Genomic deletion of GIT2 induces a premature age-related thymic dysfunction and systemic immune system disruption

Results

GIT2 genomic deletion affects total lifespan and alters indices of thymic functionality

Assessing age-related survival of male and female homozygous GIT2 knockout (GIT2KO) mice we found that GIT2KO males and females possessed a significantly shorter total lifespan compared to wildtype (WT) controls (males, p=0.0118; females, p=0.0225) (Figure 1A). The longitudinal mortality rate was accelerated in GIT2KO (male and female) compared to WT controls: in this context the males demonstrated a faster longitudinal rate of expiration compared to the females (Figure 1B). With this strong distinction in mortality rate in male GIT2KO mice we then chose to assess whether there was an alteration in the rate of thymic degradation in these males compared to WT male controls. We analyzed the presence of thymic progenitor cells, a proxy of thymic function, using FACS analysis at early time points linked to good health (i.e. 3 months of age, zero mortality) and also at a timepoint in which male GIT2KO mice first demonstrate a profound divergence in mortality rate compared to WT controls (i.e. 12 months of age, Figure 1B). A representative FACS output image of thymic progenitors (CD25/c-Kit) is shown for 3 (Figure 1C) and 12 months of age (Figure 1D). At 3 and 12 months of ages, Lin^- cells were significantly reduced (Figure 1E), as were ETPs (early thymic progenitors: Figure 1F), DN2 (Figure 1G), DN3 (Figure 1H) and DN4 (Figure 1I) in GIT2KO mice compared to similarly-aged-matched WT controls. We also assessed DP and CD4⁺, CD8⁺ cell counts in the GIT2KO mice. DP and CD4⁺ cell counts were significantly reduced at 12 months in GIT2KO mice (Figure 1J-K) compared to WT (Figure 1J-K). A non-significant trend of reduced CD8⁺ cells at 12 months of age in GIT2KO mice was noted (Figure 1L). To assess whether reductions in GIT2KO thymic DP and CD4⁺ cells were linked to apoptosis-related activity we measured thymic transcript levels of Bcl-xL, Bim, Bax, Bid and Caspase 3 (Figure S1). Pro-apoptotic GIT2KO Bid expression was not significantly altered in GIT2KO mice at either 3 or 12 months of age (Figure S1A). Caspase-3 transcript levels were consistently, albeit in a non-significant manner, lower at both time points in the GIT2KO mice compared to WT mice (Figure S1B). Bax expression was significantly reduced in 12 month-old GIT2KO mice compared to controls (Figure S1C). Anti-apoptotic Bcl-xL and Bim transcript levels were significantly reduced in 12 month-old GIT2KO thymocytes (Figure S1D, E). Therefore GIT2 deletion appears to attenuate cell support in the thymus, without the excessive induction of pro-apoptotic activity.

Figure 1. Genomic deletion of GIT2 attenuates overall murine lifespan and alters thymic T cell functionality. Male and female GIT2KO overall lifespan was assessed through comparison to control wild type (WT) littermates (A). Survival curve analysis of GIT2KO and WT mouse cohorts across their lifespan (B). Representative FACS images of a male WT and GIT2KO thymus at (C) 3 and (D) 12 months of age. The x-axes show increasing c-Kit positive and the y-axes, increasing counts of CD25⁺ cells. Quadrant 3 (Q3) (bottom R) indicates ETPs (Lin^-c-Kit⁺CD25^-), Q2 (top R) indicates DN2 (Lin^-c-Kit⁺CD25⁺), Q1 (top L) indicates DN3 (Lin^-c-Kit^-CD25⁺) and Q4 (bottom L) indicates DN4 cells (Lin^-c-Kit^-CD25^-). Significant age- and GIT2KO-dependent changes (compared to WT) in Lin^- (E), ETP (F), DN2 (G), DN3 (H) and DN4 (I) cell counts. GIT2KO mice also demonstrate GIT2KO mice demonstrate significant decreases in DP (J) and CD4⁺ (K) cell counts at 12 months of age compared to WT. A non-significant trend for a similar reduction in CD8⁺ cell counts (L) in GIT2KO thymus compared to WT at 12 months of age was observed. Values indicated are mean ± SEM (standard error of mean). WT data are indicated in this and analogous figures with solid lines, GIT2KO data with dashed lines. Months of age is abbreviated to m.o. *p<0.05, **p<0.01.

