From 0e3c82946c141c3a11426dd6b2d588bb786d661a Mon Sep 17 00:00:00 2001 From: ndwarshuis Date: Sat, 31 Jul 2021 17:47:55 -0400 Subject: [PATCH] ADD a bunch of references --- tex/references.bib | 220 +++++++++++++++++++++++++++++++++++++++++++++ tex/thesis.tex | 197 ++++++++++++++++++++-------------------- 2 files changed, 322 insertions(+), 95 deletions(-) diff --git a/tex/references.bib b/tex/references.bib index 14f6cfa..63371d7 100644 --- a/tex/references.bib +++ b/tex/references.bib @@ -1180,6 +1180,226 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec publisher = {Springer Science and Business Media {LLC}}, } +@Misc{purcellmain, + title = {{Purcell Biolytica Main Page}}, + timestamp = {2020-04-26}, + url = {http://www.percell.se/default.htm}, +} + +@InCollection{Kotancheka, + author = {Mark Kotanchek and Guido Smits and Ekaterina Vladislavleva}, + booktitle = {Genetic and Evolutionary Computation}, + publisher = {Springer {US}}, + title = {Exploiting Trustable Models via Pareto {GP} for Targeted Data Collection}, + year = {2009}, + pages = {1--18}, + doi = {10.1007/978-0-387-87623-8_10}, +} + +@Article{Witkowska2005, + author = {Anna Maria Witkowska}, + journal = {Mediators of Inflammation}, + title = {On the Role of {sIL}-2R Measurements in Rheumatoid Arthritis and Cancers}, + year = {2005}, + number = {3}, + pages = {121--130}, + volume = {2005}, + doi = {10.1155/mi.2005.121}, + publisher = {Hindawi Limited}, +} + +@Article{Mehta2018, + author = {Amit K. Mehta and Donald T. Gracias and Michael Croft}, + journal = {Cytokine}, + title = {{TNF} activity and T cells}, + year = {2018}, + month = {jan}, + pages = {14--18}, + volume = {101}, + doi = {10.1016/j.cyto.2016.08.003}, + publisher = {Elsevier {BV}}, +} + +@Article{Vudattu2005, + author = {Nalini K. Vudattu and Ernst Holler and Patricia Ewing and Ute Schulz and Silvia Haffner and Verena Burger and Silvia Kirchner and Reinhard Andreesen and Gunther Eissner}, + journal = {Immunology}, + title = {Reverse signalling of membrane-integrated tumour necrosis factor differentially regulates alloresponses of {CD}4+ and {CD}8+ T cells against human microvascular endothelial cells}, + year = {2005}, + month = {aug}, + number = {4}, + pages = {536--543}, + volume = {115}, + doi = {10.1111/j.1365-2567.2005.02190.x}, + publisher = {Wiley}, +} + +@Article{Wong2011, + author = {F. S. Wong}, + journal = {Diabetes}, + title = {Stimulating {IL}-13 Receptors on T cells: A New Pathway for Tolerance Induction in Diabetes?~}, + year = {2011}, + month = {may}, + number = {6}, + pages = {1657--1659}, + volume = {60}, + doi = {10.2337/db11-0353}, + publisher = {American Diabetes Association}, +} + +@Article{Junttila2018, + author = {Ilkka S. Junttila}, + journal = {Frontiers in Immunology}, + title = {Tuning the Cytokine Responses: An Update on Interleukin ({IL})-4 and {IL}-13 Receptor Complexes}, + year = {2018}, + month = {jun}, + volume = {9}, + doi = {10.3389/fimmu.2018.00888}, + publisher = {Frontiers Media {SA}}, +} + +@Article{Amatya2017, + author = {Nilesh Amatya and Abhishek V. Garg and Sarah L. Gaffen}, + journal = {Trends in Immunology}, + title = {{IL}-17 Signaling: The Yin and the Yang}, + year = {2017}, + month = {may}, + number = {5}, + pages = {310--322}, + volume = {38}, + doi = {10.1016/j.it.2017.01.006}, + publisher = {Elsevier {BV}}, +} + +@Article{Becher2016, + author = {Burkhard Becher and Sonia Tugues and Melanie Greter}, + journal = {Immunity}, + title = {{GM}-{CSF}: From Growth Factor to Central Mediator of Tissue Inflammation}, + year = {2016}, + month = {nov}, + number = {5}, + pages = {963--973}, + volume = {45}, + doi = {10.1016/j.immuni.2016.10.026}, + publisher = {Elsevier {BV}}, +} + +@Article{Hurton2016, + author = {Lenka V. Hurton and Harjeet Singh and Amer M. Najjar and Kirsten C. Switzer and Tiejuan Mi and Sourindra Maiti and Simon Olivares and Brian Rabinovich and Helen Huls and Marie-Andr{\'{e}}e Forget and Vrushali Datar and Partow Kebriaei and Dean A. Lee and Richard E. Champlin and Laurence J. N. Cooper}, + journal = {Proceedings of the National Academy of Sciences}, + title = {Tethered {IL}-15 augments antitumor activity and promotes a stem-cell memory subset in tumor-specific T cells}, + year = {2016}, + month = {nov}, + number = {48}, + pages = {E7788--E7797}, + volume = {113}, + doi = {10.1073/pnas.1610544113}, + publisher = {Proceedings of the National Academy of Sciences}, +} + +@Article{RonHarel2016, + author = {Noga Ron-Harel and Daniel Santos and Jonathan~M. Ghergurovich and Peter~T. Sage and Anita Reddy and Scott~B. Lovitch and Noah Dephoure and F.