The pursuit of isotopic and molecular fire tracers in the polar atmosphere and cryosphere

We present an overview of recent multidisciplinary, multi-institutional efforts to identify and date major sources of combustion aerosol in the current and paleoatmospheres. The work was stimulated, in part, by an atmospheric particle \u27sample of opportunity\u27 collected at Summit, Greenland in August 1994, that bore the 14C imprint of biomass burning. During the summer field seasons of 1995 and 1996, we collected air filter, surface snow and snowpit samples to investigate chemical and isotopic evidence of combustion particles that had been transported from distant fires. Among the chemical tracers employed for source identification are organic acids, potassium and ammonium ions, and elemental and organic components of carbonaceous particles. Ion chromatography, performed by members of the Climate Change Research Center (University of New Hampshire), has been especially valuable in indicating periods at Summit that were likely to have been affected by the long range transport of biomass burning aerosol. Univariate and multivariate patterns of the ion concentrations in the snow and ice pinpointed surface and snowpit samples for the direct analysis of particulate (soot) carbon and carbon isotopes. The research at NIST is focusing on graphitic and polycyclic aromatic carbon, which serve as almost certain indicators of fire, and measurements of carbon isotopes, especially 14C, to distinguish fossil and biomass combustion sources. Complementing the chemical and isotopic record, are direct \u27visual\u27 (satellite imagery) records and less direct backtrajectory records, to indicate geographic source regions and transport paths. In this paper we illustrate the unique way in which the synthesis of the chemical, isotopic, satellite and trajectory data enhances our ability to develop the recent history of the formation and transport of soot deposited in the polar snow and ice

Engineering a rare-cutting restriction enzyme: genetic screening and selection of NotI variants

Restriction endonucleases (REases) with 8-base specificity are rare specimens in nature. NotI from Nocardia otitidis-caviarum (recognition sequence 5′-GCGGCCGC-3′) has been cloned, thus allowing for mutagenesis and screening for enzymes with altered 8-base recognition and cleavage activity. Variants possessing altered specificity have been isolated by the application of two genetic methods. In step 1, variant E156K was isolated by its ability to induce DNA-damage in an indicator strain expressing M.EagI (to protect 5′-NCGGCCGN-3′ sites). In step 2, the E156K allele was mutagenized with the objective of increasing enzyme activity towards the alternative substrate site: 5′-GCTGCCGC-3′. In this procedure, clones of interest were selected by their ability to eliminate a conditionally toxic substrate vector and induce the SOS response. Thus, specific DNA cleavage was linked to cell survival. The secondary substitutions M91V, F157C and V348M were each found to have a positive effect on specific activity when paired with E156K. For example, variant M91V/E156K cleaves 5′-GCTGCCGC-3′ with a specific activity of 8.2 × 10(4) U/mg, a 32-fold increase over variant E156K. A comprehensive analysis indicates that the cleavage specificity of M91V/E156K is relaxed to a small set of 8 bp substrates while retaining activity towards the NotI sequence

The Complete Genome of Teredinibacter turnerae T7901: An Intracellular Endosymbiont of Marine Wood-Boring Bivalves (Shipworms)

Here we report the complete genome sequence of Teredinibacter turnerae T7901. T. turnerae is a marine gamma proteobacterium that occurs as an intracellular endosymbiont in the gills of wood-boring marine bivalves of the family Teredinidae (shipworms). This species is the sole cultivated member of an endosymbiotic consortium thought to provide the host with enzymes, including cellulases and nitrogenase, critical for digestion of wood and supplementation of the host's nitrogen-deficient diet. T. turnerae is closely related to the free-living marine polysaccharide degrading bacterium Saccharophagus degradans str. 2–40 and to as yet uncultivated endosymbionts with which it coexists in shipworm cells. Like S. degradans, the T. turnerae genome encodes a large number of enzymes predicted to be involved in complex polysaccharide degradation (>100). However, unlike S. degradans, which degrades a broad spectrum (>10 classes) of complex plant, fungal and algal polysaccharides, T. turnerae primarily encodes enzymes associated with deconstruction of terrestrial woody plant material. Also unlike S. degradans and many other eubacteria, T. turnerae dedicates a large proportion of its genome to genes predicted to function in secondary metabolism. Despite its intracellular niche, the T. turnerae genome lacks many features associated with obligate intracellular existence (e.g. reduced genome size, reduced %G+C, loss of genes of core metabolism) and displays evidence of adaptations common to free-living bacteria (e.g. defense against bacteriophage infection). These results suggest that T. turnerae is likely a facultative intracellular ensosymbiont whose niche presently includes, or recently included, free-living existence. As such, the T. turnerae genome provides insights into the range of genomic adaptations associated with intracellular endosymbiosis as well as enzymatic mechanisms relevant to the recycling of plant materials in marine environments and the production of cellulose-derived biofuels

