Evidence of the mediation of glycan molecules in the interaction between viruses and their hosts is accumulating and is now partially reflected in several online databases. Bioinformatics provides convenient and efficient means of searching, visualizing, comparing, and sometimes predicting, interactions in numerous and diverse molecular biology applications related to the -omics fields. As viromics is gaining momentum, bioinformatics support is increasingly needed. We propose a survey of the current resources for searching, visualizing, comparing, and possibly predicting host–virus interactions that integrate the presence and role of glycans. To the best of our knowledge, we have mapped the specialized and general-purpose databases with the appropriate focus. With an illustration of their potential usage, we also discuss the strong and weak points of the current bioinformatics landscape in the context of understanding viral infection and the immune response to it. Viruses are genetic mobile elements that replicate into a host cell and produce viral particles to infect new cells or organisms. The virion journey in the extracellular space is an opportunity to interact in many ways with one of the most abundant molecules in that area: glycans. These multifunctional molecules cover most of epithelial or bacterial cells in a thick layer, called glycocalyx , or as sThese host–virus interactions are dynamic and multifaceted. Some viruses have evolved to bind specific glycans in order to infect cells, like human rotaviruses binding to blood group A antigens . InfectiAll of these functions are critical for viral replication of most pathogenic viruses and could be exploited to create antiviral compounds . NonetheA handful of bioinformatics resources include the relevant pieces of information that can be put together to study glycan–virus interactions at different stages of infection. To follow the outline of a recent review highlighting the roles of glycosylation in virus biology , we haveViralZone  is a webIn fact, most viruses use the host glycosylation machinery composed of enzymes that are categorized in the CAZy database of Carbohydrate-Active enZymes  upon seqViralZone, includes detailed information on virus taxonomy, virion structures, as well as their interactions with other molecules. This latter aspect highlights the frequent occurrence of glycan mediation in receptor binding. This is shown, for example, with cross-references to SugarBindDB  that colMajor molecules involved in immunity (antibodies and antigens) are extensively covered in two very popular and long-standing databases, namely: the International Immuno-Genetics information system (IMGT)  and the Note that each and every of these resources is pointing to PubMed , the bio3, an in-house substructure search engine [The SugarBindDB entry for Norovirus Norwalk virus GrV  is associated with several glycan binders one of which being A type 1 A Lewis b [Fuc(α1–2)]Gal(β1–3)[Fuc(α1–4)] GlcNAc(β1–3)Gal), characterized in PubMed ID 12825167 . This arh engine  also avah engine . The resAnother course of exploration can be followed through the reciprocal link between SugarBindDB and GlyTouCan. A type 1 A Lewis b is registered in GlyTouCan and the corresponding record includes a link to molecules in PubChem  which, aThis example illustrates the potential for identifying protein interacting partners along with details of the glycan structures and glycan ligands that mediate this interaction. If databases were to be more complete, automated procedures could rapidly extract the relevant pieces of information. At this stage, the proposed scenario can only be performed by a user clicking on available links.A large part of the knowledge regarding glycan–virus interactions remains in the literature which is screened by database curators to be made available online. This is a slow process which mainly explains the current lacks in databases. Furthermore, this incomplete picture leads to focus on general-purpose resources such as UniProtKB or the PDB with PubMed in the background. http://www.functionalglycomics.org/glycomics/publicdata/primaryscreen.jsp). Dedicated tools were also developed specifically for visualizing [https://glycosciences.med.ic.ac.uk/data.html). However, each experimental result displayed from the database does not provide any cross-reference other than a link of a cited published article to PubMed.Substantial knowledge is also contained in the results of glycan arrays that have been used to study virus–host interactions for close to two decades. For instance, this approach revealed the subtle differences that distinguish avian from human flu based on glycan recognition by hemagglutinin . There aualizing  or mininualizing  the CFG ualizing . This shhttps://viralzone.expasy.org/5356) which contains 254 expert curated interactions. An excerpt of this table is shown in Viruses initiate infection of a host cell by attaching to specific receptors. Binding to an entry receptor triggers a chain of dynamic events that will enable cell penetration by the virus. Many virus–host receptor interactions have been described, and those are reported in the ViralZone virus receptors table  use GAGs displayed at the surface of host cells, mostly heparan sulfate, for host cell attachment and invasion ,46,47,48The field of glycosaminoglycanomics is budding, but data collection is still limited by the lack of standards to encode structural information . ProgresMatrixDB is designed and built to display interaction networks that provide a snapshot of direct and indirect physical interactions between proteins, GAGs, and small molecules with a particular focus on the precise definition of extracellular matrix multimers. Ultimately, the table shown in As visible in UniLectin includes UniLectin3D, which unsurprisingly compiles three-dimensional information regarding lectins and glycan ligands. In particular, some structures available in this database provide details of viruses binding to glycans of animal cell surfaces  and to bacterial cell walls (bacteriophage). In Norovirus, the capsid protein sequences differ among the several genotypes, resulting in a fine specificity for different human histo-blood groups. As a final comment in this section, note that new virus-related entries in SugarBindDB are very slowly finding their way into the database due to the poor funding of biocuration. This raises the question of associating experts with the database development and is hardly a hidden invitation.Bioinformatics tools have become indispensable for processing and analyzing the now popular -omics data. Raw data associated with published articles are routinely submitted in repositories, while dedicated databases are developed to shape the implicit knowledge revealed in analyzed -omics data. Such a setup is now operational to support the enlightened interpretation of new results in genomics, transcriptomics, and proteomics, as well as to maximize data sharing, reuse, and integration. Based on this interoperability, a broad range of automated procedures are implemented to collect and piece together information from databases and support the formulation of new and testable hypotheses. At this stage, glycomics has not quite yet reached that level despite the essential roles of glycans in the mediation of molecular interactions. In the particular case of virology, this lack is visible.The panoramic view of the different information resources covering interactions between virus and glycans as shown in Moreover, there are missing data that would be very useful for virology. For example, tissue localization of various glycans in vertebrates would be invaluable to correlate with virus tissue infections. This kind of resource exists for proteins , but notMononegavirales was recently published [Finally, a paradigm shift is also needed to advance the field of glycovirology. For example, a comprehensive survey of known glycosylation in ublished . Had theGlycans are prevalent molecules that offer a broad range of interplay with viruses crossing the extracellular space to infect new cells. Bioinformatics resources are critical for the analysis of the diversity of available data. We have provided a broad overview of the bioinformatics landscape of glycan–virus interactions. This has fed a reflection and discussion on the need and the means of increasing interoperability between existing databases as a key to support interaction data interpretation. The next step is surely to focus on bridging more tightly immunology and glyco-virology resources.


Parapoxvirus. We recently observed 7 cases of orf in Muslim men living in the metropolitan area of Milan, Italy, who acquired the infection after the Feast of Sacrifice.Orf (ecthyma contagiosum) is an infection of the skin caused by a DNA virus belonging to the genus  Parapoxvirus, family Poxviridae. Skin lesions  occur at sites of inoculation of the virus 3–15 days after infection. Hands are usually affected (Orf (ecthyma contagiosum) is an infection of the skin caused by a DNA virus of the genus Orf virus usually infects sheep and goats. Humans are infected by handling infected meat from these animals; orf is considered an occupational disease in shepherds, shearers, veterinarians, butchers, and cooks . One patOrf acquired during the Feast of Sacrifice was reported in 1982 in Turkey (In most reported cases, orf appeared days or weeks after the Feast of Sacrifice. This feast is celebrated 2 months and 10 days after the end of Ramadan; the exact date varies (In conclusion, in the metropolitan area of Milan, where ≈250,000 Muslims reside, we recently observed 7 patients with orf acquired during the Feast of Sacrifice. Prevention measures are difficult. For example, most of the Muslims living in the metropolitan area of Milan are Moroccans, who travel to Italy by car, carrying the infected meat; thus, no prevention measures have been taken.


