ADD discussion from paper

tex/thesis.tex

@@ -2331,6 +2331,141 @@ IL13 and IL15 were found predictive in combination with these using SR
 
 \section{discussion}
 
+% optimization of process features
+% TODO this sounds like total fluff
+
+CPPs modeling and understanding are critical to new product development and in
+cell therapy development, it can have life-saving implications. The challenges
+for effective modeling grow with the increasing complexity of processes due to
+high dimensionality, and the potential for process interactions and nonlinear
+relationships. Another critical challenge is the limited amount of available
+data, mostly small DOE datasets. SR has the necessary capabilities to resolve
+the issues of process effects modeling and has been applied across multiple
+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.
+
+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.
+
+% predictive features
+
+An in-depth characterization of potential DMS-based T-cell CQAs includes a list
+of cytokine and NMR features from media samples that are crucial in many aspects
+of T cell fate decisions and effector functions of immune cells. Cytokine
+features were observed to slightly improve prediction and dominated the ranking
+of important features and variable combinations when modeling together with NMR
+media analysis and process parameters (Fig.3b,d).
+
+Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and IL15 were
+biologically assessed in terms of their known functions and activities
+associated with T cells. T helper cells secrete more cytokines than T cytotoxic
+cells, as per their main functions, and activated T cells secrete more cytokines
+than resting T cells. It is possible that some cytokines simply reflect the
+CD4+/CD8+ ratio and the activation degree by proxy proliferation. However, the
+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
+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.
+
+% FIGURE correlation plots from supplement (as alluded to here)
+
+Moreover, many predictive metabolites found here are consistent with metabolic
+activity associated with T cell activation and differentiation, yet it is not
+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.
+
+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.
+
+% TODO not sure how much I should include here since I didn't do this analysis
+% AT ALL
+% Ethanol patterns are difficult to interpret since its production in mammalian
+% cells is still poorly understood31. Fresh media analysis indicates ethanol
+% presence in the media used, possibly utilized as a carrier solvent for certain
+% formula components. However, this does not explain the high variability and
+% trend of ethanol abundance across time (Supp.Fig.S25-S27). As a volatile
+% chemical, variation could be introduced by sample handling throughout the
+% analysis process. Nonetheless, it is also possible that ethanol excreted into
+% media over time, impacting processes regulating redox and reactive oxygen
+% species which have previously been shown to be crucial in T cell signaling and
+% differentiation32.
+
+% this looks fine since it is just parroting sources, just need to paraphrase a
+% little
+Metabolites that consistently decreased over time are consistent with the
+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.
+
 \chapter{aim 2b}\label{aim2b}
 
 \section{introduction}