mutations using reverse genetics. The challenge going forward for engineering live attenuated vaccines will be to reduce virulence to a level considered safe whilst retaining sufficient immunogenicity to raise a long-lasting protective immune response. With modern genetic engineering tools, highly efficacious, low cost and safe vaccines against viruses of significant global health importance may be on the horizon.


This region plays a vital role in the cycling of globally important nutrients as well as carbon. Although the microbial communities in this region have been assessed, the dynamics of viruses (abundances and production rates) remains understudied. To address this gap, scientific cruises during the winter and summer seasons (2013) covered the North Pacific basin to determine factors that may drive virus abundances and production rates. Along with information on virus particle abundance and production, we collected a spectrum of oceanographic metrics as well as information on microbial diversity. The data suggest that both biotic and abiotic factors affect the distribution of virus particles. Factors influencing virus dynamics did not vary greatly between seasons, although the abundance of viruses was almost an order of magnitude greater in the summer. When considered in the context of microbial community structure, our observations suggest that members of the bacterial phyla Proteobacteria, Planctomycetes, and Bacteroidetes were correlated to both virus abundances and virus production rates: these phyla have been shown to be enriched in particle associated communities. The findings suggest that environmental factors influence virus community functions ( Lysis ol shunt” , 6. Givel shunt” . NeverthPrior estimates suggest viral activity results in the lysis of an estimated ~ 20% of marine heterotrophs daily , 8–10. VObservations indicate that drivers of, and constraints on, virus activity vary geographically; therefore major oceanic basins and perhaps even regions within these basins need to be considered independently. For example, in the Sargasso Sea, chlorophyll concentrations and temperature were shown to correlate with both virus abundance and virus production rates; however, this was not seen in the temperate region of the North Atlantic Ocean, the South Pacific Ocean, or the Western Pacific Ocean , 22, 25.In the current study, we aimed to address the role host diversity plays, relative to environmental conditions, in shaping virus abundance and production rates. We completed this study in an understudied oligotrophic region, the eastern portion of the North Pacific oceanic basin. This region includes the North Pacific subtropical Gyre (NPSG) , the EarKilo Moana. No specific permissions or permits are required for these locations/activities and research did not involve endangered or protected species. Cruise KM1301 occurred between January 10, 2013 and February 7, 2013 (hereafter referred to as the “winter cruise”). The cruise transect left from Honolulu, HI traversed the NPSG and returned to Honolulu ; samplestem 7.1  using avProchlorococcus, Synechococcus, and picoeukaryotes as detailed in Johnson et al. [A FACSCalibur flow cytometer  was used to determine bacterioplankton densities as previously described . Brieflyn et al. . The totn et al. .in-situ temperatures for 3 h. Killed controls were prepared prior to incubation via addition of trichloroacetic acid (TCA) to a final concentration of 1%. Leucine incorporation was stopped via addition of TCA. Cells were then pelleted, rinsed, and dried for 12 h. Samples were resuspended in Ecoscint A  and measured on Packard Bioscience Tri-Carb 2900TR liquid scintillation counter onboard the ship. Measured activity was converted to μg C L-1D-1 as follows: disintegrations per minute (dpm) of the live samples were corrected with data from killed controls to account for abiotic association of leucine to particles. This value was converted to μCi using the value 2.22x106 dpms per μCi. Following this conversion sample size, incubation time and specific activity of the leucine were accounted. Incorporation of tritiated leucine was converted to moles of carbon through multiplication by a factor of 1.5 [Bacterial production protocols were adapted from Kirchman et al. . Brieflyr of 1.5 .Chlorophyll concentrations were determined by vacuum filtering 100 mL of seawater on 0.22-μm or 0.8-μm polycarbonate filters (Millipore). Chlorophyll was extracted in 100% methanol at -20°C for 24 h . Followi4 and NO3: QCP-NUT-1; SiOH4: CGSI1-1.The limit of detection for nutrients is as follows. Phosphorus for both cruises was 0.05 μM. A limit of detection of 0.2 μM SiOH4 for samples from KM1301 while 0.1 μM from KM1312. The detection limits for nitrogen species are as follows: the detection limit for nitrite was 0.05 μM for both cruises; and nitrate was 0.1 μM for samples collected on the winter cruise and 0.05 μM for samples from the summer KM1312 cruise.All samples for nutrient measurement were collected in HCl-cleaned, high-density polyethylene bottles (VWR#414004–110) and stored at -80°C until analysis. Subsequent nutrient analysis was performed on technical duplicates of each sample replicate on an Astoria-Pacific A2 autoanalyzer using Certified Reference Materials, (Inorganic Ventures) as follows: Nitrite: QCP-NT; POSamples for ammonium measurements were collected from Niskin bottles as described. Ammonium concentrations were determined using the fluorometric method described in Holmes et al. 1999  and verihttps://dx.doi.org/10.17504/protocols.io.iptcdnn.Virus samples for enumeration were collected from Niskin bottles and fixed with 0.5%  glutaraldehyde and flash frozen in liquid nitrogen for shipping. Upon delivery to the lab, all samples were stored at -80°C until processed. Enumeration of virus particles was performed using a protocol adapted from Ortmann and Suttle . Brieflyhttps://dx.doi.org/10.17504/protocols.io.dsp6d (Approach 1). Virus-depleted surface water was generated daily using a tangential flow filtration device  equipped with a 30kDa filter (Millipore Pellicon XL Filter). Unattached virus particles were reduced in the sample by rinsing the bacterial community three times with virus-free water over a 47 mm diameter 0.2-μm polycarbonate filter (Millipore). Following unattached virus reduction, the bacterial community was resuspended in 500 mL of virus depleted seawater and divided into 3 polycarbonate bottles which were incubated at in situ temperature and light levels  for 12 h. Samples were collected and preserved using 0.5% glutaraldehyde, flash frozen in liquid nitrogen and stored at -80°C until enumeration. For enumeration of virus like particles (VLPs) samples were vacuum filtered onto 0.02-μm pore-size, 20-mm diameter Anodisc filters . Filters were stained with SYBR Green I for 30 min in the dark. Slides were preserved with PBS and o-phenylenediamine.Virus production assays were conducted using the dilution and reoccurrence technique previously described ,14 and aFollowing staining and preservation, 20 images per slide were acquired using the Leica MM AF Acquisition software within the Leica Application Suite X. Virus-like particles were counted using the Leica MM Analysis Software with thresholds limiting analysis of samples with fewer than 100 virus-like particles per grid or greater than 600 virus-like particles per grid. Samples were enumerated automatically using the thresholds of size greater than 10 pixels and less than 0.55-μm and a border within 5% of a perfect circle. This was compared to manual counts and shown to be statistically similar (data not shown).Statistical analysis of viral distribution and production was completed in Sigmaplot using Systat functions (ver 12.5). The Shapiro-Wilk test was used to determine the normality of data. If data was non-normally distributed, a Mann-Whitney Rank Sum test was used to determine statistically significant differences between seasons. If data was normally distributed a two tailed t-test was used to determine statistical difference. As a majority of data was non-normally distributed, Spearman correlations were used to determine correlations of factors. Correlations were considered significant if the p-values were less than 0.05.E. coli bases 515–806) of bacterial 16S rRNA genes was amplified and sequenced. Hudson Alpha Genomic Services Laboratory  performed 250-bp paired end sequencing on the Illumina Miseq platform using V2 chemistry. Only reads which reached a Q-score of 30 with no ambiguous bases were used in subsequent analysis. Sequences have been depostited and are available from the NCBI under Bioproject PRJNA394135.Water was collected from the CTD as described above. Cells from 350 mL of seawater were collected onto 47-mm diameter, 0.2-μm nominal pore-size polycarbonate filters  by vacuum filtration. Following collection, samples were placed in 1 mL cryovials and stored in liquid nitrogen for transport back to the lab. After being returned to the laboratory, samples were stored at -80°C until extraction. DNA was extracted using the PowerWater® DNA Isolation Kit (Mo Bio) according to