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Comparison of gene expression time series in vitro and in vivo

Comparison of gene expression time series in vitro and in vivo


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I have to analyse two datasets consisting of time-series measurements of gene expression. One set are in vivo data from the expression profiles obtained between a few days before birth to several days post-partum. The second data set was obtained in a C2C12 cell line, where t = 0 was defined as the day in which 100% confluence was reached.

Is there an optimal way to align the two time series? Simply aligning the two series using the two t= 0 samples as starting point seems arbitrary.


Direct comparison of the course would be difficult between tissues and cultured cells. In stead of time scale, you might want to use markers. For example, Kislinger et al. show the expression peak of SIX1 is 2 days after differentiation; SMAD3, 4 days. I am not a right person who can tell which markers are good, but I believe you could find more information by literature search.

http://www.mcponline.org/content/4/7/887.full


Comparison of gene expression during in vivo and in vitro postnatal retina development

Retina explants are widely used as a model of neural development. To define the molecular basis of differences between the development of retina in vivo and in vitro during the early postnatal period, we carried out a series of microarray comparisons using mouse retinas. About 75% of 8,880 expressed genes from retina explants kept the same expression volume and pattern as the retina in vivo. Fewer than 6% of the total gene population was changed at two consecutive time points, and only about 1% genes showed more than a threefold change at any time point studied. Functional Gene Ontology (GO) mapping for both changed and unchanged genes showed similar distribution patterns, except that more genes were changed in the GO clusters of response to stimuli and carbohydrate metabolism. Three distinct expression patterns of genes preferentially expressed in rod photoreceptors were observed in the retina explants. Some genes showed a lag in increased expression, some showed no change, and some continued to have a reduced level of expression. An early downregulation of cyclin D1 in the explanted retina might explain the reduction in numbers of precursors in explanted retina and suggests that external factors are required for maintenance of cyclin D1. The global view of gene profiles presented in this study will help define the molecular changes in retina explants over time and will provide criteria to define future changes that improve this model system.


In Vitro Fertilization

In vitro fertilization (IVF) is a series of procedures used to treat fertility or genetic problems and assist with the conception of a child. The process is in vitro (Latin for in glass), in contrast to in vivo (Latin for in something alive), because fertilization occurs outside the body. One cycle of IVF involves 1) administration of follicle stimulating hormone (FSH) to stimulate follicle production in the ovaries 2) extraction of mature eggs from a woman’s ovaries, 3) retrieval of a sperm sample from a man, and then 4) manual fertilization of the egg by sperm to produce an embryo in a laboratory dish. One or more embryo(s) are implanted in the uterus. The IVF cycle takes about two weeks. Eggs and embryos from an IVF procedure can also be frozen. Because inter-gamete contact occurs and fertilization takes place, the offspring still has DNA derived from both male and female parents. The first human conceived as a result of IVF (the first “test tube baby”) was born in 1978. The CDC now estimates that 1.5% of babies born in the US are conceived using Assisted Reproductive Technologies (ARTs).

IVF can be performed with a woman’s own eggs and a male’s sperm, or can involve eggs, sperm or embryos from a donor that may be known or anonymous. In some cases, a woman who is not the egg donor can serve as a carrier or surrogate by having an embryo implanted in her uterus.


Gene expression in the in vitro- produced preimplantation bovine embryos

Recent studies have demonstrated the relevance of a gene expression profile as a clinically important key feature determining embryo quality during the in vitro preimplantation period. Although the oocyte origin can play a crucial role in blastocyst yield, the postfertilization culture period has a profound effect in determining the blastocyst quality with particular regard to the relative abundance of many developmentally and clinically important candidate genes. During the preimplantation period, the embryo undergoes several morphogenetic developmental events including oocyte maturation, minor and major forms of embryonic genome activation and transition of transcription from maternal to embryonic control. The effect of an altered gene expression pattern on the in vitro -produced bovine embryos, particularly when cultured under suboptimal conditions, was reflected by the occurrence of clinically important phenomena like apoptosis and the large offspring syndrome. This review attempts to focus on the morphogenetic embryo development and gene expression profile in the in vitro -produced bovine embryos, with special emphasis on the different parameters that may alter gene expression pattern during the critical period of in vitro culture. The effect of the in vitro system, as reflected by some clinically important phenomena like apoptosis, is also discussed.


Abstract

Novel hyperbranched dendron (HD) polymers were synthesized using a low molecular weight poly(ethyleneimine) core (BPEI). Using successive attachment of ethyleneimine moieties to the PEI core, the relative ratio of linear-to-branched structures was lowered from 1.17 to 0.70. We found that the more extensive branching of PEI enables the condensation of plasmid DNA into nanostructures with a size of 70−100 nm. The obtained complexes were stable at least for 3 weeks at 4 °C. The HD−DNA complexes prepared using secondary and tertiary amine-containing dendrons exerted a very low cytotoxicity in vitro during a coincubation with cells for 48 h. Using firefly luciferase as a marker of protein expression, we established that HD complexes were efficient in transfecting cells in the presence of serum. Under optimized conditions, the transfection activity at a nitrogen-to-phosphate (N/P) ratio of 6 was approximately six times higher than that of the commercially available polycationic transfection reagent. Bioluminescent imaging of in vivo gene expression using a luciferase reporter gene showed the increase of the signal in the liver and in submandibular lymph nodes in live mice. Our preliminary in vivo gene expression data demonstrates the potential of HD polymers as in vivo transfection agents that could be potentially useful for lymph node gene delivery.


Conclusions and Future Directions

As synthetic biology joins ranks with other medical and scientific fields to tackle global challenges, such as providing low-cost, rapidly deployable diagnostics, success will continue to be found at the interfaces between disciplines. One example may include the use of technology to augment gene circuits, like the toehold switch, with hardware. As discussed here, the engineering approaches of synthetic biology have the potential to significantly advance the translation of promising technologies into pragmatic tools suitable for real-world applications. The paper-based platform is poised to play a prominent role in in vitro applications because it fits the need for low-cost, practical, and simple diagnostic tools for use outside of the laboratory, and the underlying concept may be merged with other technologies and extended in new and exciting directions. Additionally, existing in vivo sensors will begin to transition from their current research models to patient applications for real-time monitoring and early, personalized diagnosis. It is also intriguing to imagine in vitro synthetic biology embedded into diagnostic wearables for patients, medical personal, military personal or even athletes, allowing for both personal and environmental sensing. Bio-based wearables could also serve as an interface for in vivo diagnostics, alerting patients with preexisting conditions to presymptomatic changes in physiology. Ultimately, it will be the creativity embodied in collaborative synthetic biology that will guide this field into the future.


4 DISCUSSION

The present study aimed to determine whether mechanical loading of C2C12 fibrin bioengineered SkM in vitro recapitulates the transcriptional and DNA methylation signature of human and rodent SkM after RE in vivo. Data presented herein suggests that mechanical loading of mouse bioengineered SkM in vitro induced comparable transcriptional responses to human and rodent SKM after RE in vivo. However, while some DNA methylation modifications were detected following loading, such changes did not closely mimic the DNA methylation response to acute RE in humans.

