Transcriptomic time-series analysis of cold- and heat-shock response in psychrotrophic lactic acid bacteria
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Abstract
Background: Psychrotrophic lactic acid bacteria (LAB) species are the dominant species in the microbiota of cold-stored modified-atmosphere-packaged food products and are the main cause of food spoilage. Despite the importance of psychrotrophic LAB, their response to cold or heat has not been studied. Here, we studied the transcriptome-level cold- and heat-shock response of spoilage lactic acid bacteria with time-series RNA-seq for Le. gelidum, Lc. piscium , and P. oligofermentans at 0 °C, 4 °C, 14 °C, 25 °C, and 28 °C. Results: We observed that the cold-shock protein A ( cspA ) gene was the main cold-shock protein gene in all three species. Our results indicated that DEAD-box RNA helicase genes ( cshA , cshB ) also play a critical role in cold-shock response in psychrotrophic LAB. In addition, several RNase genes were involved in cold-shock response in Lc. piscium and P. oligofermentans. Moreover, gene network inference analysis provided candidate genes involved in cold-shock response. Ribosomal proteins, tRNA modification, rRNA modification, and ABC and efflux MFS transporter genes clustered with cold-shock response genes in all three species, indicating that these genes could be part of the cold-shock response machinery. Heat-shock treatment caused upregulation of Clp protease and chaperone genes in all three species. We identified transcription binding site motifs for heat-shock response genes in Le. gelidum and Lc. piscium. Finally, we showed that food spoilage-related genes were upregulated at cold temperatures. Conclusions: The results of this study provide new insights on the cold- and heat-shock response of psychrotrophic LAB. In addition, candidate genes involved in cold- and heat-shock response predicted using gene network inference analysis could be used as targets for future studies.
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The walR regulates cell wall metabolism, ensuring structural integrity and adaptability in harsh environments such as the gastrointestinal tract.
“The walR regulates cell wall metabolism, ensuring structural integrity and adaptability in harsh environments such as the gastrointestinal tract [78].”
From Bile Salt Hydrolase Activity in the Food-Derived Strain Levilactobacillus brevis M3R3: Genomic and Functional Characterization (2026) · cites this paper as [78]
The Discussion links the WalR–WalK system to cold-shock responses in two lactic acid bacteria and says WalK has a reported role in cell-wall modification and peptidoglycan biosynthesis. It suggests, rather than demonstrates, that WalK helps adjust their cell walls to cold; it does not establish that WalR ensures structural integrity or adaptation in the gastrointestinal tract. No page number is available in our copy. (LLM, conf=0.86)
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548 · see citing works
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · see citing works
MEME SUITE: tools for motif discovery and searching
Bailey, T. L., Boden, M., Buske, F. A., Frith, M., Grant, C. E., Clementi, L., Ren, J., Li, W. W., & Noble, W. S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp335 · Cited by 10,648 · see citing works
KEGG as a reference resource for gene and protein annotation
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1070 · Cited by 7,387 · see citing works
Magnitude and complex based diffusion signal reconstruction
(2014) · HAL · doi:10.1007/978-3-319 · Cited by 6,731 · see citing works
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583 · see citing works
