Animals and plants host diverse microbial communities that are vital for their survival. In fact, the host organisms and their associated ‘microbiome’ are so closely linked that they are often described as a single entity: the holobiont unit. This suggests that when the host adapts to cope with stressful conditions, similar changes should also occur in its microbiome.

Fish are unable to maintain a stable body temperature and can be greatly affected by temperature fluctuations. Some fish are better able to tolerate cold conditions than others, but it was not known if their gut microbes are similarly affected by changes in temperature.

To investigate, Kokou et al. selectively bred tropical blue tilapia to create families of fish that could either tolerate the cold well, or that were highly sensitive to the cold. The gut microbiomes of cold-resistant fish were different from the cold-sensitive ones, even though the fish lived in the same tank. Moreover, the gut microbiomes of the cold-tolerant fish showed higher resilience to temperature changes than the microbes in the guts of the cold-sensitive fish.

It remains to be determined whether the response of the microbiome directly affects how its host fish responds to temperature changes. However, the results presented by Kokou et al. show that there are links between how the host and its microbes adapt to environmental stress. As well as helping us to understand how holobionts evolved, this knowledge could also potentially be applied broadly in clinical sciences or agriculture, for example to select for efficient crops.

Cold temperature is an environmental challenge that greatly affects metabolic and physiological processes. Thus, adaptations to temperature fluctuations are expected to be found across all organisms. When exposed to cold temperatures, animals undergo remarkable physiological adjustments in order to maintain homeostasis. Such adjustments may occur through genetic and/or non-genetic mechanisms that increase their fitness. Recent work in mice revealed that the gut microbiome facilitates key adaptations during cold exposure by promoting energy demand-driven regulation (Chevalier et al., 2015; Rosenberg and Zilber-Rosenberg, 2018). This is not surprising, as many studies have already proven the vital importance of gut microbial communities for host survival, homeostasis, development and functioning (McFall-Ngai et al., 2013; Shabat et al., 2016). In fact, the universality of host–microbe associations, either transient or tight, inspired the hologenome concept (Bordenstein and Theis, 2015; Brucker and Bordenstein, 2013; Rosenberg and Zilber-Rosenberg, 2013; Rosenberg and Zilber-Rosenberg, 2016; Rosenberg and Zilber-Rosenberg, 2018; Theis et al., 2016), which proposes that within the holobiont, units at different levels, such as genes, chromosomes or biont combinations (i.e., host and microbes), are subject to selection or neutrality. Within this concept, host–microbe interactions have an important role in the host's physiology, whilst microbiome composition may be affected by host selection. Taking this into account, we hypothesized that host selection may facilitate changes in the host-associated microbial species and in their response to environmental selection pressure.

Poikilothermic organisms, such as fish, must develop strategies to maintain homeostasis within diverse temperature gradients because environmental temperature changes would otherwise disrupt homeostasis and cause deleterious effects on vital physiological functions (Guschina and Harwood, 2006; Vinagre et al., 2012). The physiological response to stress may vary among populations or individuals and can be affected by various factors (Nitzan et al., 2016; Somero, 2010). Microbial communities in aquatic environments have also been reported to be affected by abiotic factors that act as a habitat-filtering force, such as temperature (Fuhrman et al., 2008). In poikilothermic organisms, the gut microbiome experiences temperature fluctuations that do not exist in homeotherms. There is evidence of a temperature effect on the microbial dynamics associated with invertebrate species (Beleneva and Zhukova, 2009; Carlos et al., 2013; Erwin et al., 2012; Lokmer and Mathias Wegner, 2015; Mahalaxmi et al., 2013; Preheim et al., 2011; Zurel et al., 2011), but such information is largely missing for aquatic poikilothermic vertebrates.

Taking the hologenome concept into account, we asked whether host transgenerational selection for thermal tolerance, which is instrumental to poikilothermic organisms’ fitness, involves the host–gut microbiome axis. More specifically, how does microbiome composition respond to temperature changes and does host tolerance shape this response? Here, we hypothesized that the thermal tolerance of poikilothermic vertebrates affects their associated gut microbial species, as well as the response of these microbes to temperature alterations. We focused specifically on the association of changes in the microbiome with the transgenerational low-temperature response in tropical fish, such as tilapia species. The growth of these fish species is highly affected by temperature; they are subject to growth inhibition and mortality when exposed to low temperatures (Cnaani et al., 2000). However, recent work revealed transgenerational inheritance of cold tolerance in these species (Nitzan et al., 2016). Therefore, we used a unique setup, composed of 66 fish families that are part of a transgenerational selective-breeding scheme for enhanced cold tolerance (Figure 1). In this setup, we chose fish progeny from families with the most extreme phenotypes, selected on the basis of the survival rate of their siblings at low temperatures and characterized as cold-resistant or cold-sensitive. Hence, these fish progenies had never experienced low temperature exposure before they were challenged. We used this setup to understand how selection on host thermal tolerance corresponds to microbiome shifts. Our results suggest that host selection for cold tolerance is followed by changes in the gut microbiome, which are manifested by higher resilience to temperature shifts and which may be a key factor in orchestrating cold acclimation in poikilothermic animals.

Figure 1 with 1 supplement see all

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Within the hologenome concept, host–microbe interactions are connected and make up a unit of natural selection (Rosenberg and Zilber-Rosenberg, 2013; Rosenberg and Zilber-Rosenberg, 2016; Rosenberg and Zilber-Rosenberg, 2018; Theis et al., 2016). Inspired by this concept, we hypothesized that this association between the host and its microbes may relate to the host's response to environmental changes, such as exposure to low temperature. Cold exposure led to dramatic changes in gut microbiome composition, representing a notable determinant of the microbial communities, by increasing several orders of Proteobacteria (Vibrionales and Alteromonadales) and decreasing all other phyla (Figure 2). In particular, some species of the order Alteromonadales have been shown to facilitate adaptations for survival under different conditions, including low temperatures (Alteromonas [Math et al., 2012]; Shewanella [Abboud et al., 2005]), whereas several Vibrionales species have been found to adapt to low temperatures, such as Vibrio cholerae (Townsley et al., 2016), which was enriched under our cold conditions. These changes in microbial composition also led to a substantial change in the functional profile of the gut microbial communities. Notably, our analysis revealed that pathways related to metabolism were depleted during cold exposure, in both the microbiome and host transcriptomic response (Figure 3; Figure 3—figure supplement 1), whereas KEGG (Kyoto Encyclopedia of Genes and Genomes) orthologs related to cell communication, membrane transport and structure, signaling and cell motility were enriched, indicating a potential overall stress response (Nachin et al., 2005; Tsimring et al., 1995) (Figure 3). Interestingly, such changes have also been associated with ecological fitness traits (i.e., membrane transport, signal-transduction genes, tRNA diversity, carbon metabolism) (Math et al., 2012) that assist in microbial survival strategies during cold adaptation in some Alteromonadales speciesMath et al., 2012. It should be noted that the enrichment of microbial taxonomic groups that are associated with fish hosts and temperature changes, such as the Proteobacteria orders, potentially resulted in the enrichment of functions associated with these taxa, while not necessarily being connected to cold tolerance. However, we did find several of these functions that were not only shared between the host and the microbiome but also similarly affected in the host and microbiome after cold exposure (Figure 3C; Figure 3—figure supplement 1B). Some of the functions were actually previously reported as key cell metabolism cold-induced adaptations in mice (i.e. downregulation of peroxisome proliferator-activated receptor (PPAR) signaling and cytochrome-450-related pathways; [Shore et al., 2013]), thus suggesting a potential conserved response to low temperatures.