The present study first sought to validate whether mechanical loading of mouse bioengineered SkM using the bioreactor employed herein was able to evoke a mechano-transcriptional response, similar to that previously observed using well-established bioreactor systems. Indeed, increases in mechano-sensitive genes, IGF-IEa, MGF, and MMP-9, together with a modest increase in IGF-I, were observed as previously reported following loading in the collagen-matrix bioengineered muscle (Aguilar-Agon et al., 2019 Cheema et al., 2005 Player et al., 2014 ). To directly compare the transcriptional and DNA methylation response to mechanical loading in mouse bioengineered muscle with that observed after acute RE in human and rodent muscle, mRNA expression of genes that have been recently identified to demonstrate differential methylation (following unbiased analysis of genome-wide DNA methylation) and corresponding alterations in gene expression post RE in humans (Seaborne et al., 2018a , 2018b ) were analyzed after loading. Interestingly, 93% (14 out of 15 genes, including UBR5, ODF2, RSU1, SETD3, GRIK2, RPL35a, AXIN1, TRAF1, STAG1, PLA2G16, KLHDC1, HEG1, AFF3, ZFP2, and BICC1) of these genes demonstrated no significant differences in gene expression between loaded mouse bioengineered muscle and human muscle at 0.5 h post-loading and RE, respectively. Moreover, no differences were observed between loaded bioengineered muscle and programmed RT in rodents for any of these genes. Overall, suggesting similar gene expression changes in mechanically loaded bioengineered muscle to that observed in human and rat muscle after RE in vivo. In an attempt to identify whether mechanical loading of bioengineered muscle mimicked the temporal gene expression profile after acute RE in humans over a longer timecourse, mRNA expression of genes that were upregulated across published transcriptome data sets post-acute RE in humans (Turner et al., 2019b ) with the corresponding hypomethylation of the same genes (Seaborne et al., 2018b , 2018a ) were analyzed in loaded bioengineered SkM. These genes were therefore analyzed at 0.5, 3, and 24 h post-loading in bioengineered mouse muscle to enable a direct comparison with humans after acute RE. First, the majority of these genes predominantly increased at 3 h post-loading and returned to basal levels at 24 h. This temporal gene regulatory profile has been observed several times in response to exercise in vivo in which gene expression tends to peak at 3–8 h post-exercise (Barrès et al., 2012 Chen et al., 2002 Drummond et al., 2008 Knuiman et al., 2018 Kuang et al., 2020 ) and generally returns to basal levels within 24 h (Egan & Zierath, 2012 Liu et al., 2010 Yang et al., 2005 ). When comparing the changes in gene expression at 3 h with the human transcriptome data, 83% of these genes (MSN, CTTN, FLNB, TIMP3, ITGB3, LAMA5, COL4A1, CRK, CD63, GSK3β, SMAD3, WNT9A, ITPR, STAT3, RARA, F2LR3, KDR, DOT1L) showed no significant difference in expression between loaded mouse bioengineered muscle and human muscle after acute RE. Gene expression for these genes was also compared between loaded bioengineered muscle and RT in rodents in vivo. Interestingly, only 2 out of 22 genes were statistically different, suggestive that 91% responded similarly between bioengineered and rodent muscle tissue. Overall, together with the aforementioned data above, these results suggest that mechanical loading in mouse bioengineered muscle sufficiently recapitulates the transcriptional response of SkM following RE in vivo.

It is also worth highlighting that the E3 ubiquitin ligase, UBR5, was the most upregulated gene at 3 h post-loading in bioengineered muscle alone compared with any gene analyzed in the present manuscript, significantly increased by 1.77-fold at 0.5 h and 2.34-fold at 3 h. Interestingly, this HECT domain E3 ubiquitin ligase was hypomethylated and upregulated after acute and chronic RE in untrained human participants, with enhanced hypomethylation and gene expression after later retraining (Seaborne et al., 2018a , 2018b , 2019 ). Such alterations were positively correlated with changes in lean mass (Seaborne et al., 2018a , 2018b , 2019 ). In rodent muscle, recent work has also confirmed that UBR5 gene expression and protein levels increase in response to hypertrophy in vivo, with no changes observed in the well-characterized atrogene E3 ligases, MuRF1 and MAFbx (Seaborne et al., 2019 ). Moreover, its role in muscle mass regulation has been recently determined whereby RNAi induced silencing in Drosophila results in smaller sized larvae (Hunt et al., 2019 ) and RNAi electroporated into mouse TA muscle in vivo leads to atrophy via reduced protein synthesis and dysfunctional ERK/Akt signaling (Hughes et al., 2020 ). Collectively, such data support the notion that UBR5 is important for load-induced anabolism and hypertrophy. Interestingly, UBR5 gene expression increased to a similar extent at 0.5 h post-loading in C2C12 bioengineered SkM (1.77-fold) with that observed post-acute RE in human SkM tissue (

1.7-fold Seaborne et al., 2018a , 2019 ), following programmed RT in rats (1.5-fold Seaborne et al., 2019 ) and at 3 h post mechanical loading in human myotubes in monolayer (1.6-fold, unpublished data by our group). However, no corresponding hypomethylation was observed after loading in bioengineered muscle in the present study. Indeed, GRIK2 was the only gene that was significantly hypomethylated after loading. Such findings are interesting given GRIK2 was significantly hypomethylated after a single bout of exercise in vivo, which was maintained

22 weeks later throughout training, detraining, and retraining (Seaborne et al., 2018b ), suggestive of a epigenetic memory signature. Furthermore, recent methylome analysis of acute overload in mouse plantaris muscle revealed intron region-specific hypomethylation of GRIK3 and GRIK4 genes in myonuclei and interstitial cells, respectively (Von Walden et al., 2020 ), suggestive of a cell-specific role of GRIK family genes in response to exercise/loading. Finally, TRAF1, MSN, and CTTN were significantly hypermethylated, which was in contrast to the hypomethylation observed in human muscle. The lack of DNA methylation changes, however, is interesting given that the transcriptional program was still similar. One explanation may involve the requirement for concentric contractions as the model employed herein resembles eccentric lengthening of the bioengineered muscle only. Therefore, these data may suggest that neural input induced by concentric contraction may be a more potent driver of DNA methylation perturbations in response to exercise in vivo. Indeed, active concentric contractions require substantial cycling of cytosolic calcium concentrations. This calcium signal could drive the phosphorylation of methyl CpG-binding protein 2 (MeCP2) associated with the induction of alterations in DNA methylation (reviewed in Seaborne & Sharples, 2020 ). The absence of secretory products from other cell types within the C2C12 bioengineered muscle constructs may also partially explain the differential epigenetic response to RE in vivo as recent work reports that communication between extracellular vesicles and the myofiber influences the response to loading in mouse muscle (Murach et al., 2020 ). To challenge this hypothesis, future studies, perhaps with the inclusion of electrical stimulation to drive concentric contraction and use of rodent (Khodabukus & Baar, 2015c ) or human primary muscle-derived cells (Martin et al., 2013 ) would be an important avenue of research (reviewed in Kasper et al., 2018 ).


Comparison of global gene expression between porcine testis tissue xenografts and porcine testis in situ

Center for Animal Transgenesis and Germ Cell Research, 147 Myrin Building, New Bolton Center, University of Pennsylvania, 382 West Street Road, Kennett Square, PA 19348.Search for more papers by this author

Center for Animal Transgenesis and Germ Cell Research, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, Pennsylvania

Center for Animal Transgenesis and Germ Cell Research, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, Pennsylvania

Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania

Center for Animal Transgenesis and Germ Cell Research, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, Pennsylvania

Center for Animal Transgenesis and Germ Cell Research, 147 Myrin Building, New Bolton Center, University of Pennsylvania, 382 West Street Road, Kennett Square, PA 19348.Search for more papers by this author

Abstract

Testis tissue from immature mammalian donor animals, grafted ectopically to immunodeficient mouse hosts, can undergo complete spermatogenesis with the production of fertilization-competent spermatozoa. To further characterize testis tissue xenografts as a model for testis function in situ, the objective of this study was to compare gene expression between porcine testis tissue xenografts and testis tissue in situ. Pieces of testis tissue from 1-week-old piglets were grafted onto immunodeficient male mice and a littermate piglet was raised for comparison as control. Complete spermatogenesis was present in the testis tissue xenografts at 8 months after transplantation into mouse hosts and in the 8-month-old control porcine testis tissue. Total RNA was isolated from xenografts and control tissue, and the RNA was labeled and hybridized to the porcine genome array. By analyzing the expression of 23,256 transcripts, we found that 71 genes were differentially expressed with at least a fourfold difference between xenografts and control tissue. Interestingly, none of the 56 transcripts present on the array that were annotated in porcine testis showed differential expression between xenografts and control testis. This analysis indicates that global gene expression in porcine testis xenografts appears comparable to testis tissue in situ. These findings support the hypothesis that testis tissue xenografts can provide a representative model to study mammalian spermatogenesis. Mol. Reprod. Dev. 74: 674–679, 2007. © 2006 Wiley-Liss, Inc.


RESULTS

Regional identity markers in human fetal intestine

Many genes with differential regional identity in the small intestine have been identified in the adult human and mouse intestine, where gene expression reflects adult region-specific intestinal function, as well as in the developing murine intestine. However, in the developing/fetal human intestine, genes corresponding to adult function are expressed at very low levels (Finkbeiner et al., 2015b), and therefore adult-stage markers do not faithfully identify regional identity in the embryo. Fortunately, several regional identity markers have been described in the fetal mouse intestine (Battle et al., 2008 Dusing et al., 2001 Gao and Kaestner, 2010 Sherwood et al., 2009). To identify a cohort of markers that are regionally expressed in the human intestine, we assessed expression of genes and proteins orthologous to those enriched in embryonic mouse regions by qRT-PCR, in situ hybridization and immunofluorescent staining (Fig. 1 n=5, independent biological samples ranging from 14-19 weeks of gestation). Intestines from human fetuses were obtained and divided into thirds, corresponding to the proximal, middle and distal regions of the small intestine. We observed that PDX1 and TM4SF4 were enriched in the proximal intestine, similar to the embryonic mouse proximal intestine (Sherwood et al., 2011), whereas the expression of GATA4 and ONECUT2 showed non-statistically significant trends of higher expression in the proximal human intestine (Fig. 1A). Of note, some individual samples had more pronounced region-specific expression of GATA4 and ONECUT2 when technical replicates were examined (Fig. S1), and regional-specific expression was confirmed for ONECUT2 using in situ hybridization (Fig. 1C), suggesting that region-specific gene expression may be dynamic over time, or may vary significantly between biological specimens however, additional studies at each time point will be required to more conclusively assess biological variation or time-dependent changes. Guca2a, Osr2, Muc2, Fzd10, Cib2 and several Hox genes have higher distal gene expression levels in mice (Gao and Kaestner, 2010 Sherwood et al., 2009). In the human fetal intestine, GUCA2A, OSR2 and MUC2 showed increased expression in the distal small intestine (Fig. 1B), along with HOXB6 (Fig. S1). We confirmed proximal enrichment of PDX1 and ONECUT2 and distal enrichment of MUC2 and GUCA2A using immunofluorescence and in situ hybridization (Fig. 1C). Together, these data identify a cohort of molecular markers that are regionally expressed in the human fetal small intestine and demonstrate that some of these regional identifiers are conserved between the mouse and human fetal small intestine.

