Incidence of heat-labile enterotoxin-producing Escherichia coli detected by means of polymerase chain reaction amplification

CITATION: Winterbach, R. et al. 1994. Incidence of heat-labile enterotoxin-producing Escherichia coli detected by means of polymerase chain reaction amplification. South African Medical Journal, 84:85-87.The original publication is available at http://www.samj.org.zaDiarrhoea can be caused by many different organisms, some of which are notoriously difficult to identify. One of these is enterotoxin-producing Escherichia coli. Recently a new diagnostic technique that uses polymerase chain reaction DNA amplification was developed for detection of the 'A' subunit of the labile enterotoxin-producing E. coli gene. This technique was used to evaluate the incidence of heat-labile (LT+) enterotoxin-producing E. coli in the causation of diarrhoea. The results from this study showed that LT+ E. coli is a cause of diarrhoea in the western Cape and that 5,3% of non-diagnosed diarrhoea patients in Tygerberg Hospital were infected with this pathogen. This represented less than 1% of the total number of cases of diarrhoea investigated in this hospital. The peak coincides with the wetter months in this locality and the infection rate is lower than that reported in most other countries. Given the low incidence of occurrence of this organism we do not recommend routine implementation of the diagnostic procedure. However, this test may be useful at times, e.g. to ascertain the source of a diarrhoea epidemic.Publisher’s versio

A 250-year isotopic proxy rainfall record from southern Botswana

Climate records along aridity gradients where manifestations of climate change are most profound are important for testing climate models. The Kalahari Transect spans such a gradient, but instrumental records of climate parameters are limited in the sparsely populated region. We analysed the δ13C and δ18O record from a Vachellia erioloba (E.Mey) tree from the southern Kalahari Desert in Botswana to explore its potential as a climate proxy archive. Radiocarbon dates show that the record spans the period 1758-2013 CE. Both the δ13C and δ18O records correlate with local rainfall. The isotope proxies show a weak positive correlation with sea-surface temperature reconstruction from the southwestern Indian Ocean, and a stronger correlation with the El Niño Southern Oscillation index. This appears to contradict previous evidence that higher sea-surface temperatures are associated with reduced summer rainfall over the southern African interior. Instead of eastward shifts in the temperate tropical trough synoptic system during elevated southwestern Indian Ocean temperature anomalies, the evidence supports a westwards shift. The result demonstrates the potential of Vachellia erioloba as a climate proxy archive that may yield past climate variability from the arid regions of southern Africa.The National Research Foundation (NRF) of South Africa under the Research Grant for Unrated Researchers number CSUR13092647960. AMS radiocarbon analyses were supported by the Romanian Ministry of Research and Innovation CNCS-UEFISCDI under grant PN-III-P4-ID-PCE-2016-0776, Nr. 90/2017.http://chem.ubbcluj.ro/~studiachemiaam2019Mammal Research Institut

The Evolutionary Dynamics of the Lion Panthera leo Revealed by Host and Viral Population Genomics

The lion Panthera leo is one of the world's most charismatic carnivores and is one of Africa's key predators. Here, we used a large dataset from 357 lions comprehending 1.13 megabases of sequence data and genotypes from 22 microsatellite loci to characterize its recent evolutionary history. Patterns of molecular genetic variation in multiple maternal (mtDNA), paternal (Y-chromosome), and biparental nuclear (nDNA) genetic markers were compared with patterns of sequence and subtype variation of the lion feline immunodeficiency virus (FIVPle), a lentivirus analogous to human immunodeficiency virus (HIV). In spite of the ability of lions to disperse long distances, patterns of lion genetic diversity suggest substantial population subdivision (mtDNA ΦST = 0.92; nDNA FST = 0.18), and reduced gene flow, which, along with large differences in sero-prevalence of six distinct FIVPle subtypes among lion populations, refute the hypothesis that African lions consist of a single panmictic population. Our results suggest that extant lion populations derive from several Pleistocene refugia in East and Southern Africa (∼324,000–169,000 years ago), which expanded during the Late Pleistocene (∼100,000 years ago) into Central and North Africa and into Asia. During the Pleistocene/Holocene transition (∼14,000–7,000 years), another expansion occurred from southern refugia northwards towards East Africa, causing population interbreeding. In particular, lion and FIVPle variation affirms that the large, well-studied lion population occupying the greater Serengeti Ecosystem is derived from three distinct populations that admixed recently