The last wild case of smallpox occurred in Somalia in 1977, but due to the escape of a laboratory virus at the Birmingham University Medical School the following year the UK experienced two later cases, one of them fatal. Forty years on, before memories have entirely faded, Mark Pallen has gathered together the evidence surrounding that event. He offers a new explanation for the escape, one from which an important lesson can be drawn. Pallen's ‘Last days of smallpox’ may be read as an interesting tale, which it is, but it also acts as a warning to scientists not to allow personal involvement in any related field, such as emergent pathogens, to cloud their judgement with regard to safety.The main setting of the book is Birmingham and the year 1978. There had not been autochthonous smallpox in the UK since 1906 or any significant importation of the virus since 1962; yet in 1978, when it was already thought that wild smallpox had been eradicated (which it had), a small inadequately funded English university group was still propagating pox viruses for research purposes. The aim was to be able to characterise any pox virus pathogenic for humans that might emerge in the future following the eradication of natural smallpox; but in hindsight that work should already have been abandoned. The rationale for it was doubtful and the laboratory accommodation inadequate.Pallen notes that the 1978 escape recapitulated two earlier episodes in UK, one in 1966 from the same laboratory in Birmingham, and the other in 1973 originating from a tiny facility for pox virus research at the London School of Hygiene and Tropical Medicine. In both instances circumstances resembling those of 1978 had given rise to similar misadventures. The earlier, less well known, of these involved an infected laboratory worker who caused an extensive outbreak of minor smallpox  in the community: that outbreak is usefully described in Pallen's book. The London smallpox escape in 1973 led to two deaths from major smallpox, however, and considerable disruption in the community. It was competently investigated at the time , but without regard to its wider implications. As far as Cox was concerned, at a time when the World Health Organisation was evidently well on the way to eradicating smallpox, a metropolitan laboratory was continuing to propagate its virus without due care and for poorly defined purposes.Professor Pallen then analyses the inquiry and subsequent court case that followed the later, 1978, release of smallpox virus in Birmingham. The Shooter inquiry concluded that an aerial escape of smallpox virus via ventilation ducting infected a woman who worked in a different university department one floor above, and this explanation was not overturned in court. Shooter and colleagues found much else to criticise, but uppermost in their minds seems to have been their wish to prevent any further research on a dangerous pathogen being undertaken in an inherently unsafe environment within a multi-purpose building, in particular one sited in an urban area.This was a valuable consequence of the Shooter inquiry even if its main finding of aerial spread of virus has remained open to question. Forty years on a close observer from 1978 implied to Professor Pallen that, just as at the School of Hygiene in 1973, the first Birmingham case in 1978 was probably infected not by a stray air current, but because she had gone into one of the pox laboratories. One or more unauthorised visit may have taken place though no one offered evidence of this at the time and it was impossible to ask Mrs Janet Parker directly. Although she was said to have been vaccinated 11 years earlier, as a photographer having contact with chemicals her skin may have been especially vulnerable. After an unusually prolonged illness she had died of smallpox.For the general reader, Pallen's book is by no means too heavyweight, and it contains a salutary lesson for professionals. The safety of employees, their families and the wider community is not negotiable. Nonetheless, the current concern about the emergence of new human pathogens and the anxiety that smallpox might itself yet emerge as a bio-terrorist weapon justifies the existence of a few bio-secure facilities worldwide for the study of dangerous pathogens. Beyond that, if the cost of installing appropriate microbiological containment cannot be met or strict working protocols not enforced ongoing research work has to be abandoned and diagnostic inquiries diverted elsewhere.More generally, it may be recalled that the events of 1978 were contemporaneous with the establishment of a Communicable Disease Surveillance Centre (CDSC) for England, and an equivalent for Scotland. They were both arms-length from Government, allied to national laboratory networks and competent in early outbreak recognition and subsequent case finding. It was no longer acceptable for the response to an outbreak of an unusual pathogen, whether of UK origin or imported, to be left entirely in local hands, however competent those might be. Incubating cases of infection can quickly disperse themselves nationwide and it needs a national facility to deal with that. In subsequent years, CDSC has enhanced the response to ‘new’ pathogens such as Legionnaires’ disease and norovirus, and to imported emergent infections whether rare like Lassa fever or pervasive like HIV. Every country needs one!


Nipah virus (NiV) is a deadly virus with a high mortality rate that has affected many developing countries in the past. According to the Centers for Disease Control and Prevention (CDC), many economically deprived countries such as Madagascar, Cambodia, and Thailand are also at high risk for future outbreaks. The first case of NiV was reported in 1998 and almost two decades later, little scientific progress has been made in finding a proper treatment and prevention vaccine. As many developing countries are not properly equipped to fight the infection, it is vital to properly educate the health systems. The aim of this review is to provide an epidemiological background as well as to understand the transmission routes, presentation, and the diagnosis and prevention of this deadly virus. The first case of Nipah virus (NiV) infection was initially reported in late September 1998 near Ipoh, West Malaysia. Over the months that followed, several clusters of infections were noted over different regions such as near Sikamet and Bukit Pelandok -3.The virus was first isolated in March 1999 and based on its appearance, classified as a Paramyoviridae virus . An outbIn May 2018, an outbreak of NiV was reported in the state of Kerala in India. It is believed that the human-to-human transmission during that epidemic was caused mostly by droplet infection. The 17th death occurred on May 30th and the State of Kerala announced that it has ended the monitoring on June 30th . The NatAccording to the Centers for Disease Control and Prevention (CDC), up to now, many economically deprived countries with limited infrastructure and resources to prevent and combat the spread of this disease have been affected. Several countries such as Madagascar, Cambodia, and Thailand are also at risk for future outbursts of the disease. Proper understanding of the previous outbreaks and setting up of appropriate protocols can help protect these countries against future occurrences of NiV.MethodWe conducted a review based on published articles on NiV on MEDLINE and Google Scholar. The keywords included “Nipah virus,” “epidemiology of Nipah virus,” “transmission of Nipah virus,” “clinical manifestation of Nipah virus disease,” and “prevention of Nipah virus disease.” The search was limited to articles published from January 1st, 1998 to June 30th, 2018. All authors first reviewed the articles separately and then discussed them together.What is Nipah virus?The first analysis of the virus was carried out in early March 1999 by a team from the University of Malaysia. The cerebrospinal fluid of a patient suffering from encephalitis was analyzed and the virus was isolated and studied. The name Nipah virus was proposed as the source of the sample came from a region called Kampung Sungai Nipah . From elHow is Nipah virus transmitted?Fruit bats in the genus Pteropus are the main reservoir of NiV -11. InfeTransmission to humans involves either direct bat-to-human transmission or from bats to human through an intermediate animal host. Human-to-human transmissions have also been reported in several outbreaks such as in Bangladesh and India -15.A summarized version is shown below in Figure What are the clinical findings of Nipah virus disease?Nipah virus has an incubation period of seven to 40 days . The disHow is Nipah virus disease diagnosed?While the presence of antibodies against NiV is a reference standard for diagnosis, this technique is not used as Nipah has been classified as biosafety level four. An enzyme-linked immunoassay test has a high specificity for diagnosis. Another possible test includes polymerase chain reaction .Autopsies performed on 32 victims of the Malaysian outbreak showed vasculitis resulting in disseminated microinfarction. Similar vasculitic lesions were seen in the heart, kidneys, and respiratory tract. Medium-sized blood vessels were more involved with fibrinoid necrosis and endothelial multinucleated syncytia .Magnetic resonance imaging (MRI) scans of patients with NiV have shown multiple asymmetric focal lesions of less than 5 mm in the subcortical and deep white matter without surrounding edema -21.How can Nipah virus disease be treated and what is the outcome?There is currently no vaccine against NiV. The treatment options for patients with NiV include mostly supportive care. Chong et al. have reported a lower mortality rate with ribavirin therapy . The morHow to prevent future outbreaks of Nipah virus disease?As the vaccine against NiV is still in the preclinical stage  and no oFarm animals should not be allowed to eat fruits that have been exposed to bats. Raw palm sap is also a possible source of infection and consumption should be avoided to reduce the risk of bat-to-human transmission. However, this is a challenging step for some cultures. Proper precautions, protective clothing, and gloves ought to be used while handling any sick or dead animals or patients. With the current epidemic in Kerala, India, the National Centre of Disease Control of India has strongly advised strict and proper hand hygiene after coming into contact with any sick person or animal by using soap and water. They also advised against the consumption of raw palm sap or toddy. People were warned against eating half-eaten fruits, entering abandoned wells, and to properly handle dead bodies . In JanuNipah virus is a deadly virus and has been reported in many underdeveloped countries with a high mortality rate for each outbreak. Two decades after the first reported case, there is still no improvement in treatment options or an effective vaccine. However, with proper education future outbreaks can be prevented and should be the number one priority in combating NiV outbreaks.


