ABSTRACT Virtual memory T (TVM) cells are antigen-naïve CD8+ T cells that exist in a semi-differentiated state and exhibit marked proliferative dysfunction in advanced age. High spare

respiratory capacity (SRC) has been proposed as a defining metabolic characteristic of antigen-experienced memory T (TMEM) cells, facilitating rapid functionality and survival. Given the

semi-differentiated state of TVM cells and their altered functionality with age, here we investigate TVM cell metabolism and its association with longevity and functionality. Elevated SRC is

a feature of TVM, but not TMEM, cells and it increases with age in both subsets. The elevated SRC observed in aged mouse TVM cells and human CD8+ T cells from older individuals is

11 May 2021 CD8+ T CELL METABOLISM IN INFECTION AND CANCER Article 12 May 2021 HALLMARKS OF T CELL AGING Article 13 May 2021 INTRODUCTION Previous studies have highlighted that the metabolic

phenotype of CD8+ T cells dramatically impacts their functional and survival capacities (reviewed in refs. 1,2). True naive (TN) cells are quiescent and predominantly utilise oxidative

phosphorylation (OXPHOS) to meet their low energy demands. In contrast, effector (TEFF) cells undergo transcriptional reprogramming to upregulate aerobic glycolysis after TCR stimulation.

Conventional memory (TMEM) cells revert to the predominant utilisation of OXPHOS but are characterised by a higher mitochondrial energy reserve, known as spare respiratory capacity

(SRC)3,4,5. SRC is the difference between basal and maximal oxygen consumption rates (OCR)4,5 and it reflects the mitochondrial capacity that a cell holds in reserve, which may mitigate

stress from sudden increases in energy demand. Increased SRC has been proposed to mediate both enhanced T cell functionality, in the form of metabolic memory that confers immediate

responsiveness after secondary antigen exposure5, and the increased longevity of TMEM cells5,6. The greater SRC observed in TMEM cells was in turn associated with increased mitochondrial

load and, in particular, a distinct fused mitochondrial morphology when compared to TN and TEFF cells5,6. Mitochondrial fusion was causally linked with TMEM formation and function, since

deletion of the inner mitochondrial membrane fusion protein, Opa1, abrogated the development of TMEM cells after infection, while promoting mitochondrial fusion in TEFF cells conferred a

memory phenotype6. More recently, high SRC was shown to partition preferentially with a subset of long-lived TMEM cells known as central memory (TCM) cells, rather than short-lived effector

memory (TEM) cells. Enforcing glycolysis, rather than OXPHOS, in CD8+ TMEM cells limited their ability to survive and establish the long-lived TCM population7. Importantly, many studies on

metabolic characteristics of TMEM cells have utilised memory phenotype cells generated in vitro in the context of high levels of IL-15 (refs. 3,4,5,6,8), but the specific impact of IL-15 on

these metabolic characteristics has not been well defined. TVM cells are a subset of antigen-naive, semi-differentiated CD8+ T cells. They are generated in neonatal mice9,10 independently of

antigen exposure, as evidenced by their presence in germ-free mice, antigen-free mice and CD8+ T cell populations specific for viral antigens in naive mice11,12,13,14. Common γ (γc) chain

cytokine signalling is thought to drive the semi-differentiated phenotype of TVM cells, likely via homoeostatic proliferation, with IL-15 transpresentation by CD8α+ dendritic cells (DCs)

required for their generation11,15, and they appear to develop from T cells with modestly self-reactive TCRs13,14,16,17,18. Although antigenically naive, TVM cells are functionally distinct

from true naive T (TN) cells, as TVM engage proliferation and cytokine production more rapidly upon TCR stimulation and can also respond to cytokine stimulation17,19. TVM cells are also

phenotypically distinct from both TN and TMEM cells, with high levels of CD44, a classical marker of activation, but low levels of CD49d, which is only upregulated upon strong cognate

antigen encounter11,20. Of note, the high level expression of CD44 and CD62L on TVM cells has resulted in their frequent misclassification as central memory (TCM) cells, if CD49d expression

is not assessed20. While TVM cells exhibit augmented function in the young, ageing dramatically undermines the functionality of both TVM and TMEM subsets in vitro and in vivo17,21,22.

Moreover, while TN cells decline substantially in number and proportion, both TVM and TMEM cells accumulate with advanced age17,20,23,24, with the accumulation of TVM cells partially

dependent on type I IFN-related signalling pathways25. Overall, it is unclear whether the metabolic profile of CD8+ T cell subsets changes with age and whether metabolic changes reflect

their capacity for function and survival over the lifespan. Given that TVM cells span the phenotypic and functional divide between TN and TMEM cells, we aim to dissect the metabolic

characteristics and associated mitochondrial features of this unique T cell subset to determine whether these features are indicative of their function and survival capacity during ageing.

We define TMEM cells as only those that have encountered antigen, which necessitates a reanalysis of the metabolic, survival, and functional characteristics of bona fide TMEM cells.

Collectively, this study refines our understanding of how the metabolic state impacts on CD8+ T cell function and longevity, which is ultimately key to augmenting or suppressing T cell

function using clinical interventions. RESULTS TVM AND CELLS FROM AGED MICE HAVE INCREASED SPARE RESPIRATORY CAPACITY We sought to understand whether the basal metabolic phenotype of TVM

cells is more closely aligned with the TN population or shares characteristics with conventional TMEM cells, such as increased mitochondrial load and SRC, and to understand how these

profiles change with age. We, therefore, undertook a comprehensive mitochondrial and metabolic analysis for each of these subsets isolated from the spleens of naive young and aged

specific-pathogen-free (SPF) mice. The basal mitochondrial metabolic profile of young and aged mouse CD8+ T cells subsets was determined by performing a Mito Stress test, using a Seahorse

XFe96 Bioanalyser, on sorted TN (CD44lo), TVM (CD44hiCD49dlo) and TMEM (CD44hiCD49dhi) CD8+ cells directly ex vivo. In this assay, oxygen consumption rate (OCR) is tracked in the basal state

(OCRBas) and then during treatment with various mitochondrial inhibitors to enable measurement of maximal OCR (OCRMax), with the difference between the OCRBas and OCRMax representing SRC.

TN cells from young and aged mice had comparable OCR profiles, resulting in comparable SRC for these subsets (Fig. 1a, b). TVM cells from young mice had significantly higher OCR than TN

cells, at both OCRBas and, more noticeably, at OCRMax, resulting in significantly increased SRC (Fig. 1a, b), consistent with a memory-like phenotype. The TVM cells from aged mice also had

consistently higher OCR than those from young mice leading to substantially higher SRC (Fig. 1a, b). Strikingly, TMEM cells from young mice did not exhibit higher SRC than TN cells, but

rather had the lowest SRC of all subsets (Fig. 1a, b), in contrast to previous reports4. While a significant increase in SRC was also observed in TMEM cells with age (Fig. 1a, b), it

remained significantly lower than the SRC observed for TVM cells from aged mice. These data suggest that high SRC is not a hallmark of TMEM cells but instead defines TVM cells, and that

ageing drives an increase in SRC in all memory phenotype CD8+ T cells. CORRELATION OF SRC MITOCHONDRIAL CHARACTERISTICS Increased SRC is often thought to reflect quantitative and/or

qualitative changes in the mitochondria themselves, including (i) denser mitochondrial cristae, promoting processivity and efficiency of oxygen consumption by the electron transport chain

(ETC)3, (ii) a fused mitochondrial morphology6 and (iii) increased mitochondrial load or volume5. To assess these mitochondrial characteristics, TN, TVM and TMEM cells from young and aged

mice were sorted and mitochondria were imaged directly ex vivo. There were no obvious differences in the size, density or morphology of mitochondrial cristae across cell types or ages, by

electron microscopy (Fig. 1c). When mitochondrial fusion was scored by confocal microscopy (Fig. 1d), an intermediate or fused morphology was observed only in a minority of cells, even for

TMEM cells (Fig. 1e), suggesting that mitochondrial fusion is not required for TMEM cell maintenance. In addition, no TVM cells from young mice were observed with a fused morphology (Fig. 