KAAS: an automatic genome annotation and pathway reconstruction server
Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C., & Kanehisa, M. I. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm321 · Cited by 4,128 · see citing works
SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data
Kopylova, E., Noé, L., & Touzet, H. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts611 · Cited by 3,051 · see citing works
Quantifying similarity between motifs
Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L., & Noble, W. S. (2007) · Genome Biology · doi:10.1186/gb-2007-8-2-r24 · Cited by 2,058 · see citing works
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
Psychrophilic microorganisms: challenges for life
D’Amico, S., Collins, T., Marx, J., Feller, G., Gerday, C., & Gerday, C. (2006) · EMBO Reports · doi:10.1038/sj.embor.7400662 · Cited by 849 · see citing works
Rosvall, M., Axelsson, D., & Bergstrom, C. T. (2009) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2010-01179-1 · Cited by 829 · see citing works
The lactic acid bacteria: A literature survey
Carr, F. J., Chill, D., & Maida, N. (2002) · Critical Reviews in Microbiology · doi:10.1080/1040-840291046759 · Cited by 724 · see citing works
Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data
McLeay, R. C., & Bailey, T. L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-165 · Cited by 687 · see citing works
Spoilage microbiota associated to the storage of raw meat in different conditions
Doulgeraki, A. I., Ercolini, D., Villani, F., & Nychas, G. E. (2012) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2012.05.020 · Cited by 649 · see citing works
Morita, R. Y. (1975) · Bacteriological Reviews · doi:10.1128/mmbr.39.2.144-167.1975 · Cited by 639 · see citing works
Mogk, A., Tomoyasu, T., Goloubinoff, P., Rüdiger, S., Röder, D., Langen, H., & Bukau, B. (1999) · The EMBO Journal · doi:10.1093/emboj/18.24.6934 · Cited by 596 · see citing works
Some like it cold: understanding the survival strategies of psychrophiles
Maayer, P. D., Anderson, D. E., Cary, S. C., & Cowan, D. A. (2014) · EMBO Reports · doi:10.1002/embr.201338170 · Cited by 572 · see citing works
PANNZER2: a rapid functional annotation web server
Törönen, P., Medlar, A. J., & Holm, L. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky350 · Cited by 537 · see citing works
Morita, R. Y. (1975) · Bacteriological Reviews · doi:10.1128/br.39.2.144-167.1975 · Cited by 496 · see citing works
Recent Developments in Bacterial Cold-Shock Response
Phadtare, S. (2004) · Current Issues in Molecular Biology · doi:10.21775/cimb.006.125 · Cited by 454 · see citing works
RegPrecise 3.0–a resource for genome-scale exploration of transcriptional regulation in bacteria
Novichkov, P. S., Kazakov, A. E., Ravcheev, D. A., Leyn, S. A., Kovaleva, G. Y., Sutormin, R. A., Kazanov, M. D., Riehl, W., Arkin, A. P., Dubchak, I., et al. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-745 · Cited by 451 · see citing works
Aguilar, P. S., Hernández‐Arriaga, A. M., Cybulski, L. E., Erazo, A. C., & de Mendoza, D. (2001) · The EMBO Journal · doi:10.1093/emboj/20.7.1681 · Cited by 414 · see citing works
Robison, K. E., Manson, A. L., & Church, G. M. (1998) · Journal of Molecular Biology · doi:10.1006/jmbi.1998.2160 · Cited by 353 · see citing works
Dubrac, S., Boneca, I. G., Poupel, O., & Msadek, T. (2007) · Journal of Bacteriology · doi:10.1128/jb.00645-07 · Cited by 347 · see citing works
The CspA family in Escherichia coli : multiple gene duplication for stress adaptation
Yamanaka, K., Fang, L., & Inouye, M. (1998) · Molecular Microbiology · doi:10.1046/j.1365-2958.1998.00683.x · Cited by 338 · see citing works
