Archive for the ‘Embryology’ Category

Reporter and Curator: Dr. Sudipta Saha, Ph.D.


Scientists at the Stanford University School of Medicine have completed the first-ever characterization of the meticulously timed immune system changes in women that occur during pregnancy. The findings were published in Science Immunology revealed that there is an immune clock of pregnancy and suggest it may help doctors predict preterm birth.


The timing of immune system changes follows a precise and predictable pattern in normal pregnancy. Although physicians have long known that the expectant mother’s immune system adjusts to prevent her body from rejecting the fetus, no one had investigated the full scope of these changes, nor asked if their timing was tightly controlled.


Nearly 10 percent of U.S. infants are born prematurely, arriving three or more weeks early, but physicians lack a reliable way to predict premature deliveries. Previous research at Stanford and other places suggested that inflammatory immune responses may help in triggering early labor. It suggested that if scientists identify an immune signature of impending preterm birth, they should be able to design a blood test to detect it.


The researchers used mass cytometry, a technique developed at Stanford, to simultaneously measure up to 50 properties of each immune cell in the blood samples. They counted the types of immune cells, assessed what signaling pathways were most active in each cell, and determined how the cells reacted to being stimulated with compounds that mimic infection with viruses and bacteria.


The researchers developed an algorithm that captures the immunological timeline during pregnancy that both validates previous findings and sheds new light on immune cell interaction during gestation. By defining this immunological chronology during normal term pregnancy, they can now begin to determine which alterations associate with pregnancy-related pathologies.


With an advanced statistical modeling technique, introduced for the first time in this study, the scientists then described in detail how the immune system changes throughout pregnancy. Instead of grouping the women’s blood samples by trimester for analysis, the model treated gestational age as a continuous variable, allowing the researchers to account for the exact time during pregnancy at which each sample was taken. The mathematical model also incorporated knowledge from the existing scientific literature of how immune cells behave in nonpregnant individuals to help determine which findings were most likely to be important.


The study confirmed immune features of pregnancy that were already known. Such as the scientists saw that natural killer cells and neutrophils have enhanced action during pregnancy. The researchers also uncovered several previously unappreciated features of how the immune system changes, such as the finding that activity of the STAT5 signaling pathway in CD4+T cells progressively increases throughout pregnancy on a precise schedule, ultimately reaching levels much higher than in nonpregnant individuals. The STAT5 pathway is involved in helping another group of immune cells, regulatory T cells, to differentiate. Interestingly, prior research in animals has indicated that regulatory T cells are important for maintaining pregnancy.


The next step will be to conduct similar research using blood samples from women who deliver their babies prematurely to see where their trajectories of immune function differ from normal.


This study revealed a precisely timed chronology of immune adaptations in peripheral blood over the course of a term pregnancy. This finding was enabled by high-content, single-cell mass cytometry coupled with a csEN algorithm accounting for the modular structure of the immune system and previous knowledge. The study provided the conceptual backbone and the analytical framework to examine whether disruption of this chronology is a diagnostically useful characteristic of preterm birth and other pregnancy-related pathologies.




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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


Babies born at or before 25 weeks have quite low survival outcomes, and in the US it is the leading cause of infant mortality and morbidity. Just a few weeks of extra ‘growing time’ can be the difference between severe health problems and a relatively healthy baby.


Researchers from The Children’s Hospital of Philadelphia (USA) Research Institute have shown it’s possible to nurture and protect a mammal in late stages of gestation inside an artificial womb; technology which could become a lifesaver for many premature human babies in just a few years.


The researchers took eight lambs between 105 to 120 days gestation (the physiological equivalent of 23 to 24 weeks in humans) and placed them inside the artificial womb. The artificial womb is a sealed and sterile bag filled with an electrolyte solution which acts like amniotic fluid in the uterus. The lamb’s own heart pumps the blood through the umbilical cord into a gas exchange machine outside the bag.


The artificial womb worked in this study and after just four weeks the lambs’ brains and lungs had matured like normal. They had also grown wool and could wiggle, open their eyes, and swallow. Although this study is looking incredibly promising but getting the research up to scratch for human babies still requires a big leap.