~Kyle Satterstrom and Michal Sheffer and Jessica~B. Spinelli and Steven Gygi and Joshua~D. Rabinowitz and Arlene~H. Sharpe and Marcia~C. Haigis}, + journal = {Cell Metabolism}, + title = {Mitochondrial Biogenesis and Proteome Remodeling Promote One-Carbon Metabolism for T Cell Activation}, + year = {2016}, + month = {jul}, + number = {1}, + pages = {104--117}, + volume = {24}, + doi = {10.1016/j.cmet.2016.06.007}, + publisher = {Elsevier {BV}}, +} + +@Article{Pietzke2020, + author = {Matthias Pietzke and Johannes Meiser and Alexei Vazquez}, + journal = {Molecular Metabolism}, + title = {Formate metabolism in health and disease}, + year = {2020}, + month = {mar}, + pages = {23--37}, + volume = {33}, + doi = {10.1016/j.molmet.2019.05.012}, + publisher = {Elsevier {BV}}, +} + +@Article{Vardhana2020, + author = {Santosha A. Vardhana and Madeline A. Hwee and Mirela Berisa and Daniel K. Wells and Kathryn E. Yost and Bryan King and Melody Smith and Pamela S. Herrera and Howard Y. Chang and Ansuman T. Satpathy and Marcel R. M. van den Brink and Justin R. Cross and Craig B. Thompson}, + journal = {Nature Immunology}, + title = {Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen}, + year = {2020}, + month = {jul}, + number = {9}, + pages = {1022--1033}, + volume = {21}, + doi = {10.1038/s41590-020-0725-2}, + publisher = {Springer Science and Business Media {LLC}}, +} + +@Article{Lunt2011, + author = {Sophia Y. Lunt and Matthew G. Vander Heiden}, + journal = {Annual Review of Cell and Developmental Biology}, + title = {Aerobic Glycolysis: Meeting the Metabolic Requirements of Cell Proliferation}, + year = {2011}, + month = {nov}, + number = {1}, + pages = {441--464}, + volume = {27}, + doi = {10.1146/annurev-cellbio-092910-154237}, + publisher = {Annual Reviews}, +} + +@Article{Chang2013, + author = {Chih-Hao Chang and Jonathan~D. Curtis and Leonard~B. Maggi and Brandon Faubert and Alejandro~V. Villarino and David O'Sullivan and Stanley~Ching-Cheng Huang and Gerritje~J.W. van~der~Windt and Julianna Blagih and Jing Qiu and Jason~D. Weber and Edward~J. Pearce and Russell~G. Jones and Erika~L. Pearce}, + journal = {Cell}, + title = {Posttranscriptional Control of T Cell Effector Function by Aerobic Glycolysis}, + year = {2013}, + month = {jun}, + number = {6}, + pages = {1239--1251}, + volume = {153}, + doi = {10.1016/j.cell.2013.05.016}, + publisher = {Elsevier {BV}}, +} + +@Article{Cao2014, + author = {Yilin Cao and Jeffrey C. Rathmell and Andrew N. Macintyre}, + journal = {{PLoS} {ONE}}, + title = {Metabolic Reprogramming towards Aerobic Glycolysis Correlates with Greater Proliferative Ability and Resistance to Metabolic Inhibition in {CD}8 versus {CD}4 T Cells}, + year = {2014}, + month = {aug}, + number = {8}, + pages = {e104104}, + volume = {9}, + doi = {10.1371/journal.pone.0104104}, + editor = {Michael Platten}, + publisher = {Public Library of Science ({PLoS})}, +} + +@Article{Almeida2016, + author = {Lu{\'{\i}}s Almeida and Matthias Lochner and Luciana Berod and Tim Sparwasser}, + journal = {Seminars in Immunology}, + title = {Metabolic pathways in T cell activation and lineage differentiation}, + year = {2016}, + month = {oct}, + number = {5}, + pages = {514--524}, + volume = {28}, + doi = {10.1016/j.smim.2016.10.009}, + publisher = {Elsevier {BV}}, +} + +@Article{Wang_2012, + author = {Ruoning Wang and Douglas R Green}, + journal = {Nature Immunology}, + title = {Metabolic checkpoints in activated T cells}, + year = {2012}, + month = {sep}, + number = {10}, + pages = {907--915}, + volume = {13}, + doi = {10.1038/ni.2386}, + publisher = {Springer Science and Business Media {LLC}}, +} + @Comment{jabref-meta: databaseType:bibtex;} @Comment{jabref-meta: grouping: diff --git a/tex/thesis.tex b/tex/thesis.tex index 5fbc221..ed71230 100644 --- a/tex/thesis.tex +++ b/tex/thesis.tex @@ -1873,31 +1873,34 @@ We have developed a T cell expansion system that recapitulates key features of the in vivo lymph node microenvironment using DMSs functionalized with activating mAbs. This strategy provided superior expansion with higher number of naïve/memory and CD4+ T cells compared to state-of-the-art microbead technology -(Figure 2). Other groups have used biomaterials approaches to mimic the in vivo -microenvironment13–15,17,34; however, to our knowledge this is the first system -that specifically drives naïve/memory and CD4+ T cell formation in a scalable, -potentially bioreactor-compatible manufacturing process. +(Figure 2). Other groups have used biomaterials approaches to mimic the \invivo{} +microenvironment\cite{Cheung2018, Rio2018, Delalat2017, Lambert2017, Matic2013}; +however, to our knowledge this is the first system that specifically drives +naïve/memory and CD4+ T cell formation in a scalable, potentially +bioreactor-compatible manufacturing process. Memory and naïve T cells have been shown to be important clinically. Compared to effectors, they have a higher proliferative capacity and are able to engraft for months; thus they are able to provide long-term immunity with smaller -doses19,35. Indeed, less differentiated T cells have led to greater survival -both in mouse tumor models and human patients20,36,37. Furthermore, clinical +doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have +led to greater survival both in mouse tumor models and human +patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical response rates have been positively correlated with T cell expansion, implying that highly-proliferative naïve and memory T cells are a significant -contributor18,38. Circulating memory T cells have also been found in complete -responders who received CAR T cell therapy39. +contributor\cite{Xu2014, Besser2010}. Circulating memory T cells have also been +found in complete responders who received CAR T cell therapy\cite{Kalos2011}. Similarly, CD4 T cells have been shown to play an important role in CAR T cell immunotherapy. It has been shown that CAR T doses with only CD4 or a mix of CD4 -and CD8 T cells confer greater tumor cytotoxicity than only CD8 T cells22,40. -There are several possible reasons for these observations. First, CD4 T cells -secrete proinflammatory cytokines upon stimulation which may have a synergistic -effect on CD8 T cells. Second, CD4 T cells may be less prone to exhaustion and -may more readily adopt a memory phenotype compared to CD8 T cells22. Third, CD8 -T cells may be more susceptible than CD4 T cells to dual stimulation via the CAR -and endogenous T Cell Receptor (TCR), which could lead to overstimulation, -exhaustion, and apoptosis23. Despite evidence for the importance of CD4 T cells, +and CD8 T cells confer greater tumor cytotoxicity than only CD8 T +cells\cite{Wang2018, Sommermeyer2015}. There are several possible reasons for +these observations. First, CD4 T cells secrete proinflammatory cytokines upon +stimulation which may have a synergistic effect on CD8 T cells. Second, CD4 T +cells may be less prone to exhaustion and may more readily adopt a memory +phenotype compared to CD8 T cells\cite{Wang2018}. Third, CD8 T cells may be more +susceptible than CD4 T cells to dual stimulation via the CAR and endogenous T +Cell Receptor (TCR), which could lead to overstimulation, exhaustion, and +apoptosis\cite{Yang2017}. Despite evidence for the importance of CD4 T cells, more work is required to determine the precise ratios of CD4 and CD8 T cell subsets to be included in CAR T cell therapy given a disease state. @@ -1957,25 +1960,25 @@ approach are that the DMSs are large