Luo, F., Yang, Y., Zhong, J., Gao, H., Khan, L. R., Thompson, D. K., & Zhou, J. Z. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-299 · Cited by 299 · see citing works
Bárria, C., Małecki, M., & Arraiano, C. M. (2013) · Microbiology · doi:10.1099/mic.0.052209-0 · Cited by 294 · see citing works
Ercolini, D., Ferrocino, I., Nasi, A., Ndagijimana, M., Vernocchi, P., Storia, A. L., Laghi, L., Mauriello, G., Guerzoni, M. E., & Villani, F. (2011) · Applied and Environmental Microbiology · doi:10.1128/aem.05521-11 · Cited by 285 · see citing works
CcpA-Dependent Carbon Catabolite Repression in Bacteria
Warner, J. B., & Lolkema, J. S. (2003) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.67.4.475-490.2003 · Cited by 272 · see citing works
Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology
Karp, P. D., Latendresse, M., Paley, S., Krummenacker, M., Ong, Q., Billington, R., Kothari, A., Weaver, D. S., Lee, T., Subhraveti, P., et al. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv079 · Cited by 267 · see citing works
PRODORIC: prokaryotic database of gene regulation
Münch, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg037 · Cited by 264 · see citing works
Recent approaches in food bio-preservation - a review
Singh, V. P. (2018) · Open Veterinary Journal · doi:10.4314/ovj.v8i1.16 · Cited by 239 · see citing works
Thermophilic bacteria: Ecology, physiology and technology
Zeikus, J. G. (1979) · Enzyme and Microbial Technology · doi:10.1016/0141-0229(79)90043-7 · Cited by 232 · see citing works
Bacterial spoilers of food: Behavior, fitness and functional properties
Remenant, B., Jaffrès, E., Dousset, X., Pilet, M.-F., & Zagorec, M. (2014) · Food Microbiology · doi:10.1016/j.fm.2014.03.009 · Cited by 231 · see citing works
Greene, N. P., Kaplan, E., Crow, A., & Koronakis, V. E. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00950 · Cited by 229 · see citing works
A Stress Response that Monitors and Regulates mRNA Structure Is Central to Cold Shock Adaptation
Zhang, Y., Burkhardt, D. H., Rouskin, S., Li, G., Weissman, J. S., & Gross, C. A. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.02.035 · Cited by 193 · see citing works
From Gene Networks to Gene FunctionPreprint
Schlitt, T. (2003) · Genome Research · doi:10.1101/gr.1111403 · Cited by 187 · see citing works
Reporting Key Features in Cold-Adapted Bacteria
Tribelli, P. M., & López, N. I. (2018) · Life · doi:10.3390/life8010008 · Cited by 182 · see citing works
Mechanistic insight into the ribosome biogenesis functions of the ancient protein KsgA
Connolly, K., Rife, J. P., & Culver, G. M. (2008) · Molecular Microbiology · doi:10.1111/j.1365-2958.2008.06485.x · Cited by 173 · see citing works
CollecTF: a database of experimentally validated transcription factor-binding sites in Bacteria
Kılıç, S., White, E. R., Sagitova, D. M., Cornish, J. P., & Erill, I. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1123 · Cited by 158 · see citing works
Cold shock response and low temperature adaptation in psychrotrophic bacteria.
Hébraud, M., & Potier, P. (1999) · PubMed · Cited by 158 · see citing works
SwissRegulon: a database of genome-wide annotations of regulatory sites
Pachkov, M. V., Erb, I., Molina, N., & van Nimwegen, E. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl857 · Cited by 151 · see citing works
Björkroth, J. K., Geisen, R. M., Schillinger, U., Weiss, N., Vos, P. D., Holzapfel, W. H., Korkeala, H. J., & Vandamme, P. A. R. (2000) · Applied and Environmental Microbiology · doi:10.1128/aem.66.9.3764-3772.2000 · Cited by 135 · see citing works
