Studies On The Loss Of Biodiversity Due To Parasitic .

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Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151ISSN: 2319-7706 Volume 4 Number 6 (2015) pp. 131-151http://www.ijcmas.comOriginal Research ArticleStudies on the Loss of Biodiversity due to Parasitic Adaptationin Selected Fungi - An OverviewNasrin Begum* and Sudhendu MandalUGC-DRS Department of Botany, Visva-Bharati, Santiniketan-731235, India*Corresponding sityloss, FungiFungi are eukaryotes which occur in nature as symbionts, saprophytes and parasiteswith their host on the basis of their mode of nutrition. The present study deals withthe parasitic mode of adaptation of some selected members of fungi in differentecological conditions and their effect for the silent loss of biodiversity. Fungi showan wide range of host specificity from algae to human beings. Oomycetes andChytrids occur mostly in marine ecosystems and parasitize on different algal hostranging from green algae to diatoms. The Zygomycetes fungi Piptocephalisvirginiana is a mycoparasite of another zygomycetes fungi Choanephoracucurbitarum. Fungi parasitize on bryophytes, pteridophytes and gymnosperms indifferent ecological conditions. Angiosperms are the largest host of fungi.Beauveria bassiana is an entomopathogenic fungus but act as a host of amycoparasitic fungus Syspastospora parasitica. Batrachochytrium dendrobatidiscausing Chytridiomycosis of amphibians results in dramatic population decline ofamphibian species. When fungi parasitize on human cause severe diseases likeaspergillosis, candidiasis, coccidiomycetes, etc. About 15 species of Oomycotaparasitize on algae and diatoms. Green alga Chaetomorpha media showed infectionup to 5% by the fungus Pontisma lagenidioides. About 30% of amphibians ofworld is declined by the infection of B. dendrobatidis (Longcore et al., 1999). Themajor, chronic, invasive and allergic form of aspergillosis account for around600,000 death annually worldwide (Denning et al., 2013). The mortality rate inhuman due to systemic candidiasis is 30-50%.From the observation it can beconcluded that the studied group of fungi play an important role causing differentdiseases by their parasitic mode of adaptations following the silent loss ofbiodiversity.Introductionan important role in nature. There areapproximately 100,000 described species offungi (Kirk et al., 2008), which onlyrepresent a fraction of its diversity,Parasitism is one of the most commonadaptation among eukaryotes and the worldwide distribution of fungal parasites withtheir remarkable evolved modification plays131

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151estimated to be between 1.5 and 5 millionspecies (Hawksworth and Rossman, 1997,Blackwell 2011). Importantly, one of thehallmarks of fungi is their propensity toform intimate interactions/associations withother groups of life on Earth (Vega andBlackwell, 2005). As per latest statistics in2010 according to IUCN Red list whichincorporatestheglobalamphibiansassessment and subsequent updates focusesthat about 30% of amphibians of world isdeclined by the infection of B. dendrobatidis(Longcore et al., 1999). The major, chronicinvasive and allergic form of Aspergillosisaccount for around 600,000 death annuallyworldwide (Denning et al., 2013). Themortality rate in human due to systemiccandidiasis is 30 50% (Williams and Lewis,2011). Our present research paper deals withthe investigation of the pattern of adaptationof different fungal parasites, theirinfectivity, aggressivity and their gradualmodification showing which the silentbiodiversity loss. The nature of fungalparasites and their gradual evolutionindicates their adaptability runs from simpleto complex organism. It is seen underinvestigation that the selection of host andtheir morphogenetic coevolution are closelyrelated. Infectivity, aggressively, dominanceand choice of host are not occurredrandomly, selection of all the things ismomental and modification for theirevolution runs forever.according to their host range from loweralgal group to complex human system. Onthe basis of our objective we are trying torecord the estimation of biodiversity loss bysome of the selective parasitic fungus.Application of various statistical tools We calculated the derived data into thefollowing pattern of analysisPDI (No. of aggressive population) /(Total no of population) 100RD (X/n1) - (Z/n2)RR (X/n1) / (Z/n2)A PDI / 12DEP Differential extinction Point(Considered as a hidden factor forbiodiversity loss.)(PDI -Parasitic domain incidence, RD -Riskdifference, RR- Risk ratio, X- Previous yearPDI, Z Next year PDI, n1-Previous yearaggressive population, n2- Next yearaggressive population, A- Aggressivity)We are going through software analysis(plotting data on the respect of PDI, A, RD,DEP (as an unknown factor)) by using somestatistical tools like Descriptive analysis,Clusture analysis, Ward linkage andCentroid linkage analysis between deriveddata, Cross correlation, Auto-correlation,Partial correlation, Frequency n of model and so on. Thefollowing mentioned analysis is necessaryfor tracing any link to biodiversity loss orextinction for future forecasting.Materials and MethodsStudy of organisms and their populationsThe parasitology of different fungus werestudied and recorded. Approximately 45different fungal populations among which30 major are chosen for consideration on thebasis of the IUCN and published recordeddata for year wise infection rate. It has alsobeenfocusedonthebehavioralcharacteristics of different parasitic fungusEstablishment of proper 3d-diffractivemodel by software applicationTo make the 3D diffractive model foranalysis of biodiversity loss and finding thecorrelation in between species richness (SR),Aggressivity index (AI), Parasitic domain132