1e), despite their high SRC. Strikingly, there was a substantial increase in mitochondrial fusion with age across all cell types, with this effect being most apparent in TVM cells (Fig. 1e).

Finally, mitochondrial footprint per cell as a measure of mitochondrial load was highest in TVM and TMEM cells and it increased significantly with age in TN cells and TVM cells (Fig. 1f).

Generally, mitochondrial cristae morphology, fusion or volume correlated poorly with the high SRC observed selectively in the TVM cell subset. To obtain a more direct measure of Electron

Transport Chain (ETC) capacity, the levels of mitochondrial ETC Complex IV (CIV; Cox5a) from a defined number of each cell subset was quantitated directly via blue-native PAGE and

immunoblotting. Although not an absolute correlation, the amount of CIV appeared to correlate better with SRC than mitochondrial load or morphology; namely CIV was increased in TVM compared

to TN cells from young mice, and age-related increases in CIV were most marked in the TVM population (Fig. 1g). Collectively, our analyses of mitochondrial load and morphology suggested that

they were broadly predictive of age-related increases in SRC. Expression levels of ETC CIV appeared to most accurately predict cellular SRC across age and subsets. To provide a mechanistic

basis for increased mitochondrial load/activity in TVM cells from aged mice, RNA-Seq data previously generated from TN, TVM, and TMEM subsets from young and aged mice17 was interrogated for

transcripts involved in mitochondrial biogenesis, mitophagy, and mitochondrial fusion or fission. Across all T cell subsets there was an age-dependent decrease in _Atg101_ and _Ulk1_

transcripts, which are critical for mitophagy (Supplementary Fig. 1a). There was a corresponding increase in PGC-1α transcripts (_Ppargcla_) with age in both memory phenotype populations,

which was particularly striking in the TVM subset (Supplementary Fig. 1a). Analysis of transcript levels associated with mitochondrial fusion (_Mfn1_, _Mfn2_, _Opa1_) or fission (_Dnm1l_)

revealed minimal to no change across T cell subsets or with aging (Supplementary Fig. 1c). Collectively, whilst true delineation of the impact of mitochondrial dynamics on mitochondrial load

requires more detailed biochemical analyses, these transcriptional data highlight that there may be a decrease in mitochondrial degradation and an increase in biogenesis with age,

particularly in the TVM subset, which may drive the observed increase in mitochondrial load and SRC. NO EVIDENCE OF FAO FUELLING HIGH SRC IN TVM CELLS High SRC in TMEM cells was previously

thought to be fuelled by fatty acid oxidation (FAO), a mechanism largely defined using etomoxir to inhibit carnitine palmitoyltransferase I (Cpt1), which is a rate-limiting enzyme for FAO3.

However, it was recently demonstrated that the high concentration of etomoxir used in these studies also inhibited other components of OXPHOS to reduce SRC26,27,28. To examine the impact of

etomoxir on high SRC in TVM cells, the drug was incorporated into the Mito Stress assay with young mouse TVM cells at either a high concentration (200 µM) or a low concentration (5 µM), the

latter of which is predicted to maintain specificity for Cpt1 (ref.27). A substantial decrease in OCRMax was observed with the high concentration of etomoxir, but there was only a very

modest decrease in OCRMax with the low concentration (Fig. 1h). This suggests that FAO, via Cpt1, does not facilitate the high SRC observed in TVM cells. We next investigated the possibility

that glycolysis was required to fuel the high SRC observed in TVM cells, most likely via the production of pyruvate29. The Mito Stress assay was performed on TVM cells from naive mice with

the addition of 2-Deoxy-D-glucose (2-DG), a glucose analogue that inhibits glycolysis. The addition of 2-DG had a minimal effect on OCR (Supplementary Fig. 2a), similar to the addition of

low dose etomoxir (Fig. 1h). By contrast, 2-DG addition dramatically reduced ECAR, confirming its effective inhibition of glycolysis (Supplementary Fig. 2b). These data suggest that the high

basal SRC observed in TVM cells is not exclusively dependent on either FAO or glycolysis, but may be fuelled by a substrate generated independently of both pathways. VIRUS INFECTION DRIVES

INCREASED SRC IN TVM CELLS High SRC was not observed in TMEM cells in this study (Fig. 1a, b), but these TMEM cells were isolated out of naive SPF mice and were likely generated in response

to commensal or low-pathogenicity organisms and in conditions of low inflammation. To assess SRC in infection-generated TMEM cells, mice were infected with influenza A virus (IAV) and TMEM

cells specific for tetrameric H-2Db loaded with NP366, PA224 and PB1-F262 epitopes (TMEM (IAV) cells) were isolated 20 days later. Both TMEM and TMEM (IAV) cells exhibited a similarly low

SRC (Fig. 2a, b). Interestingly, TMEM (IAV) cells consistently exhibited substantially higher basal and maximal extracellular acidification rates (ECARBas and ECARMax), compared to TN and

TMEM cells (Fig. 2c). These data illustrate that TMEM and TMEM (IAV) cells are metabolically distinct with regard to glycolytic, but not OXPHOS, capacity. Strikingly, recent IAV infection

caused a substantial elevation in the SRC of TVM cells (Fig. 2d, e), without any shift in glycolytic capacity (Fig. 2f). These data demonstrate that infection, like ageing, leads to an

environment that augments SRC selectively in TVM cells (and thus in an antigen-independent manner), and reinforce that high SRC is not a canonical feature of TMEM cells, even those induced

by infection. CONVENTIONALLY DEFINED TCM CELLS ARE PREDOMINANTLY TVM CELLS Recently, high SRC was shown to partition preferentially with the long-lived central memory (TCM; CD44hiCD62Lhi)

subset of TMEM, rather than short-lived effector memory (TEM; CD44hiCD62Llo) cells7. In that study, TCM cells appear to have been defined as CD44hiCD62Lhi CD8+ T cells obtained from mice

after acute lymphocytic choriomeningitis virus (LCMV) infection, which would include TVM cells20. To determine the extent to which metabolic characteristics of TCM cells have been conflated

with those of TVM cells, we assessed the proportion of classically defined TCM cells (CD44hiCD62Lhi) that were actually TVM cells (CD44hiCD62LhiCD49dlo) in naive young, naive aged or

LCMV-infected mice. In young and aged naive mice, the vast majority (≥85%) of TCM cells were found to be CD49dlo and therefore TVM cells (Fig. 3a). Even 40 days after acute LCMV infection,

which induces a substantial CD8+ T cell response and establishes robust antigen-specific memory populations30, over 60% of TCM cells were found to be TVM cells (Fig. 3a). This highlights the

possibility that CD8+ T cell populations previously defined as TCM cells, from young, aged or infected mice, may have been predominantly comprised of TVM cells. The reciprocal analysis was

also performed to determine the distribution of sorted TVM (CD44hiCD49dlo) and TMEM (CD44hiCD49dhi) cells from naive young, naive aged or LCMV-infected mice across classical TCM

(CD44hiCD62Lhi) and effector memory (TEM; CD44hiCD62Llo) gates. When TVM cells were overlaid onto CD44/CD62L plots, they distributed predominantly to the TCM gate (~80%; Fig. 3b). TMEM cells

were predominantly distributed to the TEM gate (49-95%; Fig. 3c), although a substantial proportion was found in the TCM gate in naive young mice. We also found that TMEM (IAV) cells were

predominantly TEM cells (88.93 ± 6.67%) with some TCM cells (11.07 ± 6.67%) (Fig. 3d). To definitively determine the relative SRC of TCM and TEM cells alongside TVM cells, we performed the

