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1168-1175.pdf - Google Drive UPD

Traditionally, multiple sclerosis has been viewed as a disease predominantly affecting white matter. However, this view has lately been subject to numerous changes, as new evidence of anatomical and histological changes as well as of molecular targets within the grey matter has arisen. This advance was driven mainly by novel imaging techniques, however, these have not yet been implemented in routine clinical practice. The changes in the grey matter are related to physical and cognitive disability seen in individuals with multiple sclerosis. Furthermore, damage to several grey matter structures can be associated with impairment of specific functions. Therefore, we conclude that grey matter damage - global and regional - has the potential to become a marker of disease activity, complementary to the currently used magnetic resonance markers (global brain atrophy and T2 hyperintense lesions). Furthermore, it may improve the prediction of the future disease course and response to therapy in individual patients and may also become a reliable additional surrogate marker of treatment effect.

1168-1175.pdf - Google Drive

According to the existing evidence, changes in GM might represent a reliable marker of disease activity and of CNS damage. The relatively less pronounced inflammation within GM is likely to result in lesser fluctuations of its changes triggered by the relapsing inflammatory activity [51]. Moreover, focal oedema and treatment-associated pseudoatrophy, which may mask the changes reflecting the activity of MS, are known to be less evident in GM [172, 173]. Therefore GM lesions and atrophy, rather than WM changes, might better reflect long-term changes which drive the accumulation of disability [174].

Cell differentiation is typically directed by external signals that drive opposing regulatory pathways. Studying differentiation under polarizing conditions, with only one input signal provided, is limited in its ability to resolve the logic of interactions between opposing pathways. Dissection of this logic can be facilitated by mapping the system's response to mixtures of input signals, which are expected to occur in vivo, where cells are simultaneously exposed to various signals with potentially opposing effects. Here, we systematically map the response of naïve T cells to mixtures of signals driving differentiation into the Th1 and Th2 lineages. We characterize cell state at the single cell level by measuring levels of the two lineage-specific transcription factors (T-bet and GATA3) and two lineage characteristic cytokines (IFN-γ and IL-4) that are driven by these transcription regulators. We find a continuum of mixed phenotypes in which individual cells co-express the two lineage-specific master regulators at levels that gradually depend on levels of the two input signals. Using mathematical modeling we show that such tunable mixed phenotype arises if autoregulatory positive feedback loops in the gene network regulating this process are gradual and dominant over cross-pathway inhibition. We also find that expression of the lineage-specific cytokines follows two independent stochastic processes that are biased by expression levels of the master regulators. Thus, cytokine expression is highly heterogeneous under mixed conditions, with subpopulations of cells expressing only IFN-γ, only IL-4, both cytokines, or neither. The fraction of cells in each of these subpopulations changes gradually with input conditions, reproducing the continuous internal state at the cell population level. These results suggest a differentiation scheme in which cells reflect uncertainty through a continuously tuneable mixed phenotype combined with a biased stochastic decision rather than a binary phenotype with a deterministic decision.

In this study we use differentiation of naive CD4+ T cells towards the Th1 and Th2 lineages as a model system to study this question. Antigen-activated CD4+ T cells can differentiate into various cell types depending mainly on the cytokines present in their environment during activation [26],[27]. Differentiation of CD4+ T cells towards the Th1 lineage is driven by the cytokine IL-12, while IL-4 drives differentiation towards the Th2 lineage (Figure 1A). Th1 cells, involved in protection against intracellular pathogens, are characterized by the expression of the lineage-specific transcription factor (TF) T-bet, and by production and secretion of effector cytokines such as IFN-γ and TNFα [26]. Th2 cells express the lineage-specific TF, GATA3; secrete the cytokines IL-4, IL-5, and IL-13; and are involved in protection against extracellular pathogens [26]. Existence of cells co-expressing IFN-γ and IL-4 was observed in both mouse and human [28],[29], but the input conditions and the status of expression of transcription factors leading to their formation are not clear.