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151incidence (PDI), Differential extinctionpoint (DEP), so that we are going throughORIGIN17.0SOFTWAREandMICROORISIS (developed by Michiganuniversity) modern software tools. We areplotting species richness in 0.1 to 1 scale ofSINCLAIR, 1997. Establishment of modelis necessary for real estimation ofbiodiversity loss.In the above mentioned cases we are gettingthe valid cluster level is 9 and it isdistributed in between 4 and 5 level in twoclusters and mentioned here. At first weassumed that there is something missing orhidden data as DEP but our calculationindicates that all the including data are valid.Ward'sminimumvariancecriterionminimizes the total within the clustervariance. At each step the pair of clusterswith minimum distance between the clustersis merged. To imply this method, at eachstep we find the pair of clusters that leads tominimum increase in total within the clustervariance after merging. This increase is aweighted squared distance between clustercenters. At the initial step, all clusters are insingletons (clusters containing a singlepoint). To apply a recursive algorithm underthis function, the initial distance betweenindividual objects must be proportional tosquared Euclidean distance.Parasitic fungus and their host: Someselected parasitic fungus and their hostranging from the primitive algal groups tocomplex carnivorous level (Table 1.1).According to SINCLAIR 0.9 SCALE we aredistributing parasitic fungal density with therelative aggressivity to different host from2005 to 2013 and the data is plotted in thefollowing graph.Results and DiscussionYear wise population of different parasiticfungus with their derived aggressivity isrecorded and their PDI, RD, RR & A iscalculated in following manner:In Centroid Linkage Clustering, a vector isassigned to each pseudo-item, and thisvector is used to compute the distancesbetween this pseudo-item and all remainingitems or pseudo-items using the samesimilarity metric as were used to calculatethe initial similarity matrix. The initialcluster distances in Ward's minimumvariance method are therefore defined to bethe squared Euclidean distance betweenpoints:The following table shows the Correlation &Descriptive analysis between PDI andAggressivity with Anova analysisThe Wilcoxon signed rank sum test is thenon-parametric version of a paired samplest-test. We are using the Wilcoxon signedrank sum test for assuming the differencebetween the two variables i.e. either they arein interval or normally distributed (wherethe difference is ordinal). We will use thesame example as above, but we will notassumethatthedifferencebetween read and write is either in intervalor normally distributed. Correlation issignificant at the 0.01 level. The significantlevel between the two variables is 0.008.In all the upper nine cases we are analyzingby hierarchical cluster analysis and makingdendrogram using centroid method andshows highest proximity (level 0 to 25) incase 3 and case 4. The minimum level ofproximity is found in case 5. Zero to fivelevel of proximity cluster is found in case of133