The Complete Genome of \u3cem\u3eTeredinibacter turnerae\u3c/em\u3e T7901: An Intracellular Endosymbiont of Marine Wood-Boring Bivalves (Shipworms)

Here we report the complete genome sequence of Teredinibacter turnerae T7901. T. turnerae is a marine gamma proteobacterium that occurs as an intracellular endosymbiont in the gills of wood-boring marine bivalves of the family Teredinidae (shipworms). This species is the sole cultivated member of an endosymbiotic consortium thought to provide the host with enzymes, including cellulases and nitrogenase, critical for digestion of wood and supplementation of the host\u27s nitrogen-deficient diet. T. turnerae is closely related to the free-living marine polysaccharide degrading bacterium Saccharophagus degradans str. 2–40 and to as yet uncultivated endosymbionts with which it coexists in shipworm cells. Like S. degradans, the T. turnerae genome encodes a large number of enzymes predicted to be involved in complex polysaccharide degradation (\u3e100). However, unlike S. degradans, which degrades a broad spectrum (\u3e10 classes) of complex plant, fungal and algal polysaccharides, T. turnerae primarily encodes enzymes associated with deconstruction of terrestrial woody plant material. Also unlike S. degradans and many other eubacteria, T. turnerae dedicates a large proportion of its genome to genes predicted to function in secondary metabolism. Despite its intracellular niche, the T. turnerae genome lacks many features associated with obligate intracellular existence (e.g. reduced genome size, reduced %G+C, loss of genes of core metabolism) and displays evidence of adaptations common to free-living bacteria (e.g. defense against bacteriophage infection). These results suggest that T. turnerae is likely a facultative intracellular ensosymbiont whose niche presently includes, or recently included, free-living existence. As such, the T. turnerae genome provides insights into the range of genomic adaptations associated with intracellular endosymbiosis as well as enzymatic mechanisms relevant to the recycling of plant materials in marine environments and the production of cellulose-derived biofuels