Topology analysis and visualization of Potyvirus protein-protein interaction network

Background: One of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new perspective is allowing the identification of direct and indirect viral targets. Such information is available for several members of the Potyviridae family, one of the largest and more important families of plant viruses. Results: After collecting information on virus protein-protein interactions from different potyviruses, we have processed it and used it for inferring a protein-protein interaction network. All proteins are connected into a single network component. Some proteins show a high degree and are highly connected while others are much less connected, with the network showing a significant degree of dissortativeness. We have attempted to integrate this virus protein-protein interaction network into the largest protein-protein interaction network of Arabidopsis thaliana, a susceptible laboratory host. To make the interpretation of data and results easier, we have developed a new approach for visualizing and analyzing the dynamic spread on the host network of the local perturbations induced by viral proteins. We found that local perturbations can reach the entire host protein-protein interaction network, although the efficiency of this spread depends on the particular viral proteins. By comparing the spread dynamics among viral proteins, we found that some proteins spread their effects fast and efficiently by attacking hubs in the host network while other proteins exert more local effects. Conclusions: Our findings confirm that potyvirus protein-protein interaction networks are highly connected, with some proteins playing the role of hubs. Several topological parameters depend linearly on the protein degree. Some viral proteins focus their effect in only host hubs while others diversify its effect among several proteins at the first step. Future new data will help to refine our model and to improve our predictions.This work was supported by the Spanish Ministerio de Economia y Competitividad grants BFU2012-30805 (to SFE), DPI2011-28112-C04-02 (to AF) and DPI2011-28112-C04-01 (to JP). The first two authors are recipients of fellowships from the Spanish Ministerio de Economia y Competitividad: BES-2012-053772 (to GB) and BES-2012-057812 (to AF-F).Bosque, G.; Folch Fortuny, A.; Picó Marco, JA.; Ferrer, A.; Elena Fito, SF. (2014). Topology analysis and visualization of Potyvirus protein-protein interaction network. BMC Systems Biology. 129(8):1-15. doi:10.1186/s12918-014-0129-8S1151298Gibbs A, Ohshima K: Potyviruses and the digital revolution. Annu Rev Phytopathol. 2010, 48: 205-223. 10.1146/annurev-phyto-073009-114404.Spence NJ, Phiri NA, Hughes SL, Mwaniki A, Simons S, Oduor G, Chacha D, Kuria A, Ndirangu S, Kibata GN, Marris GC: Economic impact of turnip mosaic virus, cauliflower mosaic virus and beet mosaic virus in three Kenyan vegetables. Plant Pathol. 2007, 56: 317-323. 10.1111/j.1365-3059.2006.01498.x.Ward CW, Shukla DD: Taxonomy of potyviruses: current problems and some solutions. Intervirology. 1991, 32: 269-296.Riechmann JL, Laín S, García JA: Highlights and prospects of potyvirus molecular biology. J Gen Virol. 1992, 73 (Pt 1): 1-16. 10.1099/0022-1317-73-1-1.Elena SF, Rodrigo G: Towards an integrated molecular model of plant-virus interactions. Curr Opin Virol. 2012, 2: 719-724. 10.1016/j.coviro.2012.09.004.Wei T, Zhang C, Hong J, Xiong R, Kasschau KD, Zhou X, Carrington JC, Wang A: Formation of complexes at plasmodesmata for potyvirus intercellular movement is mediated by the viral protein P3N-PIPO. PLoS Pathog. 2010, 6: e1000962-10.1371/journal.ppat.1000962.Chung BY-W, Miller WA, Atkins JF, Firth AE: An overlapping essential gene in the Potyviridae. Proc Natl Acad Sci. 2008, 105: 5897-5902. 10.1073/pnas.0800468105.Allison R, Johnston RE, Dougherty WG: The nucleotide sequence of the coding region of tobacco etch virus genomic RNA: evidence for the synthesis of a single polyprotein. Virology. 1986, 154: 9-20. 