enough to be filtered (approximately 300 that DMSs are also desired, digestion with dispase or collagenase may be used. Collagenase D may be selective enough to dissolve the DMSs yet preserve surface markers which may be important to measure as critical quality attributes CQAs -{Figure X}. Furthermore, our system should be compatible with -large-scale static culture systems such as the G-Rex bioreactor or perfusion -culture systems, which have been previously shown to work well for T cell -expansion12,50,51. The microcarriers used to create the DMSs also have a +{Figure X}. Furthermore, our system should be compatible with large-scale static +culture systems such as the G-Rex bioreactor or perfusion culture systems, which +have been previously shown to work well for T cell expansion\cite{Forget2014, + Gerdemann2011, Jin2012}. The microcarriers used to create the DMSs also have a regulatory history in human cell therapies that will aid in clinical translation.; they are already a component in an approved retinal pigment epithelial cell product for Parkinson’s patients, and are widely available in 30 -countries26. +countries\cite{purcellmain}. It is important to note that all T cell cultures in this study were performed up to 14 days. Others have demonstrated that potent memory T cells may be obtained -simply by culturing T cells as little as 5 days using traditional beads30. It is -unknown if the naïve/memory phenotype of our DMS system could be further -improved by reducing the culture time, but we can hypothesize that similar -results would be observed given the lower number of doublings in a 5 day -culture. We should also note that we investigated one subtype (\ptmem{}) in -this study. Future work will focus on other memory subtypes such as tissue -resident memory and stem memory T cells, as well as the impact of using the DMS -system on the generation of these subtypes. +simply by culturing T cells as little as 5 days using traditional +beads\cite{Ghassemi2018}. It is unknown if the naïve/memory phenotype of our DMS +system could be further improved by reducing the culture time, but we can +hypothesize that similar results would be observed given the lower number of +doublings in a 5 day culture. We should also note that we investigated one +subtype (\ptmem{}) in this study. Future work will focus on other memory +subtypes such as tissue resident memory and stem memory T cells, as well as the +impact of using the DMS system on the generation of these subtypes. % TODO this sounds sketchy Another advantage is that the DMS system appears to induce a faster growth rate @@ -1989,10 +1992,10 @@ the allogeneic T cell model would greatly benefit from a system that could create large numbers of T cells with naïve and memory phenotype. In contrast to the autologous model which is currently used for Kymriah and Yescarta, allogeneic T cell therapy would reduce cost by spreading manufacturing expenses -across many doses for multiple patients52. Since it is economically advantageous -to grow as many T cells as possible in one batch in the allogeneic model -(reduced start up and harvesting costs, fewer required cell donations), the DMSs -offer an advantage over current technology. +across many doses for multiple patients\cite{Harrison2019}. Since it is +economically advantageous to grow as many T cells as possible in one batch in +the allogeneic model (reduced start up and harvesting costs, fewer required cell +donations), the DMSs offer an advantage over current technology. % TODO this is already stated in the innovation section It should be noted that while we demonstrate a method providing superior @@ -2009,13 +2012,14 @@ cells, this method can theoretically be applied to any T cell immunotherapy which responds to anti-CD3/CD28 mAb and cytokine stimulation. These include \glspl{til}, virus-specific T cells (VSTs), T cells engineered to express $\upgamma\updelta$TCR (TEGs), $\upgamma\updelta$ T cells, T cells with -transduced-TCR, and CAR-TCR T cells53–58. Similar to CD19-CARs used in liquid +transduced-TCR, and CAR-TCR T cells\cite{Cho2015, Straetemans2018, Robbins2011, + Brimnes2012, Baldan2015, Walseng2017}. Similar to CD19-CARs used in liquid tumors, these T cell immunotherapies would similarly benefit from the increased proliferative capacity, metabolic fitness, migration, and engraftment potential -characteristic of naïve and memory phenotypes59–61. Indeed, since these T cell -immunotherapies are activated and expanded with either soluble mAbs or -bead-immobilized mAbs, our system will likely serve as a drop-in substitution to -provide these benefits. +characteristic of naïve and memory phenotypes\cite{Blanc2018, Lalor2016, + Rosato2019}. Indeed, since these T cell immunotherapies are activated and +expanded with either soluble mAbs or bead-immobilized mAbs, our system will +likely serve as a drop-in substitution to provide these benefits. \chapter{aim 2a}\label{aim2a} @@ -2647,19 +2651,19 @@ industries12. SR discovers mathematical expressions that fit a given sample and differs from conventional regression techniques in that a model structure is not defined a priori13. Hence, a key advantage of this methodology is that transparent, human-interpretable models can be generated from small and large -datasets with no prior assumptions14,15. +datasets with no prior assumptions\cite{Kotancheka}. Since the model search process lets the data determine the model, diverse and competitive (e.g., accuracy, complexity) model structures are typically discovered. An ensemble of diverse models can be formed where its constituent models will tend to agree when constrained by observed data yet diverge in new regions. Collecting data in these regions helps to ensure that the target system -is accurately modeled, and its optimum is accurately located14,15. Exploiting -these features allows adaptive data collection and interactive modeling. -Consequently, this adaptive-DOE approach is useful in a variety of scenarios, -including maximizing model validity for model-based decision making, optimizing -processing parameters to maximize target yields, and developing emulators for -online optimization and human understanding14,15. +is accurately modeled, and its optimum is accurately located\cite{Kotancheka}. +Exploiting these features allows adaptive data collection and interactive +modeling. Consequently, this adaptive-DOE approach is useful in a variety of +scenarios, including maximizing model validity for model-based decision making, +optimizing processing parameters to maximize target yields, and developing +emulators for online optimization and human understanding\cite{Kotancheka}. % predictive features @@ -2680,34 +2684,35 @@ exact ratio of expected cytokine abundance is less clear and depends on the subtypes present, and thus examination of each relevant cytokine is needed. IL2R is secreted by activated T cells and binds to IL2, acting as a sink to -dampen its effect on T cells16. Since IL2R was much greater than IL2 in -solution, this might reduce the overall effect of IL2, which could be further -investigated by blocking IL2R with an antibody. In T cells, TNF can increase -IL2R, proliferation, and cytokine production18. It may also induce apoptosis -depending on concentration and alter the CD4+ to CD8+ ratio17. Given that TNF -has both a soluble and membrane-bound form, this may either increase or decrease -CD4+ ratio and/or memory T cells depending on the ratio of the membrane to -soluble TNF18. Since only soluble TNF was measured, membrane TNF is needed to -understand its impact on both CD4+ ratio and memory T cells. Furthermore, IL13 -is known to be critical for Th2 response and therefore could be secreted if -there are significant Th2 T cells already present in the starting population19. -This cytokine has limited signaling in T cells and is thought to be more of an -effector than a differentiation cytokine20. It might be emerging as relevant due -to an initially large number of Th2 cells or because Th2 cells were +dampen its effect on T cells\cite{Witkowska2005}. Since IL2R