Hunger, K., Beckering, C. L., Wiegeshoff, F., Graumann, P. L., & Marahiel, M. A. (2005) · Journal of Bacteriology · doi:10.1128/jb.188.1.240-248.2006 · Cited by 123 · see citing works
Matamoros, S., Pilet, M. F., Gigout, F., Prévost, H., & Leroi, F. (2009) · Food Microbiology · doi:10.1016/j.fm.2009.04.011 · Cited by 120 · see citing works
Comparison of microbial communities in marinated and unmarinated broiler meat by metagenomics
Nieminen, T. T., Koskinen, K., Laine, P., Hultman, J., Säde, E., Paulin, L. G., Paloranta, A., Johansson, P., Björkroth, J. K., & Auvinen, P. (2012) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2012.04.016 · Cited by 117 · see citing works
Self-citationPreferential translation of cold-shock mRNAs during cold adaptation
Giuliodori, A. M., Brandi, A., Gualerzi, C. O., & Pon, C. L. (2004) · RNA · doi:10.1261/rna.5164904 · Cited by 113 · see citing works
Ishida, K., Kunibayashi, T., Tomikawa, C., Ochi, A., Kanai, T., Hirata, A., Iwashita, C., & Hori, H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1180 · Cited by 103 · see citing works
The Role of Cold-Shock Proteins in Low-Temperature Adaptation of Food-Related Bacteria
Wouters, J. A., ROMBOUTS, F. M., Kuipers, O. P., de Vos, W. M., & Abee, T. (2000) · Systematic and Applied Microbiology · doi:10.1016/s0723-2020(00)80001-6 · Cited by 95 · see citing works
Cipriano, M. J., Novichkov, P. N., Kazakov, A. E., Rodionov, D. A., Arkin, A. P., Gelfand, M. S., & Dubchak, I. L. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-213 · Cited by 91 · see citing works
Andreevskaya, M., Jääskeläinen, E. L., Johansson, P., Ylinen, A., Paulin, L. G., Björkroth, J. K., & Auvinen, P. (2018) · Applied and Environmental Microbiology · doi:10.1128/aem.00554-18 · Cited by 86 · see citing works
Self-citationPathway Tools version 28.0: Integrated Software for Pathway/Genome Informatics and Systems BiologyPreprint
Karp, P. D., Paley, S., Krummenacker, M., Kothari, A., Midford, P. E., Subhraveti, P., Swart, A., Moore, L. R., & Caspi, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.03964 · Cited by 84 · see citing works
Graumann, P. L., & Marahiel, M. A. (1994) · FEBS Letters · doi:10.1016/0014-5793(94)80355-2 · Cited by 76 · see citing works
Säde, E., & Björkroth, J. K. (2007) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2007.08.029 · Cited by 73 · see citing works
Significance of Heme-Based Respiration in Meat Spoilage Caused by Leuconostoc gasicomitatum
Jääskeläinen, E. L., Johansson, P., Kostiainen, O., Nieminen, T. T., Schmidt, G., Somervuo, P. J., Mohsina, M., Vanninen, P. S., Auvinen, P., & Björkroth, J. K. (2012) · Applied and Environmental Microbiology · doi:10.1128/aem.02943-12 · Cited by 72 · see citing works
Self-citationKim, W. S., & Dunn, N. W. (1997) · Current Microbiology · doi:10.1007/s002849900212 · Cited by 70 · see citing works
BARCOSEL: a tool for selecting an optimal barcode set for high-throughput sequencing
Somervuo, P. J., Koskinen, P., Mei, P., Holm, L., Auvinen, P., & Paulin, L. G. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2262-7 · Cited by 68 · see citing works
Self-citationYao, S., Jiang, X., Wu, W., Wang, M., Hamid, M. I., Xiang, M., & Liu, X. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.033308 · Cited by 67 · see citing works
tRNA Methylation Is a Global Determinant of Bacterial Multi-drug Resistance
Masuda, I., Matsubara, R., Christian, T., Rojas, E., Yadavalli, S. S., Zhang, L., Goulian, M., Foster, L. J., Huang, K. C., & Hou, Y. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.008 · Cited by 65 · see citing works
Johansson, P., Paulin, L. G., Säde, E., Salovuori, N., Alatalo, E., Björkroth, J. K., & Auvinen, P. (2011) · Applied and Environmental Microbiology · doi:10.1128/aem.00102-11 · Cited by 64 · see citing works