Seahorse Mito Stress assay on TN and TVM cells isolated from naive SPF mice and TEM and TCM cells isolated from LCMV-infected mice at 60 days post infection. As previously observed, TVM

cells had a significantly higher SRC than all other subsets (Fig. 3e, f). In addition, we found that TEM cells had a significantly lower SRC than all other subsets, and TCM cells had a

modestly higher SRC than TN cells (Fig. 3e, f). This comprehensively demonstrates that high SRC is not a defining feature of conventional TMEM cells, in particular the TEM cell subset, but

is instead characteristic of TVM cells. SRC CORRELATES WITH MARKERS OF SURVIVAL, NOT FUNCTIONALITY High SRC had been proposed as a TMEM cell characteristic facilitating both their enhanced

functionality and long-term survival4,5. Accordingly, we aimed to determine if high SRC in TVM cells and the SRC increase with age correlated with enhanced CD8+ T cell functionality or

survival. Three key functions of CD8+ T cells during an immune response are proliferation, cytokine production (particularly IFN-γ) and cytotoxicity. We recently found that TVM cells from

aged individuals, with the highest SRC, have very low TCR-driven proliferative capacity, although the few that can respond still produce IFN-γ17. To further evaluate the functionality of

CD8+ T cell subsets, their cytotoxic capacity immediately ex vivo was assessed. Sorted TN, TVM and TMEM cells from young and aged OT-I mice were used in an in vitro cytotoxicity assay with

ovalbumin-loaded splenocytes as targets. In young mice, TVM cells were substantially more cytotoxic than TN cells and equivalent to TMEM cells (Fig. 4a). With increasing age, the

cytotoxicity of TN cells remained low while the cytotoxicity of TVM and TMEM cells declined substantially (Fig. 4a). These data, along with our previous work17, demonstrate that TVM and TMEM

cells decline in cytotoxic and proliferative capacity with age, despite their increasing SRC. To formally test whether the capacity for any CD8+ T cell function (proliferation, cytokine

production or cytotoxicity) correlated with SRC, linear regression analyses were performed across the cell types and ages. There was no significant correlation observed for any of the

functions (Fig. 4b–d), comprehensively demonstrating that high SRC does not necessarily indicate enhanced CD8+ T cell function, particularly in CD8+ T cells from aged individuals. Our

previous work indicated that TVM cells from aged individuals, which exhibit the highest SRC, had a survival advantage following adoptive transfer17, which appeared to parallel expression of

the anti-apoptotic protein, Bcl-2. Linear regression analysis of Bcl-2 expression vs SRC across the cell types and ages revealed a significant positive correlation (Fig. 4e), highlighting

that SRC is associated with Bcl-2 expression as a surrogate for survival capacity in CD8+ T cells. INCREASED TVM CELL IL-15 SIGNALLING DRIVES INCREASED SRC Given the strong correlation of

Bcl-2 with SRC, mediators of Bcl-2 expression were assessed to define the drivers of high SRC in TVM cells with age. The cytokine, IL-15 (reviewed in ref. 31), was a strong candidate as it

has been shown to promote survival of memory phenotype CD8+ T cells through induction of STAT5 phosphorylation (pSTAT5) and expression of Bcl-2 (ref. 32). To understand how IL-15 signalling

might change with age in the different subsets, we assessed the expression of cytokine receptor subunits and downstream signalling in TN, TVM and TMEM cells from young and aged mice.

Expression of IL-15Rβ was low on TN cells, modestly higher on TMEM cells, and markedly and significantly higher on TVM cells from young mice (5-fold, _p_ < 0.0001; Fig. 5a). In addition,

while neither TN nor TMEM cells exhibited substantial age-related changes in expression, TVM cells exhibited a marked increase in IL-15Rβ expression with age (Fig. 5a). When each subset was

sorted and stimulated with soluble IL-15, the intensity of pSTAT5 tracked with receptor expression levels, with TVM cells from young mice exhibiting high pSTAT5 that increased further with

age (Fig. 5b). Together, these data indicate that, of all CD8+ T cell subsets, TVM cells exhibit the greatest sensitivity to IL-15 signalling and this sensitivity increases with age. IL-15

is regarded as a critical cytokine for both TVM cells and TMEM cells as evidenced by the fact that IL-15 knockout (KO) mice lose approximately half of their memory phenotype (CD44+) cells33

and the TVM cell population fails to develop in young mice in the absence of IL-15 or IL-15Rα14,15. To ascertain whether the IL-15Rβ expression and IL-15 responsiveness of CD8+ T cell

subsets reflects their relative dependence on IL-15, we assessed the development and persistence of each subset in young and aged IL-15 KO mice. TVM cells were absent in young IL-15 KO mice,

consistent with previous studies, and TVM cells were also absent in aged mice (Fig. 5c, d). Of note, the TMEM cell population appears to be relatively intact in aged mice (Fig. 5c, d). This

highlights that IL-15 is absolutely essential for the development of TVM cells, but is dispensable for the generation and maintenance of many TMEM cells, in aged mice. IL-15 is produced at

low levels in steady state but can be dramatically upregulated in DCs during infection in response to type I IFN signalling34. To test whether production of IL-15 during infection was

leading to the observed increase in SRC in TVM cells (Fig. 2d, e), we administered an IL-15 neutralising monoclonal antibody (mAb) to young mice infected with IAV. The mAb was administered

at days 0 and 3 after infection and SRC was assessed at day 14. Abrogating IL-15 signalling during infection was sufficient to abrogate the infection-driven increase in SRC in TVM cells

(Fig. 5e, f). Collectively, these data highlight that the metabolic profile, and in particular elevated SRC, that was previously associated with TMEM cell function and longevity, appears to

be a direct function of IL-15 exposure and independent of TMEM phenotype. In addition, the sensitivity of TVM cells to IL-15 appears to account for the high SRC observed in these cells

directly ex vivo from young mice and the increase in SRC in TVM cells with age. ELEVATED IL-15RΒ AND SRC IN OLDER HUMAN CD8+ T CELLS To determine whether our findings in mice were relevant

to humans, we first analysed expression of IL-15Rβ on young adult (20–30 yo) and older (60–80 yo) human CD8+ T cells. We found significantly higher expression of IL-15Rβ on TVM cells

compared to TN cells in young adults, and this expression increased significantly with age across all subsets (Fig. 6a, b), similar to our observations in mice. Moreover, we observed a trend

toward increased SRC in total CD8+ T cells with advanced age (_p_ = 0.1) (Fig. 6c, d). Given our previous description of diminished proliferative capacity in CD8+ T cells from older

humans35, these data suggest a similar lack of correlation between SRC and functionality in human CD8+ T cells. Moreover, these data indicate a correlation between elevated SRC and an

age-related increase in IL-15 sensitivity of human CD8+ T cells. DISCUSSION By detailed dissection of the metabolic phenotype of TVM cells compared with TN and TMEM cells, we have

demonstrated that high SRC correlates best with IL-15 sensitivity and Bcl-2 expression, and it is therefore selectively associated with the superior survival capacity of TVM cells. In

contrast, neither elevated SRC nor substantial IL-15 dependence were characteristics of conventional antigen-driven memory CD8+ T cells, and SRC did not associate with multiple measures of T

cell functionality across various cell types and ages. Our data, therefore, illustrate that previous work defining high SRC as a characteristic of TMEM cells, and TCM cells in particular,

is likely due to the conflation of these populations with TVM cells, as well as the use of high levels of IL-15 to generate the memory-phenotype CD8+ T cells studied. As previously

mentioned, TVM cells cannot be distinguished from TCM cells without inclusion of the marker for CD49d20. The original study proposing SRC as a characteristic of TMEM cells either isolated