Cryptococcus gattii is a fungal pathogen endemic in tropical and subtropical regions. Isolated cases and outbreaks have been reported in areas of North America and Europe, expanding the distribution pattern beyond warmer regions. We describe a case of ventriculo-peritoneal shunt infection by C. gattii in an immunocompetent person in Illinois. Cryptococcus gattii is a fungus found in soil and decaying organic materials  during 2005–2013  shunt with a programmable valve was placed, and the patient was discharged to home in stable condition. A 40-year-old man from Lake County, Illinois, with no known medical problems was admitted in October 2015 for evaluation of hydrocephalus. The patient reported 4 months of throbbing frontal headaches, nausea, and vomiting. Progressive confusion, altered memory, intermittent gait, and balance disturbances also were present. No visual changes, fevers, chills, or seizures were reported. The patient had no travel outside of Illinois and no ill contacts. Computed tomography (CT) scan of the brain demonstrated hydrocephalus, which was concerning because it indicated possible abnormalities in the flow of cerebrospinal fluid (CSF). A right frontal ventriculostomy catheter was placed. Repeat CT imaging of the head showed a possible mass within the right cerebellar hemisphere and surrounding vasogenic edema. To determine whether an infectious pathogen was the cause, we performed a workup that included HIV screening, Cryptococcus neoformans by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry . We then subcultured the organism to CGB Agar  to differentiate C. neoformans from C. gattii. The organism, which produced blue coloration on CGB agar, was determined to be C. gattii and was confirmed as C. gattii molecular biotype VGI by multilocus sequence typing performed at CDC . Induction with amphotericin B and flucytosine was given for 14 days and high-dose fluconazole (800 mg/d) was subsequently given as consolidation therapy for 8 weeks. The dose was then decreased (to 200 mg/d) for maintenance therapy. The patient was lost to follow-up after his first outpatient clinic visit.One month later, the patient was readmitted with recurring symptoms. CT imaging of the head showed stable ventricular size. Contrast-enhanced magnetic resonance (MR) of the brain showed abnormalities above the tentorium, possibly representing a cystic mass obstructing the foramen of Monroe bilaterally, with pronounced distention of both lateral ventricles. CSF studies showed a leukocyte count of 3/μL (reference range 0–5/μL) with lymphocytic predominance (93% [reference range 40%–80%]) and protein level of 42 mg/dL (reference range 15–45 mg/dL). We observed large round yeasts on Gram stain of CSF. The VP shunt was externalized. Cryptococcal antigen  was positive in the CSF (1:160 titer). Cultures from the CSF grew yeast that we identified as C. gattii in the literature. Only 10 cases of VP shunt infections attributable to C. neoformans have been reported; the time from shunt placement to symptom onset ranged from 10 days to 20 years  (Only 4 isolates of C. gattii are not confined to tropical and subtropical regions. The case we describe serves to extend the known range of this organism to include Illinois. Infections might be missed, given that many laboratories do not routinely differentiate C. gattii from C. neoformans. Mortality rates can range from 13% to 33% (Infections attributable to


In vitro release studies showed that the floating and sustained release time were effectively increased in gastric media by addition of octodecanol. In vivo studies with rats showed that a significant increase in gastric residence time of beads had been achieved.The purpose of this research was to prepare floating calcium alginate beads of berberine for targeting the gastric mucosa and prolonging their gastric residence time. The floating beads were prepared by suspending octodecanol and berberine in sodium alginate (SA) solution. The suspension was then dripped into a solution of calcium chloride. The hydrophobic and low-density octodecanol enhanced the sustained-release properties and floating ability of the beads. The bead formulation was optimized for different weight ratios of octodecanol and SA and evaluated in terms of diameter, floating ability and drug loading, entrapment and release.  Helicobacter pylori (H. pylori) infection plays an important role in the development of gastric ulcers and gastric cancer [H. pylori from the stomach. H. pylori lives deep inside the gastric mucus layer. Any therapeutic drug must therefore be able to penetrate the gastric mucus layer and maintain a concentration sufficient for antibacterial activity at the infected site for a suitable length of time [Gastric ulcers are a global health problem with a considerable economic burden associated with its high morbidity and mortality rates ,2. Data c cancer . Since i of time ,5. Sever of time , nanopar of time , bioadhe of time , superpo of time  and floa of time .Floating drug delivery systems are designed with low bulk density to remain buoyant in the stomach for a prolonged period of time without being affected by the gastric emptying rate. While the system is floating on the gastric contents, the drug is slowly released at a desired rate. Floating drug delivery systems have the advantage of allowing local delivery of a drug to the stomach. They also reduce the variability in bioavailability that is encountered with some current immediate and modified release systems . Lately,Sodium alginate (SA) has been used to treat the symptoms of reflux esophagitis ,16; and Berberis aristate and Coptis chinensis, which has been widely used in Chinese herbal medicine. Berberine has multiple biochemical and pharmacological effects, including anticancer, antiviral, and antibacterial activities [H. pylori and activities of H. pyloriN-acetyltransferase [Berberine is a natural isoquinoline quaternary alkaloid derived from a number of species of the barberry plant, including tivities ,21,22. Rnsferase .H. pylori infection site on the gastric epithelium. Furthermore, such treatment may lead to drug dose reduction which will be an additional valuable advantage [The floating delivery system strategy allows local delivery of a drug to the stomach, making it a promising vehicle for berberine. The work described here is concerned with the formulation of berberine floating calcium alginate beads. Such a dosage form for berberine would remain buoyant in the stomach without effecting the gastric emptying time for a prolonged period of time  and locadvantage .In this study floating alginate beads composed of berberine and octodecanol are prepared. Mean diameter, drug loading, drug entrapment, floating ability, drug release and gastric retention time in rats were evaluated as desirable parameters.2 the inner diameter of needle; dripping height from needle, and the proportion of drug and adjunctions were all important.The size and sustained release characteristics of berberin calcium alginate beads were found to be influenced by the composition formulae and process parameters ,27. AfteThe most important factor considered was viscosity and the concentration of SA. In the study up to seven types of sodium alginate with different viscosities ranging from 50 cps~1,300 cps were used. The result showed that the viscosity of SA affected the spheroid shape of the beads. Use of LVCR (50 cps viscosity) resulted in beads observed to be shrinking, while the beads prepared with Kecosol  displayed tailing. Suitable spheroid shaped beads were obtained only when SA of viscosity in the range 220 cps to 500 cps was used. The beads were perfect spheroids when the concentration of SA solution was in the range of 1~2%.The inner diameter of syringe needle was also an important factor influencing the size of beads. Four needles of 4, 6, 8 and 12 mm inner diameter gave free beads of 0.75 mm, 0.86 mm, 0.97 mm and 1.13 mm mean diameter, respectively. There was no significant difference in floating ability among the four sizes of beads. Sustained-release increased with increase in the diameter of beads, so the needle of 12 mm inner diameter was therefore selected.2 was necessary for beads that could resist being crushed by liquid droplets. Suitable spheroid shaped beads were achieved while dripping from a height of 5~8 cm. The beads aggregated when the height was less than 5 cm, and could be flat sphere at more than 8 cm.A more than 2% concentration of CaClThe size of SA beads was changed largely after drying [The mean particle size of the six formulations ranged from 0.94 ± 0.01 to 2.07 ± 0.01 mm. It was observed that an increase in the proportion of berberine (0:1 to 1:10) led to an increase in the size of beads . This in2.The percent drug loading of the various formulations was from 62.1% to 88.7%, while entrapment efficiency was found to vary between 91.3% and 95.1% . It was The floating ability of the prepared beads containing berberine was evaluated alongside dissolution studies . Beads cOn lyophilization, the beads of F5 could be dehydrated forming a porous system that floats after 5 min and remained floating for over 20 h. Although the beads had a floating time of more than 12 h, there exists a problem whereby the entire drug was realesed within 3 h. So the method of bead drying in oven was chose.in vitro drug release studies, with up to 100% release after 24 h (Formula 1 (F1) was found to be best in the ter 24 h  and a flter 24 h . The oth2” value is considered as the best fit of the release data. Drug transport inside pharmaceutical systems involves multiple steps provoked by different physical or chemical phenomenon, making it difficult, or even impossible, to get a mathematical model describing it in the correct way. In general the release models with major application and best describing drug release phenomena are zero order model, first order