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Curcumin, a plant polyphenol, has been shown to possess both anti-inflammatory and antioxidant properties, in addition to regulatory effects on gut microbiota. However, research on curcumin's impact against AFB1 toxicity in ruminants is limited. This study aims to elucidate whether AFB1 induces hepatic pyroptosis and autophagy in ruminants via the rumen microbiota-blood-liver axis and the regulatory role of curcumin. The experimental design involves the administration of AFB1 and curcumin to sheep, followed by a comprehensive observation of alterations in rumen microbiota, barrier function, and the occurrence of hepatic pyroptosis and autophagy, with the aim of elucidating the mechanism of curcumin in ameliorating AFB1-induced liver injury in sheep.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the experimental setup, 800\u00a0mg\/kg dry matter (DM) curcumin was administered as a dietary supplement to alleviate the adverse effects of AFB1 (500\u00a0\u03bcg\/kg DM) on the rumen and liver of sheep. AFB1 suppressed NH\n                      <jats:sub>3<\/jats:sub>\n                      -N and VFAs production, whereas curcumin improved VFA generation and fermentation efficiency. Curcumin mitigated AFB1-induced rumen barrier impairment by upregulating tight junction proteins (ZO-1, Occludin, Claudin-1) and reducing LPS levels, which was consistent with metagenomic data showing amelioration of microbiota dysbiosis and reduced lysis of Gram-negative bacteria. At hepatic level, curcumin downregulated the principal mediators of the TLR4-NF-\u03baB-NLRP3 signaling pathway (TLR4, p65, and NLRP3), attenuating pyroptosis and reducing serum AST, ALT, and LDH concentrations, while reversing inflammatory infiltration and hepatic cord disruption. Furthermore, curcumin restored autophagic flux by increasing the LC3-II\/LC3-I ratio and decreasing p62 accumulation, counteracting AFB1-induced autophagy inhibition.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Curcumin counteracts AFB1-induced rumen-liver axis dysfunction. It works by stabilizing the microbiota, maintaining barrier integrity, and dually regulating pyroptosis and autophagy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40168-026-02396-8","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T02:06:08Z","timestamp":1776650768000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Curcumin modulates hepatic pyroptosis-autophagy crosstalk induced by aflatoxin B1 via rumen microbiota-blood-liver axis"],"prefix":"10.1186","volume":"14","author":[{"given":"Tongwei","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijie","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiefeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"issue":"11","key":"2396_CR1","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.3390\/antiox9111059","volume":"9","author":"M Pauletto","year":"2020","unstructured":"Pauletto M, Giantin M, Tolosi R, Bassan I, Barbarossa A, Zaghini A, et al. 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Nevertheless, the vast heterogeneity of study designs and microbial community profiling strategies, and of bioinformatic and biostatistical approaches for analyses of metagenomic sequence datasets hinder the identification of bacterial targets for follow-up experimental investigations of helminth-microbiota cross-talk. Furthermore, comparative analyses of published datasets are made difficult by the unavailability of a unique repository for metagenomic sequence data and associated metadata linked to studies aimed to explore potential changes in the composition of the vertebrate gut microbiota in response to GI and\/or EI helminth infections.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here, we undertake a meta-analysis of available metagenomic sequence data linked to published studies on helminth-microbiota cross-talk in humans and veterinary species using a single bioinformatic pipeline, and introduce the 'MICrobiome HELminth INteractions database' (MICHELINdb), an online resource for mining of published sequence datasets, and corresponding metadata, generated in these investigations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>By increasing data accessibility, we aim to provide the scientific community with a platform to identify gut microbial populations with potential roles in the pathophysiology of helminth disease and parasite-mediated suppression of host inflammatory responses, and facilitate the design of experiments aimed to disentangle the cause(s) and effect(s) of helminth-microbiota relationships.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s40168-019-0782-7","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:02:15Z","timestamp":1580688135000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["MICHELINdb: a web-based tool for mining of helminth-microbiota interaction datasets, and a meta-analysis of current research"],"prefix":"10.1186","volume":"8","author":[{"given":"Riccardo","family":"Scotti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stuart","family":"Southern","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine","family":"Boinett","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy P.","family":"Jenkins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alba","family":"Cort\u00e9s","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6863-2950","authenticated-orcid":false,"given":"Cinzia","family":"Cantacessi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"key":"782_CR1","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1172\/JCI34261","volume":"118","author":"PJ Hotez","year":"2008","unstructured":"Hotez PJ, Brindley PJ, Bethony JM, King CH, Pearce EJ, Jacobson J. 