Self-citationVarmanen, P., Ingmer, H., & Vogensen, F. K. (2000) · Microbiology · doi:10.1099/00221287-146-6-1447 · Cited by 61 · see citing works
Cold-stress-induced de novo expression of infC and role of IF3 in cold-shock translational bias
Giuliodori, A. M., Brandi, A., Giangrossi, M., Gualerzi, C. O., & Pon, C. L. (2007) · RNA · doi:10.1261/rna.455607 · Cited by 61 · see citing works
Koort, J. M. K., Murros, A., Coenye, T., Eerola, S., Vandamme, P. A. R., Sukura, A., & Björkroth, J. K. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.8.4400-4406.2005 · Cited by 60 · see citing works
Rahkila, R., Nieminen, T. T., Johansson, P., Säde, E., & Björkroth, J. K. (2012) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2012.02.022 · Cited by 60 · see citing works
Systematic identification of tRNAome and its dynamics in L actococcus lactis
Puri, P., Wetzel, C., Saffert, P., Gaston, K. W., Russell, S. P., Varela, J. A. C., van der Vlies, P., Zhang, G., Limbach, P. A., Ignatova, Z., et al. (2014) · Molecular Microbiology · doi:10.1111/mmi.12710 · Cited by 58 · see citing works
Woufers, J. A., sander, J.-W., Kok, J., de Vos, W. M., Kuipers, O. P., & Abee, T. (1998) · Microbiology · doi:10.1099/00221287-144-10-2885 · Cited by 57 · see citing works
Saraoui, T., Leroi, F., Björkroth, J. K., & Pilet, M. F. (2016) · Journal of Applied Microbiology · doi:10.1111/jam.13179 · Cited by 54 · see citing works
Andreevskaya, M., Johansson, P., Laine, P., Smolander, O., Sonck, M., Rahkila, R., Jääskeläinen, E. L., Paulin, L. G., Auvinen, P., & Björkroth, J. K. (2015) · Applied and Environmental Microbiology · doi:10.1128/aem.00320-15 · Cited by 45 · see citing works
Self-citationIdentification of Cold-Temperature-Regulated Genes in Flavobacterium psychrophilum
Hesami, S., Metcalf, D. S., Lumsden, J. S., & MacInnes, J. I. (2011) · Applied and Environmental Microbiology · doi:10.1128/aem.01717-10 · Cited by 43 · see citing works
Andreevskaya, M., Johansson, P., Jääskeläinen, E. L., Rämö, T., Ritari, J., Paulin, L. G., Björkroth, J. K., & Auvinen, P. (2016) · BMC Genomics · doi:10.1186/s12864-016-2840-x · Cited by 33 · see citing works
Self-citationBrandi, A., Giangrossi, M., Paoloni, S., Spurio, R., Giuliodori, A. M., Pon, C. L., & Gualerzi, C. O. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz187 · Cited by 18 · see citing works
Responses of Lactic Acid Bacteria to Heat Stress
Varmanen, P., & Savijoki, K. (2011) · Springer eBooks · doi:10.1007/978-0-387-92771-8_3 · Cited by 17 · see citing works
Seiðr: Efficient Calculation of Robust Ensemble Gene NetworksPreprint
Schiffthaler, B., van Zalen, E., Serrano, A. R., Street, N. R., & Delhomme, N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/250696 · Cited by 17 · see citing works
Andreevskaya, M., Hultman, J., Johansson, P., Laine, P., Paulin, L. G., Auvinen, P., & Björkroth, J. K. (2016) · Standards in Genomic Sciences · doi:10.1186/s40793-016-0164-8 · Cited by 14 · see citing works
Self-citationFormation of Bacterial Glycerol-Based Membrane Lipids: Pathways, Enzymes, and Reactions
Geiger, O., Sohlenkamp, C., & López-Lara, I. M. (2019) · Springer eBooks · doi:10.1007/978-3-319-50430-8_8 · Cited by 13 · see citing works
Formation of Bacterial Glycerol-Based Membrane Lipids: Pathways, Enzymes, and Reactions
Geiger, O., Sohlenkamp, C., & López-Lara, I. M. (2017) · Springer eBooks · doi:10.1007/978-3-319-43676-0_8-1 · Cited by 3 · see citing works
Flagged first, then by citations
4 of the 85 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [42]Schiffthaler B, Serrano A, Street N, Delhomme N. Seidr: a gene meta-network calculation toolkit. bioRxiv. 2019:250696.