Identification of regionally expressed molecular markers in the human fetal intestine. (A) Genes known to be enriched in the proximal developing mouse intestine, including PDX1, GATA4, TM4SF4 and ONECUT2 were examined in different regions of the human fetal intestine (n=5 individual biological specimens proximal, blue middle, red distal, green). (B) Genes know to be enriched in the distal developing mouse intestine, including GUCA2A, OSR2, MUC2 and FZD10 were examined in different regions of the human fetal intestine (n=5 individual biological specimens proximal, blue middle, red distal, green). (C) Enrichment of PDX1 and GATA4 protein as assessed by immunofluorescence and of ONECUT2 mRNA as assessed by in situ hybridization was confirmed in the proximal region of the fetal intestine, whereas GUCA2A mRNA and MUC2 protein were enriched in the distal fetal intestine when assessed by in situ hybridization and immunofluorescence, respectively. Scale bars: 200 μm.

Identification of regionally expressed molecular markers in the human fetal intestine. (A) Genes known to be enriched in the proximal developing mouse intestine, including PDX1, GATA4, TM4SF4 and ONECUT2 were examined in different regions of the human fetal intestine (n=5 individual biological specimens proximal, blue middle, red distal, green). (B) Genes know to be enriched in the distal developing mouse intestine, including GUCA2A, OSR2, MUC2 and FZD10 were examined in different regions of the human fetal intestine (n=5 individual biological specimens proximal, blue middle, red distal, green). (C) Enrichment of PDX1 and GATA4 protein as assessed by immunofluorescence and of ONECUT2 mRNA as assessed by in situ hybridization was confirmed in the proximal region of the fetal intestine, whereas GUCA2A mRNA and MUC2 protein were enriched in the distal fetal intestine when assessed by in situ hybridization and immunofluorescence, respectively. Scale bars: 200 μm.

Prolonged WNT/FGF signaling distalizes hESC-derived intestinal organoids

To examine the effects of WNT and FGF signaling on developing human intestinal tissue, we took advantage of the hESC-derived intestinal organoid culture system (Spence et al., 2011). hESCs were exposed to activin A for 3 days to induce endoderm, which was then exposed to CHIR99021/FGF4-enriched media to induce mid/hindgut spheroid formation, as previously described (Finkbeiner et al., 2015a,b Xue et al., 2013). Spheroids began to bud from monolayer cultures after 4 days and continuously generated new spheroids for over 10 days however, incubation beyond 10 days resulted in far fewer spheroids (data not shown). Spheroids were collected from the cultures after 5 days (d5), 7 days (d7) or 10 days (d10), and embedded into matrigel (Fig. 2A). Spheroids were expanded into larger human intestinal organoids for 30-35 days in intestinal growth medium containing EGF, noggin and R-spondin 2 (Fig. 2A). qRT-PCR performed on tissues collected at progressive stages of organoid differentiation, including undifferentiated hESCs, definitive endoderm, hindgut tissue after 4 days of CHIR99021/FGF4 and organoids generated after d5, d7 and d10, showed the expected stage-specific mRNA expression of pluripotency genes (OCT4), endoderm genes (FOXA2, SOX17) and the mid-hindgut and intestinal specification gene CDX2 (Fig. 2B).

Human intestinal organoids are patterned by FGF and WNT signaling. (A) Schematic of experimental design showing spheroids generated in culture over increasing periods of time. (B) Expression of OCT4, FOXA2, SOX17 and CDX2 during differentiation in undifferentiated hESCs, in endoderm and hindgut (4 days after FGF4/CHIR99021), and in organoids derived from d5, d7 and d10 cultures (d5, blue d7, red d10, green). (C) Markers shown to be enriched in the human fetal duodenum (Fig. 1), including PDX1, GATA4, TM4SF4 and ONECUT2 were examined in d5, d7 and d10 organoids. (D) Markers shown to be enriched in the human fetal ileum (Fig. 1), including MUC2, OSR2, MUC2 and FZD10 were examined in d5, d7 and d10 organoids. (E) Immunofluorescence demonstrated that PDX1 protein expression was enriched in d5 organoids, whereas MUC2 protein expression was enriched in d7 and d10 organoids. Scale bars: 200 μm.

Human intestinal organoids are patterned by FGF and WNT signaling. (A) Schematic of experimental design showing spheroids generated in culture over increasing periods of time. (B) Expression of OCT4, FOXA2, SOX17 and CDX2 during differentiation in undifferentiated hESCs, in endoderm and hindgut (4 days after FGF4/CHIR99021), and in organoids derived from d5, d7 and d10 cultures (d5, blue d7, red d10, green). (C) Markers shown to be enriched in the human fetal duodenum (Fig. 1), including PDX1, GATA4, TM4SF4 and ONECUT2 were examined in d5, d7 and d10 organoids. (D) Markers shown to be enriched in the human fetal ileum (Fig. 1), including MUC2, OSR2, MUC2 and FZD10 were examined in d5, d7 and d10 organoids. (E) Immunofluorescence demonstrated that PDX1 protein expression was enriched in d5 organoids, whereas MUC2 protein expression was enriched in d7 and d10 organoids. Scale bars: 200 μm.

We next evaluated the effects of exposing human DE to CHIR99021 and FGF4 for different lengths of time, and examined the expression of region-specific markers of the developing human intestine identified in Fig. 1. Similar to the human fetal intestine, we found that d5 human organoids had significantly higher expression of the proximal identity marker genes PDX1, TM4SF4, GATA4 and ONECUT2 compared with d7 and d10 organoids (Fig. 2C). Conversely, distally enriched intestinal genes GUCA2A, OSR2 and MUC2 were expressed at significantly higher levels in d10 organoids compared with d5 and d7 organoids (Fig. 2D). Interestingly, whereas FZD10 was not regionally expressed in the distal human fetal intestine, it was significantly higher in d10 organoids (Fig. 2D). Although further exploration of this observation is warranted, one possible explanation for this discrepancy is that organoids may represent an earlier stage of development than the human fetal intestine used in this study. Immunostaining confirmed that PDX1 was more abundant in d5 organoids, and that MUC2 was more abundant in d7 and d10 organoids (Fig. 2E).

To further validate our findings, we conducted a series of additional experiments (Fig. S2). We treated endoderm for 5 days with FGF4 plus CHIR99021 (500 ng/ml FGF4, 2 μM CHIR99021), and then varied the concentration of CHIR99021 for the next 5 days, or alternatively, removed CHIR99021 and added IWP2, a WNT inhibitor, or the FGF and ERK inhibitors SU5402 or U0126, respectively (Fig. S2). Spheroids from all conditions were embedded in matrigel and expanded into organoids for 30 days, and were then compared for regional gene expression. As expected, d5 and d10 organoids demonstrated region-specific gene expression when grown in standard conditions (as in Fig. 2, 500 ng/ml FGF4+2μM CHIR99021). However, when CHIR99021 concentrations were reduced, d10 organoids expressed much higher levels of proximal markers but this did not lead to reduced posterior marker gene expression. Interestingly, when WNT signaling was blocked between d5 and d10 (FGF4+IWP2), expression of proximal genes was enhanced and distal gene expression was reduced when compared with d5 and d10 organoids, respectively. These data suggest that, during distalization, one of the roles of WNT/β-catenin may be to repress proximal genes while inducing posterior genes (Fig. S2). In total, these experiments show that prolonged exposure to high levels of CHIR99021 is required for expression of distal intestinal markers.