Motivated by the hologenome concept, we deduced that the microbiome response to low temperatures would also be associated with the host response and would be related to host thermal tolerance. We used two different groups of tilapias originating from mothers with different tolerance to low water temperatures (Nitzan et al., 2016). Interestingly, we found agreement between transcriptomic and microbiome responses to cold exposure, marking a host–microbiome interaction that potentially leads to higher microbiome stability and lower physiological stress in the cold-resistant fish (Figure 4). As these animals originate from different families (crosses) and not from the same population, and were kept under the same experimental conditions (same tanks) across generations, we conclude that this effect originates from differences in the genetic background, thermal tolerance and acclimation of the fish.

The decrease in microbiome richness and diversity with cold exposure in both resistant and sensitive fish could be explained in terms of selective pressure (Figure 2A). As these fish are poikilothermic organisms, their gut microbiome is directly exposed to water temperature. Warmer temperatures, which are optimal for tilapias, are more permissive and can lead to greater heterogeneity and richness of microbiome composition (Erwin et al., 2012)(Figure 2A,B). Alternatively, a decrease in temperature imposes a selective pressure that reduces microbial diversity, as we observed in both resistant and sensitive fish (Chevalier et al., 2015; Lokmer and Mathias Wegner, 2015). Intriguingly, our findings show host control of microbiome composition and response to temperature differences. Under warm conditions, we found compositional microbiome differences between the two host groups (Figure 2B,C,D), highlighting the importance of host genetic background for microbiome composition. Nonetheless, in the transition to cold temperature, these differences in the microbiome responses in sensitive and resistant fish were manifested at the taxonomic and functional levels (Figure 2B,D,E), through enrichment of taxa with potential ecological fitness traits for cold adaptation or by affecting the cold sensitivity of the core microbial communities to temperature, and thus illustrating that the pre-exposure potential of the gut microbiome is due to host selection.

Cold exposure greatly affects metabolic and physiological processes in fish, which undergo several acclimation processes to maintain their fitness. The transcriptomic or physiological responses may include changes in enzymatic activity and efficiency, membrane permeability, and metabolic rate through mitochondrial energy production (Battersby and Moyes, 1998; Itoi et al., 2003), and may vary with the life history or genetic background of the organism. Transcriptomic responses in the blue tilapia liver were in line with the microbiome response to temperature, both in the size of the effect in resistant and sensitive fish (Figure 4; Figure 4—figure supplements 1–2) and in its nature, manifested as changes in functions related to metabolism and signaling (Figure 3—figure supplement 1). Resistant fish seemed to be under lower levels of physiological stress than sensitive fish, and therefore had lower temperature-related metabolic demands. These results could potentially be connected to lower energetic demands for maintaining homeostasis. In fact, it was previously shown that these resistant fish have lower mitochondrial gene expression than their sensitive counterparts (Nitzan et al., 2016). Agreement between host transcriptomic and microbiome response, showing higher stability in the resistant animals compared to the sensitive ones (Figure 4; Figure 4—figure supplements 1–2), supports our hypothesis of host–microbe associations that potentially modulate the acclimation process.

Conclusions

Our insight into the microbial dynamics of the blue tilapia gut microbiome in response to environmental conditions shows that temperature, host genetic background and tolerance to thermal stress are major determinants of community structure and dynamics. Microbial community diversity and richness severely decreased during cold exposure; but microbial response was related to host thermal tolerance, which affected microbiome stability (Figure 5). These results indicate that host cold tolerance shapes not only gut microbiome composition but also the sensitivity of core microbial communities to temperature, and that host cold tolerance modulates the thermal acclimation of the gut microbiome. These finding are consistent with the hologenome concept, which proposes that associations exist between the microbes and their hosts, suggesting that together they consist a unit of natural selection. Thus, taking this concept into account, we highlight potential connections between host acclimation and its microbiome response to environmental stress.

Figure 5

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Data has been deposited in the SRA under accession code SRP131209.

1. 1. Fotini Kokou
   2. Goor Sasson
   3. Itzhak Mizrahi

   (2018) NCBI Sequence Read Archive

   ID SRP131209. Blue tilapia gut microbiome.

   https://www.ncbi.nlm.nih.gov/sra/SRP131209

1. 1. Abboud R
   2. Popa R
   3. Souza-Egipsy V
   4. Giometti CS
   5. Tollaksen S
   6. Mosher JJ
   7. Findlay RH
   8. Nealson KH

   (2005) Low-temperature growth of Shewanella oneidensis MR-1

   Applied and Environmental Microbiology 71:811–816.

   https://doi.org/10.1128/AEM.71.2.811-816.2005

   • PubMed
   • Google Scholar
2. 1. Anderson MJ

   (2001)

   A new method for non‐parametric multivariate analysis of variance

   Austral Ecology 26:32–46.

   • Google Scholar
3. 1. Bates DM
   2. Pinheiro JC

   (1998) Linear and nonlinear mixed-effects models

   Conference on Applied Statistics in Agriculture.

   https://doi.org/10.4148/2475-7772.1273

   • Google Scholar
4. 1. Battersby BJ
   2. Moyes CD

   (1998) Influence of acclimation temperature on mitochondrial DNA, RNA, and enzymes in skeletal muscle

   American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 275:R905–R912.

   https://doi.org/10.1152/ajpregu.1998.275.3.R905

   • Google Scholar
5. 1. Beleneva IA
   2. Zhukova NV

   (2009) Seasonal dynamics of cell numbers and biodiversity of marine heterotrophic bacteria inhabiting invertebrates and water ecosystems of the Peter the Great Bay, Sea of Japan

   Microbiology 78:369–375.

   https://doi.org/10.1134/S0026261709030163

   • Google Scholar
6. 1. Benjamini Y
   2. Hochberg Y

   (1995)

   Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society

   Series B pp. 289–300.

   • Google Scholar
7. 1. Bordenstein SR
   2. Theis KR

   (2015) Host biology in light of the microbiome: ten principles of holobionts and hologenomes

   PLOS Biology 13:e1002226.

   https://doi.org/10.1371/journal.pbio.1002226

   • PubMed
   • Google Scholar
8. 1. Brucker RM
   2. Bordenstein SR

   (2013) The capacious hologenome

   Zoology 116:260–261.

   https://doi.org/10.1016/j.zool.2013.08.003

   • PubMed
   • Google Scholar
9. 1. Butts C

   (2012)

   Yacca: yet another canonical correlation analysis package

   R package version.