Cited by
Sorted by citations
PANNZER —A practical tool for protein function prediction
Törönen, P., & Holm, L. (2021) · Protein Science · doi:10.1002/pro.4193 · Cited by 197 · see citing works
Strategies to enhance stress tolerance in lactic acid bacteria across diverse stress conditions
Derunets, A. S., Selimzyanova, A. I., Rykov, S. V., Kuznetsov, A. Y., & Berezina, O. V. (2024) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-024-03905-3 · Cited by 73 · see citing works
“Additionally, DEAD-box helicase RNA genes ( csh A and csh B) have been identified as factors playing the critical role in a response to cold shock (Duru et al. 2021).”
Thermogenetics: Applications come of age
Chee, W. K. D., Yeoh, J. W., Dao, V. L., & Poh, C. L. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.107907 · Cited by 26 · see citing works
Ma, J., Jiang, G., Ma, Q., Wang, H., Du, M., Wang, C., Xie, X., Li, T., & Chen, S. (2022) · The Analyst · doi:10.1039/d1an02104d · Cited by 24 · see citing works
Feito, J. A., Contente, D., Alonso, M. P., Díaz-Formoso, L., Araújo, C., Peña, N., Borrero, J., Gómez‐Sala, B., del Campo, R., Muñoz-Atienza, E., et al. (2022) · Microorganisms · doi:10.3390/microorganisms10030521 · Cited by 24 · see citing works
Roles of adenine methylation in the physiology of Lacticaseibacillus paracasei
Zhao, J., Zhang, M., Hui, W., Zhang, Y., Wang, J., Wang, S., Kwok, L., Kong, J., Zhang, H., & Zhang, W. (2023) · Nature Communications · doi:10.1038/s41467-023-38291-1 · Cited by 23 · see citing works
Mun, S. Y., Seo, Y. J., & Chang, H. C. (2021) · Foods · doi:10.3390/foods10081899 · Cited by 20 · see citing works
“In addition, ribosomal protein, tRNA and rRNA modification, and ABC and efflux MFS transporter genes have been suggested to be components of the cold-shock response machinery [45].”
Megur, A., Ambrutaitytė, K., Šimoliūnas, E., Lastauskienė, E., & Burokas, A. (2025) · LWT · doi:10.1016/j.lwt.2025.117547 · Cited by 19 · see citing works
Qian, Y., Yu, J.-Y., Xie, J., & Yang, S. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100162 · Cited by 17 · see citing works
Liu, M., Ding, Y., Ye, Q., Wu, S., Gu, Q., Chen, L., Zhang, Y., Wei, X., Deng, M., Zhang, J., et al. (2024) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2024.2322141 · Cited by 17 · see citing works
“In contrast, the cold adaptation process is relatively complex, and traditional characterization methods mainly revolve around growth curves to describe the variability in the entire growth process, albeit with a longer measurement period (Duru et al. 2021).”
Nguyen, P.-T., & Nguyen, H. (2024) · The Microbe · doi:10.1016/j.microb.2024.100138 · Cited by 15 · see citing works
Chaichana, N., Boonsan, J., Singkhamanan, K., Wonglapsuwan, M., Pomwised, R., Yaikhan, T., Thant, E. P., & Surachat, K. (2024) · LWT · doi:10.1016/j.lwt.2024.117197 · Cited by 13 · see citing works
Response to Cold: A Comparative Transcriptomic Analysis in Eight Cold-Adapted Yeasts
Baeza, M., Zúñiga, S., Peragallo, V., Gutierrez, F., Barahona, S., Alcaı́no, J., & Cifuentes, V. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.828536 · Cited by 12 · see citing works
Seibt, A. C. M. D., Nerhing, P., Pinton, M. B., Santos, S. P., Leães, Y. S. V., de Oliveira, F. D. C., Robalo, S. S., Casarin, B. C., dos Santos, B. A., Barin, J. S., et al. (2023) · Meat Science · doi:10.1016/j.meatsci.2023.109418 · Cited by 10 · see citing works
Teoh, C. P., Lavin, P. L., Lee, D. J. H., González‐Aravena, M., Najimudin, N., Lee, P. C., Cheah, Y. K., & Wong, C. (2021) · Polar Biology · doi:10.1007/s00300-021-02883-8 · Cited by 10 · see citing works
“SO1 and psychrotrophic lactic acid bacteria (LAB) shared similar strategies by differentially expressing the ABC transporter, chaperones, oligoribonuclease, and ribosomal proteins genes (Duru et al. 2021).”