We determined the status of recent thymic emigrant cell numbers from CD31⁺/CD31^- ratios in the spleen (Figure S2). Compared to WT, splenic GIT2KO CD8⁺CD31⁺ and CD4⁺CD31⁺ cell counts were significantly reduced at both 3 months of age (Figure S2A, B): a distinction missing at the 12 month timepoint. GIT2KO CD8⁺CD31^- and CD4⁺CD31^- cell counts were both similar to WT at 3 months of age but only CD4⁺CD31^- counts were significantly elevated in GIT2^-/- compared to WT at 12 months (Figure S2C, D). In contrast the ratios of GIT2KO CD8⁺: CD31⁺/CD31^- or CD4⁺: CD31⁺/CD31^- were both significantly lower than those found in WT mice at the 12 month timepoint (Figure S2E, F). With respect to the status of circulatory white blood cells (WBCs) no significant differences between GIT2KO or WT mice at either time-point (Figure S2G, H) were observed: a trend of reduced levels in GIT2KO mice at both 3 and 12 months of age was evident. Circulating lymphocyte percentages and total cell counts were consistently reduced in GIT2KO mice, at 3 (significant) and 12 (non-significant) months of age, compared to WT (Figure S2I-L). To further investigate the age-dependent alterations in GIT2KO immune cellular status, we investigated bone marrow (BM) cell lineages. We assessed BM cell counts of Lin^-CD127⁺, Lin^-CD127^-, LSK (Lin^-Sca1⁺c-Kit⁺), LK (Lin^-Sca1^-c-Kit⁺) and CLPs (common lymphoid progenitors) (Figure S3). Levels of Lin^-CD127⁺, Lin^-CD127^- and CLPs were similar between WT and GIT2KO mice between 3 and 12 months of age (Figure S3A-C). In contrast we found an aging-specific elevation of BM LSK (Figure S3D) and LK cells (Figure S3E) in GIT2KO mice compared to WT (FACS insets for 12 month WT: Figure S3F and GIT2^-/-: Figure S3G, LSK/LK counts). This age-dependent accumulation of LSK/LK cells in the GIT2KO BM may be associated with reduced cell motility [36] as GIT2 functions as a regulator of cytoskeletal remodeling [37]. Chemotactic targeting is also crucial for BM progenitor thymic transition. We found an age-induced decline of transcript expression in the bone marrow of GIT2KO mice for chemokine receptors involved in thymic seeding (CCR7, CCR9, CXCR4: Figure S3H-J).

GIT2KO mice exhibit early-onset physical thymic disruption

In line with the attenuated support of thymocyte function (Figure 1, S1), we next investigated the structural integrity of the thymus itself. WT and GIT2KO mice possessed similar body weights at 3 and 12 months (Figure 2A). As expected with advancing age total thymic weights were reduced in WT and GIT2KO mice from 3 to 12 months (Figure 2B) – at both time points however GIT2KO thymi were smaller than WT. An age-dependent significant decrease in thymocyte cell counts (normalized to thymic weight, i.e. thymocyte ‘density’) was only evident for GIT2KO mice (Figure 2C). Histologically we found that at the one year time point GIT2KO thymi failed to demonstrate a strongly-delineated cortex compared to WT mice (Figure 2D). Indicative of this cortical failure in GIT2KO thymi we found that thymic Troma-I/cytokeratin 8 (thymic cortical marker) gene expression (Figure 2E) was significantly lower compared to WT controls. To assess global GIT2KO molecular signaling alterations in the thymus we performed unbiased transcriptomic analysis of 12 month-old GIT2KO mice compared to WT controls. Commensurate with the profound structural effects of GIT2 deletion on gross thymic integrity we found that 777 transcripts from the GIT2KO mice were differentially regulated compared to WT (331 up-, 446 down-regulated, p<0.05; Figure 2F; Table S1). The most down-regulated transcripts in GIT2KO thymi included genes regulating: age-related stress responses (Ager [38]); T cell survival/regulation (Tsc22d3 [39]); structural integrity of tissues (Emb [40]); T cell autophagy (Ddit4 [41]); T cell expansion and differentiation (Tnfrsf4 [42]); susceptibility to thymic DNA damage (Mecp2 [43]) and cell cycle clock regulation/circadian rhythms (Fbxl3 [44], Dbp [45], Tef [46], Per1 and Per2 [47,48]). Many of the thymic transcripts up-regulated in GIT2KO thymi are associated with: advanced aging (Glo1 [49]; Sod2 [50]; Mgst1 [51]; Ndn [52]; mitochondrial pathophysiology (Tspo [53]); DNA damage responses (Tipin [54]); autophagy suppression linked to thymic disruption (Sqstm1 [55]) (Figure 2F). We assessed the actual protein expression in GIT2KO thymus (compared to WT control) for multiple significantly altered transcripts (Glo1, Sod2, Per1, Ager: Figure S4A). For each of these factors our microarray data was reproduced at the level of protein expression. In addition, through in vitro siRNA-mediated attenuation of GIT2 expression in cultured Jurkat cells resulted in the modulation of multiple factors (Glo1, Cav1, Vdac3 – upregulated; Per1, Mgst2, Tef – downregulated: Figure S4B) in a similar manner indicated by our transcriptomic array data (see Supplementary Data).