10.1016/0042-6822(86)90425-3.Domier LL, Franklin KM, Shahabuddin M, Hellmann GM, Overmeyer JH, Hiremath ST, Siaw MF, Lomonossoff GP, Shaw JG, Rhoads RE: The nucleotide sequence of tobacco vein mottling virus RNA. Nucleic Acids Res. 1986, 14: 5417-5430. 10.1093/nar/14.13.5417.Revers F, Le Gall O, Candresse T, Maule AJ: New advances in understanding the molecular biology of plant/potyvirus interactions. Mol Plant Microbe Interact. 1999, 12: 367-376. 10.1094/MPMI.1999.12.5.367.Urcuqui-Inchima S, Haenni AL, Bernardi F: Potyvirus proteins: a wealth of functions. Virus Res. 2001, 74: 157-175. 10.1016/S0168-1702(01)00220-9.Merits A, Rajamäki M-L, Lindholm P, Runeberg-Roos P, Kekarainen T, Puustinen P, Mäkeläinen K, Valkonen JPT, Saarma M: Proteolytic processing of potyviral proteins and polyprotein processing intermediates in insect and plant cells. J Gen Virol. 2002, 83: 1211-1221.Adams MJ, Antoniw JF, Beaudoin F: Overview and analysis of the polyprotein cleavage sites in the family Potyviridae. Mol Plant Pathol. 2005, 6: 471-487. 10.1111/j.1364-3703.2005.00296.x.Zheng H, Yan F, Lu Y, Sun L, Lin L, Cai L, Hou M, Chen J: Mapping the self-interacting domains of TuMV HC-Pro and the subcellular localization of the protein. Virus Genes. 2011, 42: 110-116. 10.1007/s11262-010-0538-8.Culver JN, Padmanabhan MS: Virus-induced disease: altering host physiology one interaction at a time. Annu Rev Phytopathol. 2007, 45: 221-243. 10.1146/annurev.phyto.45.062806.094422.De Las Rivas J, Fontanillo C: Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol. 2010, 6: e1000807-10.1371/journal.pcbi.1000807.Bornke F: Protein Interaction Networks. Anal Biol Netw. Edited by: Junker BH, Schreiber F. 2008, John Wiley & Sons, Inc, Hoboken, NJ, USA, 207-232. 10.1002/9780470253489.ch9.Phizicky EM, Fields S: Protein-protein interactions: methods for detection and analysis. Microbiol Rev. 1995, 59: 94-123.Brückner A, Polge C, Lentze N, Auerbach D, Schlattner U: Yeast two-hybrid, a powerful tool for systems biology. Int J Mol Sci. 2009, 10: 2763-2788. 10.3390/ijms10062763.Fields S, Song O: A novel genetic system to detect protein-protein interactions. Nature. 1989, 340: 245-246. 10.1038/340245a0.Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams S-L, Millar A, Taylor P, Bennett K, Boutilier K, Yang L, Wolting C, Donaldson I, Schandorff S, Shewnarane J, Vo M, Taggart J, Goudreault M, Muskat B, Alfarano C, Dewar D, Lin Z, Michalickova K, Willems AR, Sassi H, Nielsen PA, Rasmussen KJ, Andersen JR, Johansen LE, Hansen LH, et al: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature. 2002, 415: 180-183. 10.1038/415180a.Hu C-D, Chinenov Y, Kerppola TK: Visualization of interactions among bZIP and Rel family proteins in living cells using bimolecular fluorescence complementation. Mol Cell. 2002, 9: 789-798. 10.1016/S1097-2765(02)00496-3.Kodama Y, Hu C-D: An improved bimolecular fluorescence complementation assay with a high signal-to-noise ratio. Biotechniques. 2010, 49: 793-805. 10.2144/000113519.Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, et al: Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005, 437: 1173-1178. 10.1038/nature04209.Venkatesan K, Rual J-F, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, Hao T, Zenkner M, Xin X, Goh K-I, Yildirim MA, Simonis N, Heinzmann K, Gebreab F, Sahalie JM, Cevik S, Simon C, de Smet A-S, Dann E, Smolyar A, Vinayagam A, Yu H, Szeto D, Borick H, Dricot A, Klitgord N, Murray RR, Lin C, Lalowski M, Timm J, et al: An empirical framework for binary interactome mapping. Nat Methods. 2008, 6: 83-90. 10.1038/nmeth.1280.Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000, 403: 623-627. 10.1038/35001009.Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci. 2001, 98: 4569-4574. 10.1073/pnas.061034498.Uetz P, Dong Y-A, Zeretzke C, Atzler C, Baiker A, Berger B, Rajagopala SV, Roupelieva M, Rose D, Fossum E, Haas J: Herpesviral protein networks and their interaction with the human proteome. Science. 2006, 311: 239-242. 10.1126/science.1116804.Fossum E, Friedel CC, Rajagopala SV, Titz B, Baiker A, Schmidt T, Kraus T, Stellberger T, Rutenberg C, Suthram S, Bandyopadhyay S, Rose D, von Brunn A, Uhlmann M, Zeretzke C, Dong Y-A, Boulet H, Koegl M, Bailer SM, Koszinowski U, Ideker T, Uetz P, Zimmer R, Haas J: Evolutionarily conserved herpesviral protein interaction networks. PLoS Pathog. 