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The most common causes of PEx in CF are by bacterial infection and concomitant inflammation leading to progressive airway damage. To draw attention to the seriousness of PEx they have been labelled as \u2018lung attacks\u2019, much like a \u2018heart attack\u2019 for acute myocardial infarction. Treatment typically starts when a pwCF presents with worsening respiratory symptoms. Hence, there is a pressing need to identify indicative biomarkers of PEx onset to allow more timely intervention. Set within an ecological framework, we investigated temporal microbiota dynamics to connect changes in the lung microbiota of pwCF to changes in disease states across a PEx event.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Species-time relationships (STR) describe how the richness of a community changes with time, here STRs were used to assess temporal turnover (<jats:italic>w<\/jats:italic>) within the lung microbiota of each pwCF (<jats:italic>n<\/jats:italic>\u2009=\u200912, mean sample duration 315.9\u2009\u00b1\u200942.7\u00a0days). STRs were characterised by high interpatient variability, indicating that turnover and hence temporal organization are a personalized feature of the CF lung microbiota. Greater turnover was found to be significantly associated with greater change in lung function with time. When microbiota turnover was examined at a finer scale across each pwCF time series, <jats:italic>w<\/jats:italic>-values could clearly be observed to increase in the exacerbation period, then peaking within the treatment period, demonstrating that increases in turnover were not solely a result of perturbations caused by PEx antibiotic interventions. STR <jats:italic>w<\/jats:italic>-values have been found to have a remarkable degree of similarity for different organisms, in a variety of habitats and ecosystems, and time lengths (typically not exceeding <jats:italic>w<\/jats:italic>\u2009=\u20090.5). Here, we found <jats:italic>w<\/jats:italic>-values soon increased beyond that. It was therefore possible to use the departure from that expected norm up to start of treatment to approximate onset of PEx in days (21.2\u2009\u00b1\u20098.9\u00a0days across the study participants).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Here, we illustrate that changes in turnover of the lung microbiota of pwCF can be indicative of PEx onset in considerable advance of when treatment would normally be initiated. This offers translational potential to enable early detection of PEx and consequent timely intervention.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s40168-025-02143-5","type":"journal-article","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T06:40:26Z","timestamp":1749278426000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Species turnover within cystic fibrosis lung microbiota is indicative of acute pulmonary exacerbation onset"],"prefix":"10.1186","volume":"13","author":[{"given":"Leah","family":"Cuthbertson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren","family":"Hatfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen","family":"Gavillet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle","family":"Hardman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Marsh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damian W.","family":"Rivett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"van der Gast","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,7]]},"reference":[{"key":"2143_CR1","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1183\/09059180.00006512","volume":"22","author":"JM Bhatt","year":"2013","unstructured":"Bhatt JM. 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Pollutants from waste streams, either organic or inorganic (e.g. heavy metals), are prone to interact with their physical environment that not only affects the ecosystem health but also the livelihood of local communities. Unlike organic pollutants, heavy metals or trace metals (e.g. chromium, mercury) are non-biodegradable, bioaccumulate through food-web interactions and are likely to have a long-term impact on ecosystem health. Microorganisms provide varied ecosystem services including climate regulation, purification of groundwater, rehabilitation of contaminated sites by detoxifying pollutants. Recent studies have highlighted the potential of methanotrophs, a group of bacteria that can use methane as a sole carbon and energy source, to transform toxic metal (loids) such as chromium, mercury and selenium. 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bacteria and higher trophic levels and participate in the recycling of inorganic nutrients for regenerated primary production. Studying their activity and function in the ecosystem is challenging since most of the HFs in the ocean are still uncultured. In the present work, we investigated gene expression of natural HF communities during bacterivory in four unamended seawater incubations.