Regional identity is maintained over time in vitro

It is unclear whether organoids change over time in culture, and it is also unknown whether regional identity might also be determined by the length of time spent in culture. To test this, we generated d5, d7 and d10 organoids, and examined them after 1 month and after 90 days in culture (compare Fig. 2 with Fig. S3). Although some individual regional identity markers changed over time in culture, the trends were similar at the two different time points: d5 organoids had more abundant proximal marker expression with low distal marker expression whereas d10 organoids had low proximal marker expression with enriched distal marker expression.

Whole-transcriptome profiling by RNA sequencing demonstrates that organoids are patterned into proximal and distal intestine

Because the expression patterns of several markers of human fetal proximal-distal intestinal identity suggested that d5 organoids are similar to human duodenum and d10 organoids are similar to human fetal ileum (Figs 1 and 2), we next took an unbiased approach to confirm these results. We conducted RNA-sequencing (RNAseq) at different stages of differentiation, including undifferentiated hESCs (H9 hESC line), DE and organoids grown for 30 days from each stage of spheroid formation [5 day organoids (OD5), 7 day organoids (OD7) and 10 day organoids (OD10)].

As a first step towards an unbiased assessment of the hypothesis that d5, d7 and d10 organoids are patterned into proximal or distal intestine, we determined unique stage-specific gene expression patterns in each of our RNAseq datasets. Using non-negative matrix factorization (NNMF) (Brunet et al., 2004), we identified gene expression programs that were highly enriched at one stage among the various conditions (Fig. 3A, Table S1). Each program corresponds to a cohort of genes that are statistically enriched at only one stage. In order to determine whether d5 gene expression programs corresponded to duodenal genes and d7/10 gene expression programs corresponded to ileal genes, we conducted a hypergeometric test to compare organoid gene expression programs (enriched gene sets) against regional identity gene sets of the fetal mouse and human intestine previously identified by microarray (Sherwood et al., 2009 Wang et al., 2015) (Fig. 3B). In this analysis, it was important to compare gene expression programs with data obtained from fetal, as opposed to adult, intestine because, as we have recently demonstrated, organoids resemble fetal intestine and do not express many of the genes found in the adult organ (Finkbeiner et al., 2015b). This analysis revealed that d5 gene sets had statistically significant overlap with genes expressed in the duodenum of the mouse (Sherwood et al., 2009 ‘Sherwood duo’) and human (Wang et al., 2015 ‘Wang duo’) (P<1.0×10 −18 for both), whereas the d10 gene set had statistically significant overlap with genes expressed in the ileum of the mouse (Sherwood et al., 2009 ‘Sherwood ileum’ P<1.0×10 −18 ) and human (Wang et al., 2015 ‘Wang ileum’ P<2.22×10 −16 ) (Fig. 3B, Table S2). Importantly, we could not resolve whether d7 organoids are similar to jejunum based on Sherwood et al. and Wang et al., because the gene sets exclusively expressed in this region were very small and did not allow us to perform the statistical comparisons with confidence. Thus, we have limited our conclusions to d5 and d10 organoids. Overlapping genes identified in the duodenum/d5 organoid and ileum/d10 organoids comparisons (Fig. 3B) were further plotted as a heatmap (Fig. 3C). As a control, we also compared d5 and d10 gene expression programs against genes that are enriched in the human fetal colon (Wang et al., 2015). Here, we found no statistically significant overlap, adding confidence to our conclusion that d5 and d10 organoids are most similar to human duodenum and ileum, respectively (Fig. S4).

Bioinformatic identification of stage-enriched genes and comparison with published datasets. (A) Non-negative matrix factorization was used to identify stage-enriched genes. A normalized and curated heatmap shows representative genes (the full list is in Table S1). ES, human embryonic stem cells DE, definitive endoderm OD5, day 5 organoids OD7, day 7 organoids OD10, day 10 organoids. (B) Enriched genes in d5, d7 and d10 organoids were compared with published lists of genes whose expression is regionally restricted to the duodenum or the ileum. A hypergeometric test was used to determine the level of significance of overlapping gene sets. (C) Heatmap of representative genes found to overlap in patterned organoids and in published datasets shows enrichment for ileal genes in d10 organoids and for duodenal genes in d5 organoids.

Bioinformatic identification of stage-enriched genes and comparison with published datasets. (A) Non-negative matrix factorization was used to identify stage-enriched genes. A normalized and curated heatmap shows representative genes (the full list is in Table S1). ES, human embryonic stem cells DE, definitive endoderm OD5, day 5 organoids OD7, day 7 organoids OD10, day 10 organoids. (B) Enriched genes in d5, d7 and d10 organoids were compared with published lists of genes whose expression is regionally restricted to the duodenum or the ileum. A hypergeometric test was used to determine the level of significance of overlapping gene sets. (C) Heatmap of representative genes found to overlap in patterned organoids and in published datasets shows enrichment for ileal genes in d10 organoids and for duodenal genes in d5 organoids.


Similarities in Gene Expression Profiles during In Vitro Aging of Primary Human Embryonic Lung and Foreskin Fibroblasts

Replicative senescence is of fundamental importance for the process of cellular aging, since it is a property of most of our somatic cells. Here, we elucidated this process by comparing gene expression changes, measured by RNA-seq, in fibroblasts originating from two different tissues, embryonic lung (MRC-5) and foreskin (HFF), at five different time points during their transition into senescence. Although the expression patterns of both fibroblast cell lines can be clearly distinguished, the similar differential expression of an ensemble of genes was found to correlate well with their transition into senescence, with only a minority of genes being cell line specific. Clustering-based approaches further revealed common signatures between the cell lines. Investigation of the mRNA expression levels at various time points during the lifespan of either of the fibroblasts resulted in a number of monotonically up- and downregulated genes which clearly showed a novel strong link to aging and senescence related processes which might be functional. In terms of expression profiles of differentially expressed genes with age, common genes identified here have the potential to rule the transition into senescence of embryonic lung and foreskin fibroblasts irrespective of their different cellular origin.

1. Introduction

Cellular senescence is a terminal phase observed towards the end of a primary human fibroblast cell population after numerous cell divisions it is considered to be the cellular aging process. Cellular senescence occurs either naturally or stress induced that is, cells stop dividing after a finite number of cell divisions (termed “replicative senescence”), reaching the final cell cycle arrested state called the “Hayflick limit” [1]. The process of senescence is associated with a number of phenotypes in general, the integrity and function of tissues decline, resulting in the body being susceptible to diseases associated with age [2, 3]. Key factors driving cellular senescence are induced increase in Cyclin dependent kinase inhibitors (CDKIs) [4], oxidative stress [5], and DNA damage [6, 7]. In senescence, despite their viability and active metabolism, cells are resistant to mitogenic or apoptotic stimuli [8, 9]. On the one hand, cellular senescence results in irreversible growth arrest, limiting the proliferation of damaged cells susceptible to neoplastic transformation resulting in a decreased incidence of cancer. However, on the other hand, senescence results in in vivo aging, weakening the function and renewal of stem cells [10]. Markers are able to identify cellular senescence in vitro and in vivo: enlarged cell morphology, increase in amount of cellular debris, changes in chromatin structure, increase in Cyclin dependent kinase inhibitors (CDKIs) expression, presence of senescence associated secretory phenotype (SASP), and senescence associated ß-galactosidase (SA ß-Gal) [11–13]. DNA damage response and the p53-p21 and p16-pRb pathways are crucial for senescence induction [14], together with additional pathways including telomere uncapping, DNA damage (UV, ionizing radiation, and chemicals), cytoskeletal genes, the interferon pathway, nutrient imbalances, oncogenic activities, and oxidative stress [7, 15, 16]. In primates, the percentage of senescent skin fibroblasts increases with age in vivo [17]. Here, we therefore used primary human fibroblasts [9, 13, 18] as our model system.

Recently, we identified individual gene expression patterns during replicative senescence among five fibroblast cell lines of different cell origins [13]. In this study, we determined mRNA expression changes during different stages of their lifespan in two fibroblast cell lines of different cell origin. We analyzed the transcriptome, determined by RNA-seq, at five separate population doublings (PDs) between young and senescent embryonic lung (MRC-5) and foreskin (HFF) fibroblasts. Using both molecular and systems biology approach, we studied the growth pattern of the two fibroblast cell lines in detail. By comparing fibroblasts from two different origins we were able to determine either mRNA changes specific for one of the cell lines or common transcriptomic patterns which underlie the process of replicative senescence.

2. Materials and Methods

2.1. Cell Lines

Primary human fibroblasts (MRC-5, primary cells, 14-week-gestation male, fibroblasts from normal lung, normal diploid karyotype) were obtained from ATCC (LGC Standards GmbH, Wesel, Germany). Human foreskin fibroblasts (HFFs primary cells, fibroblasts from foreskin, normal diploid karyotype) cells were kind gifts of T. Stamminger (University of Erlangen [19]).