What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach

Ambiguity surrounding the effect of external engagement on academic research has raised questions about what motivates researchers to collaborate with third parties. We argue that what matters for society is research that can be absorbed by users. We define openness as a willingness by researchers to make research more usable by external partners by responding to external influences in their own research practices. We ask what kinds of characteristics define those researchers who are more open to creating usable knowledge. Our empirical study analyses a sample of 1583 researchers working at the Spanish Council for Scientific Research (CSIC). Results demonstrate that it is personal factors (academic identity and past experience) that determine which researchers have open behaviours. The paper concludes that policies to encourage external engagement should focus on experiences which legitimate and validate knowledge produced through user encounters, both at the academic formation career stage as well as through providing ongoing opportunities to engage with third parties.The data used for this study comes from the IMPACTO project funded by the Spanish Council for Scientific Research - CSIC (Ref. 200410E639). The work also benefited from a mobility grant awarded by Eu-Spri Forum to Julia Olmos Penuela & Paul Benneworth for her visiting research to the Center of Higher Education Policy Studies. Finally, Julia Olmos Penuela also benefited from a post-doctoral grant funded by the Generalitat Valenciana (APOSTD-2014-A-006).Olmos-Peñuela, J.; Benneworth, P.; Castro-Martínez, E. (2015). What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach. Minerva. 53(4):381-410. https://doi.org/10.1007/s11024-015-9283-4S381410534Abreu, Maria, Vadim Grinevich, Alan Hughes, and Michael Kitson. 2009. Knowledge exchange between academics and the business, public and third sectors. Cambridge: Centre for Business Research and UK-IRC.Aghion, Philippe, Mathias Dewatripont, and Jeremy C. Stein. 2008. Academic freedom, private-sector focus, and the process of innovation. RAND Journal of Economics 39: 617–635.Ajzen, Icek. 2001. Nature and operation of attitudes. Annual Review of Psychology 52(1): 27–58.Alrøe, Hugo Fjelsted, and Erik Steen Kristensen. 2002. Towards a systemic research methodology in agriculture: Rethinking the role of values in science. Agriculture and Human Values 19(1): 3–23.Audretsch, David B., Werner Bönte, and Stefan Krabel. 2010. Why do scientists in public research institutions cooperate with private firms. In DRUID Working Paper, 10–27.Baldini, Nicola, Rosa Grimaldi, and Maurizio Sobrero. 2007. To patent or not to patent? A survey of Italian inventors on motivations, incentives, and obstacles to university patenting. Scientometrics 70(2): 333–354.Bandura, Albert. 1977. Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.Barnett, R. 2009. Knowing and becoming in the higher education curriculum. Studies in Higher Education 34(4): 429–440.Becher, Tony. 1994. The significance of disciplinary differences. Studies in Higher Education 19(2): 151–161.Becher, Tony, and Paul Trowler. 2001. Academic tribes and territories: Intellectual enquiry and the culture of disciplines. McGraw-Hill International.Bekkers, Rudi, and Isabel Maria Bodas Freitas. 2008. Analysing knowledge transfer channels between universities and industry: To what degree do sectors also matter? Research Policy 37(10): 1837–1853.Belderbos, René, Martin Carree, Bert Diederen, Boris Lokshin, and Reinhilde Veugelers. 2004. Heterogeneity in R&D cooperation strategies. International Journal of Industrial Organization 22(8): 1237–1263.Benner, Mats, and Ulf Sandström. 2000. Institutionalizing the triple helix: Research funding and norms in the academic system. Research Policy 29(2): 291–301.Bercovitz, Janet, and Maryann Feldman. 2008. Academic entrepreneurs: Organizational change at the individual level. Organization Science 19(1): 69–89.Berman, Elizabeth Popp. 2011. Creating the market university: How academic science became an economic engine. Princeton University Press.Bleiklie, Ivar, and Roar Høstaker. 2004. Modernizing research training-education and science policy between profession, discipline and academic institution. Higher Education Policy 17(2): 221–236.Bozeman, Barry, Daniel Fay, and Catherine P. Slade. 2013. Research collaboration in universities and academic entrepreneurship: The-state-of-the-art. The Journal of Technology Transfer 38(1): 1–67.Collini, Stefan. 2009. Impact on humanities: Researchers must take a stand now or be judged and rewarded as salesmen. The Times Literary Supplement 5563: 18–19.D’Este, Pablo, and Markus Perkmann. 2011. Why do academics engage with industry? The entrepreneurial university and individual motivations. The Journal of Technology Transfer 36(3): 316–339.D’Este, Pablo, Oscar Llopis, and Alfredo Yegros. 2013. Conducting pro-social research: Cognitive diversity, research excellence and awareness about the social impact of research: INGENIO (CSIC-UPV) Working Paper Series.Deem, Rosemary, and Lisa Lucas. 2007. Research and teaching cultures in two contrasting UK policy contexts: Academic life in education departments in five English and Scottish universities. Higher Education 54(1): 115–133.DiMaggio, Paul J., and Walter W. Powell. 