manufacturer’s protocol. The V4 region  and deposited under SRP065205. Identification of environmental parameters correlated to changes in Prochlorococcus community structure was conducted in R .Analysis of sub-ecotypic diversity of n et al. . Briefly (2 (Mann-Whitney P = 0.596), NO3 (Mann-Whitney P = 0.991), NH4 (Mann-Whitney P = 0.932), and PO4 (Mann-Whitney P = 0.199) were statistically similar.Although the two cruise transects varied geographically within the NPSG, similar physical parameters were observed in surface waters of both winter and summer seasons. Temperatures on the winter cruise ranged from 10–23°C, and in the summer ranged from 11.1–25.5°C; covering largely the same range  = 0.06) . This si5 to 8.4 x105 cells mL-1 in winter and 4.6 x105 to 1.0 x106 in summer (Mann-Whitney P = 0.343). However, the average abundance of Prochlorococcus varied between seasons (Mann-Whitney P = 0.022), with populations ranging from 775 to 2 x 105 cells mL-1 in winter and 418 to 2.25 x 105 cells mL-1 in the summer, in each case with cell density decreasing with increasing latitude. Picoeukaryote densities (Mann-Whitney P = 0.001) were statistically different between cruises as well, with higher abundances measured in the winter. In contrast, Synechococcus abundances were not different between cruises (Mann-Whitney P = 0.918), , .5 to 3.26 x 106 while in the summer abundances were 7.64 x 106 to 4.98 x 107 (5−3.51 x 107 VLPs/mL/h) than in the winter (6.56 x 105 to 1.29 x 108 VLPs/L/d), this difference was not statistically significant , Average virus abundance was statistically lower in the winter (Mann-Whitney P<0.001), where virus-like particles (VLPs) per milliliter ranged from 7.33 x1098 x 107 . Althougs = - 0.979, P = < 0.001; summer: Rs = -0.804, P = < 0.001), pH , salinity , and light . None of the factors we measured correlated to virus production rates in the NPSG, with the exception of chlorophyll a concentrations in the 0.8-μm fraction of during the summer cruise. Many correlations were seen in relation to viral abundances, a majority of which occurred in both seasons . The virus-like particle distribution in surface waters was strongly correlated to geographical and physical parameters in both seasons, as exhibited by latitude  were correlated to virus abundance only in winter , a trend also seen when observing total bacterial cell counts . The reverse was seen in an examination of picoeukaryotic density, which was positively associated with viral abundances only in the summer  of sample sites were correlated with viral abundance or production rates, regardless of season. Beta diversity however was linked to viral abundances. Briefly, the SIMPROF analysis within Primer7 determined communities that were statistically indistinguishable from one another. Unsurprisingly, numerous microbial communities clustered into SIMPROF groupings of two or more stations. SIMPROF groups shared similar viral abundances. Subsequently the BEST analysis was used to determine what environmental factors co-vary with microbial community composition. This analysis indicated changes in microbial community structure were most strongly linked to viral abundances and temperature, .To determine whether microbial community diversity influenced virus particle distribution, we explored 16S rRNA gene amplicon libraries collected from surface waters at each station. Alpha diversity metrics .As viruses are likely dependent on the composition of the microbial host communities, we asked if certain microbial taxa (OTUs) were linked to changes in virus production or virus abundances in the North Pacific, and if these taxa changed seasonally. Virus abundance and production rates are inextricably linked, therefore we utilized a similarity matrix to determine how “related” sampling locations were based on these two metrics. A BEST analysis was done to determine which microbial taxa were most strongly correlated (spearman) to this similarity matrix. Taxa were limited to those which comprised at least 0.5% of any library. BEST analysis was run stepwise with 500 iterations, and p-values determined through 999 permutations. As viral dynamics have been shown to differ between seasons, this analysis was done for each season separately. Surprisingly, taxa which most strongly correlate to changes in viral dynamics in both seasons are not necessarily the most abundant taxa across the transects, or at a given location. BEST analysis was also used to determine which grouping of OTUs describes the largest variance in virus dynamics. In summer 72% of the variation was described by 14 OTUs (p = 0.001), while in winter 59.2% of the variation was described by 18 OTUs (p = 0.02). . The OTUs = 0.389, P = 0.007). This clade is ubiquitous in the world’s oceans. However, there are no cultured representatives of this clade so information on its metabolism is limited. Inferences from distributional patterns indicate it is likely an oligotrophic bacterium with a slow growth cycle [s = 0.1, p = 0.098), and one tentatively assigned to Kangiella . The third gamaproteobacteria was an Alteramonadales belonging to the genus Colwellia . Other Proteobacterial representatives include an unclassified Rhodobacteraceae , and an unclassified Betaproteobacterium .Overwhelmingly, the taxa linked to changes in virus concentration and production rates were heterotrophic in nature; 76% and 79% in the winter and summer, respectively  Tables. th cycle , 44. TwoRubripirellula , one Pirellula , and one belonging to the FS140-16B-02 marine group . In winter only a single Planctomycete belonging to the genus Bythopirellula  correlated to virus dynamics. In winter 2 OTUs belonging to phylum Bacteroidetes were correlative; one belonging to the family Saprospiraceae (NA), and one belonging to the genus Ulvibacter .The second most represented heterotrophic phylum differed between seasons. In the summer Planctomycetes was determined to be important in differentiating virus abundance and rates of production (3 OTUs identified). Bacteroidetes, with 2 OTUs identified, was the second most identified phylum in winter. In the summer, the abundances of 3 genera belonging to the order Planctomycetaceae contributed to the taxa which correlated to changes in virus dynamics: one s = 0.315, p = 0.032); while in summer a member of the nitrogen-fixing cyanobacterial genus Cyanothece was the only cyanobacterial OTU correlated to these changes . In our analysis Prochlorococcus, was not identified as a taxa driving changes in virus abundance and production across the transects. However, analysis Prochlorococcus community structure at the ecotypic level indicates that virus abundance and rates of virus production may be important in shaping the community structure of high light adapted strains of Prochlorococcus . Although viral parameters were correlated to sub-ecotypic community structure, viruses had less of an effect on shaping these host communities than the environmental variables we measured . For more in-depth analysis of Prochlorococcus community and factors that shape subecotypic diversity of Prochlorococcus communities in the North Pacific see Larkin et al. (2016) [Although in both seasons a majority of correlated OTUs were heterotrophic, phototrophic organisms were correlated to virus dynamics as well  Tables. . (2016) .In this study we examined basin-wide drivers of virus dynamics (production and abundance) in the North Pacific Ocean. While similar work has been undertaken over the last decade in this region , we presGlobal distribution of many viral types has long been theorized , and whia was highly correlated with virus density, suggesting that photosynthetic members of North Pacific microbial communities are, at least in part, driving virus dynamics. Chlorophyll concentration has been previously shown to correlate strongly to virus abundance in the Beaufort Sea [Given that viruses are obligate parasites, the dynamics of viruses must at least in part, be shaped by the genetic richness and / or diversity of available hosts within the microbial communities. In our study, chlorophyll fort Sea ; as wellfort Sea . This maProchlorococcus cells showed a weak, but statistically significant, negative relationship with viral abundances -II.4 clade was the only sub-ecotype for which a large proportion of variation in diversity was explained by virus abundance (Data not shown). This clade largely represents the Prochlorococcus community which dominates in oligotrophic waters in the summer [The abundance of undances ; this maeratures , 52, in e summer .Braarudospaera bigelowii, is a marine algae recently shown to harbor nitrogen-fixing endosymbionts [Cyanothece, a diazotrophic marine cyanobacterium [The only parameter that viral production rates were correlated to, in this region, was the large phytoplankton fraction (as estimated by the > 0.8 μm fraction chlorophyll concentrations). This preliminarily indicates that large phytoplankton, such as large eukaryotic algae and even picoeukaryotes, could be important drivers of virus production rates in the North Pacific. This may be due to either an indirect