Figure 2. Thymic structural dysregulation in GIT2KO mice. Significant age-dependent bodyweight variations observed between WT and GIT2KO mice (A). Thymic weight was reduced for all mice with increasing age (B) while only GIT2KO thymocytes, normalized to thymic weight, were reduced with age (C). Compared to age-matched 12 month old WT mice (left image panel) gross cortico-medullary thymic structure was disrupted in GIT2KO (right panel) mice (D: original magnification: 4x. Scale bar: 200 μm). Troma-I transcript expression is significantly reduced in GIT2KO thymus at 12 months of age compared to WT controls (E). WT and GIT2KO histogram data is indicated by black and lined bars respectively: mean ± SEM is indicated on each histogram. Months of age is abbreviated to m.o. *p<0.05, **p<0.01, ***p<0.001. Significantly-regulated (p<0.05) transcripts differentially expressed in GIT2KO versus WT thymus are indicated – specifically highlighted up- (red) or down-regulated transcripts are denoted by their official Gene Symbol (F). Ingenuity Pathway Analysis (IPA) Canonical Signaling Pathway analysis of transcripts differentially and significantly regulated (% of transcripts in pathway - upregulated in red, down-regulated in green are shown) between 12 month old GIT2KO and WT thymus (Top 10 enrichment probability pathways indicated: yellow line indicates pathway enrichment probability) (G). IPA BioFunction Z score activation analysis was performed on significantly-regulated differential transcripts from GIT2KO thymus compared to WT (Top 10 activation Z-score BioFunctions indicated) (H). Pathways/BioFunctions were only considered significantly populated with >2 transcripts at a p value of <0.05. Transcript arrays were performed in triplicate.

At the functional signaling pathway level (Figure 2G, Table S2) GIT2KO thymic transcriptomes possessed significant alterations in (i) energy metabolism (‘mitochondrial dysfunction’, ‘oxidative phosphorylation’, ‘TCA cycle’, ‘AMPK signaling’) and (ii) oxidative stress resistance pathways (‘NRF2-mediated oxidative stress response’, ‘Glutathione redox reactions’, ‘PI3K/AKT signaling’, ‘Glucocorticoid Receptor Signaling’); both processes strongly implicated in the advanced aging process [31,56,57]. Reinforcing our pathway analysis we also assessed IPA BioFunction predictions (Table S3) generated using the significantly-affected, compared to WT controls, GIT2KO thymus transcriptomic data. Upon inspection of the predicted stimulation or inhibition of these BioFunctions (generated using the input GIT2KO vs. WT thymus data) a strong pattern of immunological depression was evident, i.e. stimulation of ‘hypoplasia of lymphoid organ’ and ‘hypoplasia of primary lymphoid organ’ with a simultaneous inhibition of ‘quantity of lymphocytes’, ‘quantity of mononuclear leukocytes’, ‘quantity of leukocytes’ and ‘quantity of blood cells’ (Figure 2H, Table S3).