model the Higuchi model, Hixson-Crowell and Korsemeyer-Peppas model [2” values of 0.9973 and 0.9968 indicated the berberine release model was considered as first order and Korsemeyer-Peppas mechanism.The model that gives high “ras model . To evalFor the spheroids matrix perparations, an exponent n ≤ 0.45, indicates a Fickian diffusion drug release mechanism (dM = K△C/dt), and if 0.45 < n < 0.89, then a non-Fickian or anomalous diffusion. The exponent value of F1 (0.3837) obtained is thus indicative of Fickian diffusion release. For Fickian diffusion, the value of K was related to the surface of sphere parameters; diffusion coefficient and solubility of drug. If these parameters were constant, the mechanism of drug release accorded with zero order, compared with non-zero level for the variable parameters .The calcium alginate formed alginic acid since calcium ion of calcium alginate beads could be exchanged by hydrogen ion of HCl buffer (pH 1.2). Alginic acid is insoluble on inflation, and it was therefore suggested that the release mechanism of bererine in calcium alginate beads was pore diffusion from an insoluble polymer matrix and in accordance with first order or Korsemeyer-Peppas model.Beads retention in the stomach  showed tCompared with marketed sustained-release pellets, the free SA beads displayed a better retention performance in stomach. This remarkable gastric residence time involves the gastric muco-adhesion ability of sodium alginate , and witBerberine and octodecanol  was purchased from Nanjing Chemical Reagent Factory China. Samples of SA Nos. 1~3  were a kind gift from China Bright Moon Seaweed Group. SA samples Nos. 4~7  were a present from UK ISP Alginate Ltd. Diclofenac sodium sustained release micro-pellets were purchased from Nanjing Drum Tower Hospital. All the other chemicals were of analytical grade and used without further purification.SA was dissolved in the distilled water. Octodecanol was melted at 50 °C and added to berberine powder. The mixture of bererine and octodecanol was then added to the SA solution. The resulting suspension was dropped through a 12 mm inner diameter syringe needle from a height of 7 cm into calcium chloride solution  saturated with berberine. The formed beads were left in the same solution for 45 min to improve their mechanical strength and finally dried at 50 °C in an oven.The prepared beads (n > 100) was lined and diameter determined by Vernier caliper. Measurements for each sample were performed in triplicate. Mean diameter and its standard deviations were recorded.berberine 1 was the amount of berberine in all berberine alginate beads and weight berberine 2 was the initial berberine amount added into SA solution.The prepared beads were evaluated for percent drug loading and drug entrapment efficiency. An accurately weighed sample of beads was crushed in a mortar and dissolved in pH 1.2 HCl solution (100 mL). This mixture was then centrifuged at 4,200 rpm for 30 min and filtered using 0.22 μm microporous membrane before analysis with a UV spectrophotomer at 346 nm. The percent drug loading (DL) and drug entrapment efficiency (EE) were calculated using the following Equations (1) and (2):The floating ability was determined using China Pharmacopeia XC dissolution tester apparatus II (paddle method). Fifty beads were introduced in the vessels and the paddles rotated at 50 rpm in pH 1.2 HCl solution (500 mL), maintained at 37 ± 0.5 °C. The floating ability of the beads was measured by visual observation and the percent of floating beads recorded as average of three determinations. The preparation was considered to have buoyancy only when all beads floated on the test solution immediately or within a lag time which did not exceed 2 min.In vitro dissolution studies were performed for all the formulation combinations in hexaplicate using a China Pharmacopeia XC dissolution tester apparatus II (paddle method). An accurately weighed sample of berberine alginate beads was dropped into pH 1.2 HCl solution (900 mL) maintained at a temperature of 37 °C ± 0.5 °C at 50 rpm of paddle stirring. At different time intervals, an aliquot of the sample (10 mL) was withdrawn and filtered. The filtrate was analyzed at 346 nm using a UV spectrophotometer against pH 1.2 HCl solution as blank.Thirty male Sprague-Dawley rats were used (210 ± 10 g body weight) and treated according to the regulations for animal care of China Pharmaceutical University. The rats were allowed free access to water but were fasted for 12 h before administration. The animals were randomly divided to five groups. Each group was administrated free alginate beads, berberine alginate beads and diclofenac sodium sustained release micro-pellets coated with ethyl cellulose, respectively (10 beads or pellets per rats). The rats were allowed free access to water and then dissected 12 and 24 h after administration. The remaining piece of beads and pellets in the stomach were observed.H. pylori infection.In this study, we prepared floating alginate bead of berberine together with octodecanol and evaluated different parameters including diameter, drug loading, drug entrapment, floating ability and drug release. These parameters are applicable not only to the sustained release of drugs, but also to the targeting of the gastric mucosa. The formulation F1 exhibited the optimum sustained release of berberine, with excellent floating characteristics. Therefore, the alginate beads containing octodecanol appears to be a promising vehicle for drug delivery to the gastric mucosa in the therapy of


It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness. Disease-causing microorganisms, including viruses, bacteria, protozoa and fungi, form complex communities within animals and plants. These microorganisms can coexist harmoniously or even beneficially, or they may competitively interact for host resources. Well-studied examples include interactions between viruses and bacteria in the respiratory tract. Whilst ecological studies have revealed that some pathogens do interact within their hosts, identifying interactions from available population scale data from health authorities is challenging. This is exacerbated by a lack of large-scale data describing the infection patterns of multiple pathogens within single populations over long time frames. Furthermore, methods for evaluating whether infection frequencies of different pathogens fluctuate together or not over time cannot readily account for alternative explanations. For example, human pathogens may have related seasonal patterns depending on the age groups they infect and the weather conditions they survive in, and not because they are interacting. We developed a robust statistical framework to identify pathogen-pathogen interactions from population scale diagnostic data. This framework serves as a crucial step in identifying such important interactions and will guide new studies to elucidate their underpinning mechanisms. This will have important consequences for public health preparedness and the design of effective disease control interventions. PLOS Computational Biology Methods paper.This is a Animals and plants are exposed to a wide range of pathogenic organisms that co-circulate in time and space. When multiple pathogens infect the same tissue, they form diverse communities, effectively sharing an ecological niche that provides the opportunity for interspecific interactions –3. It isPathogens that act non-independently and their health implications is an actively growing and important area of research . PathogeVarious statistical methods are available to analyse health-related time series data. Statistical methods for handling non-stationary time series data include multiple regression and generalised additive models, which are able to capture non-linear trends and explanatory factors such as seasonality and climate as well as other confounders and typically model a univariate health outcome as opposed to a multivariate distribution of several non-independent outcomes –18. ConsAlternative approaches that focus specifically on identifying interactions include confirmatory analyses that fit observed time series data from two pathogens to models containing hypothesised interactions , 23. ExtIn contrast, exploratory approaches such as Granger-Causality and Transfer Entropy can provide robust statistical evidence for unknown interactions from multiple time series whilst accounting for confounding variables , and havA framework that can infer unknown interactions from multiple pathogens incorporating non-stationary time series data whilst adjusting for confounding factors will advance this important research area . Here, wThe conceptual framework for our new approach derives from Bayesian disease mapping models—a class of regression model that has received much attention in recent years for the analysis of spatial distributions of incidence data routinely collected by public health bodies , 28. TheModelling multiple pathogens simultaneously allows assessment of related patterns and non-independence of infection risk. Multivariate forms of disease mapping models provide a suitable framework for estimating temporal dependencies between pathogens as they naturally incorporate a between-disease (or pathogen) covariance matrix . In thisTo validate our method we conducted extensive simulation studies using synthetic data. We then applied the method to diagnostic data on five respiratory viruses  from the patient population of a major urban UK population  over a period of nine years. We chose this particular group of pathogens because i) respiratory viruses are obligate intracellular pathogens that have a strong predilection for the cells of the respiratory tract ; ii) contemporary diagnostic tests based on multiplex real-time PCR (qPCR) technology allow the simultaneous detection of multiple respiratory viruses from the same patient; and iii) multiplex qPCR was routinely used to diagnose respiratory viruses in the patient population of Glasgow during the 2005-2013 period.The framework presented infers unknown pathogen interactions adjusted for confounding factors such as seasonality, demographics and testing frequencies using time series data from multiple contemporaneous pathogens .Ymtv to denote the observed count of pathogen v during the mth month of year t conditional on expected count Emtv and relative risk RRmtvαv an intercept term specific to virus v and ϕt.. = {ϕt1., …, ϕtV.