2.2. Cell Culture

Primary human fibroblast cells were cultured in Dulbeccos Modified Eagle’s Low Glucose Medium (DMEM) with L-glutamine (PAA Laboratories, Pasching, Austria), supplemented with 10% fetal bovine serum (FBS) (PAA Laboratories) under normal air conditions in a 9.5% CO2 atmosphere at 37°C. The cells were subcultured by removing the remaining medium followed by washing in 1x PBS (pH 7.4) (PAA Laboratories) and detachment using trypsin/EDTA (PAA Laboratories). Primary fibroblasts were subcultured in a 1 : 4 (= 2 population doublings (PDs)) or 1 : 2 (= 1 PD) ratio. For stock purposes, cryoconservation of the cell lines at various PDs was undertaken in cryoconserving medium (DMEM + 10% FBS + 5% DMSO). Cells were immediately frozen at −80°C and stored for two to three days. Afterwards, cells were transferred to liquid nitrogen for long time storage. Refreezing and rethawing was not performed to avoid premature senescence [20].

A vial of each of the two fibroblast cell lines (MRC-5 and HFF) was obtained and maintained in culture from an early PD. On obtaining enough stock on confluent growth of the fibroblasts in 75 cm 2 flasks, cells were subcultured into three separate 75 cm 2 flasks and were passaged until they were senescent in culture. At five different time points of the fibroblast’s span in culture (MRC-5 = PDs 32, 42, 52, 62, and 72 and HFFs = PDs 16, 26, 46, 64, and 74), the total RNA was extracted and used for high-throughput sequencing.

2.3. Detection of Senescence Associated ß-Galactosidase (SA ß-Gal)

The SA β-Gal assay was performed as described by [11] at each of the five PDs in both MRC-5 and HFF. Paired two-sample type 2 Student’s

-tests assuming equal variances were done to examine the values obtained from SA ß-Gal assay for statistical significance [9].

2.4. Western Blotting

The protocol was carried out as explained in [9, 21]. The optimal concentration of all primary antibodies was estimated in primary human fibroblasts. Primary antibodies are as follows: anti-p21 mouse antibody (OP64 Calbiochem dilution 1 : 200), anti-p15 rabbit antibody (4822 Cell Signaling Technology 1 : 250), anti-p16 mouse antibody (550834 BD Pharmingen 1 : 200), anti-p27 rabbit antibody (sc-528 Santa Cruz 1 : 200), anti-Cyclin B1 mouse antibody (CCNB1 ab72 Abcam 1 : 1000), anti-Eg5 rabbit antibody (KIF11 ab61199 Abcam 1 : 500), anti-Histone H1.2 rabbit antibody (HIST1H1C ab17677 Abcam 1 : 1000), anti-ID3 mouse antibody (ab55269 Abcam 1 : 100), anti-Cathepsin K rabbit antibody (CTSK ab19027 Abcam 1 : 50), anti-DKK3 goat antibody (ab2459 Abcam 1 : 5000), anti-TMEM47 rabbit antibody (SAB1104840 SIGMA-Aldrich 1 : 250), anti-IGFBP7 rabbit antibody (ab74169 Abcam 1 : 500), anti-IGFBP2 rabbit antibody (ab91404 Abcam 1 : 500), anti-MMP3 rabbit antibody (ab53015 Abcam 1 : 200), anti-Thymosin beta 10 rabbit antibody (TMSB10 ab14338 Abcam 1 : 10000), anti-Egr1 mouse antibody (ab55160 Abcam 1 : 100), anti-RPS23 mouse antibody (ab57644 Abcam 1 : 200), anti-LIF mouse antibody (SAB1406083 SIGMA-Aldrich 1 : 100), anti-FBL rabbit antibody (SAB1101099 SIGMA-Aldrich 1 : 500), anti-Id1 rabbit antibody (ab52998 Abcam 1 : 500), anti-IL11 rabbit antibody (ab76589 Abcam 1 : 500), anti-CLDN11 rabbit antibody (HPA013166 SIGMA-Life Sciences 1 : 50), anti-NADH Dehydrogenase subunit 6 rabbit antibody (MT-ND6 ab81212 Abcam 1 : 1000), anti-MT-ND5 rabbit antibody (ab83985 Abcam 1 : 500), anti-Granulin rabbit antibody (GRN ab108608 Abcam 1 : 1000), anti-Cyclin D1 rabbit antibody (CCND1 2922 Cell Signaling 1 : 500), anti-Cyclin D2 mouse antibody (CCND2 ab3085 Abcam 1 : 500), anti-Cyclin A2 rabbit antibody (CCNA2 NBP1-31330 Novus Biologicals 1 : 1000), anti-Wnt16 rabbit antibody (ab109437 Abcam 1 : 500), anti-Cystatin C rabbit antibody (CST3 ab109508 Abcam 1 : 10000), anti-MOXD1 mouse antibody (SAB1409086 SIGMA-Aldrich 1 : 200), anti-PERP rabbit antibody (ab5986 Abcam 1 : 500) and anti-tubulin mouse antibody (T-9026 SIGMA-Aldrich 1 : 5000). After development of film in the Western Blots procedure, intensity of the signals was quantified using Metamorph software [22]. The signal intensity values were examined for statistical significance using unpaired two-tailed two-sample Student’s -tests assuming unequal variances.

2.5. RNA Extraction

Total RNA was isolated using Qiazol (Qiagen) according to the manufacturer’s protocol, with modifications as explained in [9].

2.6. Quantitative Real-Time PCR

Real-time PCR was performed using CFX384 thermocycler Biorad and Quantitect PCR system (Qiagen) as described earlier in [23]. Three reference genes (GAPDH, ACTB, and RAB10) were used for normalization of the CT values. Since our RNA-seq results revealed a stable expression of RAB10 for both cell lines across the PDs, it was selected as reference gene. An unpaired two-tailed two-sample Student’s -tests assuming unequal variances was used for examination for statistical significance based on the ΔCT values.

2.7. RNA Sequencing

For quality check, total RNA was analyzed using Agilent Bioanalyzer 2100 (Agilent Technologies) and RNA 6000 Nano Kit (Agilent) to ensure appropriate RNA quality in terms of degradation. The RNA integrity number (RIN) varies between 8 and 10 with an average of around 9.65. Total RNA was used for Illumina library preparation and next-generation sequencing [24]. About 2.5 μg total RNA was used for indexed library preparation using Illumina’s TruSeq RNA Sample Prep Kit v2 following the manufacturer’s instruction. Libraries were pooled and sequenced (4 samples per lane) using a HiSeq2000 (Illumina) in single read mode with 50 cycles using sequencing chemistry v3. Sequencing resulted in approximately 43 million reads with a length of 50 bp (base pairs) per sample. Reads were extracted in FASTQ format using CASAVA v1.8.2 (Illumina).

2.8. RNA-seq Data Analysis

Raw sequencing data were received in FASTQ format. Read mapping was performed using Tophat 2.0.6 [25] and the human genome references assembly GRCh37.66 (http://feb2012.archive.ensembl.org). The resulting SAM alignment files were processed using featureCounts v1.4.3-p1 [26] and the respective GTF gene annotation, obtained from the Ensembl database [27]. Gene counts were further processed using the R programming language [28] and normalized to RPKM values. RPKM values were computed using exon lengths provided by featureCounts and the sum of all mapped reads per sample.

2.9. Sample Clustering and Analysis of Variance

Spearman correlation between all samples was computed in order to examine the variance and the relationship of global gene expression across the samples, using genes with raw counts larger than zero. Correlation values were visualized using a heatmap (Figure 1). Additionally, principal component analysis (PCA) was applied using the log 2 RPKM values for genes with raw counts larger than zero. Results were visualized in a three-dimensional scatterplot (Figure 2).

2.10. Detection of Differential Expression

The Bioconductor packages DESeq 1.10.4 [29] and edgeR 3.4.2 [30] were used to identify differentially expressed genes. Both packages provide statistics for determining of differential expression in digital gene expression data using a model based on the negative binomial distribution. The nonnormalized gene counts have been used here, since both packages include internal normalization procedures. The resulting

values were adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate (FDR) [31]. Genes with an adjusted value < 0.05 found by both packages were assigned as differentially expressed. Since large sets of DEG were found more strict selection cutoffs have been used: adjusted value < 0.01 (by both packages) and absolute log 2 fold-changes > 1. See Supplemental Table 1 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/731938 for complete test results.

2.11. Comparison of RNA-seq with qRT-PCR and Protein Expression

Correlation analysis was performed using all 15 samples (3 replicates for each of the 5 PDs) for MRC-5 and HFF, respectively. Spearman correlation coefficients were estimated using the RPKM values (RNA-seq data) and

values (qRT-PCR data). For comparison of RNA-seq with Western Blot data, only the first and the last PD were used. Log 2 fold-changes were calculated based on RPKM values (RNA-seq data),

ratios (rRT-PCR), and protein expression ratios (Western Blots).