Nevertheless, if all goes well, the researchers hope to test the device on premature humans within three to five years. Potential therapeutic applications of this invention may include treatment of fetal growth retardation related to placental insufficiency or the salvage of preterm infants threatening to deliver after fetal intervention or fetal surgery.


The technology may also provide the opportunity to deliver infants affected by congenital malformations of the heart, lung and diaphragm for early correction or therapy before the institution of gas ventilation. Numerous applications related to fetal pharmacologic, stem cell or gene therapy could be facilitated by removing the possibility for maternal exposure and enabling direct delivery of therapeutic agents to the isolated fetus.





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Embryonic Stem Cell differentiation

Larry H. Bernstein, MD, FCAP, Curator



Plant Homeo Domain Finger Protein 8 Regulates Mesodermal and Cardiac Differentiation of Embryonic Stem Cells Through Mediating the Histone Demethylation of pmaip1

Yan Tang1, Ya-Zhen Hong1, Hua-Jun Bai1, Qiang Wu1, Charlie Degui Chen2, Jing-Yu Lang1, Kenneth R. Boheler3 and Huang-Tian Yang1,4,*


Histone demethylases have emerged as key regulators of biological processes. The H3K9me2 demethylase plant homeo domain finger protein 8(PHF8), for example, is involved in neuronal differentiation, but its potential function in the differentiation of embryonic stem cells (ESCs) to cardiomyocytes is poorly understood. Here, we explored the role of PHF8 during mesodermal and cardiac lineage commitment of mouse ESCs (mESCs). Using a phf8 knockout (ph8-/Y) model, we found that deletion ofphf8 in ESCs did not affect self-renewal, proliferation or early ectodermal/endodermal differentiation, but it did promote the mesodermal lineage commitment with the enhanced cardiomyocyte differentiation. The effects were accompanied by a reduction in apoptosis through a caspase 3-independent pathway during early ESC differentiation, without significant differences between differentiating wide-type (ph8+/Y) and ph8-/Y ESCs in cell cycle progression or proliferation. Functionally, PHF8 promoted the loss of a repressive mark H3K9me2 from the transcription start site of a proapoptotic gene pmaip1 and activated its transcription. Furthermore, knockdown ofpmaip1 mimicked the phenotype of ph8-/Y by showing the decreased apoptosis during early differentiation of ESCs and promoted mesodermal and cardiac commitment, while overexpression of pmaip1 or phf8 rescued the phenotype of ph8-/Y ESCs by increasing the apoptosis and weakening the mesodermal and cardiac differentiation. These results reveal that the histone demethylase PHF8 regulates mesodermal lineage and cell fate decisions in differentiating mESCs through epigenetic control of the gene critical to programmed cell death pathways. Stem Cells2016


Significance Statement

Embryonic stem cells (ESCs) have the unique ability to differentiate into derivatives of all three germ layers both in vitro and in vivo. Thus, ESCs provide a unique model for the study of early embryonic development. We report here previously unrecognized effects of histone demethylase plant homeo domain finger protein 8 (PHF8) on mesodermal and early cardiac differentiation. This effect is resulted from the regulation of PHF8 on apoptosis through activating the transcription of pro-apoptotic gene pmaip1. These findings extend the knowledge in understanding of the epigenetic modification in apoptosis during ESC differentiation and of the link between apoptosis and cell lineage decision as well as cardiogenesis.