} a vector of random effects modelled conditionally through a MCAR priorWe used Estimating expected counts enables us to adjust for potential and established confounding factors. For instance, the virus diagnostic data allowed us to use age, sex, whether the patient had attended a general practice or hospital (as a proxy for infection severity), month of year and testing frequencies. Therefore, expected counts explained a proportion of the variation in the observed counts and we attributed the remaining unexplained variation to temporal autocorrelation, virus-virus interactions and residual random variation.ϕt.. captured long term temporal tends with smoothing parameters s1, …, sV. Dependency structures between neighbouring months accounted for seasonality in pathogen infection frequencies. Two such structures were considered, namely the neighbourhood structure  models, designed to model spatially autocorrelated data based on neighbourhood relationships. Here, the parameterisation of a MCAR model captured both the seasonal trends of each pathogen via precision matrix Ω and non-independence between pathogens via Λ. Temporal effects −1. By formally testing which off-diagonal entries of This method focuses primarily on the estimation of pathogen covariance matrix ΛIn order to validate the proposed method, we performed an extensive simulation study using synthetic virus diagnostic data with a wide range of time series structures and estimated the power and type 1 error rate (i.e. rejection of the true null hypothesis) of this method for a range of correlations between viruses.Individual level data of age, sex and general practice versus hospital attendance (a proxy for infection severity) were simulated to reflect the real virus diagnostic data, and the probabilities of infection for each virus within each month were estimated. For a full data description, we refer readers to Nickbakhsh et al . Within αv, simulated uniformly, and monthly effect terms ϕv... Monthly effect sizes were simulated without constraining the nature of time series data in order to illustrate the flexibility of this framework (Generation of the matrix Ω depended on the choice of correlation structure (either neighbourhood or autoregressive). Relative risks were calculated from the virus specific intercept term ramework .An example of data simulated under the neighbourhood structure is present in Since our approach incorporated two structures that captured monthly autocorrelations (the neighbourhood structure (N) and autoregressive structure (A) either adjusting for multiple comparisons (post-mcc) or not (pre-mcc)), four possible combinations of simulation (Sim) and estimation (Est) are reported . A rangeWithout correcting for multiple comparisons (pre-mcc) the power of detecting a moderately strong correlation of 0.5 was greater than 0.8 under each of the four scenarios . Under the neighbourhood structure, we found that allowing dependencies between viruses (Λ−1 ≠ I5) provided a better fit to the data (DIC = 2795.6 versus DIC = 3583.8 for the null model). However, the autoregressive structure with Λ−1 ≠ I5 minimised DIC (DIC = 2686.4).For comparison, we first fitted a null model that assumed all five viruses to be independent by setting ΛComparing observed values to fitted values under the autoregressive model fit .More precisely, our model captured winter peaks in CoV, winter and spring peaks in MPV and irregularities in AdV and IBV validating the model fit to these data.Wneigh). Under the autoregressive structure, we found a positive covariance between RSV/MPV and a negative covariance between IBV/AdV (Wauto), with adjusted p-values for the covariances between IBV/MPV and CoV/MPV of 0.075 and 0.073 respectively.Under the neighbourhood structure, we found a positive covariance between RSV/MPV and negative covariances between IBV/MPV, CoV/MPV and AdV/IBV , Wneigh. IBV/AdV , Wauto, Our analysis showed robust statistical evidence of a facilitative form of interaction between RSV and MPV and a competitive form of interaction between IBV and AdV.Humans, animals and plants are exposed to a plethora of co-circulating pathogens, creating frequent opportunity for interactions between them. There is a growing interest in the health implications of interacting pathogens that has led to the development of new research in healthcare . HoweverTraditional regression-based approaches can handle confounding variables but do not necessarily infer non-independencies between multiple response variables –18. TimeThis paper addresses the need for a more widely applicable statistical framework that can jointly infer unknown interactions among pathogens for which multiple contemporaneous time series are available. The framework accounts for non-stationarity, confounding variables such as seasonality and patient demographics and requires no prior knowledge or specification of the underlying biological or ecological mechanisms.We presented a conceptual framework derived from Bayesian multivariate disease mapping methods that provides a powerful statistical tool for inferring pathogen-pathogen interactions from diagnostic and/or surveillance time series data. Whilst standard multivariate disease mapping frameworks investigate the joint spatial distribution of multiple diseases coinfecting a population simultaneously, our method instead analyses the joint temporal distribution of multiple infections. Because multivariate disease mapping naturally incorporates a between-disease covariance matrix, these methods conveniently lend themselves to the inference of temporal signatures of pathogen-pathogen interactions when adapted to analyse temporal dependencies. Importantly, because our method accounts for confounding variables as well as the autocorrelation structure, the method distinguishes genuine pathogen-pathogen interactions from simple correlations.By applying our framework to extensive diagnostic data accrued over a nine-year period from a well-defined patient population, our analysis provides evidence of epidemiological interactions among respiratory viruses. Acute respiratory infections are a significant cause of illness and mortality and are primarily attributed to a group of viruses that occupy a shared ecological niche in the respiratory tract. Although observational data –42 and uWe anticipate that this framework will aid in the epidemiological understanding of linked pathogen dynamics. The knowledge that specific pathogen-pathogen interactions exist and of their form (positive or negative) provides an important first step towards improving disease forecasting models. Such models could be adapted for multi-pathogen systems by incorporating pathogen-pathogen interactions through reduced or enhanced transmissibility of secondary/co-infecting pathogens. Ultimately, improved understanding of the impact of coinfections on health outcomes will improve the public health utility of such models by enabling estimation of disease burden and pressures on different sections of the healthcare system, for instance the numbers of hospital beds needed at different times of the year.In summary, we have developed a new and robust method of inferring interactions from multiple pathogen time series. Applying this approach to time series data of pathogens that co-circulate in a given population allows quantification of interactions that will lead to a better understanding of the joint epidemiological dynamics of diseases. These inferences, in combination with laboratory experiments to further elucidate the underlying mechanisms, will enhance the understanding of linked pathogen dynamics, inform the forecasting of disease incidence and improve public health preparedness. In addition, they will result in better ways to evaluate the impact of public health interventions, thus aiding the design of better measures to control infectious diseases.Our dataset derives from routinely collected clinical samples tested for respiratory viruses by the West of Scotland Specialist Virology Center (WoSSVC) for Greater Glasgow and Clyde Health Board between January 2005 and December 2013. Each sample was tested by multiplex real-time RT-PCR and test results (virus positive or negative) were available for five groups of respiratory viruses: adenovirus [AdV]; coronavirus [CoV]; human metapneumovirus [MPV]; influenza B virus [IBV]; and respiratory syncytial virus [RSV] . SamplinWhilst data are available at the individual level, we are predominantly interested in estimating correlations in temporal patterns between the five viruses at the population level. Therefore, for each virus, data were aggregated into monthly infection counts across the time frame of this study.Relative risks identify time points where observed counts are higher or lower than expected, with expected counts accounting for expected seasonality and risk factors associated with respiratory infection . We noteYi, Ei and RRi are the observed count, expected count, derived from available patient demographic data (refer to expected counts section), and relative risk for some index i  [ϕ = {ϕ1, …, ϕI} spatial random effects modelled jointly through a conditional autoregressive (CAR) distribution [Conditional autoregressive models are extensively used in the analysis of spatial data to model the relative risk of a virus or more generally a disease , 51. Thenterval)  and ϕ = ribution ϕ∼MVN distribution can jointly model ϕ by incorporating a between virus covariance matrix Λ−1 of dimension V × V :Extending this model to multiple viruses, or more generally multiple pathogens, thenD − λW, ϕ = {ϕ.1, …, ϕV.