2.12. Clustering of Expression Profiles

Genes were clustered according to their temporal profiles using a fuzzy

-means algorithm. We used the function cmeans from the package e1071 1.6-2 of the R programming language. Parameters were defined as

, iter.max = 500, d.obj.fun = 10 −8 . The number of trials for the fuzzy algorithm was set to 30. The optimum number of clusters was determined using a combination of several cluster validation indexes as described by [32]. See Supplemental Table 2 for detailed assignment of the genes to clusters.

2.13. Functional Enrichment Analysis

Singular gene set enrichment analysis was performed using FungiFun2 [33] for selected sets of genes based on the clustering results. Although FungiFun2 is mainly suited for fungal gene enrichment analysis annotation for human genes is included as well and was recently updated. Default parameters were used while significant Gene Ontology (GO) terms and KEGG pathways were selected according to FDR corrected values < 0.05. Complete lists of GO-terms and KEGG pathways are available from Supplemental Table 3. The list of GO-terms was further summarized using TreeMaps of the REVIGO online tool [34]. Default parameters and GO term with adjusted values were used as input.

2.14. Monotonically Expressed Genes

In order to identify genes that change their expression levels monotonically with age, we calculated the Spearman correlation coefficient

of each gene’s temporal profile with the linearly increasing curve

. In order to incorporate the replicates at each time point, we repeated the calculations by randomly sampling over the replicates at each time point and by calculating an average correlation coefficient from the resampled curves afterwards. values were computed using the R base function cor.test. We used the calculated correlation coefficient of gene with the linear increasing curve as a criterion to split the genes into the following three groups: if

, we considered a gene to be monotonically increasing with age, if and , the gene was considered to be monotonically decreasing with age, and if , the expression of the corresponding gene was considered nonuniformly [35]. See Supplemental Table 4 for detailed test results.

2.15. Functional Association Networks

Gene symbols were used as input for functional association network creation using the STRING database [36], Cognoscente [37], and GeneMANIA [38] online tools. For STRING we used “multiple names” input and selected “Homo sapiens” as organism. In Cognoscente we selected “Human” and “Radius = 0 with intermediates” as input parameters in addition to the list of genes. The GeneMANIA network was created using default settings. The resulting networks are shown in Figure 9 and Supplemental Figure 6.

3. Results and Discussion

We studied the growth of two primary human fibroblast cell lines, MRC-5 and HFF, throughout their span in culture from an early PD until they achieved senescence at late PDs. Analysis of their growth behaviour (Supplemental Figure 1A) and their entry into senescence (Supplemental Figure 1B), measured by the induction of SA β-Gal, revealed a cell line specific transition into senescence of these two fibroblasts (MRC-5 derived from embryonic lung and HFFs derived from human foreskin). Fibroblast cell line specific growth has been observed by us before [13, 39]. Total RNA was extracted at five different time points of the fibroblasts span in culture and was subjected to high-throughput RNA sequencing (RNA-seq).

3.1. Global Expression Profiles Cluster according to Cell Line and Age

Overall, the RNA-seq data of this study comprise 30 samples: 15 samples for each cell line (HFF and MRC-5), consisting of five different PDs, each with three biological replicates. For each sample, mapping and counting resulted in 56,299 raw gene count expression values (using Ensembl gene annotation). The largest group of these genes (21,226) belongs to the group of protein coding genes. For all the 30 samples, 19,237 genes have raw gene counts larger than zero these genes were considered for further analysis.

First, we studied primary clustering of the global gene expression. We therefore created a heatmap showing the Spearman correlation for all 30 samples using the nonzero genes (Figure 1). In this heatmap, both cell lines were clearly separated. In eight out of ten cases, the three values of the replicates were clustered together, showing the good quality of the data and low noise between the replicates. Next, we applied principal component analysis (PCA) to further investigate the effect of aging in the individual cell lines. Figure 2 shows the first three principal components which explain

97% of the variances in a three-dimensional plot. MRC-5 and HFF again were clearly separated (by PC2) and the effect of aging was covered by PC1 and PC3, with a larger separation between young and old HFF compared to MRC-5. Already at this global level, similarity between both cell lines is perceptible, since young and senescent samples are grouped concordantly.

3.2. MRC-5 and HFF Share Common Differentially Expressed Genes Regulated by Aging

Differentially expressed genes (DEG) were identified by comparing all consecutive PDs as well as the first with the last PD in MRC-5 and HFF cells (10 comparisons Table 1). Figures 3(a) and 3(c) show the absolute number of DEG found as well as the intersection of sets of DEG (indicated by color). Overall, considering all five comparisons in each cell line, we identified more DEG in HFF (14,511) compared to MRC-5 (10,517). Due to the strong effect of aging on gene expression and the large number of detected DEG, more stringent selection cutoffs ( and |log 2 fold-change| > 1) were used beyond the standard value threshold of 0.05 (Figures 3(b) and 3(d)). Figure 3 reveals that DEG were not specific for a certain PD comparison but recurred when later PDs were compared. MRC-5 and HFF shared a large fraction of DEG only a minor fraction of DEG was identified uniquely in one of the cell lines (bars on the right in Figure 3). This indicates common processes which occur during aging in both cell lines rather than cell line specific changes. Most DEG were found when comparing the first with the last PD, leading to new DEG which had not previously been detected between consecutive PDs (orange and turquoise coloured bars in Figure 3). Both cell lines differed between the absolute number of DEG as well as the increased percentage of DEG for the first two transitions in HFF (PD 16 to PD 26 PD 26 to PD 46). In MRC-5, most of the changes seemed to occur at late PDs, while HFF cells indicated larger changes already after early PDs. This effect is also perceivable by the distances in the PCA plot (Figure 2).

3.3. High Correlation of RNA-seq with qRT-PCR for Selected DEG

For validation of the RNA-seq data, qRT-PCR was applied. Here, triplicates for all five PDs were measured. Selection of genes was based on the comparison of the first with the last PD, using the strict DEG criteria ( and |log 2 fold-change| > 1), resulting in 2,117 DEG for MRC-5 and 4,651 DEG for HFF (5th and 10th bars in Figures 3(b) and 3(d)). We further filtered the intersection of those two gene sets (1,139) according to common differences in both cell lines. The majority (917) of these DEG were commonly regulated, either up (385) or down (532), showing again the similarity of gene expression changes in both cell lines. Overall 12 DEG were selected which either showed strong expression in the RNA-seq data (RPKM > 50 genes: EGR1, CCND1, CTSK, DKK3, IGFBP7, and TMEM47) or were proven to have an established role in cell cycle and senescence pathways (CCNA2, CCNB1, ID3, IGFBP2, MMP3, and WNT16). The minimal RPKM criterion was applied to ensure a strong expression signal in at least one condition for a set of genes. The expression profiles from both measurement techniques were then confronted using Spearman rank correlation in each individual cell line. The results showed high correlation coefficients indicating a good overlap of both measurement techniques and quality of high-throughput gene expression analysis (Figure 4). In 17 out of 24 cases, correlation was larger than 50% (mean correlation of 63%), while only once negative correlation was found (EGR1 in MRC-5).

3.4. Consistent Changes of mRNA and Protein Expression

Although mRNA expression changes are generally considered to consequently lead to corresponding changes in protein levels, correlation between both can be as little as 40%, as observed in large-scale proteome- and transcriptome-profiling experiments [40]. We thus asked if the detected changes of strongly altered DEG correlated with corresponding protein expression levels. Triplicates of the first and last PD were selected for comparison. Gene selection was performed as described above, either by strong expression in RNA-seq (RPKM > 35) or by functional relation to cell cycle and senescence pathways. Overall, 28 DEG (16 down- and 12 upregulated DEG) were selected (the genes mentioned above, validated by qRT-PCR, were included in this set). The results of this comparison showed consistent changes, in terms of their direction of regulation, between mRNA expression, measured by RNA-seq, and protein expression, measured by Western Blots, for all selected genes (Figures 5 and 6). 44 out of the 56 protein fold-changes exhibited significant differences between young and old PDs.

) are indicated with an asterisk:

, , and . (c) The blots show the protein expression levels in MRC-5 and HFF cells at young compared to old PDs. The up- or downregulation was signified by the presence or absence of bands in Western Blots.