Gan, W., Ding, Z., Wang, J., & Jing, X. (2025) · International Journal of Food Microbiology · doi:10.1016/j.ijfoodmicro.2025.111465 · Cited by 8 · see citing works
Dynamic Transcriptional Landscape of Mycobacterium smegmatis under Cold Stress
Grigorov, A., Skvortsova, Y. V., Bychenko, O. S., Aseev, L. V., Koledinskaya, L. S., Boni, I. V., & Azhikina, T. L. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241612706 · Cited by 7 · see citing works
“subtilis, and lactic acid bacteria [20,24,28,29].”
Popoola, O. A., Orukotan, A. A., & Agarry, O. O. (2025) · BMC Biotechnology · doi:10.1186/s12896-025-01077-y · Cited by 6 · see citing works
Cruz, G. F. D. L., Pupo, M. R., Daniel, J. L. P., & Ferraretto, L. F. (2026) · Journal of Dairy Science · doi:10.3168/jds.2025-27446 · Cited by 8 · see citing works
DNA methylation confers epigenetic changes in cold-adapted microorganisms in response to cold stress
Bu, X., Dou, X., Chen, Z., Liu, L., Mei, Y., & Ren, M. (2025) · Extremophiles · doi:10.1007/s00792-025-01381-7 · Cited by 5 · see citing works
“This conclusion has been confirmed for three species of psychrophiles: Leuconostoc gelidum , Lactococcus piscium , and Lactobacillus oligofermentans (Duru 2021).”
Yang, T., Zhang, J., Zhang, R., Zhang, J., Liu, Z., & Wu, Z. (2025) · Current Research in Microbial Sciences · doi:10.1016/j.crmicr.2025.100453 · Cited by 5 · see citing works
“In another study, CshA could bind to nucleic acids and restore their secondary structures, which was a common feature of cold growth in many cold-adapted bacteria (Duru et al., 2021).”
Zhang, Z., Xu, Q., Gou, M., Zhu, C., Guo, W., Hu, R., Wang, L., & Liu, X. (2023) · LWT · doi:10.1016/j.lwt.2023.115714 · Cited by 6 · see citing works
Matta, T., Agrawal, K., Datta, P., Kumari, L., Bishnoi, M., Chopra, K., & Kondepudi, K. K. (2026) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-025-10628-w · Cited by 4 · see citing works
Abitayeva, G., Kurmangali, D. E., Baikonys, T., & Bekshin, Z. (2025) · Foods · doi:10.3390/foods14193449 · Cited by 6 · see citing works
Qiu, Q., Li, H., Sun, X., Zhang, L., Tian, K., Chang, M., Li, S., Zhou, D., & Huo, H. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.142146 · Cited by 4 · see citing works
Microbial Genes Responsible for Cold Adaptation
Singh, V. (2021) · Springer eBooks · doi:10.1007/978-981-16-2625-8_7 · Cited by 3 · see citing works
Microbial Adaptations Under Low Temperature
Singh, D., Thapa, S., Pandiyan, K., Das, M. K., Pramanik, A., & Chakdar, H. (2021) · Springer eBooks · doi:10.1007/978-981-16-2625-8_9 · Cited by 3 · see citing works
RNA editing, RNA modifications, and transcriptional units in Listeria monocytogenesPreprint
Duru, I. C., Ylinen, A., Grigore‐Gurgu, L., Riedel, C. U., Paulin, L. G., & Auvinen, P. (2022) · Research Square · doi:10.21203/rs.3.rs-1530110/v1 · Cited by 3 · see citing works
Self-citationAgolino, G., Cristofolini, M., Ronsivalle, M. A., Cattivelli, A., Tagliazucchi, D., Caggia, C., Solieri, L., & Randazzo, C. L. (2026) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-025-10810-0 · Cited by 1 · see citing works
“The walR regulates cell wall metabolism, ensuring structural integrity and adaptability in harsh environments such as the gastrointestinal tract [78].”