   • Google Scholar
10. 1. Callahan BJ
    2. McMurdie PJ
    3. Rosen MJ
    4. Han AW
    5. Johnson AJ
    6. Holmes SP

    (2016) DADA2: High-resolution sample inference from Illumina amplicon data

    Nature Methods 13:581–583.

    https://doi.org/10.1038/nmeth.3869

    • PubMed
    • Google Scholar
11. 1. Carlos C
    2. Torres TT
    3. Ottoboni LM

    (2013) Bacterial communities and species-specific associations with the mucus of Brazilian coral species

    Scientific Reports 3:1624.

    https://doi.org/10.1038/srep01624

    • PubMed
    • Google Scholar
12. 1. Chevalier C
    2. Stojanović O
    3. Colin DJ
    4. Suarez-Zamorano N
    5. Tarallo V
    6. Veyrat-Durebex C
    7. Rigo D
    8. Fabbiano S
    9. Stevanović A
    10. Hagemann S
    11. Montet X
    12. Seimbille Y
    13. Zamboni N
    14. Hapfelmeier S
    15. Trajkovski M

    (2015) Gut microbiota orchestrates energy homeostasis during cold

    Cell 163:1360–1374.

    https://doi.org/10.1016/j.cell.2015.11.004

    • PubMed
    • Google Scholar
13. 1. Cnaani A
    2. Gall GAE
    3. Hulata G

    (2000) Cold tolerance of tilapia species and hybrids

    Aquaculture International 8:289–298.

    https://doi.org/10.1023/A:100929910

    • Google Scholar
14. 1. Dufrene M
    2. Legendre P

    (1997) Species Assemblages and Indicator Species: The Need for a Flexible Asymmetrical Approach

    Ecological Monographs 67:345–366.

    https://doi.org/10.2307/2963459

    • Google Scholar
15. 1. Dufrêne M
    2. Legendre P

    (1997)

    Species assemblages and indicator species:the need for a flexible asymmetrical approach

    Ecological Monographs 67:345–366.

    • Google Scholar
16. 1. Erwin PM
    2. Pita L
    3. López-Legentil S
    4. Turon X

    (2012) Stability of sponge-associated bacteria over large seasonal shifts in temperature and irradiance

    Applied and Environmental Microbiology 78:7358–7368.

    https://doi.org/10.1128/AEM.02035-12

    • PubMed
    • Google Scholar
17. 1. Fuhrman JA
    2. Steele JA
    3. Hewson I
    4. Schwalbach MS
    5. Brown MV
    6. Green JL
    7. Brown JH

    (2008) A latitudinal diversity gradient in planktonic marine bacteria

    PNAS 105:7774–7778.

    https://doi.org/10.1073/pnas.0803070105

    • PubMed
    • Google Scholar
18. 1. Giatsis C
    2. Sipkema D
    3. Smidt H
    4. Heilig H
    5. Benvenuti G
    6. Verreth J
    7. Verdegem M

    (2016) The impact of rearing environment on the development of gut microbiota in tilapia larvae

    Scientific Reports 5:e18206.

    https://doi.org/10.1038/srep18206

    • Google Scholar
19. Website

    1. Gordon A
    2. Hannon G

    (2010) Fastx-toolkit. FASTQ/A short-reads preprocessing tools (unpublished)

    Accessed September 2017.

    http://hannonlab. cshl. edu/fastx_toolkit 5.
20. 1. Guschina IA
    2. Harwood JL

    (2006) Mechanisms of temperature adaptation in poikilotherms

    FEBS Letters 580:5477–5483.

    https://doi.org/10.1016/j.febslet.2006.06.066

    • PubMed
    • Google Scholar
21. 1. Haygood AM
    2. Jha R

    (2016) Strategiesto modulate the intestinal microbiota of tilapia (oreochromis sp.) in aquaculture: a review

    Reviews in Aquaculture.

    https://doi.org/10.1111/raq.12162

    • Google Scholar
22. 1. Huang daW
    2. Sherman BT
    3. Lempicki RA

    (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

    Nature Protocols 4:44–57.

    https://doi.org/10.1038/nprot.2008.211

    • PubMed
    • Google Scholar
23. 1. Itoi S
    2. Kinoshita S
    3. Kikuchi K
    4. Watabe S

    (2003) Changes of carp FoF1-ATPase in association with temperature acclimation

    American Journal of Physiology. Regulatory, Integrative and Comparative Physiology 284:R153–R163.

    https://doi.org/10.1152/ajpregu.00182.2002

    • PubMed
    • Google Scholar
24. 1. Kanehisa M
    2. Goto S

    (2000) KEGG: kyoto encyclopedia of genes and genomes

    Nucleic Acids Research 28:27–30.

    https://doi.org/10.1093/nar/28.1.27

    • PubMed
    • Google Scholar
25. 1. Kokou F
    2. Adamidou S
    3. Karacostas I
    4. Sarropoulou E

    (2016) Sample size matters in dietary gene expression studies—A case study in the gilthead sea bream ( Sparus aurata L.)

    Aquaculture Reports 3:82–87.

    https://doi.org/10.1016/j.aqrep.2015.12.004

    • Google Scholar
26. 1. Kopylova E
    2. Noé L
    3. Touzet H

    (2012) SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data

    Bioinformatics 28:3211–3217.

    https://doi.org/10.1093/bioinformatics/bts611

    • PubMed
    • Google Scholar
27. 1. Langille MG
    2. Zaneveld J
    3. Caporaso JG
    4. McDonald D
    5. Knights D
    6. Reyes JA
    7. Clemente JC
    8. Burkepile DE
    9. Vega Thurber RL
    10. Knight R
    11. Beiko RG
    12. Huttenhower C

    (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

    Nature Biotechnology 31:814–821.

    https://doi.org/10.1038/nbt.2676

    • PubMed
    • Google Scholar
28. Book

    1. Lauro FM
    2. Allen MA
    3. Wilkins D
    4. Williams TJ
    5. Cavicchioli R

    (2011) Psychrophiles: Genetics, Genomics, Evolution

    In: Horikoshi K, editors. Extremophiles Handbook. Japan: Springer. pp. 865–890.

    https://doi.org/10.1007/978-4-431-53898-1_42

    • Google Scholar
29. 1. Li B
    2. Dewey CN

    (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

    BMC Bioinformatics 12:323.

    https://doi.org/10.1186/1471-2105-12-323

    • PubMed
    • Google Scholar
30. 1. Lokmer A
    2. Mathias Wegner K

    (2015) Hemolymph microbiome of Pacific oysters in response to temperature, temperature stress and infection

    The ISME Journal 9:670–682.

    https://doi.org/10.1038/ismej.2014.160

    • PubMed
    • Google Scholar
31. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
32. 1. Mahalaxmi B
    2. Revathy K
    3. Raghunathan C
    4. Anjalai K
    5. Suabshini A

    (2013)

    Distribution of microbial population associated with crabs from ennore seacoast bay of bengal north east coast of india

    International Journal of Current Microbiology and Applied Sciences 2:290–305.