Embryonic stem cells (ESCs) have the unique ability to differentiate into derivatives of all three germ layers both in vitro and in vivo. Due to this plasticity, mechanisms controlling cell autonomous and regulatory events critical to in vivo mammalian development have benefitted from the in vitro study of differentiating ESCs [1, 2]. Early embryogenesis and cavity formation as well as early ESC differentiation, for example, are accompanied by a reduction in proliferation and increased apoptosis [3-5]. Withdrawal of leukemia inhibitory factor (LIF) from mouse embryonic stem cells (mESCs) cultivated in vitro causes approximately 20%-30% of the cells to die by spontaneous (constitutive) apoptosis [4, 5]. This occurs secondary to the induction of cleaved caspase 3 [3] and apoptosis-inducing factor (AIF)-complex proteins [6]. Blockade of spontaneous apoptosis in vitro by a p38 mitogen-activated protein kinase (MAPK) inhibitor alters the differentiation markers and increases the abundance of both antiapoptotic proteins (Bcl-2, Bcl-XL) and Ca2+-binding proteins [4, 7]. In addition, Ca2+ released from type 3 inositol 1, 4, 5-trisphosphate receptors (IP3R3) negatively regulates this apoptotic response, which in turn modulates the mesodermal lineage commitment of early differentiating mESCs [5]. These findings explain, in part, how apoptosis contributes to specific lineage commitment during early development. However, in contrast to the relatively advanced knowledge of signaling pathways [8], little is known about the contribution of epigenetic regulators, especially, histone lysine demethylases (KDMs), in the regulation of apoptosis during ESC differentiation and how the affected programmed cell death by KDMs contributes to the lineage commitment.

Epigenetic regulators and dynamic histone modifications by KDMs are emerging as important players in ESC fate decisions [9]. Histone modifications coordinate transient changes in gene transcription and help maintaining differential patterns of gene expression during differentiation [10-13]. The molecular and biological functions of many KDMs, however, remain enigmatic during ESC differentiation. PHF8, an X-linked gene encoding an evolutionarily conserved histone demethylase harboring an N-terminal plant homeo domain (PHD) and an active jumonji-C domain (JmjC), is able to catalyze demethylation from histones [14, 15]. It is actively recruited to and enriched in the promoters of transcriptionally active genes [14], and it functions to maintain the active state of rRNA through the removal of the repressive H3K9me2 methylation mark at the active rRNA promoters. Mutation of PHF8 is associated with X-linked mental retardation with cleft lip/cleft palate in human [16-18]. Knockdown of phf8 in mouse embryonic carcinoma P19 cells impairs neuronal differentiation [19] and leads to brain defects in zebrafish by directly regulating the expression of the homeo domain transcription factor MSX1/MSXB [20]. However, the precise function of PHF8 in the regulation of lineage differentiation derived from other germ layers remains to be identified.

Here, we report previously unrecognized effects of the PHF8 histone demethylase on germ layer commitment and differentiation of mESCs. The results are based on an assessment of early steps of differentiation to mesodermal lineages and cardiomyocytes using phf8 knockout (phf8-/Y) and wild-type (phf8+/Y) mESCs. The data show that PHF8 regulates gene transcription of a proapoptotic gene pmaip1 (also named Noxa) [21]. Activation or repression of pmaip1 controlled by PHF8 ultimately determines mESC lineage commitment through the regulation of caspase 3-independent apoptosis during mesodermal and cardiac differentiation. Our data reveal that PHF8-mediated the demethylation of histone proteins coordinates ESC lineage commitment through the regulation of apoptosis in early differentiating ESCs.


Deletion of phf8 Promotes Mesodermal and Cardiac Lineage Commitment

The PHF8 protein was detectable in undifferentiated ESCs, but its abundance significantly increased within one day of LIF withdrawal. Then it gradually decreased to a level at day 5 lower than that observed in the undifferentiated ESCs (Fig. 1A).


Figure 1. Plant homeo domain finger protein 8 (PHF8) regulates the mesodermal and early cardiac differentiation of mouse embryonic stem cells (mESCs). (A): Western blot analysis of PHF8 expression in undifferentiated and differentiating ESCs. n = 3. (B): quantitative RT-PCR (qRT-PCR) analysis of pluripotency markers nanog, rex1, sox2, and oct4. n = 8. (C): qRT-PCR analysis of gene expression of pluripotency marker oct4; early mesodermal markers brachyury (T), gsc, eomes, and mesp1; cardiovascular progenitor markers flk-1 and nrp1; and the cardiac transcription factors hand1, tbx5, and mef2c during ESC differentiation. n = 5. (D): qRT-PCR analysis of the early ectodermal markersnestin and fgf5 during ESC differentiation. n = 3. (E): qRT-PCR analysis of early endodermal markers afp, foxa2, sox17, and gata4 during ESC differentiation. n = 3. Data are presented as mean ± SEM *, p < .05; **, p < .01; ***, p < .001 compared with the corresponding phf8+/Y value.