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review 48(2): 147–160.Downing, David B. 2005. The knowledge contract: Politics and paradigms in the academic workplace. Lincoln: Nebraska University of Nebraska Press.Donovan, Claire. 2007. The qualitative future of research evaluation. Science and Public Policy 34(8): 585–597.Durning, Bridget. 2004. Planning academics and planning practitioners: Two tribes or a community of practice? Planning Practice and Research 19(4): 435–446.Edquist, Charles. 1997. System of innovation approaches: Their emergence and characteristics. In Systems of innovation: Technologies, institutions and organizations, ed. C. Edquist, 1–35. London: Pinter.Etzkowitz, Henry, and Loet Leydesdorff. 2000. The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy 29(2): 109–123.Fromhold-Eisebith, Martina, Claudia Werker, and Marcel Vojnic. 2014. Tracing the social dimension in innovation networks. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Geuna, Aldo, and Alessandro Muscio. 2009. The governance of university knowledge transfer: A critical review of the literature. Minerva 47(1): 93–114.Gibbons, Michael, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott, and Martin Trow. 1994. The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage.Gläser, Jochen. 2012. How does Governance change research content? On the possibility of a sociological middle-range theory linking science policy studies to the sociology of scientific knowledge. Technical University Berlin. Technology Studies Working Papers. http://www.ts.tu-berlin.de/fileadmin/fg226/TUTS/TUTS-WP-1-2012.pdf . Accessed 16 Feb 2015.Goethner, Maximilian, Martin Obschonka, Rainer K. Silbereisen, and Uwe Cantner. 2012. Scientists’ transition to academic entrepreneurship: Economic and psychological determinants. Journal of Economic Psychology 33(3): 628–641.Gulbrandsen, Magnus, and Jens-Christian Smeby. 2005. Industry funding and university professors’ research performance. Research Policy 34(6): 932–950.Haeussler, Carolin, and Jeannette Colyvas. 2011. Breaking the ivory tower: Academic entrepreneurship in the life sciences in UK and Germany. Research Policy 40(1): 41–54.Hessels, Laurens K., Harro van Lente, John Grin, and Ruud E.H.M. Smits. 2011. Changing struggles for relevance in eight fields of natural science. Industry and Higher Education 25(5): 347–357.Hessels, Laurens K., and Harro Van Lente. 2008. Re-thinking new knowledge production: A literature review and a research agenda. Research Policy 37(4): 740–760.Hoye, Kate, and Fred Pries. 2009. ‘Repeat commercializers’, the ‘habitual entrepreneurs’ of university–industry technology transfer. Technovation 29(10): 682–689.Jacobson, Nora, Dale Butterill, and Paula Goering. 2004. Organizational factors that influence university-based researchers’ engagement in knowledge transfer activities. Science Communication 25(3): 246–259.Jain, Sanjay, Gerard George, and Mark Maltarich. 2009. Academics or entrepreneurs? Investigating role identity modification of university scientists involved in commercialization activity. Research Policy 38(6): 922–935.Jasanoff, Sheila, and Sang-Hyun Kim. 2013. Sociotechnical imaginaries and national energy policies. Science as Culture 22(2): 189–196.Jensen, Pablo. 2011. A statistical picture of popularization activities and their evolutions in France. Public Understanding of Science 20(1): 26–36.Kitcher, Philip. 2001. Science, truth, and democracy. Oxford: Oxford University Press.Knorr-Cetina, Karin. 1981. The manufacture of knowledge: An essay on the constructivist and contextual nature of science. Oxford: Pergamon Press.Kronenberg, Kristin, and Marjolein Caniëls. 2014. Professional proximity in research collaborations. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Krueger, Rob, and David Gibbs. 2010. Competitive global city regions and sustainable development’: An interpretive institutionalist account in the South East of England. Environment and planning A 42: 821–837.Lam, Alice. 2011. What motivates academic scientists to engage in research commercialization: ‘Gold’, ‘ribbon’ or ‘puzzle’? Research Policy 40(10): 1354–1368.Landry, Réjean, Malek Saïhi, Nabil Amara, and Mathieu Ouimet. 2010. Evidence on how academics manage their portfolio of knowledge transfer activities. Research Policy 39(10): 1387–1403.Lee, Alison, and David Boud. 2003. Writing groups, change and academic identity: Research development as local practice. Studies in Higher Education 28(2): 187–200.Lee, Yong S. 1996. ‘Technology transfer’ and the research university: A search for the boundaries of university–industry collaboration. Research Policy 25(6): 843–863.Lee, Yong S. 2000. The sustainability of university–industry research collaboration: An empirical assessment. The Journal of Technology Transfer 25(2): 111–133.Leisyte, Liudvika, Jürgen Enders, and Harry De Boer. 2008. The freedom to set research agendas—illusion and reality of the research units in the Dutch Universities. Higher Education Policy 21(3): 377–391.Louis, Karen Seashore, David Blumenthal, Michael E. Gluck, and Michael A. Stoto. 1989. Entrepreneurs in academe: An exploration of behaviors among life scientists. Administrative Science Quarterly 34(1): 110–131.Lowe, Philip, Jeremy Phillipson, and Katy Wilkinson. 