CD8+CD44hiCD62Lhi cells ex vivo from _Listeria monocytogenes_ infected mice or generated cells in vitro using high levels of exogenous IL-15 (ref. 4). Subsequent work proposed that high SRC

partitioned preferentially with the TCM cell population but TCM cells in this study also appeared to be identified as CD8+CD44hiCD62Lhi cells from LCMV-infected mice7. Based on our findings,

we contend that these in vivo strategies would have included a substantial population (60–80% of sorted cells) of TVM cells (Fig. 3a), while the in vitro strategy, which has been widely

used to define phenotypic, functional and metabolic characteristics of memory cells3,4,5,6,8,36,37, relies on robust IL-15 signalling to drive a memory-like phenotype. Recent work has

suggested that IL-15 signalling has direct effects on metabolic profiles, with vaccine-induced effector CD8+ T cells shown to depend predominantly on IL-15 for increased SRC38, and another

study showing increased expression of Bcl-2, increased IL-15Rβ and metabolic adaptations in memory CD8+ T cells (CD44hi) from aged mice, again incorporating TVM cells23. IL-15 signalling is

also known to promote OXPHOS in other cell types, with IL-15 overexpression driving oxidative metabolism in both adipose tissue and skeletal muscle39,40. Thus, we contend that the selective

sensitivity of TVM cells to IL-15 is the basis for the uniquely elevated SRC in those cells. Our data suggest that physiological IL-15 signalling can drive high SRC in TVM cells, and that

this can be further increased with recent infection and increasing age. Accordingly, we propose that high SRC is an indicator of recent IL-15 signalling in T cells, which itself appears to

be a defining characteristic of naturally generated TVM cells, rather than TMEM or TCM cells. This observation also raises the question of whether other well-accepted characteristics of TMEM

populations are primarily canonical TVM cell characteristics20, particularly with regard to IL-15. The dependence on IL-15 for survival is a well-accepted characteristic of

antigen-experienced TMEM cells, alongside increased levels of IL15Rβ, and sensitivity to IL-15 stimulation33,41. Here, we demonstrate that TVM cells express higher levels of receptor, are

more sensitive to IL-15 signalling, and are entirely dependent on IL-15 for their generation and maintenance, while TMEM cells are surprisingly resistant to a lack of IL-15. Furthermore,

IL-15 has been traditionally regarded as a key mediator of CD4 help for antigen-specific CD8+ T cell responses, with CD4 T cells providing help by engaging through CD40L/CD40 interactions

with DCs to amplify their IFN-induced production of IL-15 (ref. 42). The provision of CD4 help, including type I IFN and IL-15, during an antigen-specific CD8+ T cell response limits CD8+ T

cell contraction, resulting in a memory population responsive to secondary antigenic stimulation41. However, these cytokines also directly expand memory-phenotype populations in an antigen

non-specific manner34,43 and are crucial for generation of TVM cell populations14,15. To better delineate the qualitative and quantitative impact of IL-15 on antigen-experienced TMEM vs

antigen-naive TVM cells, it is imperative to perform more nuanced analyses using _bona fide_ antigen-experienced TMEM cell populations. This distinction is more than semantic; it is

essential to accurately define the fundamental effects of IL-15 on T cell populations and to determine whether treatments using IL-15 or downstream targets will primarily act upon

antigen-induced memory T cell populations or for the improved recruitment of antigenically naive CD8+ T cells following primary antigen encounter. Of note, this data highlights that high SRC

is not necessarily linked to superior CD8+ T cell function. Previously, high SRC was proposed to facilitate accelerated proliferation and increased cytokine production with in vitro

generated TMEM cells5. We and others have previously demonstrated that proliferative capacity is highest in TVM cells, followed by TN and TMEM cells directly ex vivo in young

individuals9,17,19,22, which tracks with their SRC. However, any correlation breaks down with ageing, as proliferative capacity is highest in TN cells and relatively poor in TVM and TMEM

cells17,22 and the latter two populations exhibit elevated SRC. We propose that this lack of correlation between SRC and function in aging is compounded by an age-related defect in TCR

signalling that uncouples metabolic potential in resting cells from metabolic engagement and functionality after activation. Dysfunctional TCR-driven proliferation in TVM cells from aged

individuals and dysregulation of signalling cascades downstream of TCR engagement are evident with age17,22,44,45. This dysregulation may prevent TVM cells from efficiently engaging any

increased mitochondrial reserve upon TCR signalling. To test this paradigm further, it would be informative to assess SRC in other T cell subsets, such as stem cell memory T cells, which are

highly proliferative, and resident memory T cells, which retain a level of proliferative capacity in situ and are highly IL-15 dependent. Unfortunately, the low frequency of these cells

precludes their analysis using the current Seahorse technology. Interestingly, our mouse data appears to partly contradict earlier work suggesting that human naive CD8+ T cells exhibit an

age-related reduction in SRC, although an age-related increased mitochondrial load was observed46. This study used older individuals up to the age of 85 yo, which may be older than the

equivalent mouse age of 18–20 mo characterised here. Thus, it is possible that the SRC is reduced in TN cells at extreme ages. Irrespective, our results in mice highlight that a dramatic

metabolic shift occurs within the CD8+ T cell population from aged mice, coincident with our analyses of dysfunction and leading to a substantial increase in SRC in memory-phenotype cells.

This also appears to be supported by our observation of elevated SRC in older human CD8+ T cells. Our data, and others, suggests that high SRC might be a more relevant indicator of T cell

longevity, rather than functionality. The elevated SRC observed in TVM cells is paralleled by our previous data suggesting these cells have a survival advantage both in vivo and in

vitro13,17. Moreover, IL-15 is necessary for both elevated SRC and essential for TVM cell survival15. Finally, SRC has been found to be directly responsible for survival of myocytes under

conditions of hypoxia or nutrient deprivation47. We propose that the differential sensitivity of distinct CD8+ T cell subsets to IL-15 exacerbates its effects in states of increased IL-15

production, such as infection or ageing. IL-15 is produced at low levels in the steady state but it can be dramatically upregulated in DCs during infection, in response to type I IFN

signalling34. Type I IFN and IL-15 signalling may also increase with age, as a result of heightened inflammation, or inflammageing48. Increased IL-15 transcription has been observed in bone

marrow49 and lymph node stromal cells with age50. Bcl-2 was similarly seen to be increased in CD8+ T cells that survived after transfer into a lymphopenic environment, which also correlated

with metabolic shifts51. While SRC and IL-15 sensitivity are clearly linked, the mechanism by which IL-15 signalling drives increased SRC remains unclear. It may upregulate Bcl-2 - Bcl-2

family members have been shown to increase mitochondrial capacity by increasing CIV expression52 and inhibiting mitophagy53. It remains unclear, however, whether there is a causal link

between SRC and survival or whether these are independently mediated by IL-15. Additionally, while our results show that IL-15 is necessary for high SRC in TVM cells, it may not be entirely

sufficient, and additional signalling pathways may be required to co-ordinate with IL-15 to drive increased SRC. In the T cell field, mitochondrial fusion has been largely associated with

advantageous outcomes; namely elevated SRC and memory T cell formation3,6, although this association was not observed in this study, while a more fragmented (or fissed) mitochondria is

associated with effector T cells6. However, while fission of mitochondria is essential for cytokinesis and for isolating dysfunctional mitochondria to allow autophagic degradation54, fusion

of mitochondria is generally considered to reflect an age- or senescence-related stress response to promote survival of the cell by enabling functional complementation, whereby damaged or

dysfunctional mitochondria fuse with intact mitochondria, to maintain ATP production and functionality55,56. We speculate that mitochondrial fusion in this biological scenario reflects an

age-related functional complementation. Collectively, our study underlines how CD8+ T cell subset function and survival is controlled by molecular and metabolic pathways, knowledge of which

is essential for design of interventions that restore functionality or modify survival in T cells from aged individuals. METHODS MICE Female C57BL/6 mice and male CD45.1+ OT-I mice on a

C57BL/6 background were bred and housed in specific-pathogen-free (SPF) conditions at the Monash Animal Research Platform (MARP) and Animal Research Facility (ARL) at Monash University.