Early-onset thymic disruption engenders cervical parathymicand DN3 cell count lobe development in GIT2KO mice

In addition to alterations in gross thymic structure/functionality (Figure 2) in GIT2KO mice at 12 months these mice specifically at this timepoint presented novel large ‘parathymic lobes’ (PTLs: Figure 3A). We hypothesized these novel organs could represent modified lymphoid tissues, induced in-part as a compensatory mechanism for T cell development by the early-onset deterioration of the GIT2KO thymus. Cellular analysis of the extracted GIT2KO PTL tissues revealed similar total cell counts compared to inguinal lymph nodes (ILNs) from WT or GIT2KO mice, as well as GIT2KO thymic tissue (Figure 3B). PTLs however demonstrated significantly-reduced cell counts compared to WT thymic tissue (Figure 3B). Similar to the disrupted GIT2KO thymus, PTLs exhibited significantly reduced Troma-I/cytokeratin 8 transcript expression levels (Figure 3C). Assessing the immune cell lineages in these GIT2KO-specific PTLs, we found that PTLs possessed similar levels of Lin^- cells as WT thymi (Figure 3D). This PTL Lin^- level was significantly greater than that of GIT2KO thyme or WT/GIT2KO ILNs. GIT2KO PTL tissues demonstrated ETP, DN2 and DN3 cell counts significantly greater than any ILNs or thymi of WT or GIT2KO origin (Figure 3E-G). With respect to the levels of CD8⁺ (Figure 3H) or CD4⁺ (Figure 3I) cells PTLs possessed counts similar to those seen in WT or GIT2KO ILNs as opposed to the CD8⁺/CD4⁺ count levels seen in WT or GIT2KO thymi. Therefore it appears that the GIT2KO PTLs represent, at a functional level, a ‘pseudo-thymus’ (e.g. Lin^- cell counts) potentially from a lymph node origin (e.g. CD8⁺/CD4⁺) that still possesses some degree of unique functional nature (e.g. ETP/DN2/DN3 counts).

Figure 3. Development of idiosyncratic parathymic lobes in GIT2KO mice. Large parathymic lobes (PTLs) were consistently observed in 12 month old GIT2KO mice only (A: 1-thymus, 2-PTL). Total cell count data measured in WT and GIT2KO inguinal and mesenteric lymph nodes (ILN and MLN respectively), thymus and PTLs (GIT2KO only) (B). (C) PTLs express significantly lower Troma-I expression compared to WT or GIT2KO thymus (C). PTLs also demonstrate significantly-distinct patterns (compared to WT and GIT2KO thymus, ILN and MLN) of total counts for Lin^- (D), ETP (E), DN2 (F) and DN3 (G) T cell precursors, as well as for CD8⁺ (H) and CD4⁺ (I) cells. All values indicated are mean ± SEM. For histograms WT data are represented by solid black objects, with GIT2KO data represented by lined objects. Months of age is abbreviated to m.o. *p<0.05, **p<0.01, ***p<0.001.