Qin, S., Wang, H., Xu, X., Jia, Z., Lü, S., Wang, H., & Zhang, Q. Q. (2025) · Food Bioscience · doi:10.1016/j.fbio.2025.107809 · Cited by 1 · see citing works
Exploring bacterial cold adaptation: genomic and multiomics insights into physiological adaptations
Datta, B., Kumar, A., Ahmed, S., Rai, S., Chhetri, P., & Kumar, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-27622-4.00010-9 · Cited by 1 · see citing works
Sun, J., Liu, X., Huang, L., Ma, W., & Jin, G. (2024) · Food Science and Human Wellness · doi:10.26599/fshw.2024.9250361 · Cited by 1 · see citing works
Duru, I. C., Ylinen, A., Grigore‐Gurgu, L., Riedel, C. U., Paulin, L. G., & Auvinen, P. (2024) · Research Square · doi:10.21203/rs.3.rs-3996292/v1
Self-citationEvans, R. T., Lee, Y., Hesketh, A. R., Cho, B., Smith, C. P. S., & Bucca, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.11.648403
Liu, R., Miao, S., Xia, K., Wang, Y., Li, L., & Zhang, A. (2025) · Animal Diseases · doi:10.1186/s44149-025-00171-0
“This factor was identified as HrcA, a heat-inducible transcriptional repressor and a key regulator of the bacterial heat shock response (Duru et al. 2021; Versace et al., 2021).”
Thermal shifts can override the effects of Transcription-targeting AntibioticsPreprint
Jagadeesan, R., Arsh, A. M., Chauhan, V., Dash, S., & Ribeiro, A. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.16.682818
Quantitative proteomic and phenotypic responses of urinary pathogens to CuO/Cu₂O nanoparticles
Sung, K., Park, M., Kweon, O., Savenka, A. V., Paredes, A. M., Khan, S. A., Min, S., & Foley, S. L. (2025) · Nanomedicine · doi:10.1080/17435889.2025.2579616
“Similarly, cold temperatures have been shown to induce ribosome protein upregulation in lactic acid bacteria [59].”
Lucas-Hernández, V. L., Lugo‐Zarate, L., Olivo-Ramírez, D. P., Cruz, E. Y. H., Pedraza-Chaverri, J., & Jiménez‐Osorio, A. S. (2026) · Applied Biosciences · doi:10.3390/applbiosci5020027
Ko, H. I., Kim, S.-R., Lee, M., Jeong, C., Na, N., Eun, J., & Kim, T.-W. (2026) · Food Research International · doi:10.1016/j.foodres.2026.119781
Rodríguez-Alvarez, Y., González, N., Araya, M. P., Moreno‐Switt, A. I., Melotto, M., & Jacob, C. (2026) · Food Microbiology · doi:10.1016/j.fm.2026.105327
- Authors
- Duru, I. C., Ylinen, A., Belanov, S., Pulido, A. A., Paulin, L., & Auvinen, P.
- Affiliations
- University of Helsinki (FI)
- Corresponding author
- Duru, Ilhan Cem
- Venue
- BMC Genomics
- Published
- 2021-01-07
- Volume
- 22
- Issue
- 1
- Pages
- 28-28
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC7788899
- OpenAlex
- W3114833742
- Cited by
- 40
- References
- 85
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 7 · 2023: 5 · 2022: 5 · 2021: 5
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Työväentutkimus Vuosikirja (submittedVersion)
- Funders
- Academy of Finland (307856, 323576)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Heat shock proteins research
- Keywords
- psychrotrophic lactic acid bacteria, cold shock response, heat shock response, DEAD-box RNA helicase, Clp protease, chaperone genes, food spoilage, modified atmosphere packaging, Leuconostoc gelidum, Lactococcus piscium, gene regulatory networks, ribosomal proteins
- MeSH terms
- Cold Temperature, Food Microbiology, Heat-Shock Response, Lactobacillales, Transcriptome
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