IL-15 knockout (KO) mice were housed in SPF conditions at the Biomedical Research Facility in the Department of Microbiology and Immunology (DMI) at the University of Melbourne. Young mice

were defined as 2–3 months old (mo), and aged mice were ex-breeding stock that were 18–20 mo, unless otherwise noted in the figure legends. During tissue harvests, mice were examined for

gross abnormalities, tumours, and enlarged lymph nodes and spleens, and excluded from analyses if these were evident. All animal experimentation was conducted following the Australian

National Health and Medical Research Council Code of Practice for the Care and Use of Animals for Scientific Purposes guidelines for housing and care of laboratory animals and performed in

accordance with Institutional regulations after pertinent review and approval by the University of Melbourne and Monash University Animal Ethics Committees. SEAHORSE MITO STRESS ASSAY A

Seahorse XFe96 Bioanalyser (Agilent) was used to determine OCR and ECAR for sorted CD8+ T cell subsets. Sorted cells were washed in assay media (XF Base media (Agilent) with glucose (10 mM),

sodium pyruvate (1 mM) and L-glutamine (2 mM) (Gibco), pH 7.4 at 37 °C) before being plated onto Seahorse cell culture plates coated with Cell-Tak (Corning) at 2×105 cells per well. After

adherence and equilibration, cell OCR and ECAR was measured during a Seahorse Mito Stress assay (Agilent), with addition of oligomycin (1 µM), carbonyl cyanide 4-(trifluoromethoxy)

phenylhydrazone (FCCP; 1.2 µM) and antimycin A and rotenone (0.5 μM each)). To assess pathways contributing to SRC, we added etomoxir at either 200 or 5 μM (Fig. 1h) or 500 mM of

2-deoxy-D-glucose (Supplementary Fig. 2), after FCCP and prior to antimycin A/rotenone. Assay parameters were as follows: 3 min mix, no wait, 3 min measurement, repeated 3–4 times at basal

and after each addition. SRC was calculated as oxygen consumption rate (OCR) at maximum rate (OCRMax) − OCR in basal state (OCRBas). ELECTRON MICROSCOPY To prepare for transmission electron

microscopy, at least 1 × 106 sorted CD8+ TN, TVM, and TMEM cells from young and aged mice were fixed in 2.5% glutaraldehyde in 100 mM sodium cacodylate buffer for 2 h, washed in cacodylate

buffer, and post-fixated (1% osmium tetroxide, 1.5% potassium ferricyanide, 65 mM sodium cacodylate buffer) for 1 h before storage in cacodylate buffer. Samples were serially dehydrated with

increasing concentrations of ethanol, then propylene oxide before being embedded in serial ratios (3:1, 2:1, 1:1, 1:2 and 1:3) of propylene oxide:epon araldite resin, with samples in 100%

epon araldite resin polymerised at 60˚C for 48 h. Embedded blocks were then microtomed into 60 nm sections that were stained with 2.5% uranyl acetate for 15 min then Reynold’s lead citrate

for 3 min. Sections were imaged on a JEOL JEM-1400 electron microscope operated at 80 kV using a sCMOS Matataki Flash camera. CONFOCAL MICROSCOPY To prepare for confocal microscopy, at least

1 × 106 sorted TN, TVM, and TMEM cells from young and aged mice were seeded on 0.1% gelatin-coated Fluorodishes (World Precision Instruments), allowed to settle, adhered for 30 min and

fixed with 4% (w/v) paraformaldehyde in PBS (pH 7.4) for 10 min. After permeabilisation with 0.5% (w/v) Triton X-100 in PBS, cells were incubated with primary antibody against Cytochrome C

(CytC, mouse monoclonal, BD Biosciences, 556432; 1:500 in 3% BSA-1xPBS) for 60 min at room temperature. The primary antibody was labelled with Alexa-Fluor-488 conjugated anti-mouse-IgG

(Molecular Probes, 1:500 in 3% BSA-1xPBS). Hoechst 33258 (1 μg/ml) was used to stain nuclei. Confocal microscopy was performed on a Leica TCS SP8 confocal microscope (405 nm, 488 nm, 552 nm,

647 nm; Leica Microsystems) equipped with HyD detectors using a 63×/1.40 NA oil immersion objective (HC PLAPO, CS2, Leica Microsystems). Microscopy data was recorded using the Leica LAS X

Life software. Images in all experimental groups were obtained using the same settings. Z-sectioning was performed using 150-nm slices. Leica.lif files were converted to multi-colour.tiff

composite stacks using custom-written Fiji/ImageJ macros (Version 1.52n). Images were analysed using Fiji and custom-written macros. Mitochondrial network area (footprint) was evaluated

using the MINA plugin for Fiji based on maximum intensity projections of 3D-image stacks. Mitochondrial organelle morphology quantitation into three subcategories (fragmented, intermediate,

fused) was evaluated manually based on confocal z-stacks from three independent experiments totalling to 140 cells. All graphical representations and statistical analysis were carried out on

Prism (v7.0a, GraphPad) using two-way ANOVA or Student’s _t_-tests. For representational figures, images were median filtered (1px) using ImageJ and Fiji. BLUE-NATIVE PAGE To assess

expression levels of ETC components, 5 × 105 sorted TN, TVM and TMEM cells from young and aged mice were solubilized in 1% digitonin solubilization buffer, subjected to BN-PAGE and

transferred to PVDF membrane57 before immunoblotting for COX5A. Complex IV was detected using antibodies to COX5A (Santa Cruz sc-376907) and horseradish peroxidase coupled secondary

antibodies and ECL chemiluminescent substrate (BioRad) were used for detection on a BioRad ChemiDoc XRS+ imaging system. The PVDF membrane was also stained with Coomassie Blue (50% methanol,

7% Acetic acid, 0.05% Coomassie Blue R) to assess relative protein loading. IDENTIFICATION, SORTING AND PHENOTYPING OF T CELL SUBSETS For identification and isolation of mouse TN, TVM, and

TMEM cells, samples were processed and subsets were identified using the staining panel and gating strategy described previously17. For sorting, TN cells were defined as CD44lo (the bottom

30% of CD44 expression based on gating in a young, untreated control mouse); CD44int cells were not included in sorted populations. For characterisation of surface expression of CD69, CD5,

CD127 and CD122 on mouse subsets, the following panel was used: LIVE/DEAD Fixable AquaBlue Viability Dye (Life Technologies), anti-Dump (B220, CD4, CD11c, CD11b, F4/80, NK1.1)-FITC (BD

Pharmingen; all 1:400), anti-CD8-PacBlue (53-6.7; BD Pharmingen; 1:200), anti-CD49d-AF647 (R1-2; Biolegend; 1:400), anti-CD44-APC-Cy7 (IM7; Biolegend; 1:400) and either anti-CD69:PE (H1:2F3;

Biolegend; 1:400), anti-CD5:PE (53-7.3; BD Pharmingen; 1:200), anti-CD127:PE (A7R34; eBioscience; 1:400), or anti-CD122:PE (TM-β1; BD Pharmingen; 1:400). For overlays of TCM/TEM gating with

TN, TVM and TMEM subsets, the following panel was used: LIVE/DEAD Fixable Near IR Viability Dye (Life Technologies), anti-Dump (B220, CD4, CD11c, CD11b, F4/80, NK1.1; all 1:400)-FITC (BD

Pharmingen), anti-CD8-BUV395 (53-6.7; BD Pharmingen; 1:400), anti-CD49d-AF647 (R1-2; Biolegend; 1:400), anti-CD44-PE-Cy7 (IM7; Biolegend; 1:1000) and anti-CD62L:BV605 (MEL-14; BD Pharmingen;