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151case 5, case 8, case 6, case 1, and case 2.Five to ten level of proximity cluster isfound in case 7 and case 9.It is mentionedhere that the highest proximity cluster showsclosest correlation and the smallestproximity cluster shows distant correlationbetween two clusters. Cluster proximityanalysis is important for measuringsignificance level between two consecutivedata.where n is the sample size,is the sampleautocorrelation at lag k, and h is the numberof lags being tested. Underthe statisticQ follows a. For significance level ,the critical region for rejection of thehypothesis of randomness isIn case of ANOVA analysis we are gettingconvergence. Convergence is due to smallchange or static in cluster centers. Themaximum absolute coordinate changes forany centres are 0. The minimum distancebetween initial centres is 26.085.Box-Ljungshows that all the correlated data arepositivelysignificant.Partialautocorrelation reflects that in case of PDI andA, some data are positively significant andsome are negatively correlated with A, sotherefore we can assume that (A) isinversely proportional to PDI. In case of onesample correlation or paired samplecorrelation (with PDI and A) we are gettingpositively related data (where correlation issignificant in 0.01 level). Non parametriccorrelation with Kendall s tau b andspearmann s rho shows a significant positiveresult. Wilcoxon signed ranked test gives thepositive emphasis and shows sometimesVAR00002 VAR00001 and sometimesVAR00002 VAR00001(whereVAR00002 denoting A, VAR00001denoting PDI), so we can clearly turn intothe indication that (A), is an independentfactor, correlation comes in different domethrough phylogenetic evolved line. The Ftests should be used only for descriptiveprocesses. Proximity analysis between RDand A, showing 100% valid data, and closecross linkage between the two (A).We are getting by this analysis 4 validclusters. At a time we considered DEP as ahidden factor, now it is under valid cluster.So, therefore we are going through 3DHere in the following table, we areconsidering two variables as VAR00001 asPDI and VAR00002 as Aggressivity, andconnecting the two with cross correlation tofind out the significance level.VAR00001 considering as PDI andVAR00002 considered as aggressivity andcreate auto correlation, and partialcorrelation between the two. The Ljung Boxtest (named for Greta M. Ljung and GeorgeE.P.Box) is a type of statistical test ofwhetheranyofagroupof autocorrelations ofa timeseries aredifferentfromzero.Insteadoftesting randomness at each distinct lag, ittests the "overall" randomness based on anumber of lags, and is thereforea portmanteau test.The Ljung Box test can be defined asfollows.H0: The data are independently distributed(i.e. the correlations in the population fromwhich the sample is taken are 0, so that anyobserved correlations in the data result fromrandomness of the sampling process).Ha: The datadistributedarenotindependently134

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151diffractive model and get some point oftraces of DEP, which forecasts the silentbiodiversity loss.Table.1 Parasitic fungus and their host: Some selected parasitic fungus and their host rangingfrom the primitive algal groups to complex carnivorous levelName Of The Parasitic FungusName Of The HostPARASITIC FUNGUSChytridium polysiphoniaeCoenomyces sp.Ectrogella perforansLindra thalasiaeLabyrinthula sp.Olphidium rostriferumOlphidiopsis porphyraePontisma lagenioidesPetersenia pollagasterPythium porphyraeSchizochytriumPARASITIC FUNGUSPiptocephalis virginianaALGAL HOSTCentroceros clavulatum (Raghukumar 1987a&b)Cladophora sp, Rhizoclonium sp (Raghukumar,1994)Lichmorpha sp (LI Wei et al., 2010)Sargassum sp. (Sharma et al., 1994)Rhizoclonium (Raghukumar, 1994)Cladophora frascatti (Raghukumar 1986a, 1987a)Bangia, Porphyra (LI Wei et al., 2010)Chaetomorpha media (Raghukumar, 1987a & b)Chondrus crispus. (LI Wei et al., 2010)Porphyra sp. (LI Wei et al., 2010)Thalassonema nitzchioides (Gaertner,1979)FUNGAL HOSTSyspastospora parasiticaVerticillium biguttatumPARASITIC FUNGUSChoanephora cucurbitarum (Manochaand RoyaGolesorkhi, 1979)Beauveria bassiana (Humber et al 2004)Rhizoctonia solani (Van Den Boogert and Velvis, 1991)BRYOPHYTEAN HOSTLamprospora carbonicolaLamprospora miniataNeottiella albocinctaNeottiella vividaOctospora grimmiaeOctospora humosaOctospora ithacaensisOctospora leucolomaTyphrocybe palustrisFunaria hygrometrica (Benkert D. 1976)Barbula convoluta (Benkert, 2009)Atrichum undulatum (Benkert, 1987c)Polytrichum strictum (Benkert, 1995)Grimmia pulvinata (Benkert, 2009)Pogonatum aloides (Dobbeler & Itzerott,1981)Marchantia polymorpha (Benkert, 2009)Bryum argenteum (Benkert, 1998c)Sphagnum sp. (Peck, 1872)PARASITIC FUNGUSPTERODOPHYTEAN HOSTMixia osmundaePARASITIC FUNGUSOsmunda regalis, O. Cinnamomea (Kramer,1958)GYMNOSPERMEAN HOSTGymnosporium juniper-verginianaePARASITIC FUNGUSJuniperus virginiana (Peterson, 1967)ANGIOSPERMIC HOSTArmillaria melleaAlbugo candidaAlternaria spCryphonectria parasiticaForest and fruit trees (O Reilly, 1963)Crucifers (Alexopoulosn et al.,1996)Potato, Tomato (Rotem, 1994)Chestnut tree (Roane et al., 1986)135