relationship, such as their contribution to organic carbon concentrations or their potential role as a platform for attached bacteria. However, we cannot rule out a direct relationship due to their own infection and lysis. To this end, our efforts here may be underestimating the importance of large eukaryotic phytoplankton and archaea in viral dynamics in the North Pacific ,53. Indeymbionts . This isacterium . This maUnlike other pelagic regions, bacterial production rates in our study were not correlated with viral abundance or viral production rates, further supporting the idea that photosynthetic members of the community may be important drivers in this region. Targeted (16S rRNA gene) metagenomes were sampled in coordination with viral measurements that allowed for the linkage of virus abundance to bacterial community structure. A variety of new hypotheses for how certain members of the bacterial community could drive concentrations of virus in the environment exist: these range from effects of burst size differences to decreased competition due to viral lysis of other bacterial phylotypes . InteresThere were, however, some bacterial members of the microbial community that did correlate to the viral parameters measured, with a majority being heterotrophic in nature . Most of these OTUs were classified as Proteobacteria, which may indicate that this phylum is an important player in the density and rates at which viruses are being produced in the North Pacific. Furthermore, the OTUs that correlated to the viral parameters we measured changed between seasons, indicating that although Proteobacteria may be important regardless of season, the specific taxa that influence virus abundances and production rates change seasonally. Furthermore, members of the phyla Bacteroidetes and Planctomycetes were important in winter and summer, respectively. Unfortunately, for many of the important OTUs there are very few, if any, cultured representatives, so information on their potential metabolisms are limited. However, many of these taxa have been shown to be disproportionately associated with marine particles, including Alphaproteobacteria, Gammaproteobacteria, Planctomycetacia and Bacteroidetes , 56–58.Although this data must be interpreted carefully, this study indicates a potentially important role for Proteobacteria-infecting viruses in the regeneration of nutrients in the North Pacific. It is important to note that microbial community structure is generally stable in oligotrophic gyres, therefore the most dominant taxa are similar across our transects, thus implying that these taxa may be infected at similar rates across these transects. To this end, while these taxa may be important drivers of abundance and production they will not be identified by this analysis. Instead, we identified taxa whose changing abundances correlated with increased concentration or production of viruses. In our analysis these changes correlated with taxa that are enriched in particle-associated fractions of metagenomes , 56–58. 6 to 1.5x107 particles mL-1) despite slight differences in seasonal sampling, sample collection methodology and VLP enumeration protocols [5 VLPs mL-1 d-1) and North Atlantic (8.2 to 28.0 x 105 VLPs mL-1 d-1) [Summer viral abundances were found to be comparable to previous studies in the region  may vary across different oceanic regions [The observation that factors related to viral abundance vary between regions is unsurprising given that virus particle distribution is likely not influenced by a singular parameter, but likely by environmental interactions. Furthermore, it has been noted that environmental parameters that affect decay rates of viruses Click here for additional data file.S2 TableOTUs Identified by BEST analysis as explaining the most variance in virus abundance and production rates over the winter transect. BEST analysis was conducted stepwise with 500 random restarts; P-values were determined using 999 permutations. OTUs which increased spearman’s rho of >0.01 were included in the analysis.(PDF)Click here for additional data file.S3 TableOTUs Identified by BEST analysis as explaining the most variance in virus abundance and production rates over the summer transect. BEST analysis was conducted stepwise with 500 random restarts; P-values were determined using 999 permutations. OTUs included must increase spearman’s rho by >0.01 to be identified in the analysis.(PDF)Click here for additional data file.S4 TableTop 50 most abundant OTUs identified as chloroplast DNA in Mothur. A representative sequence was chosen from the OTU and top cultured BLAST hit is reported.(PDF)Click here for additional data file.S1 Fig2 values which indicate the correlation strength between the variable of interest and the distribution of stations in the ordination space.Numbers in parentheses are R(PDF)Click here for additional data file.


Since 2013, Zika virus (ZIKV) and chikungunya virus (CHIKV) have infected millions of people in the Americas via urban transmission cycles. Nonhuman primates (NHP) are involved in sylvatic transmission cycles maintaining ZIKV and CHIKV in the Old World. We tested NHP sampled during 2012 to 2017 in urban and peri-urban areas severely affected by ZIKV and CHIKV in Brazil. Seroprevalence and antibody titers were low for both viruses. Additionally, we found evidence for infection by heterologous viruses eliciting cross-reactive antibodies. Our data suggest that urban or peri-urban NHP are not easily infected by ZIKV and CHIKV despite intense local transmission. These data may imply that the ZIKV and CHIKV outbreaks in the Americas cannot be sustained in urban or peri-urban NHP once human population immunity limits urban transmission cycles. Investigation of diverse animals is urgently required to determine the fate of the ZIKV and CHIKV outbreaks in the Americas. Chikungunya virus (CHIKV) and Zika virus (ZIKV) emerged in the Americas in 2013. Limited antigenic variability of CHIKV and ZIKV may restrict urban transmission cycles due to population protective immunity. In Africa, sylvatic transmission cycles involving nonhuman primates (NHP) are known for CHIKV and ZIKV, causing cyclic reemergence in humans. To evaluate whether sylvatic cycles can be expected in Latin America, we tested 207 NHP collected between 2012 and 2017 in urban and peri-urban settings in Brazil for infection with ZIKV and CHIKV. No animal tested positive for viral RNA in genus-specific and species-specific reverse transcription-PCR (RT-PCR) assays. In contrast, six animals (2.9%) from the families Atelidae, Callitrichidae, and Cebidae showed ZIKV-specific antibodies and 11 (5.3%) showed CHIKV-specific antibodies in plaque reduction neutralization tests (PRNT). Reactivity was monotypic against either ZIKV or CHIKV in all cases, opposing unspecific virucidal activity of sera. PRNT endpoint titers were low at 1:40 in all NHP, and positive specimens did not correspond to the likely dispersal route and time of introduction of both arboviruses. All antibody-positive samples were therefore tested against the NHP-associated yellow fever virus (YFV) and Mayaro virus (MAYV) and against the human-associated dengue virus (DENV) by PRNT. Two ZIKV-positive samples were simultaneously DENV positive and two CHIKV-positive samples were simultaneously MAYV positive, at titers of 1:40 to 1:160. This suggested cross-reactive antibodies against heterologous alphaviruses and flaviviruses in 24% of ZIKV-positive/CHIKV-positive sera. In sum, low seroprevalence, invariably low antibody titers, and the distribution of positive specimens call into question the capability of ZIKV and CHIKV to infect New World NHP and establish sylvatic transmission cycles.IMPORTANCE Since 2013, Zika virus (ZIKV) and chikungunya virus (CHIKV) have infected millions of people in the Americas via urban transmission cycles. Nonhuman primates (NHP) are involved in sylvatic transmission cycles maintaining ZIKV and CHIKV in the Old World. We tested NHP sampled during 2012 to 2017 in urban and peri-urban areas severely affected by ZIKV and CHIKV in Brazil. Seroprevalence and antibody titers were low for both viruses. Additionally, we found evidence for infection by heterologous viruses eliciting cross-reactive antibodies. Our data suggest that urban or peri-urban NHP are not easily infected by ZIKV and CHIKV despite intense local transmission. These data may imply that the ZIKV and CHIKV outbreaks in the Americas cannot be sustained in urban or peri-urban NHP once human population immunity limits urban transmission cycles. Investigation of diverse animals is urgently required to determine the fate of the ZIKV and CHIKV outbreaks in the Americas. Flavivirus in the family Flaviviridae, whereas the chikungunya virus (CHIKV) is a member of the genus Alphavirus in the family Togaviridae. Both arthropod-borne viruses (arboviruses) originated in Africa and were likely introduced into Brazil during 2013, affecting millions of individuals since then (The Zika virus (ZIKV) is a member of the genus Aedes aegypti mosquito vectors and humans as vertebrate hosts (Similarly to the human-associated