2009, 5: e1000570-10.1371/journal.ppat.1000570.Rodrigo G, Carrera J, Ruiz-Ferrer V, del Toro FJ, Llave C, Voinnet O, Elena SF: A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS One. 2012, 7: e40526-10.1371/journal.pone.0040526.Newman MEJ: The structure and function of complex networks. SIAM Rev. 2003, 45: 167-256. 10.1137/S003614450342480.Watts DJ, Strogatz SH: Collective dynamics of "small-world" networks. Nature. 1998, 393: 440-442. 10.1038/30918.Albert R, Barabási A-L: Statistical mechanics of complex networks. Rev Mod Phys. 2002, 74: 47-97. 10.1103/RevModPhys.74.47.Boccaletti S, Latora V, Moreno Y, Chávez M, Hwang D: Complex networks: structure and dynamics. Phys Rep. 2006, 424: 175-308. 10.1016/j.physrep.2005.10.009.Barabási A-L, Oltvai ZN: Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004, 5: 101-113. 10.1038/nrg1272.Albert R, DasGupta B, Hegde R, Sivanathan GS, Gitter A, Gürsoy G, Paul P, Sontag E: Computationally efficient measure of topological redundancy of biological and social networks. Phys Rev E. 2011, 84: 036117-10.1103/PhysRevE.84.036117.Cho D-Y, Kim Y-A, Przytycka TM: Chapter 5: network biology approach to complex diseases. PLoS Comput Biol. 2012, 8: e1002820-10.1371/journal.pcbi.1002820.Russell RB, Aloy P: Targeting and tinkering with interaction networks. Nat Chem Biol. 2008, 4: 666-673. 10.1038/nchembio.119.Winterbach W, Mieghem PV, Reinders M, Wang H, de Ridder D: Topology of molecular interaction networks. BMC Syst Biol. 2013, 7: 90-10.1186/1752-0509-7-90.Pržulj N: Protein-protein interactions: making sense of networks via graph-theoretic modeling. Bioessays. 2011, 33: 115-123. 10.1002/bies.201000044.Yook S-H, Oltvai ZN, Barabási A-L: Functional and topological characterization of protein interaction networks. Proteomics. 2004, 4: 928-942. 10.1002/pmic.200300636.Pržulj N, Wigle DA, Jurisica I: Functional topology in a network of protein interactions. Bioinformatics. 2004, 20: 340-348. 10.1093/bioinformatics/btg415.Elena SF, Carrera J, Rodrigo G: A systems biology approach to the evolution of plant-virus interactions. Curr Opin Plant Biol. 2011, 14: 372-377. 10.1016/j.pbi.2011.03.013.Zilian E, Maiss E: Detection of plum pox potyviral protein-protein interactions in planta using an optimized mRFP-based bimolecular fluorescence complementation system. J Gen Virol. 2011, 92: 2711-2723. 10.1099/vir.0.033811-0.Lin L, Shi Y, Luo Z, Lu Y, Zheng H, Yan F, Chen J, Chen J, Adams MJ, Wu Y: Protein-protein interactions in two potyviruses using the yeast two-hybrid system. Virus Res. 2009, 142: 36-40. 10.1016/j.virusres.2009.01.006.Guo D, Rajamäki M-L, Saarma M, Valkonen JPT: Towards a protein interaction map of potyviruses: protein interaction matrixes of two potyviruses based on the yeast two-hybrid system. J Gen Virol. 2001, 82: 935-939.Shen WT, Wang MQ, Yan P, Gao L, Zhou P: Protein interaction matrix of papaya ringspot virus type P based on a yeast two-hybrid system. Acta Virol. 2010, 54: 49-54. 10.4149/av_2010_01_49.Kang S, Ws L, Kh K: A protein interaction map of soybean mosaic virus strain G7H based on the yeast two-hybrid system. Mol Cells. 2004, 18: 122-126.Yambao MLM, Masuta C, Nakahara K, Uyeda I: The central and C-terminal domains of VPg of Clover yellow vein virus are important for VPg-HCPro and VPg-VPg interactions. J Gen Virol. 2003, 84: 2861-2869. 10.1099/vir.0.19312-0.Evidence for network evolution in an Arabidopsis interactome map. Science. 2011, 333: 601-607. 10.1126/science.1203877.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.Fouss F, Francoisse K, Yen L, Pirotte A, Saerens M: An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification. Neural Netw Off J Int Neural Netw Soc. 2012, 31: 53-72. 10.1016/j.neunet.2012.03.001.Bass JIF, Diallo A, Nelson J, Soto JM, Myers CL, Walhout AJM: Using networks to measure similarity between genes: association index selection. Nat Methods. 2013, 10: 1169-1176. 10.1038/nmeth.2728.Newman MEJ: Assortative mixing in networks. Phys Rev Lett. 2002, 89: 208701-10.1103/PhysRevLett.89.208701