Transcriptomic characterization of GIT2KO cervical PTLs

To gain an unbiased and comprehensive appreciation of the functional nature of the novel GIT2KO PTL organs we analysed the relative transcriptomic expression patterns present in PTLs and compared these to i) inguinal and mesenteric lymph nodes (ILNs and MLNs respectively) and ii) thymi of WT and GIT2KO mice. Using Principal Component Analysis (PCA), we found that global PTL transcriptomic data clustered independently of peripheral lymph nodes (ILN/MLN) from either WT or GIT2KO mice (Figure 4A). With a more targeted PCA, comparing thymus and ILNs from WT/GIT2KO with the PTLs, we found that PTLs shared one component (PC2) with ILN data, regardless of genotype, while sharing one component with GIT2KO thymic data (PC1) (Figure 4B). To analyze the differences in significantly-altered transcripts between PTLs and WT or GIT2KO tissues we performed multiple pair-wise transcriptomic comparisons. We compared significantly-regulated PTL transcriptomic expression patterns to those in both GIT2KO thymus (Table S4) and WT thymus (Table S5). 3-way Venn analysis was employed to identify any potential relationships between PTL-specific transcriptomic patterns (PTL transcriptomes compared to WT or GIT2KO thymus as ‘controls’) and those specific to the GIT2KO thymus (from Table S1, i.e. GIT2KO thymus compared to control WT thymus) (Table S6). Venn analysis revealed there were 22 upregulated, 30 contra-regulated (i.e. divergent expression polarities in at least two datasets), and 26 down-regulated transcripts shared across all the comparisons (Figure 4C). It is likely that the 30 contra-regulated common transcripts (all displaying expression polarity reversals between the PTL vs. GIT2KO thymus comparison and GIT2KO vs. WT thymus comparison) represents an indication of the core functional activity of the PTL compared to the dysfunctional GIT2KO thymus as well as a WT thymus. To further validate this specific data subset from the 30 contra-regulated common transcripts we independently measured Hsp90β1 and Itgb7 transcript levels in WT thymus, GIT2KO thymus and PTLs in 12 month old animals. These factors were chosen using GeneIndexer-based prioritization [58,59] using interrogator terms related to T cell function. In accordance with our transcriptomic array data, both Hsp90β1 and Itgb7 were upregulated in PTLs compared to WT and GIT2KO thymus (Figure 4D, E: Tables S4, S5). A large proportion of this PTL-based dataset is strongly linked with metabolic aging and clock regulation [60,61], cell senescence [62,63] and age-related immune dysfunction [64] (Figure 4F). For each of the significantly-regulated PTL transcripts in these general categories (Figure 4F) it was evident that their regulation extent (z ratio magnitude) was consistently greater when compare to the dysfunctional GIT2KO thymus as opposed to the WT thymus. This supports our suggestion that PTL ‘functionality’ may be a reflexive compensatory process for the accelerated thymic disruption seen in the GIT2KO mice. Supportive of this posit is our identification of PTL activation of transcripts linked to T cell selection and development (Irf8 [65]; Irf1 [66]), T cell differentiation (Bach2 [67]; Btla [68]), immune cell ion channel regulation (Hvcn1 [69]), cellular chemotaxis (H2dmb1 [70]), interferon regulation of immune function (Gvin [71]), T cell activity regulation (Hmgb2 [72]) and protein sorting/endocytosis (Chmp1b [73]; Ehd1 [74]).

Figure 4. Transcriptomic analysis of GIT2KO parathymic lobes. Principal component analyses were performed upon transcriptomic data from thymus (WT thymus = TWT; GIT2KO thymus = T GIT2^-/-), WT/GIT2KO ILN and MLN, as well as PTLs from GIT2KO mice. PTL transcriptome data separates with PCA from ILN and MLN from both WT and GIT2KO mice (A). PTLs share component 2 with GIT2KO thymi and component 1 with WT and GIT2KO ILN (B). Venn diagram analysis of significantly-regulated transcripts generated by the following tissue transcriptome comparisons: GIT2KO PTLs vs. GIT2KO thymus (black circle); GIT2KO PTL vs. WT thymus (green circle); GIT2KO thymus vs. WT thymus (blue circle). For the Venn diagram numbers in italics represent upregulated transcripts, underlined numbers represent downregulated transcripts, red numbers represent transcripts possessing diverse expression polarities (C). RT-PCR validations of selected PTL transcripts Hsp90β1 (D) and Itgb7 (E: **p<0.01, ***p<0.001). Transcriptomic z ratios of 30 common and coherently regulated (same expression polarity across three Venn sectors) transcripts generated from Venn separation of PTL-associated transcripts (F).

To further investigate the functionality of this unique 30 transcript PTL dataset we employed two orthogonal latent semantic indexing (LSI) informatic platforms, Textrous [75]! and Genes2WordCloud [76]. Textrous! and Genes2Wordcloud facilitate the creation of natural language-based scientific interpretations of small datasets. Using the collective processing mode of Textrous! a hierarchical wordcloud was generated that indicated a strong functional bias towards age-dependent, presenilin-focused and pro-degenerative activities such as amyloid processing (Figure 5A: Table S7 - for Cosine Similarity Scores, Probability Values and Z-scores associated with the wordcloud). Among the top 20 highest frequency words semantically associated with the input 30 transcript dataset were: early, onset, age, Alzheimer and amyloid. The strongest risk factor for most neurodegenerative conditions, including Alzheimer’s disease, is aging and therefore in this GIT2KO model it is not surprising that molecular signatures of advanced aging are present across multiple tissues in the body. It is interesting to note that presenilin-dependent gamma-secretase activity (processing Notch) has been demonstrated to modulate thymocyte development [77] and that significant immune function disruption is found in mutant presenilin-1 transgenic mice [78]. Using the mammalian phenotype analytical data module of Genes2Wordcloud to annotate the core 30 PTL transcripts we found that wordcloud interpretation reinforced the demonstration of a GIT2-specific immunological aging phenotype (Figure 5B). Among the top 20 highest frequency mammalian phenotype-based words semantically associated with the input 30 transcript dataset were: immune, lifespan, hematopoietic and homeostasis /metabolism.