    • Google Scholar
33. 1. Math RK
    2. Jin HM
    3. Kim JM
    4. Hahn Y
    5. Park W
    6. Madsen EL
    7. Jeon CO

    (2012) Comparative genomics reveals adaptation by Alteromonas sp. SN2 to marine tidal-flat conditions: cold tolerance and aromatic hydrocarbon metabolism

    PLOS ONE 7:e35784.

    https://doi.org/10.1371/journal.pone.0035784

    • PubMed
    • Google Scholar
34. 1. McDonald D
    2. Price MN
    3. Goodrich J
    4. Nawrocki EP
    5. DeSantis TZ
    6. Probst A
    7. Andersen GL
    8. Knight R
    9. Hugenholtz P

    (2012) An improved greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea

    The ISME Journal 6:610–618.

    https://doi.org/10.1038/ismej.2011.139

    • PubMed
    • Google Scholar
35. 1. McFall-Ngai M
    2. Hadfield MG
    3. Bosch TC
    4. Carey HV
    5. Domazet-Lošo T
    6. Douglas AE
    7. Dubilier N
    8. Eberl G
    9. Fukami T
    10. Gilbert SF
    11. Hentschel U
    12. King N
    13. Kjelleberg S
    14. Knoll AH
    15. Kremer N
    16. Mazmanian SK
    17. Metcalf JL
    18. Nealson K
    19. Pierce NE
    20. Rawls JF
    21. Reid A
    22. Ruby EG
    23. Rumpho M
    24. Sanders JG
    25. Tautz D
    26. Wernegreen JJ

    (2013) Animals in a bacterial world, a new imperative for the life sciences

    PNAS 110:3229–3236.

    https://doi.org/10.1073/pnas.1218525110

    • PubMed
    • Google Scholar
36. 1. Möller AM
    2. Korytář T
    3. Köllner B
    4. Schmidt-Posthaus H
    5. Segner H

    (2014) The teleostean liver as an immunological organ: intrahepatic immune cells (ihics) in healthy and benzo[a]pyrene challenged rainbow trout (oncorhynchus mykiss)

    Developmental & Comparative Immunology 46:518–529.

    https://doi.org/10.1016/j.dci.2014.03.020

    • PubMed
    • Google Scholar
37. 1. Nachin L
    2. Nannmark U
    3. Nyström T

    (2005) Differential roles of the universal stress proteins of Escherichia coli in oxidative stress resistance, adhesion, and motility

    Journal of Bacteriology 187:6265–6272.

    https://doi.org/10.1128/JB.187.18.6265-6272.2005

    • PubMed
    • Google Scholar
38. 1. Nitzan T
    2. Slosman T
    3. Gutkovich D
    4. Weller JI
    5. Hulata G
    6. Zak T
    7. Benet A
    8. Cnaani A

    (2016) Maternal effects in the inheritance of cold tolerance in blue tilapia (Oreochromis aureus)

    Environmental Biology of Fishes 99:975–981.

    https://doi.org/10.1007/s10641-016-0539-0

    • Google Scholar
39. 1. Preheim SP
    2. Boucher Y
    3. Wildschutte H
    4. David LA
    5. Veneziano D
    6. Alm EJ
    7. Polz MF

    (2011) Metapopulation structure of vibrionaceae among coastal marine invertebrates

    Environmental Microbiology 13:265–275.

    https://doi.org/10.1111/j.1462-2920.2010.02328.x

    • PubMed
    • Google Scholar
40. 1. Raymond-Bouchard I
    2. Whyte LG

    (2017) From transcriptomes to metatranscriptomes: cold adaptation and active metabolisms of psychrophiles from cold environments, in: R

    Margesin (Ed.), Psychrophiles: From Biodiversity to Biotechnology. Springer International Publishing, Cham pp. 437–457.

    https://doi.org/10.1007/978-3-319-57057-0_18

    • Google Scholar
41. 1. Roeselers G
    2. Mittge EK
    3. Stephens WZ
    4. Parichy DM
    5. Cavanaugh CM
    6. Guillemin K
    7. Rawls JF

    (2011) Evidence for a core gut microbiota in the zebrafish

    The ISME Journal 5:1595–1608.

    https://doi.org/10.1038/ismej.2011.38

    • PubMed
    • Google Scholar
42. 1. Rosenberg E
    2. Zilber-Rosenberg I

    (2013) The hologenome concept: human, animal and plant microbiota

    Springer.

    https://doi.org/10.1007/978-3-319-04241-1

    • Google Scholar
43. 1. Rosenberg E
    2. Zilber-Rosenberg I

    (2016) Microbes drive evolution of animals and plants: the hologenome concept

    mBio 7:e01395–e01315.

    https://doi.org/10.1128/mBio.01395-15

    • PubMed
    • Google Scholar
44. 1. Rosenberg E
    2. Zilber-Rosenberg I

    (2018) The hologenome concept of evolution after 10 years

    Microbiome 6:78.

    https://doi.org/10.1186/s40168-018-0457-9

    • PubMed
    • Google Scholar
45. 1. Schulte PM
    2. Healy TM
    3. Fangue NA

    (2011) Thermal performance curves, phenotypic plasticity, and the time scales of temperature exposure

    Integrative and Comparative Biology 51:691–702.

    https://doi.org/10.1093/icb/icr097

    • PubMed
    • Google Scholar
46. 1. Segata N
    2. Izard J
    3. Waldron L
    4. Gevers D
    5. Miropolsky L
    6. Garrett WS
    7. Huttenhower C

    (2011) Metagenomic biomarker discovery and explanation

    Genome Biology 12:R60.

    https://doi.org/10.1186/gb-2011-12-6-r60

    • PubMed
    • Google Scholar
47. 1. Shabat SK
    2. Sasson G
    3. Doron-Faigenboim A
    4. Durman T
    5. Yaacoby S
    6. Berg Miller ME
    7. White BA
    8. Shterzer N
    9. Mizrahi I

    (2016) Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants

    The ISME Journal 10:2958–2972.

    https://doi.org/10.1038/ismej.2016.62

    • PubMed
    • Google Scholar
48. 1. Shore AM
    2. Karamitri A
    3. Kemp P
    4. Speakman JR
    5. Graham NS
    6. Lomax MA

    (2013) Cold-induced changes in gene expression in brown adipose tissue, white adipose tissue and liver

    PLOS ONE 8:e68933.

    https://doi.org/10.1371/journal.pone.0068933

    • PubMed
    • Google Scholar
49. 1. Somero GN

    (2010) The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine 'winners' and 'losers'

    Journal of Experimental Biology 213:912–920.

    https://doi.org/10.1242/jeb.037473

    • PubMed
    • Google Scholar
50. 1. Sun H
    2. Jami E
    3. Harpaz S
    4. Mizrahi I

    (2013) Involvement of dietary salt in shaping bacterial communities in european sea bass (dicentrarchus labrax)

    Scientific Reports 3:e1558.

    https://doi.org/10.1038/srep01558

    • Google Scholar
51. 1. Theis KR
    2. Dheilly NM
    3. Klassen JL
    4. Brucker RM
    5. Baines JF
    6. Bosch TC
    7. Cryan JF
    8. Gilbert SF
    9. Goodnight CJ
    10. Lloyd EA
    11. Sapp J
    12. Vandenkoornhuyse P
    13. Zilber-Rosenberg I
    14. Rosenberg E
    15. Bordenstein SR

    (2016) Getting the hologenome concept right: an eco-evolutionary framework for hosts and their microbiomes

    mSystems 1:.

    https://doi.org/10.1128/mSystems.00028-16

    • PubMed
    • Google Scholar
52. 1. Townsley L
    2. Sison Mangus MP
    3. Mehic S
    4. Yildiz FH

    (2016) Response of vibrio cholerae to low-temperature shifts: cspv regulation of type vi secretion, biofilm formation, and association with zooplankton