2013. Why social scientists should engage with natural scientists. Contemporary Social Science 8(3): 207–222.Martín-Sempere, María José, Belén Garzón-García, and Jesús Rey-Rocha. 2008. Scientists’ motivation to communicate science and technology to the public: Surveying participants at the Madrid Science Fair. Public Understanding of Science 17(3): 349–367.Martin, Ben. 2003. The changing social contract for science and the evolution of the university. In Science and innovation: Rethinking the rationales for funding and governance, eds. A. Geuna, A.J. Salter, and W.E. Steinmueller, 7–29. Cheltenhan: Edward Elgar.Merton, Robert K. 1973. The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.Miller, Thaddeus R., and Mark W. Neff. 2013. De-facto science policy in the making: how scientists shape science policy and why it matters (or, why STS and STP scholars should socialize). Minerva 51(3): 295–315.Muthén, Bengt O. 1998–2004. Mplus Technical Appendices. Muthén & Muthén. Los Angeles, CA.: Muthén & Muthén.Nedeva, Maria. 2013. Between the global and the national: Organising European science. Research Policy 42(1): 220–230.Neff, Mark William. 2014. Research prioritization and the potential pitfall of path dependencies in coral reef science. Minerva 52(2): 213–235.Nelson, Richard R. 2001. Observations on the post-Bayh-Dole rise of patenting at American universities. The Journal of Technology Transfer 26(1): 13–19.Nowotny, Helga, Peter Scott, and Michael Gibbons. 2001. Re-thinking science: Knowledge and the public in an age of uncertainty. Cambridge: Polity Press.Olmos-Peñuela, Julia, Paul Benneworth, and Elena Castro-Martínez. 2014a. Are ‘STEM from Mars and SSH from Venus’? Challenging disciplinary stereotypes of research’s social value. Science and Public Policy 41: 384–400.Olmos-Peñuela, Julia, Elena Castro-Martínez, and Manuel Fernández-Esquinas. 2014b. Diferencias entre áreas científicas en las prácticas de divulgación de la investigación: un estudio empírico en el CSIC. Revista Española de Documentación Científica. doi: 10.3989/redc.2014.2.1096 .Ouimet, Mathieu, Nabil Amara, Réjean Landry, and John Lavis. 2007. Direct interactions medical school faculty members have with professionals and managers working in public and private sector organizations: A cross-sectional study. Scientometrics 72(2): 307–323.Perkmann, Markus, Valentina Tartari, Maureen McKelvey, Erkko Autio, Anders Brostrom, Pablo D’Este, Riccardo Fini, et al. 2013. Academic engagement and commercialisation: A review of the literature on university-industry relations. Research Policy 42(2): 423–442.Philpott, Kevin, Lawrence Dooley, Caroline O’Reilly, and Gary Lupton. 2011. The entrepreneurial university: Examining the underlying academic tensions. Technovation 31(4): 161–170.Rutten, Roel, and Frans Boekema. 2012. From learning region to learning in a socio-spatial context. Regional Studies 46(8): 981–992.Sarewitz, Daniel, and Roger A. Pielke. 2007. The neglected heart of science policy: reconciling supply of and demand for science. Environmental Science & Policy 10(1): 5–16.Sauermann, Henry, and Paula Stephan. 2013. Conflicting logics? A multidimensional view of industrial and academic science. Organization Science 24(3): 889–909.Schein, Edgar H. 1985. Organizational culture and leadership: A dynamic view. San Francisco, CA: Jossey-Bass.Shane, Scott. 2000. Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science 11(4): 448–469.Spaapen, Jack, and Leonie van Drooge. 2011. Introducing ‘productive interactions’ in social impact assessment. Research Evaluation 20(3): 211–218.Stokes, Donald E. 1997. Pasteur’s quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.Tartari, Valentina, and Stefano Breschi. 2012. Set them free: scientists’ evaluations of the benefits and costs of university–industry research collaboration. Industrial and Corporate Change 21(5): 1117–1147.Tinker, Tony, and Rob Gray. 2003. Beyond a critique of pure reason: From policy to politics to praxis in environmental and social research. Accounting, Auditing & Accountability Journal 16(5): 727–761.van Rijnsoever, Frank J., Laurens K. Hessels, and Rens L.J. Vandeberg. 2008. A resource-based view on the interactions of university researchers. Research Policy 37(8): 1255–1266.Venkataraman, Sankaran. 1997. The distinctive domain of entrepreneurship research: An editor’s perspective. Advances in Entrepreneurship, Firm Emergence, and Growth 3: 119–138.Verspagen, Bart. 2006. University research, intellectual property rights and European innovation systems. Journal of Economic Surveys 20(4): 607–632.Villanueva-Felez, Africa, Jordi Molas-Gallart, and Alejandro Escribá-Esteve. 2013. Measuring personal networks and their relationship with scientific production. Minerva 51(4): 465–483.Watermeyer, Richard. 2015. Lost in the ‘third space’: the impact of public engagement in higher education on academic identity, research practice and career progression. European Journal of Higher Education (online first, doi: 10.1080/21568235.2015.1044546 ).Weingart, Peter. 2009. Editorial for Issue 47/3. Minerva 47(3): 237–239.Ziman, John. 1996. ‘Postacademic science’: Constructing knowledge with networks and norms. Science Studies 1: 67–80.Zomer, Arend H., Ben W.A. Jongbloed, and Jürgen Enders. 2010. Do spin-offs make the academics’ heads spin? The impacts of spin-off companies on their parent research organisation. Minerva 48(3): 331–353