Figure 5. LSI-based natural language investigation of the core 30 GIT2KO PTL-associated transcripts. The noun-phrase wordcloud for the 30 core PTL-associated coherently regulated transcripts was generated using the collective processing module of Textrous! coupled to proportional text representation output with Wordle. Noun-phrase frequency score analysis (histogram on left of panel A) of the resultant wordcloud was performed using WriteWords text analysis (A). In addition to LSI-based interpretation of the core 30 PTL-associated coherently regulated transcripts and orthogonal analysis was performed using Genes2wordcloud (Mammalian Phenotype annotation database: B). The wordcloud output in panel B is from Genes2wordcloud as is the associated histogram displaying the top 20 word frequencies from the cloud. For both wordclouds in panels A and B, the text size is directly proportional to the word/phrase frequency generated from either Textrous! or Genes2wordcloud.

Complementary signaling pathway analysis of GIT2KO PTLs and thymus reveals effective compensatory functional ‘transposition’

To complement our natural language processing-based interpretation of the core 30 PTL-specific transcripts we performed classical signaling pathway analysis to investigate the potential compensatory functionality of the de novo PTL in the GIT2KO mice. IPA-based canonical signaling pathway analysis was performed with significant transcriptomic data comparing GIT2KO thymus vs. WT thymus (Table S8) and PTL vs. GIT2KO thymus (Table S9). We directly compared the most-downregulated (assessed by % of specific pathway transcripts downregulated – indicative of the ‘loss’ of thymic functionality in the GIT2KO mice) signaling pathways in the GIT2KO thymus (Table S8) with the most upregulated (assessed by % of specific pathway transcripts upregulated) – indicative of potential compensatory signaling pathways in the GIT2KO PTL (Table S9). Differential signaling activity in the dysfunctional GIT2KO thymus (Table S8) revealed a large number of significantly downregulated transcripts enriching pathways involved in cell growth and development (‘GADD45 signaling’, ‘EGF receptor signaling’, ‘Role of p14/p19ARF in tumor suppression’), T cell functionality (‘T helper cell differentiation’, ‘Antigen presentation pathway’) and cytokine signaling (‘Role of Jak family kinases in IL-6 type cytokine signaling’, ‘IL-9 signaling’, ‘interferon signaling’) (Figure 6A – top 10 downregulated – Table S8). Similar analysis of pathways modulated differentially between the PTL and GIT2KO thymus (Figure 6B – top 10 upregulated; Table S9). Many pathways that contained the greatest number of transcripts up-regulated in the PTL were functionally similar (although oppositely-regulated) to those representing this loss of function (downregulated transcripts) in GIT2KO thymus, e.g. ‘T helper cell differentiation’, ‘Antigen presentation pathway’ and ‘Role of Jak2 family kinase in IL-6-type cytokine signaling’ (Figure 6B). To more comprehensively assess our hypothesized functional ‘transposition’ from the dysfunctional GIT2KO thymus to the PTLs, we assessed how much signaling pathway complementarity crossover existed between GIT2KO thymus and PTLs. Using signaling pathways containing the most down-regulated transcripts (pathways with >5% of transcripts down-regulated: Table S8) in GIT2KO thymus, and the corresponding group of signaling pathways containing the most up-regulated transcripts in the PTLs (pathways with >5% of transcripts up-regulated: Table S9), we found a considerable (25 signaling pathways; 50% of GIT2KO thymus and 25% of PTL pathways) functional cross-over between these two sets (Figure 6C). We analyzed these common functional groups and found an almost universal polarity reversal of regulation (with respect to percentage up- or downregulation of pathway-populating transcripts) between those in GIT2KO thymus data and the same pathways found using PTL data, indicating perhaps that multiple signaling functions lost in disrupted GIT2KO thymus were being reflexively stimulated in the PTLs (Figure 6D).