dengue virus (DENV), the American ZIKV and CHIKV epidemics rely on urban transmission cycles involving predominantly urban te hosts . Howeverte hosts , 6. Thiste hosts .Papio cynocephalus) and African green monkeys (Chlorocebus pygerythrus) , patas monkeys (Erythrocebus patas), Guinea baboons (Papio papio) (Chlorocebus sabaeus)  and forest-associated mosquitoes , 9. Whicrythrus) . For CHIo papio) , and gresabaeus) . A high sabaeus) . Blood msabaeus) , which isabaeus) –17.Aedes africanus, Aedes albopictus, Aedes apicoargenteus, and Aedes furcifer  comprise the superfamilies Hominoidea, including humans, and Cercopithecoidea . EvidencAedes species may be among the prime suspects for potential sylvatic transmission cycles. Brazil has approximately 28 Aedes species that have only very limited overlap with Old World Aedes species other than the widespread A. aegypti and A. albopictus  and capuchin monkeys (Sapajus libidinosus), available only as a non-peer-reviewed preprint so far, attracted substantial attention in the scientific community and the media  in northeastern and central-western Brazil, namely, Lucena, Sapé, and Santa Rita, where free-ranging animals were sampled ; Fig. 1.Flavivirus and Alphavirus  assays targeting the genera phavirus , 30 or iphavirus , 32. ThiP > 0.05).In contrast, six animals showed neutralizing antibodies against ZIKV 2.9%). Noticeably, two of these animals were sampled as early as the projected introduction of ZIKV into Brazil in 2013  33). Fo. Fo33). .9%. NotiP > 0.05). For ZIKV, only the 12.5% seroprevalence within Atelidae differed significantly from the 1.4% seroprevalence within Cebidae . Hypothetically, the relatively higher ZIKV seroprevalence in Atelidae may result from differences in the ZIKV-associated mortality between different NHP species, as observed with YFV  endpoint titers were invariably low at a 1:40 serum dilution for both ZIKV and CHIKV in all cases. For comparison, PRNT titers in ZIKV-infected humans can be several orders of magnitude higher using the same PRNT protocol  Fig. 2AFig. 2A. Flavivirus  were exposed to heterologous alphaviruses or flaviviruses.Cross-reactivity of antibodies in serological tests is commonly observed within the genus avivirus , 39 and omplexes  even wheomplexes . Therefoomplexes . No ZIKVFinally, ZIKV and CHIKV were likely introduced into different regions of northeastern Brazil upon their arrival in mainland southern America in 2013. ZIKV was introduced to the coastal region of northeastern Brazil, whereas CHIKV was introduced to the hinterland of northeastern Brazil . The geoExtensive spread of ZIKV and CHIKV in humans may cause population protective immunity that causes the outbreaks to stop due to lack of acute cases . PersistSapajus and Callithrix monkeys  titers observed in experimental infections of squirrel monkeys (Saimiri boliviensis and Saimiri sciureus), owl monkeys (Aotus nancymaae), and marmosets  ranging from 1:80 to 1:320 after seroconversion  and midazolam . After clinical examination, up to 5 ml of blood was collected from the lateral or jugular saphenous veins by a trained veterinarian. Animals were classified according to morphological characteristics such as hair color and size by trained biologists. Sampling and export were approved by Brazilian and German authorities .Flavivirus and Alphavirus genera (All samples were analyzed for viral RNA using broadly reactive nested RT-PCR assays targeting the s genera , 30, as s genera  and CHIKs genera . Serolog


Analysis of 75 colonies sourced in 2010 revealed that VDV1 was present in only 2 colonies (2.7%), suggesting its recent spread. We also detected newly emerged recombinants between the US strains of VDV1 and DWV. The presence of these recombinants poses additional risk, because similar VDV1-DWV recombinants constitute the most virulent honeybee viruses in the UK.RNA viruses impact honey bee health and contribute to elevated colony loss rates worldwide. Deformed wing virus (DWV) and the closely related Varroa destructor virus-1 (VDV1), are the most widespread honey bee viruses. VDV1 is known to cause high rates of overwintering colony losses in Europe, however it was unknown in the United States (US). Using next generation sequencing, we identified VDV1 in honey bee pupae in the US. We tested 603 apiaries the US in 2016 and found that VDV1 was present in 66.0% of them, making it the second most prevalent virus after DWV, which was present in 89.4% of the colonies. VDV1 had the highest load in infected bees (7.45*10 The economic impact of animal pollination services worldwide has been estimated at US$235–577 billion (in 2009) annually1, with honey bee pollination provide the bulk of these services . Overall, pollinator services provide 9.5% of the total agricultural economy2. Honey bees and other pollinators are challenged by abiotic and biotic stress factors that cause queen and worker mortality and impact colony health4 resulting in more than 50% of hives being lost annually since 2006 in the United States8.The European honey bee 9. Pathogens shared with honeybees could affect wild insect pollinators11 thereby posing additional risks to the services provided by wild pollinators12. In Europe and the US, elevated losses of honeybee colonies are associated with the mite Varroa destructor13. This parasite feeds on the internal tissues of both pupal and adult honey bees, and in the process can vector a number of RNA viruses14. These include Deformed wing virus (DWV)15, also referred to as DWV-A16, and DWV-like viruses such as the Varroa destructor virus 1 (VDV1)17, 19. DWV and VDV1 are not only the most prevalent viruses in honey bees, but are also likely to be the most significant in terms of their impact on honey bee colony health22.Parasites and pathogens are strongly implicated in honey bee colony losses, impacting pollination services17, Fig. , also re25. Both VDV1 and VDV1-DWV recombinants outcompete DWV and are more virulent than DWV (DWV-A) strains26, although the mechanisms of the their increased virulence remain unknown. On its own, VDV1 was identified as a major predictor of elevated winter losses in Germany27.Varroa-mediated transmission of DWV selects for highly virulent strains and decreases overall virus population diversityLittle is known about the prevalence and range of VDV1 in the US, a serious knowledge gap considering the potential role of this virus in observed high colony loss rates. In this study, we report that VDV1 is currently widespread in the US, alongside DWV. There are at least two distinct sequence variants of VDV1 present in the US, one of which is closely related to the European VDV1 strains. The prevalence of VDV1 in tested colonies (65%) was significantly lower than that of DWV (89%). Nevertheless, average VDV1 loads in virus-positive colonies were higher than those of DWV. Although the major VDV1 variants in the samples tested by NGS were full-length, we demonstrated that in addition to the full-length VDV1 and DWV, US honey bees harbor VDV1-DWV recombinants, similar to those found in Europe with respect to genome organization, but showing sequence-level traits from the VDV1 and DWV parental virus strains typical of the US. The widespread nature of VDV1 in the USA is worrisome. When planning antiviral treatments, including RNAi-based methodologies, consideration of the sequence differences between VDV1 and DWV will be important to ensure their efficacy.15  and in 2010 (n = 75). VDV1 and DWV qPCR primers targeting the structural gene block and the polymerase regions, respectively, were designed so as to not to cross-amplify, i.e. VDV1 primers did not detect DWV and vice versa , distributed throughout the US Fig. , contain2 = 0.025, F = 6.53, 387, 1, p = 0.0127). Neither Nosema prevalence nor Nosema spore load were associated with VDV1. We correlated the levels of VDV1 and other honey bee viruses quantified from the same samples during the National Honey Bee Disease Survey22, including DWV, Kashmir bee virus (KBV), Acute bee paralysis virus (ABPV), Israel acute paralysis virus (IAPV), Chronic bee paralysis virus (CBPV), and Lake Sinai virus 2 (LSV2). Although VDV1 was detected in a lower proportion of apiaries than DWV , the average number of genome copies of VDV1 in VDV1-positive apiaries, (7.45*1012 ± 1.62*1012) was the highest for all viruses quantified in this study, including DWV in DWV-positive apiaries (1.04*1012 ± 0.53*1012). Notably, the average genome copy numbers of other tested RNA viruses in their respective positive samples were more than 100 times lower than that of VDV1, ranging from 1.12*109 ± 0.84*109 for KBV to 5.31*1010 ± 3.27*1010 for IAPV (average number of virus genome copies per bee ± standard error),  than did VDV1-free apiaries . We observed an association between VDV1 levels and Varroa load in VDV1 prevalent colonies  Fig. .29. To determine if these sequences were present in bees dually infected with DWV and VDV1, we used the four possible combinations of strain-specific primers flanking the expected recombination region between the capsid protein (CP) genes and the non-structural (NS) genes, positions 