1:400). PE-labelled IAV-specific tetramers were included in Fig. 2 and Fig. 3d and were a pool of H2-Db-based tetramers loaded with NP366, PA224 and PB1-F262 epitopes. IAV INFECTION, LCMV

INFECTION AND IL-15 NEUTRALISATION For influenza A virus (IAV) infection, mice were anesthetized by isoflurane inhalation and infected intranasally with 1 × 104 plaque-forming units of the

HKx31 (H3N2) IAV strain in 30 μL of PBS and spleens were harvested at indicated timepoints. For lymphocytic choriomeningitis virus (LCMV) infection, mice were administered 3000

plaque-forming units (PFU) of LCMV (strain WE) intravenously and spleens were harvested at indicated timepoints. For IL-15 neutralisation, 25 μg of an IL-15/Ra neutralising monoclonal

antibody (mAb) (GRW15PLZ; eBioscience) was administered intraperitoneally on day 0 immediately prior to infection and at day 3 after IAV infection. CYTOTOXICITY ASSAYS Effector TN, TVM and

TMEM cells were sorted from young and aged male CD45.1+ OT-I mice. Target splenocytes from male C57BL/6 mice were stained with intermediate or high concentrations of Cell Trace Violet (CTV,

Molecular Probes) and left unloaded (CTV low) or loaded with Ovalbumin-derived peptide (SIINFEKL) at 0.1 µM (CTV high). Unloaded and Ova-loaded targets were mixed in equal proportions with

beads (1:1:1) and then effector cells were added at ratio of 5:1 with Ova-loaded target cells. Cultures were incubated overnight at 37 °C in 5% CO2. To identify live cells and differentiate

effector cells from target cells, the sample was stained with Propidium Iodide (Molecular Probes), anti-CD45.1:APC-Cy7 (A20; Biolegend; 1:400) and anti-CD45.2:PE (104; Biolegend; 1:400). The

ratio of live loaded target cells to beads after incubation with each effector cell type was normalised back to the ratio of live loaded target cells to beads in samples that were not

incubated with effectors, to calculate the % lysis for each effector cell type. PHOSPHORYLATION ASSAYS TN, TVM and TMEM cells were sorted and left unstimulated, or stimulated with IL-7 (10 

ng/mL) or IL-15 (100 ng/mL) in cRPMI at 37˚C in 5% CO2 for 15 min. Cells were processed with Lyse/Fix solution and Perm Buffer II (BD Biosciences) before staining with anti-phospho-STAT5

(CST; 1:400) followed by phycoerythrin (PE)-conjugated anti-rabbit secondary mAb (CST; 1:500). HUMAN ANALYSES Human experimental work was conducted according to the Declaration of Helsinki

Principles and to the Australian National Health and Medical Research Council (NHMRC) Code of Practice. Signed informed consent was obtained from all blood donors before the study. The study

was approved by the University of Melbourne Human Ethics Committee (HREC 1443389). Donor details are shown in Table 1. Briefly, PBMCs were defrosted and rested in complete RPMI overnight.

One sample was taken and stained35 to identify CD8+ T cell subsets (TN, TVM, TCM, TEM and TEMRA) based on CD45RA, CD27, Pan-KIR and NKG2A expression, along with anti-human IL-15Rβ-BV421

(TU27; Biolegend; 1:200) and Propidium Iodide was substituted as the viability dye. The remaining sample was negatively enriched for CD8+ T cells using the human CD8+ T Cell Isolation Kit

(Miltenyi Biotec) as per manufacturer’s instructions, plated at 2 × 105 cells per well and run in a standard Seahorse Mito Stress assay, as detailed above. STATISTICAL ANALYSES Data were

analysed in Graphpad Prism (version 7.0a) using the unpaired, two-tailed _t_-test without correction for multiple comparisons, as indicated in figure legends. REPORTING SUMMARY Further

information on research design is available in the Nature Research Reporting Summary linked to this article. DATA AVAILABILITY RNASeq data accessed during this study are available at GEO

under the accession code GSE112304. All other data that support the findings of this study are available from the corresponding authors upon request. Raw data for all figures are provided in

the Source data file. Source data are provided with this paper. CHANGE HISTORY * _ 09 JULY 2020 An amendment to this paper has been published and can be accessed via a link at the top of

the paper. _ REFERENCES * Almeida, L., Lochner, M., Berod, L. & Sparwasser, T. Metabolic pathways in T cell activation and lineage differentiation. _Semin. Immunol._ 28, 514–524 (2016).

CAS  PubMed  Google Scholar  * van der Windt, G. J. & Pearce, E. L. Metabolic switching and fuel choice during T-cell differentiation and memory development. _Immunol. Rev._ 249, 27–42

(2012). PubMed  PubMed Central  Google Scholar  * O’Sullivan, D. et al. Memory CD8(+) T cells use cell-intrinsic lipolysis to support the metabolic programming necessary for development.

_Immunity_ 41, 75–88 (2014). PubMed  PubMed Central  Google Scholar  * van der Windt, G. J. W. et al. Mitochondrial respiratory capacity is a critical regulator of CD8+ T cell memory

development. _Immunity_ 36, 68–78 (2012). PubMed  Google Scholar  * van der Windt, G. J. W. et al. CD8 memory T cells have a bioenergetic advantage that underlies their rapid recall ability.

_Proc. Natl Acad. Sci. USA_ 110, 14336–14341 (2013). ADS  PubMed  PubMed Central  Google Scholar  * Buck, M. D. et al. Mitochondrial dynamics controls T cell fate through metabolic

programming. _Cell_ 166, 63–76 (2016). CAS  PubMed  PubMed Central  Google Scholar  * Phan, A. T. et al. Constitutive glycolytic metabolism supports CD8(+) T cell effector memory

differentiation during viral infection. _Immunity_ 45, 1024–1037 (2016). CAS  PubMed  PubMed Central  Google Scholar  * Geltink, R. I. K. et al. Mitochondrial priming by CD28. _Cell_ 171,

385–397.e11(2017). * Akue, A. D., Lee, J.-Y. & Jameson, S. C. Derivation and maintenance of virtual memory CD8 T cells. _J. Immunol._ 188, 2516–2523 (2012). CAS  PubMed  Google Scholar 

* Smith, N. L. et al. Developmental origin governs CD8+ T cell fate decisions during infection. _Cell_ 174, 117–130.e114 (2018). CAS  PubMed  Google Scholar  * Haluszczak, C. et al. The

antigen-specific CD8+ T cell repertoire in unimmunized mice includes memory phenotype cells bearing markers of homeostatic expansion. _J. Exp. Med._ 206, 435–448 (2009). CAS  PubMed  PubMed

Central  Google Scholar  * La Gruta, N. L. et al. Primary CTL response magnitude in mice is determined by the extent of naive T cell recruitment and subsequent clonal expansion. _J. Clin.

Invest._ 120, 1885–1894 (2010). PubMed  PubMed Central  Google Scholar  * Quinn, K. M. et al. Heightened self-reactivity associated with selective survival, but not expansion, of naïve

virus-specific CD8+ T cells in aged mice. _Proc. Natl Acad. Sci. USA_ 113, 1333–1338 (2016). ADS  CAS  PubMed  PubMed Central  Google Scholar  * White, J. T. et al. Virtual memory T cells

develop and mediate bystander protective immunity in an IL-15-dependent manner. _Nat. Commun._ 7, 11291 (2016). ADS  CAS  PubMed  PubMed Central  Google Scholar  * Sosinowski, T. et al.