Figure 6. Functional signaling transposition between the GIT2KO thymus and PTLs. Signaling analysis of transcripts differentially and significantly regulated between GIT2KO and WT thymus (A). The top 10 pathways containing the most downregulated transcripts are indicated. Upregulated (red) or downregulated (green) transcripts populating these specific pathways are indicated in the histogram. Signaling analysis of transcripts differentially and significantly regulated between the PTL and the GIT2KO thymus (B). Venn diagram comparison of the functional cross-over between GIT2KO thymus pathways containing the greatest number of downregulated transcripts (>5%) with the GIT2 PTL pathways containing the greatest number of upregulated transcripts (>5%) (C). Histograms indicate the functional transcript expression nature of the 25 shared signaling pathways (from C) common between the GIT2KO thymus and the PTLs (D).

GIT2 genomic deletion generates a systemic alteration of age-related clock gene functionality in the immune system

From ours and previous research it is clear that GIT2 plays a profound role in immune system regulation: here we demonstrate that the immune system can also compensate for GIT2-associated disruption. To investigate the systemic actions of GIT2 deletion in multiple immune-related tissues we assessed the presence of consistent significantly-regulated transcriptomic expression patterns between the thymus (Table S1), ILNs (Table S10), MLNs (Table S11) and Spleen (Table S12) and of GIT2KO mice compared to WT controls. As previously demonstrated for GIT2KO thymus tissue (Figure S4) we also assessed the correlation between transcriptomic data and protein expression levels (for Ndufb10, Rnase4, Per1, Sin3a) in for example the spleen (Figure S5). For each factor we found that protein levels of these factors closely mirrored our transcriptomic data. Venn analysis of the significantly-regulated transcripts found in all of the four tissues studied (Figure 7A) revealed a core of 40 coherently-regulated transcripts common to all of the tissues (13 commonly upregulated, 27 commonly downregulated) (Table S13). We have previously shown that the Collective Processing module of Textrous! can efficiently generate meaningful biomedical semantic output from small data corpi [75]. Using the 40 commonly-regulated transcripts from GIT2KO ILN, MLN, spleen and thymus we found a remarkably strong and focused hierarchical wordcloud was created (Figure 7B: Table S14). The collective processing module of Textrous! employs latent semantic indexing (LSI) to assess the strength of correlation between all the members of the input dataset (40 coherently-regulated GIT2KO ILN/MLN/spleen/thymus transcripts) and semantically-associated nouns and noun-phrases from multiple curated biomedical databases. Biomedical textmining Cosine Similarity scores, ranging from 0 to a theoretical perfect textual correlation score of 1 [59], are employed to indicate the strength of the semantic correlation between datasets and specific words – here we found five output nouns possessing a near perfect Cosine Similarity score of over 0.9, i.e. ‘oscillators’, ‘clocks’, ‘circadian’, ‘rhythms’ and ‘rhythmicity’ (Table S14). Therefore it appears that the disruption of circadian clock-related transcript expression observed previously in the GIT2KO thymus is reproduced across multiple tissues involved in immune regulation across the body. Collective Textrous! processing attempts to find commonalities of textual output from a complete corpus of input data. Alternatively, the Individual Processing mode of Textrous! reveals the strongest individual transcript-word associations. With the same 40 transcript used for Collective Processing we found that the dataset possessed multiple input transcripts strongly linked to ‘clock regulation’, ‘transcription’, ‘metabolism/stress’ and ‘immune’ functionalities (Figure S6A). Creating a summated Z-score (addition of individual transcript z ratio scores (GIT2KO vs. WT expression)) for each of these functional categories of activity it was evident that a profound inhibitory activity towards ‘clock regulation’ was present in the input dataset (Figure S6B). Inspection of the individual transcripts coherently regulated across ILN/MLN/ spleen/thymus in GIT2KO mice demonstrated that, aside from the expected changes in immune-related factors (Btla [79], Tnfrsf4 [80], AI467606 [81]), there were significant reductions in multiple clock-related transcripts that are also associated with premature aging and DNA damage repair actions (DDR) (Per1 [22], Per2 [48], Tef [82], Dbp [83], LOC100044862-Fbxl3-like [84] as well as transcripts related to age-related stress/metabolism alterations and cell senescence (Glo1 [85], Ndufb10 [86], Ddit4 [87], Sin3a [88], Rnase4 [89] (Figure 7C). In addition to our LSI-based interpretation of the systemic effects of GIT2 genomic deletion we also individually annotated each significant GIT2KO dataset (ILN, MLN, spleen, thymus) using Ingenuity Pathway Analysis canonical signaling pathways (ILN Table S15; MLN Table S16; spleen Table S17, thymus Table S1). As with our analysis at the transcript level we aimed to identify the qualitative and quantitative nature of the coherently-regulated signaling pathways common to each of the experimental tissues (Figure 8A). Venn analysis revealed the presence of 13 coherently upregulated (calculated by creating a Z score of Up/Down regulated transcripts that significantly populate that specific pathway) and 4 coherently downregulated signaling pathways common to all the immune tissues (Table S18). The pathway Z scores, indicating the relative degree of activation (positive Z score) or inhibition (negative Z score) of the total of 17 coherently-regulated pathways are represented in Figure 8B. Corroborating our multiple observations of an aberrant aging process in the GIT2KO mice we found consistent and significant GIT2KO-specific alterations in aging-related pathways linked to clock gene disruption: ‘Mitochondrial dysfunction’ [90]; ‘AMPK Signaling’ [91]; ‘P2Y Purinergic Receptor Signaling pathway’ [92]; ‘P70S6K Signaling’ [93]; ‘Telomerase Signaling pathway’ [94]; ‘Superpathway of Cholesterol Biosynthesis’ [95]; ‘PI3K/AKT Signaling pathway’ [96]. In the context of these multiple interconnected pathways there are also a group of inter-related pathways linking immune cell function (‘PKCθ Signaling in T Lymphocytes’, ‘CD28 Signaling in T Helper Cells’, ‘iCOS-iCOSL Signaling in T Helper Cells’) with Ephrin receptor signaling and clathrin-mediated endocytic subcellular trafficking [97]. Taken together from both LSI-based and classical signaling pathway analysis it is evident that GIT2 genomic deletion engenders a premature state of cellular senescence/aging across multiple immune-related tissues.