4.9–6.5 kb of the VDV1 and DWV genomes, to amplify VDV1, DWV and possible recombinants in the RNA samples used for NGS by RT-PCR as described previously30 , we demonstrated that full-length VDV1 sequences were present alongside DWV  could increase chances of the mite-mediated spread of VDV1 which has higher virulence compared to DWV27, perfectly fitting the model that increases in host density lead to the dominance of more virulent strains of pathogens33. On the other hand, long-term surveys by the Bee Informed Partnership (www.beeinformed.org) did not find higher disease incidents in colonies from large versus small apiaries. Additional monitoring of VDV1 and Varroa load over time will be necessary to determine whether this trend is consistent and important to virus transmission.In this study a weak association was found between the VDV1 load and Varroa load in VDV1-infected colonies. Given a clearly demonstrated connection between Varroa mite infestation and the incidence and pathogenicity of all DWV-like virusesWe observed both a positive correlation between DWV and VDV1 loads in dually-infected colonies Fig.  as well 28. We were particularly interested in analyzing recombination points in the central genomic region because these VDV1-DWV recombinants were highly virulent in the UK24. By using RT-PCR we detected and sequenced recombinants containing VDV1-derived 5′ proximal structural (CP) and DWV-derived non-structural (NS) 3′ proximal gene blocks which were similar to those found in the UK28, Israel29, and France25. Moreover, we demonstrated that these recombinants derived from the US VDV1 and DWV parent viruses  × DWV(NS) combination may have selective advantages over the parental viruses. There is an urgent need to monitor VDV1 and recombinants to determine sources and possible control strategies for these viruses. Thanks to widespread samples available from the National Honey Disease Health Survey22, it should be possible to determine the source, dispersal, and arguably the bee population consequences of VDV1 during the past seven years. Along with reducing the risk of further introductions, knowledge of VDV1 and the rest of the DWV-like viruses can aid novel antiviral therapies including RNAi-based strategies.Although analysis of the NGS data clearly showed that full-length VDV1 sequences were present alongside the full-length DWV, we also found strong evidence for VDV1-DWV recombinants, both by direct sequencing of RT-PCR products Figs  and 5 an22. Quantification of virus RNA and the honeybee pRP49 transcript were performed using the qRT-PCR SYBR-green kit (Eurogentec) as described previously22. The VDV1 specific primers were designed to exclude cross detection of DWV 34, based on the Blaker method with a conservative 95% sensitivity and specificity. For NGS analysis and amplification of the RT-PCR fragments for sequencing, total RNA was extracted from individual honey bee pupae collected in Maryland and Florida in 2015 and 2017, respectively, as in previous studies28.Honey bee samples were collected as a part of the APHIS honey bee surveyV Table S as descr35. First, extra bases and adapters were trimmed using the file of Illumina Truseq and Nextera adapters sequences included with the BBTools package (parameters: ref = adapters.fa ftm = 5 ktrim = r k = 23 mink = 11 hdist = 1 tpe tbo). Next, PhiX contaminant sequences were removed using a Kmer filtering approach and the PhiX reference sequence included with the BBTools package (parameters: ref = phix174_ill.ref.fa.gz k = 31 hdist = 1). Finally, data was quality trimmed on both ends of the read to Q6 using the Phred algorithm and length filtered such that only those reads with a minimum length of 75 bp after all pre-analysis processing steps were retained (parameters: qtrim = rl trimq = 6 minlength = 75).Pre-analysis processing of the raw Illumina data (paired-end 150 nt reads) was performed in three steps with BBDuk (version 36.92), a tool in the BBTools package15 (GenBank Accession GU109335), VDV117 (GenBank accession AY251269) and DWV-C16 or the recombinant RT-PCR product . Counts, coverage and Shannon’s diversity index estimates for each nucleotide position were calculated from SAMtools mpileup37 (version 1.2) output. In order to correct Illumina sequencing chemistry errors, the Shannon diversity index estimate was error corrected38. Briefly, the error model was calibrated using data from an Illumina library from a bee injected with a cloned virus of known sequence. Data from positions 5230 to 6600 of the sequence, representing a low diversity region of the virus, were used to calculate an alphahat of 9.87546E-05 after excluding any position with a Shannon diversity index above 0.004 due to the presence of background virus diversity.Cleaned data from each sample library were individually aligned to reference sequences of DWV39 (version 0.1.2). Structural variants were detected using the lumpy smooth script from the LUMPY package40 (version 0.2.13) and additional genotype and coverage metrics for each variant were calculated using SVTyper, a script within the SpeedSeq package. Breakpoint positions representing recombinations between two different viral sequences were considered for further analysis. Recombination points with evidence from more the 10 supporting events based on split or discordant reads, which did not resulted in inversions, deletions or insertions, were considered.For detection of recombination breakpoints, cleaned data from each sample NGS library were individually aligned to the VDV1 and DWV references using SpeedSeq41, and the neighbour-joining trees were produced and bootstrapped using the PHYLIP package42. Bootstrap values were obtained from 1000 replications.The viral nucleotide sequences were aligned using CLUSTALSupplementary Information


A customized bioinformatics protocol has been implemented to analyze the microarray hybridization data by including a new normalization procedure and a stepwise filtering algorithm, which resulted in the highly accurate (96.33%) detection of positive/negative signals. This microarray has been tested with 57 subtype B HIV-1 clinical samples extracted from multi-treated patients, showing an overall identification of 95.53% and 89.24% of the queried PR and RT codons, respectively, and enough sensitivity to detect minority subpopulations representing as low as 5–10% of the total quasispecies. The developed genotyping platform represents an efficient diagnostic and prognostic tool useful to personalize antiviral treatments in clinical practice.The response of human immunodeficiency virus type 1 (HIV-1) quasispecies to antiretroviral therapy is influenced by the ensemble of mutants that composes the evolving population. Low-abundance subpopulations within HIV-1 quasispecies may determine the viral response to the administered drug combinations. However, routine sequencing assays available to clinical laboratories do not recognize HIV-1 minority variants representing less than 25% of the population. Although several alternative and more sensitive genotyping techniques have been developed, including next-generation sequencing (NGS) methods, they are usually very time consuming, expensive and require highly trained personnel, thus becoming unrealistic approaches in daily clinical practice. Here we describe the development and testing of a HIV-1 genotyping DNA microarray that detects and quantifies, in majority and minority viral subpopulations, relevant mutations and amino acid insertions in 42 codons of the  The (TCC)5 spacer sequence was used instead of the (T)15 one when the latter could induce partial self-complementarity with poly-A tracks present at the specific sequence. Sequence and length of the oligonucleotides were stepwise optimized in preliminary versions of the microarray (data not shown) to avoid formation of stable hairpins and to ensure a proper hybridization with the queried wild type (wt) and mutant codons in the context of their most common flanking regions, as shown in HIV-1 sequence databases (http://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html and http://hivdb.stanford.edu). The calculated melting temperature of all the oligonucleotides was 49–55°C, most of them being in the range of 49–51°C  were designed as internal hybridization positive controls (IHC) (Table A in All oligonucleotides were diluted in 1× spotting solution (Telechem-Arrayit) at 50 μM final concentration, and spotted onto super-epoxy-coated glass slides (Telechem-Arrayit). Spots containing spotting solution with no oligonucleotide were used as additional negative controls. Microarrays were printed using a GMS 417 Arrayer (Genetic Microsystems) and defined four grids or subarrays per slide. Each subarray contained 360 spots arranged in two regions (PR and RT), every oligonucleotide being printed in duplicate spots of 150 μm in diameter, with a center-to-center distance of 250 μm  buffer and 0.1% N-lauryl sarcosine, for 2 min at room temperature, followed by 2 additional min in 2× SSC, as described ,52. ThisThe phosphorylated strand of the HIV-1 DNA targets to be hybridized was specifically