3.5. Common Genes Ruling the Transition into Senescence in MRC-5 and HFF

Then, we asked if common cellular markers are involved in the transition into senescence. We thus studied the genes most differentially expressed with age commonly in MRC-5 and HFF fibroblasts. We noticed that a large number of genes among the most differentially expressed genes belonged to the secretory phenotype (Figure 8(a), as explained in Section 3.7). The list of genes included CTSK, normally stimulated by inflammatory cytokines released after tissue injury [41], GRN, a previously functionally validated gene responsible for wound healing [42], CST3, associated with sarcopenia [43], and PERP, a p53 apoptosis effector, the mRNA expression level of which is upregulated in human mesenchymal stem cells [44]. We detected significant upregulation of IGFBP2 which was found upregulated with senescence in retinal pigment epithelial cells [45, 46] and BJ fibroblasts [47]. ID1, ID3, CCNA2, and CCNB1 showed significant downregulation with age in our study for both human fibroblasts. Downregulation of ID1 and ID3 expression with senescence was detected in BJ foreskin, WS1 fetal skin, and LF1 lung human fibroblasts [48] and of CCNA2 in IMR-90 and WI-38 [49]. Targeting CCNB1 expression inhibits proliferation of breast cancer cells [50]. The list of most differentially expressed genes also included IGFBP7 and MMP3 which encode protein receptors predominantly located on the cell surface. Both IGFBP7 and MMP3 are upregulated with senescence in human melanocytes [51–54]. Recently we found that overexpression of recombinant IGFBP7 proteins induced premature senescence in early PD MRC-5 fibroblasts [13].

Among the genes significantly upregulated with age in both MRC-5 and HFFs we identified DKK3, having a role in Wnt signaling [55–57]. DKK3 has tumor suppressor activity in breast cancer patients [58] and in papillary thyroid carcinoma [57]. However, we had failed to demonstrate an induction of premature senescence in early PD HFFs on overexpression of recombinant DKK3 proteins [13]. Though not significantly differentially expressed with age in MRC-5 fibroblasts, one of the genes which were most significantly upregulated with age in HFFs was the SFRP4 gene, an antagonist for Wnt signalling [59]. SFRP4 acts as a tumor suppressor in gastric carcinoma [60] and epithelial ovarian cancer cell lines [61]. In a separate study, we functionally validated the expression of SFRP4 in early PD HFF and MRC-5 fibroblasts by treating them separately with human recombinant SFRP4 protein. This treatment resulted in premature senescence induction in HFFs but not in early PD MRC-5 fibroblasts [13]. Here, induction of SFRP4 mRNA expression was not detected by RNA-seq, explaining the lack of premature senescence induction in early PD MRC-5 fibroblasts. SFRP4 expression thus showed cell line specific differences.

3.6. Clustering of the Expression Profiles Shows Similar Pattern in Both Cell Lines

We found many differentially expressed genes commonly regulated in both MRC-5 and HFF. Next, we asked if both cell lines exhibit common temporal expression profiles rather than showing different effects for the same set of genes. Therefore, we applied fuzzy -means clustering comparing the expression profiles of both cell lines. We used 1,803 genes found to be differentially regulated between the four consecutive PDs and between the first and the last PD in both cell lines, according to the strict cutoffs as shown in Figures 3(b) and 3(d) (FDR < 0.01 |log 2 fold-change| > 1). Using several cluster validation indexes, an optimal number of five clusters were estimated, and each selected DEG was assigned to one out of these five groups (Figure 7). The majority of DEG exhibits similar temporal expression profiles in MRC-5 and HFF. 811 DEG were upregulated (clusters 3 and 5) and 722 are downregulated (clusters 2 and 4). Stronger differences between both cell lines were found for genes grouped in clusters 1 and 4. Interestingly, most genes follow a monotonic profile (either up or down) while only few genes exhibit a parabolic-like shape. Clusters 1 and 3 show major changes in MRC-5 between the second to last and the last PD, while cluster 4 groups genes with large differences between the first and the second PD in HFF. This effect was already observed when comparing DEG between the consecutive PDs (see DEG section above). Figure 7 summarizes the gene expression profiles by showing only the scaled and centred mean and standard deviation of the DEG clustered. In most of the cases, the absolute expression values were different between both cell lines (indicated by the dashed horizontal lines) but the trends of the actual changes across the five PDs were similar. For instance, cluster 3 contains genes which show larger mean expression values for MRC-5 but are upregulated with increasing PDs in both cell lines (vice versa in cluster 2).

3.7. Identification of Functional Categories Significantly Enriched for Genes with Common Expression Profiles

Next, we deduced the main biological processes driven by the differentially expressed gene sets obtained from the cluster analysis. Using gene set enrichment analysis, for each of the five clusters significant GO categories and KEGG pathways could be identified. The results indicated a strong connection of upregulated genes (grouped in clusters 3 and 5) to “extracellular space” (GO:0005615) and “membrane” (GO:0016020) components (Figure 8(a)). Corresponding KEGG pathways, found for these genes, were for example, “ECM-receptor interaction” (hsa04512) and “ABC transporters” (hsa02010 see Supplemental Table 3). ABC proteins transport various molecules across extra- and intracellular membranes and are involved in aging and age-related diseases [62]. Cluster 3 shows stronger upregulation at late PD for MRC-5 cells while HFF cells are upregulated more clearly in cluster 5 (see above). Comparing the GO-terms found for these single clusters, we found links of the stronger upregulation in MRC-5 with “integral component of plasma membrane” (GO:0005887) and the “Golgi apparatus” (GO:0005794), while “sarcolemma” (GO:0042383) and “nucleosome” (GO:0000786) were more specific for upregulation in HFF (Supplemental Figure 2). The structure of the secretion regulating Golgi complex is altered in senescent cells [63]. While our results indicate cell line specific differences during replicative senescence, the GO-term comparison revealed that in both cell lines many genes were similarly upregulated. A large set of GO-terms associated with upregulated genes were related to the senescence associated secretory phenotype [64].

Downregulated genes (grouped in clusters 2 and 4) were associated to strongly enriched GO processes related to, for example, “cell cycle” (GO:0007049), “cell division” (GO:0051301) and “DNA replication” (GO:0006260) (Figure 8(b)). Here, differences between both cell lines are more obvious. After a slight initial gain, expression in MRC-5 cells declined strongly between PD 46 and PD 64. In HFFs, strong decline already started after PD32 without larger changes for late PD (Figure 7 cluster 4). Most of these cell cycle related genes, which account for the above-mentioned profiles, are related to the cellular component “nucleoplasm” (GO:0005654). Associated GO-terms for cluster 2, which depicts moderate downregulation, were more widespread and covered processes like “positive regulation of nitric oxide biosynthetic process” (GO:0045429), “endoderm formation” (GO:0001706), and “response to cAMP” (GO:0051591 Supplemental Figure 3).

Cluster 1 showed the largest differences between both cell lines. While, in MRC-5, genes are downregulated strongly at the last PD, no clear up- or downregulation is observed for HFF. Significantly enriched GO-terms associated to these genes were, for example, “vasculogenesis” (GO:0001570), “response to lipopolysaccharide” (GO:0032496), and “cell adhesion” (GO:0007155) (Supplemental Figure 4).

3.8. Monotonically Regulated Genes in MRC-5 and HFF Are Connected in Functional Association Networks

Since senescence is a continuous cellular process, it can be hypothesized that genes possessing key relevance for senescence change their expression values monotonically over time, while genes with irregular temporal expression patterns might be associated with response to environmental conditions, with the circadian rhythm or other processes.

Amongst others, continuous increasing and decreasing profiles were found by the clustering analysis. In addition to this nonbiased approach, we intended to identify genes with a strong monotonic behaviour across the PDs investigated here. We calculated the Spearman correlation coefficient of each gene’s temporal profile with a linearly increasing sequence. Replicates for each PD were incorporated by a random sampling approach. Subsequently, we classified genes into three classes according to their behaviour with age: (a) monotonically upregulated genes, (b) monotonically downregulated genes, and (c) nonuniformly regulated genes (Table 2).

for complete test results.