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Helminthosporium oryzaePhytophthora infestansPuccinia graminisPolyporus spUstilago spPARASITIC FUNGUSBeauveria bassianaOphiocordyceps unilateralisPARASITIC FUNGUSBatrachochytrium dendrobatidisPARASITIC FUNGUSPithomyces chartarumPARASITIC FUNGUSMicrosporum canisPARASITIC FUNGUSAspergillus fumigatusAspergillus nigerCandida albicansCoccidioides immitisTrychophyton rubrumRice (Alexopoulosn et al.,1996)Potato (Ingram and Williams, 1991)Wheat (Roelfs and Bushnell, 1985)Woody trees (Alexopoulosn et al.,1996)Corn,Wheat (Christensen,1963; Joshi et al.,1983)INSECT HOSTTermites,White flies,Thrips,Aphids and Beetles (Bassi,1835)Camporotus leonardi (Wallace, 1859)AMPHIBIAN HOSTFrogs (Longcore et al., 1999)HERBIVOROUS HOSTCallttle, sheep, deer, goats etc (Di Menna et al., 2010)CARNIVOROUS HOSTDogs and CatsHUMAN HOSTBronchopulmonary of human(Jean Paul Latge,1999;Smith and Denning, 2011)Human ear (Vrabee et al., 2006)Oral and Gastrointestinal tract (Williams and Lewis,2011)Human body (Dickson,1937)Human foot,hair,skin,nail (Kane, 1997)Table.2 Showing year wise population of different fungus and their PDI, RD, RR and A(* PDI Parasitic Domain Incidence. RD Risk Difference. RR- Risk ratio. A- Aggessivity.)136

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Table.3 Showing Correlation & Descriptive analysis between PDI and Aggressivity with Anovaanalysis(VAR00001- PDI, VAR00002- Aggressivity)137

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Table.4 Showing Non-parametric Correlations between PDI & A138

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Table.5 Showing Cluster analysis between PDI & A139

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Table.6 Showing Cluster analysis through Ward Linkage and Centroid Linkage140

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Table.7 Showing Cross Correlation between PDI & A141

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Table.8 Showing Auto Correlation between PDI & A142

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Table.9 Showing comparison between auto correlation and partial auto correlation of PDI & A143

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Table.10 Showing proximity analysis between RD & A144

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Fig.1 X-axis with the average relative parasite richness & Y-axis with consecutive yearFig.2 X-axis with two different variables i.e. Original Population (OP) & Expected Population(EP); Y-axis with consecutive year145

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Fig.3 X-axis with rate of infection on plants and animals; Y-axis with consecutive year146