degraded using lambda exonuclease (New England Biolabs), and the resulting ssDNA was fluorescently labeled with Alexa Fluor 647, using the Ulysis Alexa Fluor 647 Nucleic Acid Labeling Kit (Life Technologies) as described ,53. HybrThe training set included microarray hybridization data from 17 pure HIV-1 clonal samples (268 hybridization experiments) as well as mixtures of two of these clonal samples (either pWT/pINS or 1.95c9/2.94c64) at different ratios (in %): 0/100, 1/99, 5/95, 10/90, 50/50, 90/10, 95/5, 99/1 and 100/0 (110 hybridizations). The test set was composed of hybridization data of HIV-1 quasispecies successfully amplified from 53 (PR) and 51 (RT) clinical samples (119 hybridization experiments).Hybridization images were processed using GenePix Pro 6.0.0.68 (Molecular Devices) software in GenePix Scanner, and ScanArray Express 2.0 (Perkin Elmer) in Agilent one. No automatic normalization of images was performed. A number of variables corresponding to the scanned spots were quantified by both software packages, including the median fluorescence intensity in each spot and its respective local background. Additionally, the microarrays scanned using the GenePix system contained information about the number of background pixels (BP), while microarrays processed using the ScanArray platform showed information about the diameter of the spot (DS) and the percentage of foreground signal higher than the background minus its standard deviation (BSD).A stepwise quality control protocol was implemented to exclude low quality hybridization signals from further analyses. Quality was evaluated at four different stages: individual spots, overlapping probe signals, duplicated probes and full microarrays. Initially, spot filters based on either BP or DS were implemented to remove the experimental noise arising from hybridization experiments and not due to biological variation. The election of such filters was based on a preliminary study of the distribution of quantified hybridization data binned as correctly classified or not . Spots which did not satisfy this quality control were not considered for further analysis. The remaining quality control stages are explained below.pol gene, two regions  were separately normalized in each microarray by means of their own mean positive signal. These two values were used as the normalization factor for each probe by applying the following function:PSǁ is the normalized probe signal, NRP is the total number of replicas per hybridized probe , IRP is the signal intensity of a replica probe i, NPS is the total number of positive probe signals per array region (either PR or RT), and IPS is the intensity of a positive probe signal i within such a region.Normalization of the data has been recognized as a necessary pre-processing step in a variety of high-throughput technologies, including DNA microarrays. All known normalization methods rely on assumptions about data features that are expected to be invariant across samples. Different normalization protocols have been developed for gene expression microarrays , though To evaluate the classification accuracy of the method, tables showing the theoretical hybridization signal of each probe with each hybridized target molecule (the previously sequenced samples of the training set) were required. The theoretical hybridization tables were based on the sequence complementarity between probes and targets: a positive hybridization signal was expected when probe and target are fully complementary, while the presence of any single mismatch between them (including those at the 5’ or 3’ ends of their hybridizing sequences) was assigned to a negative signal. Therefore, partial hybridizations were not allowed in the theoretical tables, assuming that this stringent criterion would assign any incomplete hybridization events that resist the washing step  to false positive signals. In turn, when the hybridized target sample contained a binary mixture of two HIV-1 clones from the training set, a theoretical consensus table was generated based on the minimum percentage of each sequence in the mixture that could be detected. This allowed defining a preliminary threshold value for each probe, which was further used for the genotyping of clinical samples belonging to the test set.Although the 160 spotted probes were designed to show similar theoretical hybridization temperatures  were designed and tested. Because of their good performance, some of the probes containing the same interrogating codon were maintained in the final version of the microarray. Thus, to avoid redundant information we selected, in each hybridization experiment, the equivalent probe that showed the lowest overlap between its positive and negative distribution functions.pol gene: on average, a probe for each wt codon sequence and two probes for its alternative mutants. Since the expected number of positive signals for any sample of the training set should be around 1/3 of the probes included in each microarray, those showing a higher number of hybridization signals were discarded.Once all useful information from the hybridized microarrays had contributed to the quality control of the probes, the last step consisted in excluding those microarrays showing an excess of positive signals. The spotted probes explored a variable number of codons for each position of interest in the PR or RT regions of the Once a clinical sample from the test set was hybridized to the microarray, the cumulative probability difference to belong to either the positive or negative distribution was computed for each normalized probe signal. Probabilities were based on the distribution functions previously obtained for each probe using the training set. As stated above, when either positive or negative hybridization data were lacking for a given probe, the general reference curves were used. When the absolute cumulative probability difference was smaller than 0.05, the signal was classified as ‘undefined’ because of the high chance of a wrong classification. A theoretical hybridization table was built for clinical HIV-1 samples of the test set, based on the previous sequencing and alignment of a number of clones (13 to 34) of each sample, assuming the preliminary detection threshold per probe derived from the hybridization of binary mixtures (training set). Thus, to evaluate the classification accuracy of the method for clinical samples, once a signal was classified as positive or negative, it was checked if that was in agreement with the theoretical hybridization table. This rendered the final classification of each hybridization signal as true positive (TP), true negative (TN), false positive (FP), false negative (FN) or undefined (UD), and allowed defining the accuracy and sensitivity of the developed genotyping microarray for detecting minority subpopulations within HIV-1 quasispecies in clinical samples.15 and (CCT)5 spacers were either not included or shortened (range tested: 7–13 nts), when the interrogating region of the oligonucleotide was shorter than 15 nts (range tested: 9–14 nts), and when the queried codon occupied positions outside the interval 7–11 within the interrogating 15-mer. The nucleotide sequence of the spotted probes was also stepwise optimized based on their individual performance in five successive versions (termed I to V) of the microarray: for each codon, only the probes showing the highest performance were maintained in the final version of the genotyping microarray . Additionally, preliminary experiments showed a decrease of the overall hybridization efficiency when aldehyde-coated slides were used, probe oligonucleotides were spotted at concentrations lower than 50 μM (range tested: 5–45 μM), hybridization solution contained 50% formamide, and hybridization time was shorter or longer than 3 h (range tested: 1 to 4 h). Microarray hybridization and washing temperatures were also optimized for point mutation discrimination in systematic preliminary experiments . As a result, the use of the optimized amplification, labelling and hybridization conditions described in Materials and Methods rendered useful hybridization data for most of the clinical samples  belonging to the test set, irrespectively of their viral load. Indeed, the discarded clinical samples did not produce amplification by RT-nested PCR in any of the conditions and primer combinations assayed, probably due to the presence of a high number of mutations in their primer-binding regions. The whole set of raw microarray hybridization data used in this study has been uploaded to Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/), with the accession number GSE90621.Preliminary experiments based on our previous experience ,61–63 weTwo examples of microarray hybridizations of wild type PR and RT samples using the optimized experimental conditions described in Materials and Methods are shown in Figure A in Other examples of hybridized microarrays included signals produced by different mutant targets belonging to the training and test sets. Figure B in To quantitatively analyse the performance of the genotyping microarray