More monotonically up- and downregulated genes were found for HFFs compared to MRC-5 (888 versus 179). Only a small subset of these genes were commonly regulated in both cell lines (9 up and 14 down) but even less genes showed an opposite monotonic expression profiles (8 see Supplemental Figure 5 and Supplemental Table 4). The 23 commonly monotonically up- or downregulated genes were studied in more detail. Since in both cell lines the regulation of these genes strongly correlated with an increase of senescence, they might play an essential role in cellular aging and may rule common regulatory process. We used several online resources in order to find potential or validated interactions between these genes. The STRING database [36] only provides the interactions between four out of all the 23 genes (Supplemental Figure 6A). Using Cognoscente [37], 17 out of 23 genes were connected within one interaction graph (Supplemental Figure 6B). More interactions could be found using GeneMANIA [38], leading to a network which is widely connected by coexpression and common pathways like, for example, “epithelial cell proliferation” and “extracellular matrix organization” (Figure 9). Both of the latter tools integrate intermediate genes which were not in the input list. Hub genes in these networks included ATF7, MAF, UBC, and ELAVL which are interesting candidates for further studies. All the four of these genes were functionally associated with tumorigenesis. Members of the ubiquitin family including UBC have been associated with tumor progression [65]. In terms of ATF7, the activating transcription factor family is associated with cell proliferation and oncogenesis [66]. Both MAF and ELAV1 have been associated with oncogenesis and tumor progression [67, 68]. Thus all the four genes had an association with cell proliferation. Then, we investigated the biological relevance of the monotonically up- and downregulated genes in both fibroblast cell lines. The list of monotonically downregulated genes included NLE1, AMMECR1, FIBCD1, ENPP2, TMTC4, ANPEP, MYC, EFNB3, HCLS1, FERMT1, FABP5, SPHK1, GOS2, and RPL36A. The genes monotonically upregulated included LRP10, TMCO3, CAV2, ADAMTS5, C5orf15, SDC2, ANKH, PCDHB16, and TGFB2. A number of genes in the above list have been functionally associated with proliferation.

3.8.1. Monotonically Downregulated Genes

NLE plays a role in regulating the Notch activity and is involved in embryonic development in mammals by affecting the CDKN1A and Wnt pathways [69]. Forced expression of miR-26 inhibits the growth of stimulated breast cancer cells and tumor in xenograft models by reducing the mRNA expression levels of AMMECR1 and other genes [70]. AMMECR1 is associated with Alport syndrome, mental retardation, midface hypoplasia, and elliptocytosis [71]. FIBCD1 (fibrinogen C domain containing 1) binds to chitin of invading parasites [72]. FIBCD1 is primarily present in the gastrointestinal tract of humans however, their presence in skin has been highly debated [73, 74]. ENPP2 facilitates cell motility and progression and is related to the invasion of ductal breast carcinomas [75]. TMTC4 is a gene contributing to embryonic brain development it interacts with Wntless, an integral Wnt regulator [76]. EFNB3, a member of the ephrin gene family, is associated with neural development [77]. ANPEP is a well-known marker for acute myeloid leukemia and tumor invasion it has a regulatory role in angiogenesis [78, 79]. FERMT1 is overexpressed in colon and lung carcinomas [80]. The MYC oncogene is associated with cell growth regulation by driving proliferation via upregulation of Cyclins and downregulation of p21 [81, 82]. HCLS1 gene which is monotonically downregulated with age is associated with antigen receptor signaling and clonal expansion as well as deletion of lymphoid cells [83]. The FABP5 gene encodes the fatty acid binding protein in epidermal cells and is upregulated in psoriatic tissues [84]. SPHK1 has been previously associated with melanoma progression and angiogenesis [85, 86]. The GOS2 gene promotes apoptosis by binding to BCL2, hence preventing the formation of protective BCL2-BAX its mRNA and protein levels are downregulated in type 2 diabetic patients [87, 88]. Thus, almost all genes, monotonically downregulated with age in both fibroblast cell lines, are associated with proliferation and cell survival.

3.8.2. Monotonically Upregulated Genes

LRP10, a negative regulator of Wnt signalling, was found monotonically upregulated with age [89]. CAV2 is a scaffolding protein within the caveolar membrane modulating cancer progression [90]. ADAMTS5 enables the destruction of aggrecan in patients with arthritic disease which is prevalent with aging [91]. The ANKH gene, associated with regulation of tissue calcification and in turn susceptibility to arthritis, is also monotonically upregulated with age in both fibroblast cell lines [92]. Syndecan-2 protein (SDC2) is upregulated in skin and lung tissues of patients suffering from (age-associated) systemic sclerosis and fibrosis [93, 94]. mRNA expression of PCDHB16 is upregulated in patients with (age-associated) Alzheimer’s disease [95]. TGFB2, also monotonically upregulated with age in fibroblasts, has suppressive effects on interleukin-2 dependent T cell proliferation and displays effector functions [96].

In summary, the genes, which we found here monotonically up- and downregulated with age in both fibroblast cell lines, have been studied before. In this study, we explicitly show for the first time the age-associated regulation of these genes in primary human fibroblast cells of two different origins. In a following study we will determine the protein expression of all age-related genes and functionally validate the expression of these genes.

4. Conclusion

We studied molecular aspects of cellular aging by determining the differential expression of genes during the aging of two primary human fibroblasts, MRC-5 and HFFs. RNA-seq data analysis encompassed different levels, starting from the complete set of annotated and expressed genes, proceeding to different gene subsets and functional categories. Most of the detected changes were found to be common in both cell lines, as indicated by the large number of overlapping DEG and common expression profiles identified by clustering. We validated the expression patterns for selected genes, demonstrating an association of almost all most differentially expressed genes with proliferation or cell cycle arrest, consistent with previous senescence studies. Investigating expression changes across five consecutive PDs and comparing young with senescent cells enabled us to identify both monotonically up- and downregulated genes as well as the most differentially expressed genes. Both sets of genes strongly contributed to the transition into cellular senescence. Thus, we quantitatively describe similarities in gene expression profiles during the aging of two fibroblast cell lines of different origin.

Data Deposition

The RNA-seq data discussed in this publication have been deposited in NCBIs Gene Expression Omnibus and are accessible through GEO Series accession number GSE63577.

Conflict of Interests

The authors declare that they have no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Shiva Marthandan and Steffen Priebe contributed equally to this work.

Acknowledgments

The work described here is part of the research program of the Jena Centre for Systems Biology of Ageing (JenAge). The authors acknowledge JenAge funding by the German Ministry for Education and Research (Bundesministerium für Bildung und Forschung (BMBF) support code: 0315581). The authors would like to thank Sabine Ohndorf and Sabine Gallert for excellent technical assistance.

Supplementary Materials

Suppl. Figure 1: (A) Growth curve of MRC-5 and HFF fibroblasts derived from a single vial and maintained in culture as triplicates from an early PD until senescence at late PDs. Data points of all measurements are displayed (not the mean). PDs specified in the plot denoted the time points were the total RNA was collected and subjected to RNA-seq. Black line denotes MRC-5 and green denotes HFF (B) Percentage of SA-β Gal positive cells at different time points of their growth in culture in MRC-5 and HFF fibroblasts. Each curve is measured in triplicate, the mean value is displayed with error bar (± S.E). Red line denotes MRC-5 and black denotes HFF.

Suppl. Figure 2: TreeMaps produced using REVIGO (http://revigo.irb.hr) summarizing: (A) 12 GO cellular component (CC) terms found for cluster 3. (B) 21 GO CC terms found for cluster 5. Both clusters contain up-regulated genes but exhibit differences between MRC-5 and HFF.

Suppl. Figure 3: TreeMaps produced using REVIGO (http://revigo.irb.hr) summarizing: (A) 59 GO biological processes (BP) terms found for cluster 2. (B) 162 GO BP terms found for cluster 4. Both clusters contain down-regulated genes.

Suppl. Figure 4: TreeMaps produced using REVIGO (http://revigo.irb.hr) summarizing 24 GO BP terms found for cluster 1.

Suppl. Figure 5: Venn plots showing the number of detected significantly monotonically expression pattern. (A, B): up-/down-regulated in both MRC-5 and HFF. (C) up-regulated in MRC-5 and down-regulated in HFF. (D): vice versa of (C).

Suppl. Figure 6: Functional association networks generated using (A) STRING DB and (B) Cognoscente including 23 genes found to be monotonically expressed across the five PD. (A) Only connections between four genes were found using STRING DB. Different line colors represent the types of evidence for the association, explained on the right. (B) Nodes outlined in red are the 23 input genes. There were no interactions found for PCDHB16. 19 intermediates were included by the tool. The edge colors indicate the type of interaction, as explained in the legend on top.

Column 1-4: gene annotation.

Column 5-10: RPKM values for each single sample.

Column 11-13: mean RPKM values and log2 fold-change.

Column 14-15: adjusted p-values (FDR) by DESeq and edgeR.

Column 1-4: gene annotation.

Column 5-14: mean RPKM values of MRC-5 and HFF and all five PD.

Column 15: cluster membership number [1-5].

Supplementary Table 3: Excel table with functional enrichment analysis (FungiFun). The file contains 9 sheets: One sheet for each single cluster used for GO enrichment analysis two sheets with GO results for combined clusters and two sheets with KEGG results for combined clusters. Each sheet contais the pathway/term IDs, the corresponding names, toplevels, exact p-values, adjusted p-values, number of genes per pathway/term and per input list.

Column 1-3: gene annotation.

Column 4-5: Spearman correlation values and p-values (cor.test) for MRC-5.

Column 6-7: Spearman correlation values and p-values (cor.test) for HFF.

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Copyright © 2015 Shiva Marthandan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.