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The most abundant species growing in our incubations belonged to the taxonomic groups MAST-4, MAST-7, Chrysophyceae, and Telonemia. Gene expression dynamics were similar between incubations and could be divided into three states based on microbial counts, each state displaying distinct expression patterns. The analysis of samples where HF growth was highest revealed some highly expressed genes that could be related to bacterivory. Using available genomic and transcriptomic references, we identified 25 species growing in our incubations and used those to compare the expression levels of these specific genes.<\/jats:p>\n                \n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our results indicate that several peptidases, together with some glycoside hydrolases and glycosyltransferases, are more expressed in phagotrophic than in phototrophic species, and thus could be used to infer the process of bacterivory in natural assemblages.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s40168-023-01571-5","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T05:01:52Z","timestamp":1686805312000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Gene expression dynamics of natural assemblages of heterotrophic flagellates during bacterivory"],"prefix":"10.1186","volume":"11","author":[{"given":"Aleix","family":"Obiol","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"L\u00f3pez-Escard\u00f3","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric D.","family":"Salomaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monika M.","family":"Wi\u015bniewska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irene","family":"Forn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabet","family":"S\u00e0","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dolors","family":"Vaqu\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Kol\u00edsko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramon","family":"Massana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"key":"1571_CR1","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1038\/s41579-019-0222-5","volume":"17","author":"R Cavicchioli","year":"2019","unstructured":"Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. 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The next decade is poised to focus on microbial traits and function. Most existing methods for doing this perform pathway analysis using reference databases. This has both benefits and drawbacks. Function can go undetected if reference databases are coarse-grained or incomplete. Likewise, detection of a pathway does not guarantee expression of the associated function. Finally, function cannot be connected to specific microbial constituents, making it difficult to ascertain the types of organisms exhibiting particular traits\u2014something that is important for understanding microbial success in specific environments. A complementary approach to pathway analysis is to use the wealth of microbial trait information collected over years of lab-based, culture experiments.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Here, we use journal articles and <jats:italic>Bergey<\/jats:italic>\u2019<jats:italic>s Manual of Systematic Bacteriology<\/jats:italic> to develop a trait-based database for 971 human skin bacterial taxa. We then use this database to examine functional traits that are over\/underrepresented among skin taxa. Specifically, we focus on three trait classes\u2014binary, categorical, and quantitative\u2014and compare trait values among skin taxa and microbial taxa more broadly. We compare binary traits using a Chi-square test, categorical traits using randomization trials, and quantitative traits using a nonparametric relative effects test based on global rankings using Tukey contrasts.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We find a number of traits that are over\/underrepresented within the human skin microbiome. For example, spore formation, acid phosphatase, alkaline phosphatase, pigment production, catalase, and oxidase are all less common among skin taxa. As well, skin bacteria are less likely to be aerobic, favoring, instead, a facultative strategy. They are also less likely to exhibit gliding motility, less likely to be spirillum or rod-shaped, and less likely to grow in chains. Finally, skin bacteria have more difficulty at high pH, prefer warmer temperatures, and are much less resilient to hypotonic conditions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our analysis shows how an approach that relies on information from culture experiments can both support findings from pathway analysis, and also generate new insights into the structuring principles of microbial communities.