} and ϕv. = {ϕv1, …, ϕIv} [In this case, Ω =  …, ϕIv} , 54.j, through the conditional expectation of ϕj|ϕj−1Temporal autocorrelations may be induced in this model, at time point s controls the level of temporal autocorrelation such that s = 0 implies no autocorrelation whereas s = 1 is equivalent to a first order random walk [The parameter dom walk . Typicaldom walk .v in month m of year t, Ymtv is modelled in terms of the expected count Emtv and relative risk RRmtv:αv an intercept term specific to virus v and ϕt.. = {ϕt1., …, ϕtV.} a vector of random effects modelled conditionally through a MCAR priorWe model monthly time series count data from multiple viruses simultaneously over a nine year period. We index over monthly time intervals and so monthly autocorrelations are modelled in terms of the precision matrix and yearly autocorrelations are modelled in terms of the conditional expectation in a similar fashion to the multivariate spatial-temporal model detailed above. The observed count of virus s1, …, sV. The conditional expectation of ϕt.. depends on the previous year ϕt−1.., capturing long term temporal trends. By allowing dependencies between neighbouring months, we account for seasonality in viral infection frequencies.This parameterisation of a MCAR model captures both the seasonal trends of each virus via Ω and long-term temporal trends via The covariance structure of the MCAR distribution used to model random seasonal-temporal effects is the Kronecker product of precision matrices Ω and Λ.−1:−1, although we note that other parameterisations are available [The between-virus precision matrix Λ accounts for dependencies between viral relative risks in terms of monthly trends. Wishart priors can be used for unstructured precision matrices such as Λ , howeverelations . This pavailable .W. We will consider two possible constructions of W: neighbourhood structure and autoregressive structure.Matrix Ω captures seasonal trends in terms of monthly dependencies defined through a proximity matrix W can be defined such that wij = 1 if months i and j are neighbouring months and wij = 0 if months i and j are not neighbouring months. Neighbours were fixed as the previous and subsequent three months. Taking a neighbourhood approach, we setD a 12 × 12 diagonal matrix with i [Assuming neighbouring months are more similar than distant months,  month i , 58.W was defined through an autoregressive process and the corresponding matrix denoted by Wauto. We set the ijth entry of Wauto (i ≠ j) to be dij the distance between months i and j and ρ a temporal correlation parameter satisfying ρ < 1. We defined distance as the number of months between i and j.Under this construction, D a diagonal matrix with Taking an autoregressive approach, we setructures , 59.m, the log odds of virus v, logit(pmv), was estimated through fixed effects of age, sex and severity  and a yearly random effect. The standardised probability of virus v in month m, Naslt was the number of people of age a, sex s and infection severity l in year t; a, sex s with infection severity l in year t testing positive for virus v in month m; and Nmv the number of swabs tested for virus v in month m. The estimated probabilities of each virus in each month are therefore standardised for age, sex and severity and account for yearly differences in circulation.We required expected counts of each virus at each time point in this study. Since individual level data were available, a series of logistic regressions were used to estimate the probability of testing positive for a virus at a given time point. For month of the year v in month m of year t was thenNmtv the number of of patient episodes of illness tested for virus v in month m in year t.The expected count for virus This model was implemented in jags  using thFor each covariance parameter, higher posterior density intervals (HPDI) were estimated. Posterior probabilities were then estimated to assess the probability of zero being included in each interval, synonymous to Bayesian p-values defined in terms of lower tail posterior probabilities , 65. Cov−1. We prove the validity of our proposed model , βgender ∼ N and βseverity ∼ N. Within each year, we randomly sampled between 20 and 200 samples per month per virus in order to reflect differing testing frequencies within and between viruses. Standardised probabilities of each virus within each month We began by simulating individual level data reflecting the virological diagnostic data. For each sample, an age, sex and severity were drawn from the observed virological diagnostic data distributions . Regressd) with an autoregression (of order p) and a moving average model (of order q) to obtain a non-seasonal ARIMA model. In addition seasonal components were included through seasonal differencing (D), autoregression (P) and a moving average model (Q) over period m therefore simulating from a SARIMA12 with period 12 since we are dealing with monthly data. Within each simulation, we used differencing d = 1 with a second or first order autoregression and moving average p, q ∈ {1, 2}. Likewise, we used either no or a seasonal differencing D ∈ {0, 1} and no or a first order autoregression and moving average p, q ∈ {0, 1}. These parameter settings allowed for a wide range of seasonal and non-stationary time series data. Monthly effect sizes were simulated using the sarima package  in R 6262. We chϕ were drawn from multivariate normal distributions with yearly smoothing parameters and monthly smoothing parameter s1, s2, s3 and λ simulated uniformly between 0 and 0.9 and precision matrix equal to the Kronecker product of matrices Ω and Λ. Matrix Ω was dependent on the choice of structure used to simulate data. In this case we simulated from both the neighbourhood and autoregressive structure (ρ uniformly between 0 and 0.9 (method described in MCAR prior specification section).Random effects tructure . In the Matrix Λ was the virus correlation matrix that we aimed to estimate. We simulated data from three viruses with one virus pair, virus 1 and virus 2, non-independent of each other but both independent of the remaining virus, virus 3. We explored a variety of correlations between virus 1 and virus 2 ranging from 0.2 to 0.5. This range was chosen to reflect weakly related viruses (0.2) to moderate to strongly related viruses (0.5). We anticipated that as the strength of correlation increased, the power would also increase whilst still controlling the type 1 error rate.α1, α2, α2 simulated uniformly and random effects ϕ. Observed counts were the product of expected counts and relative risks. Relative risks were then taken additively as the exponential of virus intercept terms We fitted both models . In eachUnder each scenario we estimated higher posterior density intervals (HPDI) for covariance parameters Click here for additional data file.S2 Appendixy = x). Correlations and p-values between fitted and observed values are given for each virus.Fitted values based on the best fitting autoregressive model plotted against observed values with the line of equality ((PDF)Click here for additional data file.


Mashua virus Y (MasVY), first isolated in 1984 from a plant of the Andean tuber crop mashua . There was a 70% nucleotide identity between MasVY and a genomic sequence of Verbena virus Y.We present the complete genomic sequence of a new potyvirus we tentatively call  Mashua virus Y (MasVY), first isolated in 1984 from a plant of the Andean tuber crop mashua . There was a 70% nucleotide identity between MasVY and a genomic sequence of Verbena virus Y.We present the complete genomic sequence of a new potyvirus we tentatively call  Tropaeolum tuberosum, family Tropaeolaceae)  following the manufacturer’s instructions. The indexed library was sequenced on a MiSeq instrument (Illumina) with a 600-cycle v3 kit. The resulting 748,695 paired reads were 3′ trimmed to a quality score of 20 with Sickle in paired-end mode (Verbena virus Y (VVY) (GenBank accession number NC_010735), from the ornamental plant Verbena × hybrid (family Verbenaceae) reported from the United States (Potyviridae (Mashua virus Y (MasVY), and the three mashua potyviruses named previously  including optional DNase treatment, total RNA was extracted from infected leaf material from yviridae . Establiviously 35 awaits MH680824 (genome) and MH680823 . Raw data were deposited in the SRA under BioSample number SAMN10081141, which is part of BioProject PRJNA491634.The sequences described here were deposited in GenBank under accession numbers