The two input signals (top) drive the GRN that controls differentiation of CD4+ T cells. The levels of the two lineage-specifying transcription factors, T-bet and GATA3, tune (bar graphs) from a Th1 state (left) to a Th2 state (right), through a continuum of intermediate states in which both factors are co-expressed. Cytokine expression upon restimulation is stochastic. The fraction of cells that express IFN-γ or IL-4 is biased by the levels of the corresponding transcription factors, as well as by other factors (dashed arrows). These two stochastic processes are independent. This model results in a heterogonous cell population (scatter plots, right), with cells expressing only IFN-γ (yellow ellipse), only IL-4 (blue), both cytokines (green), or neither (white). The fraction of cells in each of the four subpopulations continuously tunes with changing inputs. Expression levels of all four factors are represented schematically by the cell populations at the bottom. The internal color represents levels of T-bet and GATA3 tuning from Th1 (yellow, T-bet high, GATA3 low) to Th2 (blue, T-bet low, GATA3 high), through intermediate levels of green. The outer color represents cytokine expression upon restimulation, showing a higher level of heterogeneity. For clarity, we don't show here noise in gene expression (for example, cells cultured under Th1 conditions express different levels of T-bet, and similarly for the other proteins and conditions). Note that other factors influence this differentiation process (TCR stimulation strength and duration, other cytokines), which we assume here to be constant across all conditions.

Several factors could drive the marked increase in INSIG1 mRNA during lactation. For example, up-regulation of INSIG1 expression might be a consequence of SREBF isoform mRNA up-regulation and increased activity (i.e., induction of gene expression) of the corresponding proteins. SREBP1a and SREBP2 directly regulate INSIG1 gene expression [84]. Given the lipogenic capacity of mammary tissue, it is more likely that SREBP1c is the more abundant isoform. Thus, INSIG1 up-regulation in bovine mammary tissue could be under control of SREBF2 (Figure 4). Another reason for marked INSIG1 mRNA up-regulation might be its very short half-life [84], or as a necessary mechanism to sense low mammary cholesterol levels in order to regulate de novo FA synthesis.

Articular cartilage is composed of a specialized matrix of collagen, proteoglycans, and non-collagen components such as chondrocytes [35]. This balance can be disrupted due to ageing or joint disorders, and consequently, the rate of loss of collagen and proteoglycans from ECM may exceed the rate of production of newly synthesized molecules [36]. The loss of joint homeostasis because of an imbalance between the anabolic and catabolic processes is driven by inflammatory cytokine cascades during pathogenesis. Cartilage damage begins when proteoglycans are broken down by MMPs. Other inflammatory mediators, such as COX-2, 5-LOX, FLAP, PGE2, and LTB4, also drive OA progression. MMP-2 is a collagenase that is the main proteolytic enzyme among the MMPs. In addition to MMP-2, MMP-9, also known as gelatinase B, plays a role in ECM degradation. Both MMP-3 and stromelysin-1 can degrade a variety of ECM substrates, including collagen, laminin, fibronectin, osteopontin, and proteoglycans, while also demonstrating proteolytic activity on cell surface protein ectodomains [37]. High levels of MMP-13 and gelatinase can cause degradation of the basement membrane [38]. In this study, we demonstrated that 5-Loxin-mediated suppression of COX-2 and 5-LOX led to a significant decrease in PGE2 and LTB4. Moreover, the inhibition of inflammatory cytokines, including TNF-α, IL-1β, and IL-6, by 5-Loxin treatment resulted in a reduction in levels of cartilage-degrading enzymes, MMP-2 and 13, within both the serum and cartilage tissue. These results are consistent with the data reported in previous studies that showed treatment with 5-Loxin inhibited the expression of 5-LOX and FLAP in LPS-stimulated THP-1 monocytes and synovial MMP-3 in patients with OA. 041b061a72


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