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s40168-019-0698-2","type":"journal-article","created":{"date-parts":[[2019,7,5]],"date-time":"2019-07-05T17:02:32Z","timestamp":1562346152000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Trait-based analysis of the human skin microbiome"],"prefix":"10.1186","volume":"7","author":[{"given":"Sharon","family":"Bewick","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eliezer","family":"Gurarie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JL","family":"Weissman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jess","family":"Beattie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cyrus","family":"Davati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Flint","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Thielen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Breitwieser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Karig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William F.","family":"Fagan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"issue":"5847","key":"698_CR1","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1126\/science.1146689","volume":"318","author":"JA Huber","year":"2007","unstructured":"Huber JA, Welch DBM, Morrison HG, Huse SM, Neal PR, Butterfield DA, et al. 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Genomic selection to modify the microbiome function provides a permanent and accumulative solution, which may have also favourable consequences in other traits of interest (e.g. methane emissions). Possibly due to a lack of data, this strategy has never been explored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      This study provides a comprehensive identification of ruminal microbial mechanisms under host genomic influence that directly or indirectly affect the content of unsaturated fatty acids in beef associated with human dietary health benefits C18:3n-3, C20:5n-3, C22:5n-3, C22:6n-3 or\n                      <jats:italic>cis-9<\/jats:italic>\n                      ,\n                      <jats:italic>trans-11<\/jats:italic>\n                      C18:2 and\n                      <jats:italic>trans-11<\/jats:italic>\n                      C18:1 in relation to hypercholesterolemic saturated fatty acids C12:0, C14:0 and C16:0, referred to as N3 and CLA indices. We first identified that ~27.6% (1002\/3633) of the functional core additive log-ratio transformed microbial gene abundances (\n                      <jats:italic>alr<\/jats:italic>\n                      -MG) in the rumen were at least moderately host-genomically influenced (HGFC). Of these, 372\n                      <jats:italic>alr<\/jats:italic>\n                      -MG were host-genomically correlated with the N3 index (\n                      <jats:italic>n<\/jats:italic>\n                      =290), CLA index (\n                      <jats:italic>n<\/jats:italic>\n                      =66) or with both (\n                      <jats:italic>n<\/jats:italic>\n                      =16), indicating that the HGFC influence on beef fatty acid composition is much more complex than the direct regulation of microbial lipolysis and biohydrogenation of dietary lipids and that N3 index variation is more strongly subjected to variations in the HGFC than CLA. Of these 372\n                      <jats:italic>alr<\/jats:italic>\n                      -MG, 110 were correlated with the N3 and\/or CLA index in the same direction, suggesting the opportunity for enhancement of both indices simultaneously through a microbiome-driven breeding strategy. These microbial genes were involved in microbial protein synthesis (\n                      <jats:italic>aroF<\/jats:italic>\n                      and\n                      <jats:italic>serA<\/jats:italic>\n                      ), carbohydrate metabolism and transport (\n                      <jats:italic>galT<\/jats:italic>\n                      ,\n                      <jats:italic>msmX<\/jats:italic>\n                      ), lipopolysaccharide biosynthesis (\n                      <jats:italic>kdsA<\/jats:italic>\n                      ,\n                      <jats:italic>lpxD<\/jats:italic>\n                      ,\n                      <jats:italic>lpxB<\/jats:italic>\n                      ), or flagellar synthesis (\n                      <jats:italic>flgB<\/jats:italic>\n                      ,\n                      <jats:italic>fliN<\/jats:italic>\n                      ) in certain genera within the Proteobacteria phyla (e.g.\n                      <jats:italic>Serratia<\/jats:italic>\n                      ,\n                      <jats:italic>Aeromonas<\/jats:italic>\n                      ). A microbiome-driven breeding strategy based on these microbial mechanisms as sole information criteria resulted in a positive selection response for both indices (1.36\u00b10.24 and 0.79\u00b10.21 sd of N3 and CLA indices, at 2.06 selection intensity). When evaluating the impact of our microbiome-driven breeding strategy to increase N3 and CLA indices on the environmental trait methane emissions (g\/kg of dry matter intake), we obtained a correlated mitigation response of \u22120.41\u00b10.12 sd.