Solanaceae, comprising meaningful crops , can benefit from a symbiosis with arbuscular mycorrhizal fungi (AMF), which improve plant fitness and support plant defense against pathogens. Currently, those crops are likely the most impacted by Potato virus Y (PVY). Unfortunately, the effects of AM symbiosis on the severity of disease induced by PVY in solanaceous crops remain uncertain, partly because the interplay between AMF and PVY is poorly characterized. To shed some light on this issue, available studies on interactions in tripartite association between the host plant, its fungal colonizer, and viral pathogen were analyzed and discussed. Although the best-documented PVY transmission pathway is aphid-dependent, PVY infections are also observed in the absence of insect vector. We hypothesize the existence of an additional pathway for virus transmission involving AMF, in which the common mycorrhizal network (CMN) may act as a potential bridge. Therefore, we reviewed (1) the significance of AM colonization for the course of disease, (2) the potential of AMF networks to act as vectors for PVY, and (3) the consequences for crop breeding and production of AM biofertilizers. Potato virus Y (PVY) belonging to the genus Potyvirus within the Potyviridae family represents a serious threat due to high incidence and worldwide distribution. This phytopathogen is particularly destructive to solanaceous crops, i.e. potato (Solanum tuberosum L.), tomato (Solanum lycopersicum L.), pepper (Capsicum spp.), eggplant (Solanum melongena L.), and tobacco (Nicotiana tabacum L.)  .PVY exerts the highest economic impact on potato, as it is the third most consumed food crop after rice and wheat . This waThe economic losses caused by PVY are quite considerable. According to recent estimations, PVY is able to affect up to 50% of potato crops in China, which is the world’s largest potato producer . In othePotato Virus Y”), we dedicate our attention to ecological function of arbuscular mycorrhizal fungi  and then concentrate on its role in PVY biocontrol in potato, which is discussed in the light of virus-plant interactions in Solanaceae (“The Interactions Between AMF and Viruses in Potato and Other Solanaceae”). Finally, we propose the emerging hypothesis on the role of the mycorrhizal soil networks in PVY spreading (“Do AMF Participate in PVY Transmission?”).In this review, after a short summary on the PVY transmission pathways taking potato as a model host  or (2) horizontally, i.e. mechanically and by many vectors, including insects, soil-borne zoosporic parasitic fungi and protists, nematodes, and mites (Potyviridae spreading (Potyvirus (Plant viruses are transmitted (1) vertically, i.e. from infected plant to the progeny  acting as a “molecular bridge” mediates reversible retention of virions in the insect’s mouthparts. When aphids feed on a healthy plant, PVY particles are released from the stylet to inoculate the tissue , including species such as ral ones . Highly ral ones . These npartners . Host plpartners . In agripartners . Beneficpartners . Furtherpartners . In partpartners , by moduSeveral protective mechanisms against phytopathogens are involved in mycorrhizal systems, which include the promotion of plant growth, reduction of colonization sites available for attackers, alteration in root morphology, increase in damage compensation, changes in both root exudate and rhizosphere microbiome composition, and eventually activation of plant immune system . A numbeGlomus etunicatum-inoculated barley plants were more susceptible to the fungal pathogen Erysiphe graminis. G. mosseae increased the severity of disease caused by phytoplasma strains (class Mollicutes) to periwinkle plants. Furthermore, G. etunicatum-associated sour orange and Duncan grapefruit seedlings to be strongly affected by Citrus tristeza virus and Citrus leaf rugose virus, respectively.Currently, large body of articles is focused on bioprotective function of mycorrhiza and AMF-mediated phytopathogen biocontrol , while rSurely, as an active participants of symbiosis, mycorrhizal fungi display the ability to influence the interaction between plant and its enemies. Abovepresented data show that AMF can play a dual role in plant pathogenesis; however, the factors  or processes  underlying these observations are not clear. In such multi-species relationship, which involves both mutualistic (plant-AMF) and antagonistic (plant-enemy) interactions, the specific outcome of AMF-phytopathogen interaction is difficult to predict, thus each experimental system should be treated individually.There is a gap in the knowledge of the direct AMF-PVY interaction in potato plant. For this reason, in this section, we discuss data for experimental settings with different solanaceous host plants, various species of arbuscular fungi, and different viruses (summarized in Tomato yellow leaf curl Sardinia virus (TYLCSV) infection in tomato previously colonized by F. mosseae compared to non-inoculated plants. Moreover, virus exerted no effect on the level of root colonization by AMF maintained after the TYLCSV infection. Similarly, G. intraradices, although the fungus itself exerted no influence on the growth of virus-free plant. However, the protective role of AMF may occur at some conditions. Significant attenuation of virus-induced plant disease following mycorrhizal establishment was shown in two research papers . Maffei Tomato aucuba mosaic virus (TAMV) and Potato virus X (PVX) from tomatoes (S. lycopersicum) forming mycorrhiza with Gigaspora macrocarpum (previously Endogone macrocarpa). Tobacco mosaic virus (TMV) in roots of tomatoes associated with Glomus sp. compared with non-mycorrhizal controls. The similar observation was done by G. intraradices-colonized tobacco infected with TMV. The process underlying this phenomenon is not clear yet. However, the authors hypothesized the existence of a AMF-triggered regulatory pathway suppressing plant defense mechanisms, which enhanced sensitivity of AMF-tobacco to viral attack. Furthermore, G. intraradices increased the virus activity and reproduction rate. This in turn exacerbated the plant disease symptoms, resulting in reduced potato growth. The authors suggested that raised accumulation of PVY following potato mycorrhization might be at least partially attributed to an improved nutrition of the plant. More recently, G. mossae negatively affected a plant defense response to Tomato spotted wilt virus based on transcriptomic analysis. The researchers found differences in gene expression patterns between roots and aboveground parts of mycorrhizal plants. The overall number of genes expressed in the shoot of AMF-colonized tomato decreased upon viral infection in comparison with non-mycorrhizal plants. Additionally, the upregulation of fewer defense genes with simultaneous weaker downregulation of primary metabolism genes was revealed in shoot transcriptome of mycorrhizal tomato. This observation suggested the contribution of AMF to increased plant sensitivity to viral pathogen.In contrast, several studies indicated that the symbiosis with AMF promoted multiplication of viral particles in the host plants . Daft anG. intraradices DAOM 197198) preferentially colonized some specific plant genotypes, while the others were less favored and developed significantly lower level of the symbiosis. Interestingly, the host-AMF compatibility was shown to be partially modified by growing conditions , which means that this aspect of mycorrhiza is environment-dependent and thus variable even for the same fungus-plant combination. Nevertheless, it is suggested that the bioprotective function of mycorrhiza  depends on the compatible interaction between the partners  may be variable . The funpartners . This mapartners .Thus far, the interaction between potato, PVY and AMF was studied in one setting where mycorrhizal development was established after viral infection . TherefoInteractions in tripartite association between host plant, its symbiotic colonizer – arbuscular mycorrhizal fungus, and viral pathogen remain an interesting research topic, due to limited information on the impact of arbuscular mycorrhiza on viral infection and disease development. Conclusions based on available studies seem to be contradictory, because some researchers indicated alleviating effects and thereby bioprotective function of AMF, whereas others demonstrated the AMF-dependent stimulation of viral activity . Due to Synchytrium endobioticum and Spongospora subterranea, were suggested to be implicated in transmission of Potato virus X (PVX) and Potato-mop top virus (PMTV), respectively , and (3) release . Alternatively, it would be interesting to know if any possible mechanisms, which would prevent plant virus from entering AMF cells, exist. These could be based on a lack of receptors on the AMF wall surface, on a physical protection , or cell death programming to isolate all or part of the infected mycorrhizal structures. This may be achieved (1) by testing detection methods of PVY with molecular and microscopic approaches in mycorrhizal propagules , (2) by incubating mycorrhizal mycelium with a PVY solution and to try to detect the eventual presence of viral particles (transmission microscopy and molecular tracking) within structures after washing as well as phenotypic observation of the hyphae, and (3) to follow eventual transmission of the virus from infected to non-infected plants interconnected by mycorrhizal mycelium. Ultimately, chemical or biochemical protection from mycorrhizal fungi against viral particles may be conceivable and may be studied by the monitoring of the PVY integrity and infection potential after incubation of viral particles with mycelium exudates or lysates.In case of PVY transmission, only insects are well documented to carry this pathogen. However, other hypothetical vectors should not be excluded until additional studies are made. One possible pathway for PVY spreading may involve endomycorrhizal fungi . AMF intectively . Neverthectively . The fircastanea . Later oisolates ; howeverto cells . AlthougConsidering the fragmentary knowledge of AMF-plant-virus interactions, the questions regarding potential contribution of AMF to PVY-induced disease development or even transmission in the frame of the CMN cannot be answered yet. Surely, AMF can play a dual role in PVY infection, which is reflected in the proposed hypotheses ; howeverED-S prepared the first version of manuscript and graphics, LM, and CB participated in the preparation of the manuscript. KH determined the first concept of the review and participated in the preparation of the manuscript. All authors revised the manuscript and approved the publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Giant vesicles (GVs) are widely-used model systems for biological membranes. The formulation of these vesicles, however, can be problematic and artifacts, such as degraded molecules or left-over oil, may be present in the final liposomes. The rapid formulation of a high number