in terms of accuracy, specificity and sensitivity, a detailed bioinformatics analysis was designed and carried out. It was assumed that, although a high number of interrogating probes were tested and optimized along the different versions of the microarray, some probe-target hybridizations could be either hindered or impeded due to nucleotide mutations eventually present in the sequences of the training set at positions adjacent to the queried codons. Thus, once the consensus sequences of the samples were obtained, the sequence complementarity between the spotted probes and the hybridized targets was evaluated. This allowed constructing the theoretical hybridization tables of the training set  that compared the hybridization with targets from the training set with the expected results derived from the theoretical hybridization tables. As a result, microarray spots scanned with the GenePix platform passed the filter if BP > 300, a criterion that discarded most of the FP but also some of the TP signals. In turn, spots of microarrays analysed with the ScanArray platform were accepted if either DS ≤ 150 μm, or DS > 150 μm being BSD ≤ 40: these criteria eliminated FP signals while avoiding the filtering of TN ones . Such normalized probe signals ǁPositive and negative distribution functions presented certain degree of overlapping in some of the probes. Thus, though 76% of probes discriminated correctly (overlapping <10% between the positive and negative distribution curves), 24% of probes showed curves overlapping >10%. Such distinct behaviours were exemplified by probes Y188a and M230-3 in The information derived from the spots that passed the first filtering step was used to filter the probes showing a suboptimal performance. Then, the last quality control step allowed discarding the whole hybridized microarrays when an excess of positive signal was produced due to unspecific hybridization. To do so, both the number of positive signals per microarray and the related false positive vs. false negative ratios were plotted (data not shown). This allowed establishing that microarrays with more than 12 and 35 positive signals at the PR and RT regions, respectively (around 1/3 of the spotted probes), had to be excluded from further analysis. Thus, 4.46% of the microarrays that were hybridized with the training set and 4.16% of those hybridized with the test set in the PR region were discarded, while all the microarrays hybridized with the test and training sets in the RT region passed this last filtering step .The overview of the classification of all hybridization signals before filtering is shown in i.e. I46-PR, K65-2, D67a-2 and E219) and FN ones (Y181) were clearly identified.The increased classification performance upon the 4-step, sequential filtering protocol applied is shown in The distribution of genotyping errors per probe was further analysed in all the hybridised clinical samples belonging to the test set  or pWT/pINS (48 microarrays). The theoretical hybridization tables of these pure samples and their mixtures are shown in Figure J in Each of the probes presented a characteristic detection sensitivity for minority genomes. In the mixture 1.95c9/2.94c64, probes K211-2 and R211-2 showed positive signals in all hybridization experiments where their specific targets were present at percentages of at least 10%. Furthermore, in 50% of the experiments (31 of 62 microarrays) these probes showed a neat hybridization signal when their specific targets were present at proportions of 5% in the mixture. In turn, using the mixture pWT/pINS, 4 out of the 5 selected discriminating probes showed a positive signal when their specific targets were present at percentages ranging from 10% to 50%, being D67a-2 the only probe able to detect minority genomes at proportions of 10%. Unfortunately, the probe Ins69c produced FP signals even when its specific target (pINS) was not present in the mixture . Still, the results obtained with the mixture 1.95c9/2.94c64 suggest that the maximum sensitivity of the microarray for detecting minority genomes could reach a value of 5%.Before hybridizing the microarrays with the test set samples, the genetic variants present in the quasispecies at each queried codon were characterized by sequencing between 13 to 34 clones (mean: 22) isolated from each sample. After aligning the sequences, the percentage of clones containing each queried codon was quantified, and the results were graphically depicted for the PR and RT regions  were hybridized to microarrays, and the resulting signals were filtered using the previously described stepwise protocol. Then, the comparison of the theoretical hybridization tables  complementary to HIV-1 codons involved in drug- and multidrug-resistance to PIs, NRTIs and NNRTIs. Of interest, not only nucleotide mutations but also codon insertions and deletions have been included in the spotted oligonucleotide variants. 17 clonal HIV-1 samples and selected mixtures of them at different ratios have been used as the training set, while 57 HIV-1 clinical samples extracted from multi-treated infected patients constituted the test set.Once the hybridization protocol was optimized, the developed bioinformatics filtering protocol allowed discarding a number of low quality individual spots and probes without compromising the number of good quality, full microarrays to be kept for analysis. Therefore, such a stepwise procedure maximized the recovery of useful information from the experimental data. Indeed, before applying such filtering, 9.7% of the microarrays that were hybridized with samples from the training set and 12.6% of those hybridized with clinical samples (8.4% of the PR region and 4.2% of the RT) showed an excess of positive signals that would have recommend their elimination. Nevertheless, after the individual filtering of spots and probes, the amount of full microarrays to be discarded decreased to 4.46% in the training set and 4.16% (PR arrays) or even 0% (RT arrays) in the test set .It became evident that quality control filters must be applied sequentially and following a specific order to maximize classification accuracy as each filter progressively and differentially decreases the rate of FP and FN signals. Thus, with our proposed four-steps sequential filtering protocol, the ratio of correctly classified data significantly increased in both the training and the test sets, concomitantly to a neat reduction of FP and FN signals . On the In turn, a fraction of the detected FN signals is due to the limitation that some target samples show a number of mutant nucleotides at the vicinity of the queried codon, thus preventing the correct hybridization to its corresponding and adjacent probes. This is exemplified by the target pINS of the training set: a TP signal is consistently obtained at probe Ins69c, while probes designed to cover positions 67, 68, 69 and 70 cannot hybridize to this target due to the silent mutations that accompany the genotype T69SSS ,48. CertDuring the quality control protocol performed, 30 out of the 154 spotted probes were discarded. Interestingly, the vast majority of the discarded probes corresponded to redundant versions of some of them, which were designed to hybridize to the queried codons in different sequence contexts. Thus, probes complementary to the key HIV-1 codons involved in antiviral resistance and multidrug-resistance to NRTI and NNRTI  [pol gene. Also, a panel of relevant resistance mutations corresponding to non-B subtype HIV-1 strains could be considered, thus improving the clinical applicability of this genotyping platform. Finally, the overall performance of the pre-hybridization steps should be further optimized to increase the PCR-based amplification efficiency for samples showing viral loads lower than 104 HIV-1 RNA copies/ml.The filtering protocol performed was also useful for identifying individual probes that compromise the overall accuracy of the genotyping microarray, thus being clear candidates to be redesigned in further versions of this genotyping platform. As an example, the NNRTI resistance-related probe Y181  HIV-1 subpopulations. The microarray has been designed as an open, flexible genotyping platform that can be updated to include different spotted oligonucleotide probes containing newly detected codon mutations, insertions or deletions involved in drug resistance. It allows the high-throughput genotyping of up to 12 clinical samples in a working day, without requiring skilled technical personnel along the experimental work-flow. The current protocol is adapted to the use of at least two different types of microarray scanners to get the hybridization signals. The developed bioinformatics algorithm provides a highly accurate profiling of filtered hybridization signals of relevant majority and minority drug-resistance variants. Thus, this genotyping microarray is proposed as an open and customizable platform useful for drug-resistance diagnosis and prognosis in clinical practice focused on personalized antiretroviral therapies.S1 FileFigures A-L and Tables A-D.(PDF)Click here for additional data file.