    Applied and Environmental Microbiology 82:4441–4452.

    https://doi.org/10.1128/AEM.00807-16

    • PubMed
    • Google Scholar
53. Book

    1. Trewavas E

    (1983) Tilapiine Fishes of the Genera Sarotherodon, Oreochromis and Danakilia

    British Museum.

    https://doi.org/10.5962/bhl.title.123198

    • Google Scholar
54. 1. Tsimring L
    2. Levine H
    3. Aranson I
    4. Ben-Jacob E
    5. Cohen I
    6. Shochet O
    7. Reynolds WN

    (1995) Aggregation patterns in stressed bacteria

    Physical Review Letters 75:1859–1862.

    https://doi.org/10.1103/PhysRevLett.75.1859

    • PubMed
    • Google Scholar
55. 1. Tsuchiya C
    2. Sakata T
    3. Sugita H

    (2008) Novel ecological niche of cetobacterium somerae, an anaerobic bacterium in the intestinal tracts of freshwater fish

    Letters in Applied Microbiology 46:43–48.

    https://doi.org/10.1111/j.1472-765X.2007.02258.x

    • PubMed
    • Google Scholar
56. 1. Vinagre C
    2. Madeira D
    3. Narciso L
    4. Cabral HN
    5. Diniz M

    (2012) Effect of temperature on oxidative stress in fish: Lipid peroxidation and catalase activity in the muscle of juvenile seabass, Dicentrarchus labrax

    Ecological Indicators 23:274–279.

    https://doi.org/10.1016/j.ecolind.2012.04.009

    • Google Scholar
57. 1. Wang Q
    2. Garrity GM
    3. Tiedje JM
    4. Cole JR

    (2007) Naive bayesian classifier for rapid assignment of rrna sequences into the new bacterial taxonomy

    Applied and Environmental Microbiology 73:5261–5267.

    https://doi.org/10.1128/AEM.00062-07

    • PubMed
    • Google Scholar
58. 1. Ye J
    2. Coulouris G
    3. Zaretskaya I
    4. Cutcutache I
    5. Rozen S
    6. Madden TL

    (2012) Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction

    BMC Bioinformatics 13:134.

    https://doi.org/10.1186/1471-2105-13-134

    • PubMed
    • Google Scholar
59. 1. Zak T
    2. Deshev R
    3. Benet-Perlberg A
    4. Naor A
    5. Magen I
    6. Shapira Y
    7. Ponzoni RW
    8. Hulata G

    (2014) Genetic improvement of Israeli blue (Jordan) tilapia, Oreochromis aureus (Steindachner), through selective breeding for harvest weight

    Aquaculture Research 45:546–557.

    https://doi.org/10.1111/are.12072

    • Google Scholar
60. 1. Zurel D
    2. Benayahu Y
    3. Or A
    4. Kovacs A
    5. Gophna U

    (2011) Composition and dynamics of the gill microbiota of an invasive indo-pacific oyster in the eastern mediterranean sea

    Environmental Microbiology 13:1467–1476.

    https://doi.org/10.1111/j.1462-2920.2011.02448.x

    • PubMed
    • Google Scholar

Thank you for submitting your article "Support for the hologenome theory as host adaptive capacity shapes and modulates its microbiome response to temperature" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This manuscript by Kokou et al. proposes that Tilapia fish and its microbiome evolve as a unit of selection, based on measured correlations between host genetic makeup and microbiome responses to temperature. The consensus opinion is that, at best, the results claimed by the authors show a relationship (i.e. correlation) between host resistance and microbiome "resilience". This is very interesting, but far from showing support for the hologenome theory. Therefore, the paper needs to be completely rewritten to focus on the experiments actually done and omit the tie-in to the hologenome theory, or transgenerational experiments need to be performed.

Essential revisions:

The authors have measured a microbiome response to temperature within one generation rather than transgenerational changes in the microbiome that would more directly support the hologenome concept of evolution. More generally, it is important to note that the hologenome concept of evolution is an eco-evolutionary hypothesis, while the terms holobiont are hologenome are structural definitions used to define the host-microbiome ecosystem and their genomes.

Although multilevel selection is an interesting concept that could apply to host-microbiome associations, extraordinary claims demand extraordinary evidence, and this paper falls short of providing such evidence. In particular, the study shows no data on evolution, only ecological changes in microbiome composition correlated to host genetics. Multi-level selection or coevolution would require transgenerational experiments to establish, not possible in the two month revision window. Given that this paper is not about multilevel selection, presenting that as the main point of this study appears misleading. This is why the paper should be rewritten to say that it is "consistent with the hologenome theory", with this being either a minor component or omitted entirely, or new data supporting evolutionary adaptation effects need to be provided through new experiments.

Focusing on the main data of the paper, which describes the correlation between host resistance to stress and microbiome composition and response, the main result is expressed in this sentence:

"Interestingly, when fish from both groups were kept under their optimal temperature conditions of 24°C, the sensitive fish exhibited higher microbial diversity (Shannon H') than the resistant ones (Supplementary Figure 2; P = 0.04), suggesting that host adaptive capacity is involved in shaping microbiome composition."

What this shows is that the host genetics and physiology (not its adaptive capacity) conditions the composition of the microbiome, at least in this broad statistical sense. This is a strong result which the authors would expand more to provide some insight into processes and or mechanisms.

Other claims, however, are only weakly supported if at all. Part of the problem is the use of statistical descriptors that do not capture the multiple dimensions of the data. The authors choose to do the analysis of microbiome composition and susceptibility to temperature at the level of non-informative summary statistics, like diversity indexes. These numbers hide a lot of information about what is going on in the data, and the authors derive conclusions from simple statistical comparisons of diversity distributions that are not well supported.

For instance, Figure 2A shows that there are many individuals with similar diversity at 24 and 12°C, but that the 24°C control has a tail of high diversity microbiomes that drives the statistically significant difference between treatment and control. What would happen if we stratify the data by diversity in order to compare individuals with similar diversity indexes in the two families? Would all low-diversity microbiomes behave the same regardless of family? In other words, is the effect we see simply a population average driven by a few more variable individuals, or can it be pinned down to features of the individual microbiome composition? This is an example of how the analysis presented by the authors hides a lot of important information that prevents the reader from getting away with more than a few headlines and no insight into the actual processes.

One of the main points of the paper is that the microbiome of resistant individuals is more "resilient" to changes in temperature. However, if diversity is already low in the resistant family and if temperature stress decreases diversity, it stands to reason that diversity will drop less in the resistant family compared to the sensitive one, whose microbiomes still have more microbial taxa "to spare". In other words, drops in diversity may saturate, either because there is a biological lower bound to it or because the index has a non-linear behavior. To assess "resilience" it would be better to work with the set of taxa that are present in all microbiomes (the core), renormalize the data and study fold changes. Again, the problem described in this paragraph stems from the attempt to study changes in high dimensional species composition profiles with single numbers like richness or Shannon diversity.

Unfortunately, the taxonomic analysis, which could deal with part of the problem, is done only at the level of shallow clades. For instance, most organisms in Figure 2B are proteobacteria. As the authors must know, this is an incredibly diverse set of microbes. Which proteobacteria? The authors should describe fold-changes in taxa at various taxonomic levels, starting with exact sequence variants.