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151147

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151148

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Fig.4 Showing the Dendrogram analysis of the data149

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151Fig.5 Exploration of 3d-diffractive model and trace of silent biodiversity lossPathol., Vol. 7. Academic Press, SanDiego.Jean Paul Latge, 1999. Aspergillusfumigatus and aspergillosis. Clin.Microbiol. Rev., 12(2): 310 350.Joshi, L.M., et al., 1983. Karnal bunt: Aminor disease that is now a threat towheat. Bot. Rev., 49: 309 330.Kane, J. 1997. Laboratory hand book ofdermatophytes: clinical guide andlaboratoryhandbookofdermatophytes and other filamentousfungi from skin, hair and nails. StarPub., Belmont, CA.LI Wei, Zhang Tianyu, Tang Xuexi, WangBingyao, 2010. Oomycetes andfungi: important parasites on marinealgae. Acta Oceanol., 29(5): 74-81.Longcore, J.E., Pessier, A.P., Nichols, D.K.1999.Batrachochytriumdendrobatidis gen. et sp. nov., aReferencesChristensen, J.J. 1963. Corn smut caused byUstilago maydis. Am. Phytopathol.Soc. Monogr., 2: 1 41.Denning, D.W., Pleuvry, A., Cole, D.C.2013. Global burden of chronicPulmonaryaspergillosiscomplecating Sarcoidosis. Eur. Resp.J., 41(3): 621 6.Di Menna, M.I., Smith, B.L., Miles, C.O.2010. A history of facial eczema(Pithomycotoxicosis) research. N.ZJ. Agricult. Res., 52(4): 345 376.Dickson, E.C. 1937. Valley fever of the SanJoaquinVallenandfungusCoccidioides California West. Med.,47: 151 155.Ingram, D.S., Williams, P.H. (Eds.) 1991.Phytophthora infestans: the cause oflate blight of potato. Adv. Plant150

Int.J.Curr.Microbiol.App.Sci (2015) 4(6): 131-151chytrid pathogenic to amphibians.Mycologia, 91: 219 227.Manocha, M.S., Roya Golesorkhi, ronMicroscoptofPiptocephalisvirginiana Infection in Compatibleand Incompatible Hosts. Mycologia,71(3): 565 576.Molina, F. 1986. Petersenia pollagaster(Oomycete): an invasive pathogen ofChondrus crispus (Rhodophyceae).In: Moss, S.T. (Ed.) Biology ofmarine fungi. Cambridge Universitypress, Cambridge. Pp. 165 75.Raghukumar, C. 1986a. Fungal parasite ofthe marine green algae Cladophoraand Rhizoclonium. Bot. Mar. 29:289 97.Raghukumar, C. 1986b. The occurance ofChytridium polysiphoniae, a fungalpathogenontheredalgaCentroceros clavulatum (C. Agardh)Montage from Goa. Indian J. Mar.Sci., 15: 42 4.Roane, M.K., Griffin, G.J., Elkins, J.R.1986.Chestnutblight,otherEndothia diseases, and the genusEndothia. APS Monograph Series.APS Press. St. Paul, Minnesota.Roelfs, A.P., Bushnell, W.R. (Eds). 1985.The cereal rusts. Vols.1 and 2.Academic press, Orlando, Florida.Rotem, J. 1994. The genus Alternaria. APSPress, St. Paul, Minnoesota.Smith, N., Denning, D.W. 2011. Underlyingconditions in chronic . Eur. Resp. J., 37(4):865 872.Van Den Boogert, P.H.J.F., Velvis, H. 1991.Populationdynamicsofthemycoparasite Verticillium biguttatumand it s host, Rhizoctonia solani. SoilBiol. Bio-chem., 24: 157 164.Williams, D., Lewis, M. 2011. Pathogenesisand treatm

Studies on the Loss of Biodiversity due to Parasitic Adaptation in Selected Fungi - An Overview . other groups of life on Earth (Vega and Blackwell, 2005). As per latest statistics in . parasites and their gradual evolution indicates their adaptability runs from simple to complex organism. It is seen under

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