Synthetic Biology: Mapping the Scientific Landscape

This article uses data from Thomson Reuters Web of Science to map and analyse the scientific landscape for synthetic biology. The article draws on recent advances in data visualisation and analytics with the aim of informing upcoming international policy debates on the governance of synthetic biology by the Subsidiary Body on Scientific, Technical and Technological Advice (SBSTTA) of the United Nations Convention on Biological Diversity. We use mapping techniques to identify how synthetic biology can best be understood and the range of institutions, researchers and funding agencies involved. Debates under the Convention are likely to focus on a possible moratorium on the field release of synthetic organisms, cells or genomes. Based on the empirical evidence we propose that guidance could be provided to funding agencies to respect the letter and spirit of the Convention on Biological Diversity in making research investments. Building on the recommendations of the United States Presidential Commission for the Study of Bioethical Issues we demonstrate that it is possible to promote independent and transparent monitoring of developments in synthetic biology using modern information tools. In particular, public and policy understanding and engagement with synthetic biology can be enhanced through the use of online interactive tools. As a step forward in this process we make existing data on the scientific literature on synthetic biology available in an online interactive workbook so that researchers, policy makers and civil society can explore the data and draw conclusions for themselves

Constitutive Secretion in Tetrahymena thermophila ▿ † ‡

The growth, survival, and life cycle progression of the freshwater ciliated protozoan Tetrahymena thermophila are responsive to protein signals thought to be released by constitutive secretion. In addition to providing insights about ciliate communication, studies of constitutive secretion are of interest for evaluating the utility of T. thermophila as a platform for the expression of secreted protein therapeutics. For these reasons, we undertook an unbiased investigation of T. thermophila secreted proteins using wild-type and secretion mutant strains. Extensive tandem mass spectrometry analyses of secretome samples were performed. We identified a total of 207 secretome proteins, most of which were not detected in a set of abundant whole-cell protein identifications. Numerous proteases and other hydrolases were secreted from cells grown in rich medium but not cells transferred to a nutrient starvation condition. On the other hand, we detected the starvation-enhanced secretion of a small number of cytosolic proteins, suggestive of an exosome-like pathway in T. thermophila. Subsets of proteins from the T. thermophila regulated secretion pathway were detected with differential representation across strains and culture conditions. Finally, many secretome proteins had a predicted N-terminal signal sequence but no other annotated characteristic or functional classification. Our work provides the first comprehensive analysis of secreted proteins in T. thermophila and establishes the groundwork for future studies of constitutive protein secretion biology and biotechnology in ciliates