Pseudomonas putida S12 contains many such mobile elements that play a major role in the regulation and adaptation to various stress conditions, including the regulation of expression of the solvent efflux pump SrpABC. We recently sequenced the genome of P. putida S12. Detailed annotation identified a threefold higher copy number of the mobile element ISS12 in contrast to earlier observations. In this study, we describe the mobile genetic elements and elaborate on the role of ISS12 in the establishment and maintenance of solvent tolerance in P. putida. We identified three different variants of ISS12 of which a single variant exhibits a high translocation rate. One copy of this variant caused a loss of solvent tolerance in the sequenced strain by disruption of srpA. Solvent tolerance could be restored by applying selective pressure, leading to a clean excision of the mobile element.Organic solvent‐tolerant bacteria are outstanding and versatile hosts for the bio‐based production of a broad range of generally toxic aromatic compounds. The energetically costly solvent tolerance mechanisms are subject to multiple levels of regulation, involving among other mobile genetic elements. The genome of the solvent‐tolerant  Pseudomonas putida S12 is a Gram‐negative bacterium that was isolated on styrene as a sole carbon source  and a megaplasmid 584 Kbps , CP009975 (plasmid pTTS12)]. Surprisingly, this genome showed a high number of one specific insertion sequence, ISS12, while in the previous studies, fewer ISS12 copy numbers were observed in P. putida S12 using Southern blot analysis  led to an interesting observation. To our surprise, the P. putida S12/A could not tolerate a second phase of styrene or sudden solvent shock of toluene as reported for the original strain. In this study, the role of insertion sequences  in the regulation of solvent tolerance was investigated. We compared the P. putida S12/A to its original stocks of P. putida S12 currently available at TNO (the Netherlands) and the ATCC collection (USA).Investigating the high occurrence of ISS12 mobile element in the recently sequenced P. putida S12, to confirm the high occurrence of mobile elements in the P. putida S12/A. An overview of characterized mobile elements in P. putida S12/A is summarized in Table Pseudomonas strains, using BLAST search and comparison with the ISFinder database . These include other P. putida strains as well as P. pseudoalcaligenes, P. aeruginosa, P. mandelii and P. stutzeri. Other insertion sequences were observed with < 70% similarity, mainly in Marinobacter, Burkholderia and Polaromonas species.With 33 copies, ISS12 elements comprise half of the total insertion sequences in the AF292393, Wery et al., up) and one at the downstream of the ATPase AAA encoding gene (Pdown)  and Pdown promoter sequences were cloned in a Tn7 promoter‐probe vector , indicating that the putative SoxR binding motif is not involved.ISS12 consists of 2596 bp and is one of the largest insertion sequences. It contains two open‐reading frames (ORFs), encoding an integrase that enables the transposition and an ATPase AAA helper protein . This was probably caused during the assembly process at that specific region due to high occurrence of this insertion sequence. The ISS12_C variant has four copies in the P. putida S12/A genome, and the reading frames of the two encoded genes and start/stop codons are retained. The encoded proteins of the ISS12_C variant show an 84% and 85% identity at the amino acid level to the integrase and ATPaseAAA encoding genes of ISS12. The inverted repeats (IR) are different although there are still several candidate IRs present in this ISS12 variant . The single occurrence of this ISS12 variant indicated that it might be a silent insertion sequence due to an early stop codon in ORF1 and a larger repeat region at its left flank compared to other ISS12 variants. Similar as ISS12_C, ISS12_D has the highest (~85% based on amino acid sequence) similarity to the ISPsy14. ISS12_A appears to exhibit a high translocation rate in P. putida S12/A with 28 copies.We identified two other variants of the insertion sequence after alignment of all ISS12 insertion sequences. According to our analysis, all ISS12 variants were already present in previous sequences of P. putida S12, a detailed comparison was performed with previously available sequencing data (Table AYKV00000001) and two unpublished sequences of phenol‐ and cinnamic acid‐producing mutants , we performed PCRs on 33 regions in both the original and the recently sequenced strains. Ten sequence regions of ISS12 showed a PCR product of 3 kb in the original strain, while the remaining regions showed a PCR product of 400 bp and thus no ISS12 insertions, as expected  do not disrupt a known ORF. In total, 14 genes were clearly disrupted on the chromosome and 11 on the megaplasmid. Besides insertions in the ORFs, insertions in regions that alter rates of for example of transcription‐like promoter regions can alter the function of genes as well and may cause a phenotype. The disrupted and neighbouring genes for each insertion in the sequence are documented in Table et al., et al., P. aeruginosa ; different dilutions were plated on LB agar and incubated inside and outside an exsiccator with a saturated toluene atmosphere at 30°C. Surprisingly, under these conditions, a small number of colonies appeared after 2 days on the plates under the toluene atmosphere  selected from plates incubated under toluene atmosphere, while colonies selected from plates outside of the exsiccator retained the ISS12 element in this gene . To confirm that these colonies are the result of a genuine reversion, from six colonies gDNA was isolated and the presence of all other 33 ISS12‐elements was verified by PCR. Except for the ISS12 in srpA, no consistent translocation from other sites was observed in these six revertants (Table S2). As disruption of srpS gene is also known to increases solvent tolerance in P. putida S12 . The amplified products of srpS was the size of an intact gene, and no disruption by ISS12 was observed in P. putida S12/A nor in any of the revertant strains.As expected from disruption of et al., srpA by ISS12_A caused specific and dramatically reduced solvent tolerance in this strain. Loss of this specific insertion sequence under stress conditions restored srpA function and concomitantly, solvent tolerance. We observed a lower excision rate (~10−7) of ISS12 from srpA compared to early observations of insertion into srpS . For this study, the deposited P. putida S12 at ATCC and the wild‐type strain present at TNO were acquired and compared to the recently sequenced S12/A strain (CP009974 and CP009975). Permanent cultures were stored in glycerol stocks at −80°C.600) using a spectrophotometer .All strains were cultivated on Lysogeny broth (LB), containing 10 g/l tryptone, 5 g/l yeast extract and 5 g/l sodium chloride using shake flasks. For solvent containing media, experiments were performed in 250‐ml tightly closed Boston bottles, containing 20‐ml medium, in a horizontal shaker  at 200 rpm and 30°C. Growth was monitored by measuring the optical density at 600 nm (ODP. putida S12/A were inoculated using 1:50 dilution and grown up to mid‐log stage (OD600 ≈ 0.7) in LB medium. The number of viable cells under solvent stress was determined by spreading suitable dilutions on LB plates and incubating inside and outside an exsiccator saturated with 200 ml toluene at 30° C for 2 days. The colonies from these plates were analysed in solvent shock experiments, and PCR was performed to confirm the presence or absence of insertion sequences.For organic solvent adaptation, overnight cultures of P. putida S12 strains were diluted 1:50 in 50 ml of fresh LB medium and grown up to an OD600 of about 0.7. At this mid‐log phase, 16 ml of culture was supplemented with 4 ml of toluene using tightly closed Boston bottles and incubated in horizontal shaker at 30°C for 30 min. As control, 16‐ml culture was supplemented with 4 ml water and grown under same conditions. The tolerance of the strains to solvent shock was determined by spreading suitable dilutions or total cell content on LB plates and scoring for growth or no growth.For organic solvent shock, overnight cultures of up, Pup_soxR and Pdown sequences were amplified by nested PCR using primers listed in Table S1 from gDNA of P. putida S12. These sequences were subsequently cloned into mini‐Tn7 probe vector using PacI and AvrII restriction sites , and for sequence analysis, Phusion polymerase  was used according to the manufacturers’ manual. All primers were purchased from Sigma‐Aldrich  and are listed in Table S1. All PCRs were analysed by size specific gel electrophoresis on 1% (w/v) TAE agarose gels containing 5 μl ethidium bromide per 100 ml agarose solution in an electric field . The PCR fragments were isolated from the agarose gel using the High Pure PCR Preparation Kit (ThermoFisher) and were either cloned into pJET1.2 (ThermoFisher) before sequencing or directly sequenced at Macrogen . Pgeneious software , unless otherwise stated in the text. BLAST search of insertion sequence ISS12 was performed at the NCBI website (Altschul et al., −5 were used as cut‐off. The selected sequences were aligned using MAFFT (Katoh and Standley, et al., et al., et al., All analysis was performed using Fig. S1. Cladogram of similar insertion sequences to ISS12.Fig. S2. Alignment of different ISS12 variants in P. putida S12.Fig. S3. Activity of ISS12‐putative promoter sequences under oxidative stress.Fig. S4. Sequence alignment of cloned ISS12‐variant B in P. putida S12/A.Fig. S5. Presence or absence of ISS12 insertion sites in P. putida S12.Fig. S6. Sequence alignment of srpA and PCR amplification of srpS in P. putida S12/A revertants.Table S1. List of primers used in this study.Table S2. PCR analysis of ISS12 elements in P. putida S12/A and the revertants after toluene stress.Click here for additional data file.