Figure 7. GIT2 genomic deletion engenders a consistent transcriptomic signature across multiple immune tissues. Venn diagram analysis of the significant transcriptomic effects of GIT2 deletion in the ILN, MLN, spleen and thymus of GIT2KO mice compared to age-matched WT controls. In the Venn diagram numbers in italics represent upregulated transcripts, underlined numbers represent downregulated transcripts, red numbers represent transcripts possessing diverse expression polarities (A). Hierarchical wordcloud generated using the collective processing mode of Textrous! to investigate the functional nature of the 40 coherently-regulated transcripts common across GIT2KO ILN, MLN, spleen and thymus (B). Physical proximity of semantically-associated scientific words in public biomedical database curated documents indicates their strength of relationship. The most strongly associated words (with the entire input 40 transcript dataset) occur in the more intense red-hued regions of the cloud. A cumulative z ratio representation of the 40 coherently-regulated cross-tissue (ILN – black bars; MLN – red bars; spleen – green bars; thymus – blue bars) GIT2KO-spepcific factors indicates the strong presence of pro-aging/stress phenotype that is closely linked with clock gene dysfunction (stress/clock gene related transcripts possess Gene Symbols in bold typeface) (C).

Figure 8. GIT2 genomic deletion engenders a consistent signaling pathway signature across multiple immune tissues. Venn diagram analysis of the significant signaling pathway modulatory effects of GIT2 deletion (assessed using IPA-based canonical signaling pathway annotation of GIT2KO-specific significantly-regulated transcripts in ILN, MLN, spleen and thymus tissues) in the ILN, MLN, spleen and thymus of GIT2KO mice compared to age-matched WT controls. For each significantly-enriched signaling pathway derived from the respective transcriptomic datasets a pathway activation score was derived by subtraction of the number of downregulated transcripts from the number of upregulated transcripts that mediated the enrichment of the specific signaling pathway. In the Venn diagram numbers in italics represent upregulated transcripts, underlined numbers represent downregulated transcripts, red numbers represent transcripts possessing diverse expression polarities (A). The Venn diagram in panel A indicates that there are 17 coherently-regulated signaling pathways common to all tissues studied – 13 upregulated and 4 downregulated. The respective pathway scores of these pathways and their functional identities are indicated in the histogram in panel B. Pathways with a 0 pathway activation score were considered positive – a zero score indicated an even number of up and downregulated enriching transcripts.

Acknowledgments

We thank AML Laboratories for technical assistance with Oil Red O staining and the NIA Clinical Core laboratory for help with cell counts.

Conflicts of Interest

The authors declare no competing financial interests.

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