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This research provides insight on the possibility of using the ruminal functional microbiome as information for host genomic selection, which could simultaneously improve several microbiome-driven traits of interest, in this study exemplified with meat quality traits and methane emissions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40168-022-01352-6","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:02:42Z","timestamp":1664928162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions"],"prefix":"10.1186","volume":"10","author":[{"given":"Marina","family":"Mart\u00ednez-\u00c1lvaro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Mattock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Auffret","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqing","family":"Weng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carol-Anne","family":"Duthie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard J.","family":"Dewhurst","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew A.","family":"Cleveland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mick","family":"Watson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rainer","family":"Roehe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"1352_CR1","doi-asserted-by":"publisher","first-page":"674","DOI":"10.3181\/0711-MR-311","volume":"233","author":"AP Simopoulos","year":"2008","unstructured":"Simopoulos AP. 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The experiment was approved by the Animal Experiment Committee of SRUC and was conducted following the requirements of the UK Animals (Scientific Procedures) Act 1986.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"166"},{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:20:49Z","timestamp":1785471649762,"version":"3.56.0"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Microbiome"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      The plant holobiont concept emphasizes the critical role of the microbiome in host plant health and resilience. Microbial communities have been shown to enhance plant resistance to abiotic stresses, such as drought and salinity, and to mitigate the impact of phytopathogens. Traditional microbiome engineering approaches face challenges due to the complexity of microbial interactions. To overcome these limitations, recent advances in transcriptomics and metataxonomics analyses enable the identification of microbiome-associated phenotypes, co-occurrence networks, and key host genes-microbiome interactions. We present a novel framework combining co-occurrence network analyses and transcriptome-microbiota correlations to identify keystone belowground microorganisms and host genes potentially involved in olive (\n                      <jats:italic>Olea europaea<\/jats:italic>\n                      L.) tolerance to Verticillium wilt, a devastating disease caused by the soil-borne, fungal vascular pathogen\n                      <jats:italic>Verticillium dahliae<\/jats:italic>\n                      Kleb. Our approach aims to identify microbiome-regulating host genes and keystone bacteria and fungi that could be instrumental as genetic and microbiological markers in olive breeding programs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the root endosphere, cultivars qualified as tolerant to Verticillium wilt of olive (VWO) exhibited an enrichment of the bacterial genera\n                      <jats:italic>Actinophytocola<\/jats:italic>\n                      ,\n                      <jats:italic>Kibdelosporangium<\/jats:italic>\n                      and\n                      <jats:italic>Nocardia<\/jats:italic>\n                      . Keystone taxa analyses revealed clearly different profiles when comparing the microbial co-occurrence networks of the VWO-tolerant genotypes with those varieties described as susceptible to\n                      <jats:italic>V. dahliae<\/jats:italic>\n                      . Thus, tolerant cultivars harbored bacteria predominantly displaying negative interactions with the mycobiome. In contrast, VWO-susceptible cultivars displayed microbial hubs with positive fungal correlations. Transcriptomic analyses of olive roots identified 1,143 differentially expressed genes (DEGs), with 309 upregulated genes in tolerant cultivars, highlighting biological processes like defense response, carbohydrate metabolism, and amino acid transport. Key microbial taxa (\n                      <jats:italic>Actinophytocola<\/jats:italic>\n                      ,\n                      <jats:italic>Kibdelosporangium<\/jats:italic>\n                      ,\n                      <jats:italic>Nocardia<\/jats:italic>\n                      ,\n                      <jats:italic>Aquabispora<\/jats:italic>\n                      , and\n                      <jats:italic>Fusarium<\/jats:italic>\n                      ) strongly correlated with DEGs associated with plant defense.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Keystone microbial taxa, particularly\n                      <jats:italic>Actinophytocola<\/jats:italic>\n                      and\n                      <jats:italic>Nocardia<\/jats:italic>\n                      , are proposed to play an important role against\n                      <jats:italic>V. dahliae<\/jats:italic>\n                      within the indigenous olive root microbiota under natural conditions. Moreover, our findings underscore the importance of studying keystone taxa along with essential host plant genes to holistically understand plant-microbiota interactions and explore their potential in disease management. This integrative approach provides insights into the complex dialogue taking place between the host plant and its microbiota, offering potential targets for microbiome engineering to enhance olive resilience against VWO.