Flux-dependent graphs for metabolic networks

Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic fluxes and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species’ population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate ‘intactness scores’: the remaining proportion of an ‘intact’ reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region’s major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/ taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

Preliminary syntaxonomic scheme of vegetation classes for the Central Bushveld of South Africa

Data from 29 vegetation studies done in the socalled Central Bushveld, South Africa (a large region N and NW of Pretoria), were subject to syntaxonomic synthesis using TWINSPAN and further refined with traditional tablesorting procedures of the floristic-sociological approach to classification of vegetation. The analysis revealed four major groups of communities, interpreted at this stage as zonal vegetation classes which we preliminarily name: Commiphoromollis-Colophospermetea mopani, Panico maximi-Acaciatea tortilis, Terminalio sericeae-Combretetea apiculati and Englerophyto magalismontani-Acacietea caffrae

Conservation implications of brown hyaena (Parahyaena brunnea) population densities and distribution across landscapes in Botswana

The brown hyaena (Parahyaena brunnea) is endemic to southern Africa. The largest population of this near-threatened species occurs in Botswana, but limited data were available to assess distribution and density. Our objectives were to use a stratified approach to collate available data and to collect more data to assess brown hyaena distribution and density across land uses in Botswana. We conducted surveys using track counts, camera traps and questionnaires and collated our results and available data to estimate the brown hyaena population based on the stratification of Botswana for large carnivores. Brown hyaenas occur over 533 050 km² (92%) of Botswana. Our density estimates ranged from 0 brown hyaenas/100 km² in strata of northern Botswana to 2.94 (2.16–3.71) brown hyaenas/100 km² in the southern stratum of the Central Kalahari Game Reserve. We made assumptions regarding densities in strata that lacked data, using the best references available. We estimated the brown hyaena population in Botswana as 4642 (3133–5993) animals, with 6.8% of the population in the Northern Conservation Zone, 73.1% in the Southern Conservation Zone, 2.0% in the smaller conservation zones and 18.1% in the agricultural zones. The similar densities of brown hyaenas in the Central Kalahari Game Reserve and the Ghanzi farms highlight the potential of agricultural areas in Botswana to conserve this species. The conservation of brown hyaenas in the agricultural landscape of Botswana is critical for the long-term conservation of the species; these areas provide important links between populations in South Africa, Namibia and Zimbabwe. Conservation implications: Botswana contains the core of the brown hyaena population in southern Africa, and conflict mitigation on agricultural land is crucial to maintaining connectivity among the range countries