Finally, the functional analysis is strongly biased by taxonomic changes. For example, the statement that the "host selects for taxa with a specific functional landscape that is better suited to cold exposure" is a bold claim lacking supporting evidence, What is shown is simply an enrichment of functions that are more frequently found in proteobacteria. The type of functional categories found have no relationship to anything related to adaptation to low temperatures. Incorporation of David Sloan Wilson's concepts of "selection for" vs. "selection of" traits might be helpful here.

In sum, the finding that the microbiome composition is related to the genetic makeup of the host is interesting, but the authors should dig deeper into processes and, if possible, mechanisms. A better statistical analysis that goes beyond simple summary statistics like average richness and diversity and focuses on specific sequence variants and their relationship to the resistance/sensitivity of individual hosts could make the paper much better. The authors should refrain from making grandiose claims about multilevel selection, microbiome resilience, etc., and simply focus on providing a more in-depth analysis of their data.

Essential revisions:

The authors have measured a microbiome response to temperature within one generation rather than transgenerational changes in the microbiome that would more directly support the hologenome concept of evolution. More generally, it is important to note that the hologenome concept of evolution is an eco-evolutionary hypothesis, while the terms holobiont are hologenome are structural definitions used to define the host-microbiome ecosystem and their genomes.

Although multilevel selection is an interesting concept that could apply to host-microbiome associations, extraordinary claims demand extraordinary evidence, and this paper falls short of providing such evidence. In particular, the study shows no data on evolution, only ecological changes in microbiome composition correlated to host genetics. Multi-level selection or coevolution would require transgenerational experiments to establish, not possible in the two month revision window. Given that this paper is not about multilevel selection, presenting that as the main point of this study appears misleading. This is why the paper should be rewritten to say that it is "consistent with the hologenome theory", with this being either a minor component or omitted entirely, or new data supporting evolutionary adaptation effects need to be provided through new experiments.

The manuscript was adjusted to focus on the experiments done and new analysis was also added according to the reviewers’ suggestions (see below). In addition, the tie-in to the hologenome concept was omitted from the title and moderated to be more conceptual.

Focusing on the main data of the paper, which describes the correlation between host resistance to stress and microbiome composition and response, the main result is expressed in this sentence:

"Interestingly, when fish from both groups were kept under their optimal temperature conditions of 24 °C, the sensitive fish exhibited higher microbial diversity (Shannon H') than the resistant ones (Supplementary Figure 2; P = 0.04), suggesting that host adaptive capacity is involved in shaping microbiome composition."

What this shows is that the host genetics and physiology (not its adaptive capacity) conditions the composition of the microbiome, at least in this broad statistical sense. This is a strong result which the authors would expand more to provide some insight into processes and or mechanisms.

Thank you for this comment. According to your suggestion, we further analysed our data to highlight how host genetics/cold tolerance and physiology condition the microbiome. We first performed a similarity test to compare the overall microbiome composition between the cold-resistant and cold-sensitive hosts using Bray-Curtis metric (non-parametric t-test with 1000 Monte Carlo permutations) at the control conditions (24^oC) to see potential differences stemming from the genetic selection. We found that the microbiome response within the resistant individuals was more similar compared to the sensitive (Figure 2Ci). We further aimed to get a better insight into the microbiome composition of these host groups, thus we compared the microbiome composition between the cold-resistant and cold-sensitive hosts under any temperature condition; more specifically, we asked whether microbiomes of similar (Shannon H’) diversity were also sharing the same microbiome compositions. To this end, we stratified our data and examined individuals from each host group with similar diversity (see in the next comments for more details). This analysis showed that the microbiomes clustered significantly according to host group/tolerance under any temperature conditions (Figure 2—figure supplement 6), thus suggesting that host genetic background (genetic selection for cold tolerance) has a strong effect on shaping the microbiome composition. We next explored microbial taxa associated to genetic background effects and applied the indicator species analysis (this measure identifies habitat-associated species based on their fidelity and relative abundance in different environments; see Materials and methods). We found different microbes to be associated with the cold-resistant and cold-sensitive fish (Figure 2D), namely Cetobacterium somerae species were mostly enriched in the resistant, while a higher diversity of taxa such as Streptococcus luteciae, Bacteroides fragilis, Prevotella sp., Clostridiaceae and Succinivibrionaceae species were enriched in the sensitive (Table S5 in Supplementary file 1). Interestingly, in the cold conditions we found an enrichment of Vibrionales species in the resistant fish, which have been shown to include several psychrophilic taxa or taxa with adaptations for cold tolerance, thus indicating that cold acclimation of the host may be selecting for specific microbes, with some of them potentially carrying fitness traits for cold tolerance (see below for more information).

These results and insights were obtained after we re-analysed all our data using DADA2 (Callahan et al., 2016) as suggested by the reviewers (see below and in the Materials and methods section). Our overall results and figures, generated using the new data at the exact sequence variant level (ESV) originating from DADA2, show the same patterns and conclusions (Figures 2, 3, 4; See also supplementary figures).

Other claims, however, are only weakly supported if at all. Part of the problem is the use of statistical descriptors that do not capture the multiple dimensions of the data. The authors choose to do the analysis of microbiome composition and susceptibility to temperature at the level of non-informative summary statistics, like diversity indexes. These numbers hide a lot of information about what is going on in the data, and the authors derive conclusions from simple statistical comparisons of diversity distributions that are not well supported.

For instance, Figure 2A shows that there are many individuals with similar diversity at 24 and 12 °C, but that the 24 °C control has a tail of high diversity microbiomes that drives the statistically significant difference between treatment and control. What would happen if we stratify the data by diversity in order to compare individuals with similar diversity indexes in the two families? Would all low-diversity microbiomes behave the same regardless of family? In other words, is the effect we see simply a population average driven by a few more variable individuals, or can it be pinned down to features of the individual microbiome composition? This is an example of how the analysis presented by the authors hides a lot of important information that prevents the reader from getting away with more than a few headlines and no insight into the actual processes.

This is a valid point by the reviewers. We thank you for this comment as it allowed us to better present our results and understand our observations.

Following this suggestion, we stratified our data by diversity to compare individuals with similar diversity indices. We selected the 10 most extreme values from both temperatures (lowest and highest Shannon H’ diversity; Table S2 in Supplementary file 1) and we performed multivariate and clustering analysis (Two-way Permanova, Tables S3 and S4 in Supplementary file 1; Principal Coordinate Analysis, Figure 2—figure supplement 6). Our results show that, even when we select individuals with similar diversity indices, either high or low, their posterior gut microbiomes cluster significantly according to temperature and host genetics (tolerance) group (Two-way Permanova in Tables S3 and S4 in Supplementary file 1; Figure 2—figure supplement 6).

In addition, in our attempt to identify specific features that contribute to these changes, we performed the Dufrene-Legendre Indicator Species Analysis (Dufrene and Legendre, 1997; function indval with 1000 permutations, package "labdsv" in R), as described now in the Materials and methods section (subsection “Comparison of gut communities”). This analysis calculates the indicator value of a species in clusters or types, given by the following formula:

IndValij = Specificityij × Fidelityij × 100

where IndVali j is the Indicator Value of an 'i' species in relation to a 'j' type of site; Specificity ij is the proportion of sites of type 'j' with species 'i', and Fidelity ij is the proportion of the number of individuals (abundance) of species 'i' that are in a 'j' type of site.