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40168-025-02216-5","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T15:36:50Z","timestamp":1764171410000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Unveiling essential host genes and keystone microorganisms of the olive tree holobiont linked to Verticillium wilt tolerance"],"prefix":"10.1186","volume":"13","author":[{"given":"Antonio J.","family":"Fern\u00e1ndez-Gonz\u00e1lez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alicia","family":"Serrano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Luque","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Fern\u00e1ndez-L\u00f3pez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jes\u00fas","family":"Mercado-Blanco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"2216_CR1","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/s41579-020-0412-1","volume":"18","author":"P Trivedi","year":"2020","unstructured":"Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. 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Enormous sequencing depth can be necessary to compile sufficient information for identification of a certain pathogen. Generic HTS combining with in-solution capture enrichment can markedly increase the sensitivity for virus detection in complex diagnostic samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A virus panel based on the principle of biotinylated RNA baits was developed for specific capture enrichment of epizootic and zoonotic viruses (VirBaits). The VirBaits set was supplemented by a SARS-CoV-2 predesigned bait set for testing recent SARS-CoV-2-positive samples. Libraries generated from complex samples were sequenced via generic HTS (without enrichment) and afterwards enriched with the VirBaits set. For validation, an internal proficiency test for emerging epizootic and zoonotic viruses (African swine fever virus, Ebolavirus, Marburgvirus, Nipah henipavirus, Rift Valley fever virus) was conducted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The VirBaits set consists of 177,471 RNA baits (80-mer) based on about 18,800 complete viral genomes targeting 35 epizootic and zoonotic viruses. In all tested samples, viruses with both DNA and RNA genomes were clearly enriched ranging from about 10-fold to 10,000-fold for viruses including distantly related viruses with at least 72% overall identity to viruses represented in the bait set. Viruses showing a lower overall identity (38% and 46%) to them were not enriched but could nonetheless be detected based on capturing conserved genome regions. The internal proficiency test supports the improved virus detection using the combination of HTS plus targeted enrichment but also points to the risk of cross-contamination between samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The VirBaits approach showed a high diagnostic performance, also for distantly related viruses. The bait set is modular and expandable according to the favored diagnostics, health sector, or research question. The risk of cross-contamination needs to be taken into consideration. The application of the RNA-baits principle turned out to be user friendly, and even non-experts can easily use the VirBaits workflow. 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However, the virome composition in anaerobic digestion is still under-investigated. A viral induction experiment was conducted on separate batches undergoing a series of DNA-damaging stresses, in order to coerce temperate viruses to enter the lytic cycle.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The sequencing of the metagenome revealed a viral community almost entirely composed of tailed bacteriophages of the order\n                      <jats:italic>Caudovirales<\/jats:italic>\n                      . Following a binning procedure 1,092 viral and 120 prokaryotic genomes were reconstructed, 64 of which included an integrated prophage in their sequence.\n                    <\/jats:p>\n                    <jats:p>\n                      Clustering of coverage profiles revealed the presence of species, both viral and microbial, sharing similar reactions to shocks. A group of viral genomes, which increase under organic overload and decrease under basic pH, uniquely encode the\n                      <jats:italic>yopX<\/jats:italic>\n                      gene, which is involved in the induction of temperate prophages. Moreover, the in-silico functional analysis revealed an enrichment of sialidases in viral genomes. These genes are associated with tail proteins and, as such, are hypothesised to be involved in the interaction with the host.\n                      <jats:italic>Archaea<\/jats:italic>\n                      registered the most pronounced changes in relation to shocks and featured behaviours not shared with other species. Subsequently, data from 123 different samples of the global anaerobic digestion database was used to determine coverage profiles of host and viral genomes on a broader scale.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Viruses are key components in anaerobic digestion environments, shaping the microbial guilds which drive the methanogenesis process. In turn, environmental conditions are pivotal in shaping the viral community and the rate of induction of temperate viruses. This study provides an initial insight into the complexity of the anaerobic digestion virome and its relation with the microbial community and the diverse environmental parameters.