was much greater +than IL2 in solution, this might reduce the overall effect of IL2, which could +be further investigated by blocking IL2R with an antibody. In T cells, TNF can +increase IL2R, proliferation, and cytokine production\cite{Mehta2018}. It may +also induce apoptosis depending on concentration and alter the CD4+ to CD8+ +ratio\cite{Vudattu2005}. Given that TNF has both a soluble and membrane-bound +form, this may either increase or decrease CD4+ ratio and/or memory T cells +depending on the ratio of the membrane to soluble TNF\cite{Mehta2018}. Since +only soluble TNF was measured, membrane TNF is needed to understand its impact +on both CD4+ ratio and memory T cells. Furthermore, IL13 is known to be critical +for Th2 response and therefore could be secreted if there are significant Th2 T +cells already present in the starting population\cite{Wong2011}. This cytokine +has limited signaling in T cells and is thought to be more of an effector than a +differentiation cytokine\cite{Junttila2018}. It might be emerging as relevant +due to an initially large number of Th2 cells or because Th2 cells were preferentially expanded; indeed, IL4, also found important, is the conical cytokine that induces Th2 cell differentiation (Fig.3). The role of these cytokines could be investigated by quantifying the Th1/2/17 subsets both in the starting population and longitudinally. Similar to IL13, IL17 is an effector -cytokine produced by Th17 cells21 thus may reflect the number of Th17 subset of -T cells. GM-CSF has been linked with activated T cells, specifically Th17 cells, -but it is not clear if this cytokine is inducing differential expansion of CD8+ -T cells or if it is simply a covariate with another cytokine inducing this -expansion22. Finally, IL15 has been shown to be essential for memory signaling -and effective in skewing CAR-T cells toward the Tscm phenotype when using -membrane-bound IL15Ra and IL15R23. Its high predictive behavior goes with its -ability to induce large numbers of memory T cells by functioning in an -autocrine/paracrine manner and could be explored by blocking either the cytokine -or its receptor. +cytokine produced by Th17 cells\cite{Amatya2017} thus may reflect the number of +Th17 subset of T cells. GM-CSF has been linked with activated T cells, +specifically Th17 cells, but it is not clear if this cytokine is inducing +differential expansion of CD8+ T cells or if it is simply a covariate with +another cytokine inducing this expansion\cite{Becher2016}. Finally, IL15 has +been shown to be essential for memory signaling and effective in skewing CAR-T +cells toward the Tscm phenotype when using membrane-bound IL15Ra and +IL15R\cite{Hurton2016}. Its high predictive behavior goes with its ability to +induce large numbers of memory T cells by functioning in an autocrine/paracrine +manner and could be explored by blocking either the cytokine or its receptor. Moreover, many predictive metabolites found here are consistent with metabolic activity associated with T cell activation and differentiation, yet it is not @@ -2715,23 +2720,25 @@ clear how the various combinations of metabolites relate with each other in a heterogeneous cell population. Formate and lactate were found to be highly predictive and observed to positively correlate with higher values of total live CD4+ TN+TCM cells (Fig.5a-b;Supp.Fig.28-S30,S38). Formate is a byproduct of the -one-carbon cycle implicated in promoting T cell activation24. Importantly, this -cycle occurs between the cytosol and mitochondria of cells and formate -excreted25. Mitochondrial biogenesis and function are shown necessary for memory -cell persistence26,27. Therefore, increased formate in media could be an -indicator of one-carbon metabolism and mitochondrial activity in the culture. +one-carbon cycle implicated in promoting T cell activation\cite{RonHarel2016}. +Importantly, this cycle occurs between the cytosol and mitochondria of cells and +formate excreted\cite{Pietzke2020}. Mitochondrial biogenesis and function are +shown necessary for memory cell persistence\cite{van_der_Windt_2012, + Vardhana2020}. Therefore, increased formate in media could be an indicator of +one-carbon metabolism and mitochondrial activity in the culture. In addition to formate, lactate was found as a putative CQA of TN+TCM. Lactate is the end-product of aerobic glycolysis, characteristic of highly proliferating -cells and activated T cells28,29. Glucose import and glycolytic genes are -immediately upregulated in response to T cell stimulation, and thus generation -of lactate. At earlier time-points, this abundance suggests a more robust -induction of glycolysis and higher overall T cell proliferation. Interestingly, -our models indicate that higher lactate predicts higher CD4+, both in total and -in proportion to CD8+, seemingly contrary to previous studies showing that CD8+ -T cells rely more on glycolysis for proliferation following activation30. It may -be that glycolytic cells dominate in the culture at the early time points used -for prediction, and higher lactate reflects more cells. +cells and activated T cells\cite{Lunt2011, Chang2013}. Glucose import and +glycolytic genes are immediately upregulated in response to T cell stimulation, +and thus generation of lactate. At earlier time-points, this abundance suggests +a more robust induction of glycolysis and higher overall T cell proliferation. +Interestingly, our models indicate that higher lactate predicts higher CD4+, +both in total and in proportion to CD8+, seemingly contrary to previous studies +showing that CD8+ T cells rely more on glycolysis for proliferation following +activation\cite{Cao2014}. It may be that glycolytic cells dominate in the +culture at the early time points used for prediction, and higher lactate +reflects more cells. % TODO not sure how much I should include here since I didn't do this analysis % AT ALL @@ -2753,18 +2760,18 @@ primary carbon source (glucose) and essential amino acids (BCAA, histidine) that must be continually consumed by proliferating cells. Moreover, the inclusion of glutamine in our predictive models also suggests the importance of other carbon sources for certain T cell subpopulations. Glutamine can be used for oxidative -energy metabolism in T cells without the need for glycolysis30. Overall, these -results are consistent with existing literature that show different T cell -subtypes require different relative levels of glycolytic and oxidative energy -metabolism to sustain the biosynthetic and signaling needs of their respective -phenotypes33,34. It is worth noting that the trends of metabolite abundance here -are potentially confounded by the partial replacement of media that occurred -periodically during expansion (Methods), thus likely diluting some metabolic -byproducts (i.e. formate, lactate) and elevating depleted precursors (i.e. -glucose, amino acids). More definitive conclusions of metabolic activity across -the expanding cell population can be addressed by a closed system, ideally with -on-line process sensors and controls for formate, lactate, along with ethanol -and glucose. +energy metabolism in T cells without the need for glycolysis\cite{Cao2014}. +Overall, these results are consistent with existing literature that show +different T cell subtypes require different relative levels of glycolytic and +oxidative energy metabolism to sustain the biosynthetic and signaling needs of +their respective phenotypes\cite{Almeida2016,Wang_2012}. It is worth noting that +the trends of metabolite abundance here are potentially confounded by the +partial replacement of media that occurred periodically during expansion +(Methods), thus likely diluting some metabolic byproducts (i.e. formate, +lactate) and elevating depleted precursors (i.e. glucose, amino acids). More +definitive conclusions of metabolic activity across the expanding cell +population can be addressed by a closed system, ideally with on-line process +sensors and controls for formate, lactate, along with ethanol and glucose. \chapter{aim 2b}\label{aim2b}