To determine the significance of phf8 gene expression on ESC fate decision, we knocked out the X-chromosome-encoded phf8 gene in one allele of male SCR012 ESCs by deletion of exons 7 and 8 through Cre-mediated recombination (Supporting Information Fig. S1A). Gene inactivation was confirmed by the lack of Phf8 mRNA and PHF8 protein expression in these targeted ESCs (Supporting Information Fig. S1B). Transcripts for pluripotency marker genes nanog, rex1 (zfp42), sox2, and oct4 (pou5f1) were not significantly different between phf8+/Y and phf8-/Y ESCs (Fig. 1B). No significant difference was observed in cell morphology (Supporting Information Fig. S1C) of undifferentiated phf8+/Y and phf8-/Y ESCs or in alkaline phosphatase activity (Supporting Information Fig. S1D). Immunofluorescence staining confirmed that the expression of pluripotency marker SOX2 and SSEA-1 did not differ between the phf8+/Y and phf8-/Y ESCs (Supporting Information Fig. S1E). These results indicate that phf8 may be dispensable for normal growth and maintenance of mESCs.

We then analyzed the role of PHF8 in the mesodermal and cardiac lineage commitment. By microarray analysis of differentiating phf8+/Y and phf8-/Y cells from days 0, 1, to 3.5, we found a significant decrease in transcripts for pluripotency markers, accompanied by a significant increase in transcripts for ectoderm, mesoderm and endoderm, while in phf8-/Y cells some transcripts for mesodermal and cardiac lineage commitment were significantly enhanced compared with those in phf8+/Y cells (Supporting Information Fig. S2A). These differentiation-dependent changes in transcript abundance were confirmed by qRT-PCR for early mesodermal markers brachyury (T) [28], goosecoid (gsc),eomes[29], and mesp1[30], cardiovascular progenitor marker flk-1[31, 32] and neuropilin 1 (nrp1) [33]. Early cardiac transcription factors, including myocyte enhancer factor 2C (mef2c) [34], hand1[35], and tbx5[36, 37] were also up-regulated in phf8-/Y cells at differentiation day 5, while no difference in the expression levels of pluripotent markersoct4 (Fig. 1C), rex1, and nanog (Supporting Information Fig. S2B) were detected between phf8+/Y and phf8-/Y cells at the time points examined.

Because mESCs can differentiate into all three germ layers, we also examined whether phf8 affected ectodermal and endodermal differentiation. qRT-PCR analysis did not show any significantly difference in the expression of early ectodermal markers nestin and fgf5 between the phf8+/Y and phf8-/Y cells (Fig. 1D). Moreover, in the induced early ectodermal differentiation system [23], the expression of ectodermal markers nestin, fgf5, and pax6 were comparable between the phf8+/Y and phf8-/Y cells (Supporting Information Fig. S3A). Besides, the expression of endodermal markers afp, foxa2, sox17, and gata4 were not significantly different between the phf8+/Y and phf8-/Y cells (Fig.1E). Consistently, the expression of endodermal markers foxa2, sox17, and gata4 were comparable during induced endodermal differentiation [24] between the phf8+/Y andphf8-/Y cells (Supporting Information Fig. S3B). Thus, phf8 appears not to affect early ectodermal and endodermal differentiation.