This analysis showed several ESV features to be host genetics (tolerance)-associated in the low and high diversity microbiomes, as shown in Table S5 in Supplementary file 1. The resistant fish with low diversity microbiomes were more enriched with species of the Vibrionales order, which include taxa that are cold-resistant (Raymond-Bouchard, Whyte, 2017; Townsley et al., 2016), while in the warm temperature, we saw in the high diversity microbiomes an enrichment of Cetobacterium somerae, which has been previously reported in the tilapia gut environment (Tsuchiya et al., 2008; Giatsis et al., 2015; Haygood, Jha, 2016). In the high diversity microbiome of the sensitive group, we observed enrichment of microbes within the Succinivibrionaceae and Clostridiaceae family (unknown species), with the latter being previously reported in several warm water fish species including tilapia, along with Streptococcus luteciae, Bacteroides fragilis and Prevotella species.

Looking at host genetics-associated posterior gut microbiome at different taxonomic levels (Figure 2D; Figure 2—figure supplement 8; Table S6 in Supplementary file 1), similarly we found significant enrichment of the Vibrionales order in the cold-resistant fish, while, as mentioned above, a higher microbial order diversity such as Clostridiales, Methanobacteriales and Actinomycetales in the sensitive fish. Altogether, our results suggest that host genetic background shapes the microbiome composition, with some members that potentially carry fitness traits for cold tolerance (i.e. species from the Vibrionales order).

One of the main points of the paper is that the microbiome of resistant individuals is more "resilient" to changes in temperature. However, if diversity is already low in the resistant family and if temperature stress decreases diversity, it stands to reason that diversity will drop less in the resistant family compared to the sensitive one, whose microbiomes still have more microbial taxa "to spare". In other words, drops in diversity may saturate, either because there is a biological lower bound to it or because the index has a non-linear behavior. To assess "resilience" it would be better to work with the set of taxa that are present in all microbiomes (the core), renormalize the data and study fold changes. Again, the problem described in this paragraph stems from the attempt to study changes in high dimensional species composition profiles with single numbers like richness or Shannon diversity.

Following your suggestion, we assessed the resilience of microbiomes that were present in >50% of the individuals in both temperatures (the core microbiome). We renormalized the data (re-calculated their relative abundance within the core microbiome) and examined the fold changes between the two temperatures within each tolerant group (Figure 2E). Overall, we observed that the fold change of these core microbes was significantly less affected by the temperature change in the resistant fish compared to the sensitive ones (Figure 2Eii). In addition, we designed specific primers in order to measure the absolute numbers for the most abundant and prevalent core taxa (>95% of the individuals; Pseudomonas veronii and Janthinobacterium lividum) and quantified their counts within the samples using quantitative PCR (See in the Materials and methodssubsection “Comparison of gut communities”). Our results agree with the sequencing data, showing a significantly less pronounced effect on the absolute abundances of the core microbes in the gut of resistant fish. These results, from both re-normalization of the core microbes’ abundance and the absolute numbers of some of these core taxa using real-time PCR, support our findings by showing that the changes in the microbiome composition are significantly less pronounced in the resistant fish compared to the sensitive ones (Figure 2E; P < 0.05), thus supporting our hypothesis that host thermal tolerance modulates microbiome response to temperature changes.

Unfortunately, the taxonomic analysis, which could deal with part of the problem, is done only at the level of shallow clades. For instance, most organisms in Figure 2B are proteobacteria. As the authors must know, this is an incredibly diverse set of microbes. Which proteobacteria? The authors should describe fold-changes in taxa at various taxonomic levels, starting with exact sequence variants.

As suggested by the reviewers, we presented the taxonomic analysis of the microbiome at each taxonomic level (Figure 2B; Figure 2—figure supplement 4), as well as calculated the fold change from control to cold conditions for each group presented in Figure 2—figure supplement 5. In addition, as described above, we performed indicator species analysis for each of the taxonomic levels that showed several host-associated taxonomic groups.

As suggested we re-analysed all our data using DADA2 (Callahan et al., 2016) in order to correct any potential sequencing errors and presented our data at the Exact Sequence Variant (ESV) level (100% sequence identity dataset). In addition to using this tool, we took even more stringent approach in which we eliminated sequences present in less than two samples (doubletons) from the 100% sequence ID data sets. This overall analysis is described in the Materials and methods section (subsection “Sequencing of gut microbiome”). Essentially, our overall results and figures, generated using the new ESV data originating from DADA2, show the same patterns and conclusions (Figures 2, 3, 4; See also supplementary figures).

As presented in Figure 2A and B, the sensitive animals had a higher diversity of microbial orders compared to the resistant ones, such as species from the families Clostridiaceae, Succinivibrionaceae and from the genera Prevotella and Streptococcus (Figure 2—figure supplement 7-8). These species consistently existed in most of the individuals and show significant association for the sensitive (genetic) group as it emerges from the indval analysis (Figure 2D; 6 in Supplementary file 1 and Figure 2—figure supplement 8). Similarly, we found significant association to the resistant hosts of taxa originating mostly from the Vibrionales order (Figure 2D; Table S6 in Supplementary file 1 and Figure 2—figure supplement 8). Interestingly, taxa from this order has been reported as resistant to low temperatures or isolated from deep sea waters (Bouchard and Whyte, 2017). More specifically, Vibrio cholerae, a naturally occurring species in the freshwater fish gut (Haygood and Jha, 2018), which we found enriched in the resistant fish gut after cold exposure (Figure 2—figure supplement 5F), has been reported to be resistant to low temperatures through adaptation in its physiology (Townsley et al., 2016). Such results support our claims, that host genetic background, but also cold acclimation phenotype is associated with specific microbiome composition, with some of them potentially carrying fitness traits for cold tolerance.

Finally, the functional analysis is strongly biased by taxonomic changes. For example, the statement that the "host selects for taxa with a specific functional landscape that is better suited to cold exposure" is a bold claim lacking supporting evidence, What is shown is simply an enrichment of functions that are more frequently found in proteobacteria. The type of functional categories found have no relationship to anything related to adaptation to low temperatures. Incorporation of David Sloan Wilson's concepts of "selection for" vs. "selection of" traits might be helpful here.

Our claim of ‘host selects for taxa with a specific functional landscape that is better suited to cold exposure’ was removed from the text. We additionally discuss and cite David Sloan Wilson's concepts considering our results in the discussion. The taxonomic groups that we found to be enriched in the gut are associated with the hosts and the environmental changes; this also results in the enrichment of functions associated with these taxa, with this not necessarily being connected to cold tolerance. Moreover, to investigate whether there are shared responses between the host and the microbiome after cold exposure that could suggest cold acclimation or cold stress response, we compared the significantly affected KEGG orthologs within the microbiome (Figure 3A) to that of the host transcriptome. Interestingly, we found several functions that were not only shared, but also similarly affected (Figure 3C; Figure 3—figure supplement 1B) between the host and the microbiome. Some of the functions affected were previously reported as key cell metabolism cold-induced adaptations in mice (i.e. down-regulation of PPAR signalling, cytochome 450; Shore et al., 2013) and thus suggesting a potential conserved response to low temperatures.