of artifact-free vesicles of uniform size using standard laboratory equipment is, therefore, highly desirable. Here, the gentle hydration method of glass bead-supported thin lipid films has been enhanced by adding a vortexing step. This led to the formulation of a uniform population of giant vesicles. Batches of glass beads coated with different lipids can be combined to produce vesicles of hybrid lipid compositions. This method represents a stable approach to rapidly generate giant vesicles. Lipid vesicles or liposomes are 3D structures made from a bilayer of phospholipid molecules enclosing an aqueous compartment. Giant vesicles (GVs) typically have a diameter of >1 µm, which enables easy visualization and manipulation under an optical microscope. Today, many technologies exist to formulate lipid vesicles [Giant vesicles can be formulated by adding an aqueous buffer to a dried thin film of lipids ,3. To acThe agarose swelling method is based on spreading a lipid-containing solvent on a thin dried agarose film . Upon buAdditionally, giant vesicles can be formed by creating double-emulsion droplets followed by oil removal , or by tStill, none of the GV formulation techniques is universal, and, e.g., there is a lack of uniform phospholipid bilayers for precise biophysical measurements . Therefosn-glycero-3-phosphocholine (DPPC) and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) were purchased from Lipoid  or Avanti Polar Lipids  and used without further purification. Laurdan [N,N-dimethyl-2-naphthylamine) and Brand cavity slides with three cavities were purchased from Sigma-Aldrich . Glass beads, unwashed, 212–300 µm  were bought from Sigma-Aldrich . The glass beads were stirred in concentrated nitric acid for 2 h, washed with ultra-pure water followed by methanol and acetone, and dried under high vacuum (40 µbar) overnight.1,2-dipalmitoyl- Laurdan ,26 (6-doThe beads (2 g) were weighed in a round bottom glass flask and the appropriate volume (see paragraph “liposome formulation on glass beads”) of the lipid stock solution in chloroform (720 µg of lipid in 1 mL chloroform) was added on top plus 1 mL of chloroform to ensure optimal solvent coverage of the bead bed. For each experiment, a ratio of beads and lipids corresponding to a theoretical bead coverage with ten bilayers was used. For fluorescence imaging of the liposomes the membrane dye Laurdan (1 mol %) was included in the lipid mixture. The solvent was removed under reduced pressure using a rotary evaporator at mild rotation speed to ensure optimal coverage of the glass beads with the lipids. After complete solvent removal, the beads were dried under high vacuum overnight and stored at −20 °C under argon. The beads can be stored for at least one year see .5–2.8 × 105 µm2. For the formulation of GVs 200 mg of lipid-coated 212–300 µm-sized beads (corresponding to 72 µg of phospholipids) were weighed into a 2-mL Eppendorf vial. The appropriate swelling liquid, namely 500 µL of ultra-pure water (>18 MΩ·cm) or sucrose buffer  was added and the vial was shaken at 65 °C for 1 h at the indicated speed using a PHMT-PSC24 thermoshaker . Typically, only sucrose is used in GV formulation experiments. Here, phosphate-buffered saline was employed to create an environment of high ionic strength that inhibits vesicle formulation in many other techniques [The different bead sizes qualitatively yielded the same results, therefore we focused on one bead size only, namely beads with an average size of 212–300 µm, corresponding to a surface of 1.4 × 10For the experiment where vesicles were formulated from different phospholipids on different glass beads, two different types of lipid-coated beads were prepared containing either DOPC or DPPC. Then 100 mg of each bead were mixed together in an Eppendorf tube with 500 µL hydrating liquid and the mixture was treated as stated above.−3 m2 for a 25-mL flask. Given a molecular size of 63 Å2 per DPPC molecule [The first step of a typical vesicle preparation in a round bottom flask is the formation of a thin film. Slow evaporation of the solvent under rotation creates a visible lipid film on about half of the flask’s inner surface, i.e., 2.5 × 10molecule , the thiDuring the formulation procedure, the vial containing the coated beads and the swelling liquid was heated above the main phase transition temperature of the corresponding lipid bilayers while simultaneously shaking the vial. We hypothesized that the shaking would influence the size of the GVs formed and that smaller diameter GVs would be formed when the speed of the shaking increases. Newly-formed vesicles would be mechanically reduced in diameter during the shaking step.In order to compare the new method with other formulation techniques, the bead gentle hydration experiment was repeated following the standard protocol but omitting the shaking step.For the electroformation experiments, a swelling chamber was constructed by placing a Teflon ring  onto an ITO-coated microscopy slide . Then, 5 µL of a lipid solution (2 mg/mL) was spread into the Teflon ring. The organic solvent was removed under high vacuum for 1 h at 30 °C. The lipid film was hydrated with 500 µL of ultra-pure water or sucrose in PBS buffer and a second ITO-coated slide was positioned to close the chamber. Vesicles were grown by applying a sinusoidal wave (1 V and 10 Hz for 1 h at RT (DOPC) or 55 °C (DPPC or DPPC/DOPC-mixture)) using a potentiostat. For microscopy imaging 20 µL of the sucrose buffered vesicle suspension were diluted five times with glucose buffer. The vesicle suspensions formulated in ultra-pure water were used neat. For imaging, a Ti Eclipse confocal microscope  was used with an excitation wavelength of 405 nm. The emitted light was split using a prism detector. The images were saved as Nikon Viewer software .nd2 files, exported as .tif files, and treated with Fiji/ImageJ software ,29,30. FGP) measurements were performed using the emission wavelengths at 441 nm (gel phase) and 495 nm . The resulting images were analyzed using the GP-plugin for Fiji/ImageJ [The generalized polarization (i/ImageJ . The genGP value would represent a liposomal gel phase membrane and a low GP represents a liposome membrane in a liquid crystalline phase [A high ne phase .In addition to producing artifacts, many of the current GV preparation techniques suffer from the fact that a highly-polydispersed sample population is formed, slowing the process of finding suitable vesicle candidates for biophysical analysis. In order to more quantitatively compare the different vesicle formulation techniques, DPPC and DOPC vesicles were prepared and their diameter and uniformity were assessed by measuring a series of liposomes see . The twoAll the different techniques tested here yielded GVs in ultra-pure water see . For DPPNext, we formulated the same lipids again, but this time in a sucrose/glucose gradient at 400 mOsm/L PBS buffer concentration see . These hWe then examined the formulation of GVs made from a 1:1 mixture of DPPC and DOPC. Unlike the other methods, where a lipid film of specific composition has to be prepared for each type of vesicles, our new approach opens the way for rapid formulation of various hybrid GVs. For this, we took equal weights of glass beads that were each coated with DPPC or DOPC and combined them in an Eppendorf tube. Bead gentle hydration of this mixture lead to the formulation of two separate populations of DPPC or DOPC GVs, each sized, respectively, at 5.3 μm ± 2.8 μm. In contrast to that, when the shaking step was added to the formulation procedure, hybrid liposomes formed at diameters between 8.3 μm ± 1.4 μm for 500 rpm and 4.6 μm ± 1.0 μm at 1400 rpm. This makes it possible to formulate GVs of various compositions starting from only a few batches of lipid-coated beads. Thus, glass beads coated with different thin lipid films can be combined to form hybrid GVs.In another experiment, the hybrid vesicles were formulated at high ionic strength in a sucrose/glucose gradient see . Here, tThe possibility to combine different phospholipids into single hybrid vesicles just by changing the combination of a limited number of lipid-coated glass beads is, of course, quite powerful in terms of combinatorial potential but raises questions about how well the vesicles compare to vesicles formed from phospholipid combinations on a single glass bead. We, therefore, utilized the polarity-sensitive dye Laurdan in order to quantify membrane properties. Due to its sensitivity to the polarity of the immediate environment, Laurdan can be used to visualize changes in membrane fluidity ,26. FiguShifts in the emission wavelengths of Laurdan can be quantified in a generalized polarization analysis on the various GV combinations allowing a quantification of the extent of gel and liquid crystalline phases in each liposome ensemble ,32. The The formulation using the shaking method yielded various results with preferences for either phospholipid see . Salt-frWe have presented a rapid formulation technique for monodisperse giant vesicles. Glass beads coated with dehydrated stacks of lipid bilayers can be conveniently prepared, stored at low temperature, and be easily distributed to other laboratories. Once needed, the glass beads are placed in an aqueous buffer solution and shaken at the appropriate speed and temperature. The initial coating of the glass beads with a thin lipid film requires a chemistry laboratory. After the solvent removal, no additional steps require any handling of organic solvents. The coated beads can be stored at low temperature for over one year. The quantities of lipids needed can be very low. Furthermore, beads covered with different lipids can be easily combined avoiding measuring very small amounts of hygroscopic phospholipids. After a one-hour hydration and vortexing step, giant vesicles are formed. This procedure offers facile access to hybrid GVs.