CD8alpha+ dendritic cell trans presentation of IL-15 to naive CD8+ T cells produces antigen-inexperienced T cells in the periphery with memory phenotype and function. _J. Immunol._ 190,

1936–1947 (2013). CAS  PubMed  Google Scholar  * Drobek, A. et al. Strong homeostatic TCR signals induce formation of self-tolerant virtual memory CD8 T cells. _EMBO J_. 37, e98518(2018). *

Quinn, K. M. et al. Age-related decline in primary CD8+ T cell responses is associated with the development of senescence in virtual memory CD8+ T cells. _Cell Rep._ 23, 3512–3524 (2018).

CAS  PubMed  Google Scholar  * Rudd, B. D. et al. Nonrandom attrition of the naive CD8+ T-cell pool with aging governed by T-cell receptor:pMHC interactions. _Proc. Natl Acad. Sci. USA_ 108,

13694–13699 (2011). ADS  CAS  PubMed  PubMed Central  Google Scholar  * Lee, J.-Y., Hamilton, S. E., Akue, A. D., Hogquist, K. A. & Jameson, S. C. Virtual memory CD8 T cells display

unique functional properties. _Proc. Natl Acad. Sci. USA_ 110, 13498–13503 (2013). ADS  CAS  PubMed  PubMed Central  Google Scholar  * Chiu, B.-C., Martin, B. E., Stolberg, V. R. &

Chensue, S. W. Cutting edge: central memory CD8 T cells in aged mice are virtual memory cells. _J. Immunol._ 191, 5793–5796 (2013). CAS  PubMed  Google Scholar  * Lanzer, K. G., Cookenham,

T., Reiley, W. W. & Blackman, M. A. Virtual memory cells make a major contribution to the response of aged influenza-naïve mice to influenza virus infection. _Immun. Ageing_ 15, 17

(2018). PubMed  PubMed Central  Google Scholar  * Renkema, K. R., Li, G., Wu, A., Smithey, M. J. & Nikolich-Zugich, J. Two separate defects affecting true naive or virtual memory T cell

precursors combine to reduce naive T cell responses with aging. _J. Immunol._ 192, 151–159 (2013). PubMed  Google Scholar  * Davenport, B. et al. Aging of antiviral CD8+ memory T cells

fosters increased survival, metabolic adaptations, and lymphoid tissue homing. _J. Immunol._ 202, 460–475 (2019). CAS  PubMed  Google Scholar  * Goronzy, J. J., Fang, F., Cavanagh, M. M.,

Qi, Q. & Weyand, C. M. Naive T cell maintenance and function in human aging. _J. Immunol._ 194, 4073–4080 (2015). CAS  PubMed  Google Scholar  * Martinet, V. et al. Type I interferons

regulate eomesodermin expression and the development of unconventional memory CD8(+) T cells. _Nat. Commun._ 6, 7089 (2015). ADS  CAS  PubMed  Google Scholar  * Divakaruni, A. S. et al.

Etomoxir inhibits macrophage polarization by disrupting CoA homeostasis. _Cell Metab._ 28, 490–503.e7 (2018). CAS  PubMed  PubMed Central  Google Scholar  * O’Connor, R. S. et al. The CPT1a

inhibitor, etomoxir induces severe oxidative stress at commonly used concentrations. _Sci. Rep._ 8, 6289 (2018). ADS  PubMed  PubMed Central  Google Scholar  * Raud, B. et al. Etomoxir

actions on regulatory and memory T cells are independent of Cpt1a-mediated fatty acid oxidation. _Cell Metab._ 28, 504–515.e7 (2018). CAS  PubMed  PubMed Central  Google Scholar  * Quinn, K.

M., Palchaudhuri, R., Palmer, C. S. & La Gruta, N. L. The clock is ticking: the impact of ageing on T cell metabolism. _Clin. Transl. Immunol._ 8, e01091 (2019). Google Scholar  * Butz,

E. A. & Bevan, M. J. Massive expansion of antigen-specific CD8+ T cells during an acute virus infection. _Immunity_ 8, 167–175 (1998). CAS  PubMed  PubMed Central  Google Scholar  *

Raeber, M. E., Zurbuchen, Y., Impellizzieri, D. & Boyman, O. The role of cytokines in T-cell memory in health and disease. _Immunol. Rev._ 283, 176–193 (2018). CAS  PubMed  Google

Scholar  * Lai, Y. G. et al. IL-15 modulates the balance between Bcl-2 and Bim via a Jak3/1-PI3K-Akt-ERK pathway to promote CD8alphaalpha+ intestinal intraepithelial lymphocyte survival.

_Eur. J. Immunol._ 43, 2305–2316 (2013). CAS  PubMed  Google Scholar  * Kennedy, M. K. et al. Reversible defects in natural killer and memory CD8 T cell lineages in interleukin 15-deficient

mice. _J. Exp. Med_ 191, 771–780 (2000). CAS  PubMed  PubMed Central  Google Scholar  * Zhang, X., Sun, S., Hwang, I., Tough, D. F. & Sprent, J. Potent and selective stimulation of

memory-phenotype CD8+ T cells in vivo by IL-15. _Immunity_ 8, 591–599 (1998). CAS  PubMed  Google Scholar  * Quinn, K. M. et al. Age-related decline in primary CD8(+) T cell responses is

associated with the development of senescence in virtual memory CD8(+) T cells. _Cell Rep._ 23, 3512–3524 (2018). CAS  PubMed  Google Scholar  * Manjunath, N. et al. Effector differentiation

is not prerequisite for generation of memory cytotoxic T lymphocytes. _J. Clin. Invest._ 108, 871–878 (2001). CAS  PubMed  PubMed Central  Google Scholar  * Weninger, W., Crowley, M. A.,

Manjunath, N. & von Andrian, U. H. Migratory properties of naive, effector, and memory CD8(+) T cells. _J. Exp. Med_ 194, 953–966 (2001). CAS  PubMed  PubMed Central  Google Scholar  *

Klarquist, J. et al. Clonal expansion of vaccine-elicited T cells is independent of aerobic glycolysis. _Science Immunol._ 3, eaas9822 (2018). * Barra, N. G. et al. Interleukin-15 Modulates

Adipose Tissue by Altering Mitochondrial Mass and Activity. _PLoS ONE_ 9, e114799–114722 (2014). ADS  PubMed  PubMed Central  Google Scholar  * Quinn, L. S., Anderson, B. G., Conner, J. D.

& Wolden-Hanson, T. IL-15 overexpression promotes endurance, oxidative energy metabolism, and muscle PPARδ, SIRT1, PGC-1α, and PGC-1β expression in male mice. _Endocrinology_ 154,

232–245 (2013). CAS  PubMed  Google Scholar  * Schluns, K. S., Williams, K., Ma, A., Zheng, X. X. & Lefrançois, L. Cutting edge: requirement for IL-15 in the generation of primary and

memory antigen-specific CD8 T cells. _J. Immunol._ 168, 4827–4831 (2002). CAS  PubMed  Google Scholar  * Greyer, M. et al. T cell help amplifies innate signals in CD8(+) DCs for optimal

CD8(+) T cell priming. _Cell Rep._ 14, 586–597 (2016). CAS  PubMed  Google Scholar  * Tough, D. F., Borrow, P. & Sprent, J. Induction of bystander T cell proliferation by viruses and

type I interferon in vivo. _Science_ 272, 1947–1950 (1996). ADS  CAS  PubMed  Google Scholar  * Lanna, A. et al. A sestrin-dependent Erk-Jnk-p38 MAPK activation complex inhibits immunity

during aging. _Nat. Immunol._ 18, 354–363 (2017). CAS  PubMed  PubMed Central  Google Scholar  * Lanna, A., Henson, S. M., Escors, D. & Akbar, A. N. The kinase p38 activated by the

metabolic regulator AMPK and scaffold TAB1 drives the senescence of human T cells. _Nat. Immunol._ 15, 965–972 (2014). CAS  PubMed  PubMed Central  Google Scholar  * Moskowitz, D. M. et al.