In sum, the finding that the microbiome composition is related to the genetic makeup of the host is interesting, but the authors should dig deeper into processes and, if possible, mechanisms. A better statistical analysis that goes beyond simple summary statistics like average richness and diversity and focuses on specific sequence variants and their relationship to the resistance/sensitivity of individual hosts could make the paper much better. The authors should refrain from making grandiose claims about multilevel selection, microbiome resilience, etc., and simply focus on providing a more in-depth analysis of their data.

Thank you for this remark. As mentioned above, we now re-analysed our data obtaining a more in-depth taxonomic information at the Exact Sequence Variant level using DADA2. Nonetheless, we further developed our insights on the microbial composition changes due to the host genetic makeup and thermal tolerance.

We have shown that when fish from both groups were kept under their optimal temperature conditions of 24^oC, the sensitive fish exhibited higher microbial diversity (Shannon H’) than the resistant ones (Figure 2Aii), as well as a higher individual variability. Indeed, when we compared the microbial community structure (β-diversity) of the two groups at these optimal conditions, we found that cold-resistant fish had a higher within-group microbiome similarity compared to the sensitive fish (Figure 2Ci), potentially due to the selection process that resistant fish have been subjected to regarding the cold resistant trait and thus showing a host control on the microbiome composition.

We further aimed to get a better insight into the microbiome composition of these host groups. To do so, we first compared the microbiome composition between the cold-resistant and cold sensitive hosts under any temperature condition; more specifically, we asked whether microbiomes of similar (Shannon H’) diversity are also sharing the same microbiome compositions. We stratified our data and examined individuals from each host group with similar Shannon H’ diversity (individuals from each group with the 10 highest or 10 lowest Shannon H’ Diversity values; Table S2 in Supplementary file 1). We found that the individuals from both high- and low-diversity microbiomes (Principal Coordinate Analysis; Figure 2—figure supplement 6) clustered significantly according to the host group (Permanova analysis, Tables S3 and S4 in Supplementary file 1), suggesting that host genetic background (genetic selection for cold tolerance) has a strong effect on shaping the microbiome composition.

Our next step was to explore microbial taxa associated to genetic background effects and applied the indicator species analysis (this measure identifies habitat-associated species based on their fidelity and relative abundance in different environments; see above and Materials and methods). This analysis revealed several microbial taxa to be significantly associated with host genetic background, previously described as naturally occurring strains in the tilapia gut (Giatsis et al., 2015; Haygood and Jha, 2018) (Tables S5 and S6 in Supplementary file 1; Figure 2—figure supplement 7-8). Namely, microbial species such as Cetobacterium somerae (Tsuchiya et al., 2008) and species from the Vibrionales order (i.e. Vibrio cholerae; Figure 2—figure supplement 5F), described also as psychrophiles or cold-resistant microbes surviving in different environments (Raymond-Bouchard, Whyte, 2017; Townsley et al., 2016) were enriched in the resistant fish gut, while a higher diversity of taxa was enriched in the sensitive fish gut including Prevotella sp., Streptococcus luteciae, and Bacteroidetes species, as well as species from the Christensenellaceae, Succinivibrionaceae and Clostridiaceae families. Overall, the gut of the sensitive fish was more diverse in microbial taxa and more variable between individuals during the warm conditions compared to the resistant fish, which could potentially stem from the fact that the resistant fish have been subjected to a selection process towards the cold resistant trait, while sensitive ones still contain higher genetic variability, however this still remains to be evaluated in future experiments. Altogether, our results suggest that host genetic background shapes the microbiome composition by selecting for specific microbes, with some of them potentially carrying fitness traits for cold tolerance.

Finally, as host fish were specifically selected according to their thermal tolerance, and our findings indicated that temperature and host genetic background affect the fish gut microbiome, we further asked whether the microbiome's response to temperature stress is also affected by its host's tolerance. When exposed to the cold temperature of 12^oC, both resistant and sensitive hosts exhibited a decrease in their microbiome diversity and richness (Figure 2A; Figure 2—figure supplement 3), as well as changes in its composition (Figure 2B; Figure 2—figure supplement 4-5). However, when we compared the β-diversity within each of these host groups in relation to temperature change, we found that after exposure to the stressful low-temperature conditions, the microbiomes of cold-resistant families were less affected than those of sensitive ones (Figure 2Cii). More specifically, when we compared the similarity of the microbial compositions between 24^oC and 12^oC, we found it to be significantly higher in the resistant families than in the sensitive ones, showing a stronger effect of temperature change in the sensitive fish (Figure 2Cii). We next asked whether this differential response to temperature of the microbiome in the two host groups also applies to microbes that are shared between the two host groups. To this end, we assessed the microbiome resilience in 11 selected taxa that were present in >50% of the individuals from each temperature (core microbiome). After renormalizing the core microbes abundance (expressed as percentage within the core microbiome community; Figure 2E), we evaluated the fold changes in their abundance from 24^oC to 12^oC in the cold-resistant and cold-sensitive fish. Similar to the overall microbiome response (Figure 2Cii), the fold changes in the relative abundance of the core microbiome in the resistant fish was significantly lower that the sensitive ones (Figure 2Eii). In addition, we further designed specific primers in order to measure the absolute copy numbers for the two most abundant and prevalent core taxa within both host groups and quantified their abundance using quantitative PCR (see Materials and methods). Our results were in agreement with the sequencing data (Figure 2Ei, ii), showing a significantly less pronounced effect of the temperature on the abundance of the core microbes in the gut of the resistant fish compared to the sensitive ones (Figure 2Eiii). This means that the sensitivity of the shared-core microbes to cold temperatures was higher in the sensitive fish and thus supporting our hypothesis that host thermal tolerance modulates its microbiome response to temperature changes.

Author details

1. Fotini Kokou

   1. Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
   2. Department of Poultry and Aquaculture, Institute of Animal Sciences, Agricultural Research Organization, Rishon LeZion, Israel

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Visualization, Methodology, Writing—original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3675-3835

2. Goor Sasson

   Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

3. Tali Nitzan

   Department of Poultry and Aquaculture, Institute of Animal Sciences, Agricultural Research Organization, Rishon LeZion, Israel

   Contribution

   Data curation

   Competing interests

   No competing interests declared

4. Adi Doron-Faigenboim

   Department of Vegetable and Field Crops, Institute of Plant Science, Agricultural Research Organization, Rishon LeZion, Israel

   Contribution

   Data curation, Software

   Competing interests

   No competing interests declared

5. Sheenan Harpaz

   Department of Poultry and Aquaculture, Institute of Animal Sciences, Agricultural Research Organization, Rishon LeZion, Israel

   Contribution

   Conceptualization, Project administration

   Competing interests

   No competing interests declared

6. Avner Cnaani

   Department of Poultry and Aquaculture, Institute of Animal Sciences, Agricultural Research Organization, Rishon LeZion, Israel

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Writing—original draft, Project administration

   Competing interests

   No competing interests declared

7. Itzhak Mizrahi

   Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Writing—original draft, Project administration

   For correspondence

   imizrahi@bgu.ac.il

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6636-8818

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