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40168-022-01316-w","type":"journal-article","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T21:02:28Z","timestamp":1660510948000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Analysis of the anaerobic digestion metagenome under environmental stresses stimulating prophage induction"],"prefix":"10.1186","volume":"10","author":[{"given":"Alessandro","family":"Rossi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Silvia","family":"Morlino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Gaspari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arianna","family":"Basile","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Kougias","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Treu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Campanaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"key":"1316_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2021.125915","volume":"341","author":"S Dutta","year":"2021","unstructured":"Dutta S, He M, Xiong X, Tsang DCW. 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In order to understand this, the salivary microbiome was analyzed with 16S rRNA gene amplicon sequencing, and saliva viral titers were analyzed with quantitative polymerase chain reaction (qPCR) with primers specific for Epstein-Barr virus (EBV), herpes simplex virus (HSV), and varicella zoster virus (VZV) from 10 astronauts pre-flight, in-flight, and post-flight.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      <jats:italic>Streptococcus<\/jats:italic>\n                      was the most abundant organism in the saliva, making up 8% of the total organisms detected, and their diversity decreased during spaceflight. Other organisms that had statistically significant changes were\n                      <jats:italic>Proteobacteria<\/jats:italic>\n                      and\n                      <jats:italic>Fusobacteria<\/jats:italic>\n                      which increased during flight and\n                      <jats:italic>Actinobacteria<\/jats:italic>\n                      which decreased during flight. At the genus level,\n                      <jats:italic>Catonella<\/jats:italic>\n                      ,\n                      <jats:italic>Megasphera<\/jats:italic>\n                      , and\n                      <jats:italic>Actinobacillus<\/jats:italic>\n                      were absent in more than half of saliva samples collected pre-flight but were then detected during flight. In those subjects that already had these genera pre-flight, their relative abundances increased during flight. Correlation analyses between the microbiome and viral titers revealed a positive correlation with\n                      <jats:italic>Gracilibacteria<\/jats:italic>\n                      ,\n                      <jats:italic>Absconditabacteria<\/jats:italic>\n                      , and\n                      <jats:italic>Abiotrophia<\/jats:italic>\n                      and a negative correlation between\n                      <jats:italic>Oribacterium<\/jats:italic>\n                      ,\n                      <jats:italic>Veillonella<\/jats:italic>\n                      , and\n                      <jats:italic>Haemophilus<\/jats:italic>\n                      . There was also a significant positive correlation between microbiome richness and EBV viral titers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This is the first study to look at how the salivary microbiome changes as a result of spaceflight and the search for bacterial biomarkers for viral reactivation. Further studies examining the role of specific organisms that were shown to be correlative and predictive in viral reactivation, a serious problem in astronauts during spaceflight, could lead to mitigation strategies to help prevent disease during both short and long duration space missions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40168-020-00830-z","type":"journal-article","created":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T13:04:08Z","timestamp":1587387848000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["The influence of spaceflight on the astronaut salivary microbiome and the search for a microbiome biomarker for viral reactivation"],"prefix":"10.1186","volume":"8","author":[{"given":"Camilla","family":"Urbaniak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hernan","family":"Lorenzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Thissen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Crystal","family":"Jaing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Crucian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clarence","family":"Sams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duane","family":"Pierson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kasthuri","family":"Venkateswaran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satish","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"key":"830_CR1","unstructured":"WHO NCD Surveillance strategy [https:\/\/www.who.int\/ncd_surveillance\/strategy\/en\/]."},{"issue":"10152","key":"830_CR2","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1016\/S0140-6736(18)31992-5","volume":"392","author":"NCD Countdown 2030","year":"2018","unstructured":"NCD Countdown 2030. 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