Epigenomics of human CD8 T cell differentiation and aging. _Sci. Immunol._ 2, eaag0192 (2017). PubMed  PubMed Central  Google Scholar  * Pfleger, J., He, M. & Abdellatif, M.

Mitochondrial complex II is a source of the reserve respiratory capacity that is regulated by metabolic sensors and promotes cell survival. _Cell Death Dis._ 6, e1835 (2015). CAS  PubMed 

PubMed Central  Google Scholar  * Franceschi, C. & Campisi, J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. _J. Gerontol. A Biol. Sci.

Med. Sci._ 69, S4–S9 (2014). PubMed  Google Scholar  * Pangrazzi, L. et al. Increased IL-15 production and accumulation of highly differentiated CD8+ effector/memory T cells in the bone

marrow of persons with cytomegalovirus. _Front. Immunol._ 8, 715 (2017). PubMed  PubMed Central  Google Scholar  * Cui, G. et al. Characterization of the IL-15 niche in primary and secondary

lymphoid organs in vivo. _Proc. Natl Acad. Sci. USA_ 111, 1915–1920 (2014). ADS  CAS  PubMed  PubMed Central  Google Scholar  * Xu, A. et al. IL-15 signaling promotes adoptive effector

T-cell survival and memory formation in irradiation-induced lymphopenia. _Cell Biosci._ 6, 30 (2016). PubMed  PubMed Central  Google Scholar  * Wali, J. A. et al. Loss of BIM increases

mitochondrial oxygen consumption and lipid oxidation, reduces adiposity and improves insulin sensitivity in mice. _Cell Death Differ._ 25, 217–225 (2018). CAS  PubMed  Google Scholar  *

Hollville, E., Carroll, R. G., Cullen, S. P. & Martin, S. J. Bcl-2 family proteins participate in mitochondrial quality control by regulating Parkin/PINK1-dependent mitophagy. _Mol.

Cell_ 55, 451–466 (2014). CAS  PubMed  Google Scholar  * Chan, D. C. Fusion and fission: interlinked processes critical for mitochondrial health. _Annu. Rev. Genet._ 46, 265–287 (2012). CAS

  PubMed  Google Scholar  * Nakada, K. et al. Inter-mitochondrial complementation: mitochondria-specific system preventing mice from expression of disease phenotypes by mutant mtDNA. _Nat.

Med._ 7, 934–940 (2001). CAS  PubMed  Google Scholar  * Schon, E. A. & Gilkerson, R. W. Functional complementation of mitochondrial DNAs: mobilizing mitochondrial genetics against

dysfunction. _Biochim. Biophys. Acta_ 1800, 245–249 (2010). CAS  PubMed  Google Scholar  * Formosa, L. E. et al. Characterization of mitochondrial FOXRED1 in the assembly of respiratory

chain complex I. _Hum. Mol. Genet._ 24, 2952–2965 (2015). CAS  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS We thank Adam Costin and Joan Clark for technical assistance in

electron microscopy, and staff at Monash University Ramaciotti Centre for Cryo-Electron Microscopy, Monash University FlowCore, Monash Micro Imaging, and Monash University Animal Facilities.

This work was supported by a Sylvia and Charles Viertel Senior Medical Research Fellowship, an Australian Research Council (ARC) Future Fellowship FT170100174, a National Health and Medical

Research Council (NHMRC) Program grant APP1071916 (to N.L.L.), a Rebecca L. Cooper Foundation Medical Research Grant (to K.M.Q), a Viertel-Belberry Senior Medical Research Fellowship (to

K.L.G.-J.), Monash Graduate Scholarship and Monash International Postgraduate Research Scholarship (to T.H.) a Monash University Biomedicine Discovery Scholarship (to L.C.) and funding from

the Bonn and Melbourne Research and Graduate School (GRK2168). C.E.S. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie

Skłodowska-Curie grant agreement No. 792532 and University of Melbourne McKenzie Fellowship laboratory support. KK is supported by a NHMRC Senior Research Fellowship Level B (GNT#1102792).

AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia Kylie

M. Quinn, Tabinda Hussain, Felix Kraus, Luke E. Formosa, Wai K. Lam, Lisa M. Assmus, Lucy Cooper, Kim L. Good-Jacobson, Michael T. Ryan & Nicole L. La Gruta * School of Health and

Biomedical Sciences, RMIT University, Bundoora, VIC, Australia Kylie M. Quinn * Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology,

University of Melbourne, Parkville, VIC, 3010, Australia Michael J. Dagley, Eleanor C. Saunders, Malcolm J. McConville & Georg Ramm * Institute of Experimental Immunology, University

Hospital Bonn, 53127, Bonn, Germany Lisa M. Assmus * Department of Microbiology and Immunology, University of Melbourne, Peter Doherty Institute for Infection and Immunity, Parkville, VIC,

Australia Erica Wynne-Jones, Liyen Loh, Carolien E. van de Sandt, Katherine Kedzierska & Laura K. Mackay * Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory,

Amsterdam UMC, University of Amsterdam, 1066CX, Amsterdam, Netherlands Carolien E. van de Sandt * Monash Ramaciotti Centre for Cryo-EM, Monash University, Clayton, VIC, Australia Georg Ramm

Authors * Kylie M. Quinn View author publications You can also search for this author inPubMed Google Scholar * Tabinda Hussain View author publications You can also search for this author

inPubMed Google Scholar * Felix Kraus View author publications You can also search for this author inPubMed Google Scholar * Luke E. Formosa View author publications You can also search for

this author inPubMed Google Scholar * Wai K. Lam View author publications You can also search for this author inPubMed Google Scholar * Michael J. Dagley View author publications You can

also search for this author inPubMed Google Scholar * Eleanor C. Saunders View author publications You can also search for this author inPubMed Google Scholar * Lisa M. Assmus View author

publications You can also search for this author inPubMed Google Scholar * Erica Wynne-Jones View author publications You can also search for this author inPubMed Google Scholar * Liyen Loh

View author publications You can also search for this author inPubMed Google Scholar * Carolien E. van de Sandt View author publications You can also search for this author inPubMed Google

Scholar * Lucy Cooper View author publications You can also search for this author inPubMed Google Scholar * Kim L. Good-Jacobson View author publications You can also search for this author

inPubMed Google Scholar * Katherine Kedzierska View author publications You can also search for this author inPubMed Google Scholar * Laura K. Mackay View author publications You can also

search for this author inPubMed Google Scholar * Malcolm J. McConville View author publications You can also search for this author inPubMed Google Scholar * Georg Ramm View author

publications You can also search for this author inPubMed Google Scholar * Michael T. Ryan View author publications You can also search for this author inPubMed Google Scholar * Nicole L. La

Gruta View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS K.M.Q. and N.L.L. designed the study, K.M.Q. performed the majority of experiments

and analysed results, T.H. performed assays of T cell function, F.K. performed confocal microscopy, L.M.A. performed cytotoxicity assays, L.E.F. performed western blots, W.K.L. and G.R.

performed electron microscopy, L.C., E.W-J., L.L., C.E.S, and K.K. contributed to the procurement and processing of samples, E.C.S. and M.J.D. optimised Seahorse XFe96 experiments, N.L.L.,

K.M.Q., M.T.R., K.G-J, L.M., and M.J.M. supervised the research, K.M.Q. and N.L.L. wrote the initial draft of the paper and all authors participated in writing the final paper. CORRESPONDING

AUTHORS Correspondence to Kylie M. Quinn or Nicole L. La Gruta. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PEER REVIEW

INFORMATION _Nature Communications_ thanks the anonymous reviewers for their contribution to the peer review of this work. Peer review reports are available. PUBLISHER’S NOTE Springer Nature

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CITE THIS ARTICLE Quinn, K.M., Hussain, T., Kraus, F. _et al._ Metabolic characteristics of CD8+ T cell subsets in young and aged individuals are not predictive of functionality. _Nat

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