The increased mesodermal and cardiac marker expressions were associated with a significant increase in the total number of cardiac progenitors and cardiomyocytes in differentiating phf8-/Y cells. By flow cytometry analysis, marked increases in the population of FLK-1 positive (FLK-1+) cells were detected in phf8-/Y cells at differentiation day 3 and day 4 (Fig. 2A). Consistently, the percentage of contracting EBs was higher in phf8-/Y cells than in phf8+/Y cells (Fig. 2B). The transcripts for progenitor marker nrp1, early cardiac transcription factor tbx5, and cardiac specific genes tnnt2, myh6, myl2, and gja1 were higher in phf8-/Y EBs than those in phf8+/Y ones (Fig. 2C). The areas of immunostained EBs positive for the cardiac cytoskeletal and myofilamental proteins α-actinin and TNNT2 were also greater in phf8-/Y than in phf8+/Y EBs (Fig. 2D). Flow cytometry analysis of MYH6+ (Fig. 2E) and TNNT2+ (Fig. 2F) cells at differentiation day 9 further confirmed the increase of cardiomyocytes in phf8-/Y cells. Taken together, these data indicate that the phf8 deletion facilitates the differentiation of mesodermal and cardiac linage commitment.

Figure 2. phf8 deletion promotes cardiac differentiation of mouse embryonic stem cells (mESCs). (A): Left, representative flow cytometry plots showing FLK-1 expression at differentiation day 3 (n = 6), day 4 and day 5 (n = 3 each). Right, the quantification of flow cytometry data. (B): Differentiation profile of cardiomyocytes during embryoid bodies (EB) outgrowth. n = 6. (C): qRT-PCR analysis of ESCs for the expression of cardiac markers at differentiation day 14. n = 3. (D): Immunofluorescence analysis of TNNT2 and α-actinin in day 14 EBs. Scale bars = 400 μm. (E) Flow cytometry analysis of MYH6 positive cells and (F) TNNT2 positive cells in day 9 EBs. n = 3 each. Data are presented as mean ± SEM *, p < .05; **, p < .01; ***, p < .001 compared with the corresponding phf8+/Y value.

PHF8 Inactivation Increases Cell Viability but not Proliferation of the Differentiating ESCs

Differentiation of both phf8+/Y and phf8-/Y ESCs via EB formation produced normal round shaped EBs but, by day 3, phf8-/Y EBs were larger than those generated from phf8+/YESCs, and the size differences were visibly obvious at differentiation days 5 and 7 (Fig. 3A). Although no significant differences in cell viability could be demonstrated between undifferentiated phf8+/Y and phf8-/Y ESCs (Fig. 3B), the viability of phf8-/Y cells was significantly greater than that in phf8+/Y cells at differentiation days 3 to 7 (Fig. 3C). However, no significant change in BrdU staining was detected by flow cytometry between phf8+/Y and phf8-/Y ESCs at differentiation days 0, 3, or 5 (Fig. 3D). Moreover, no significant difference in the cell cycle distribution between the differentiating Phf8+/Y and Phf8-/Y ESCs was detected, although the percentage of cells in S phase gradually decreased while those in G1 phase increased upon differentiation (Fig. 3E). Knockout of phf8 thus increases cell numbers in the early differentiating ESCs through the improvement of cell viability without changes in cell proliferation or cell cycle progression.

Figure 3. phf8 deletion increases cell viability in differentiating mouse embryonic stem cells (mESCs) without affecting cell proliferation. (A): Left, phase-contrast images of embryoid bodies (EB) morphology during EB formation from ESCs. Scale bar = 200 μm. Right, the diameter of EB formed from ESCs. (B): Cell viability of undifferentiated and (C): differentiating ESCs analyzed by MTT assay for seven consecutive days. n = 3. (D): Flow cytometry analysis of BrdU positive proportion of undifferentiated (n = 4) and differentiating ESCs at day 3 and day 5.n = 5 each. (E): Flow cytometry analysis of cell cycle distribution by propidium iodide (PI) staining at differentiation day 3 (n = 6) and day 5 (n = 3). Data are presented as mean ± SEM *, p < .05; ***, p < .001 compared with the corresponding phf8+/Y value.

PHF8 Regulates Apoptosis During the Early Stage of Cardiac Lineage Commitment

We then examined whether cell death might account for the differences in the cell viability observed between the differentiating phf8+/Y and phf8-/Y ESCs. In undifferentiated ESCs, no significant difference was demonstrated with Annexin V (an early apoptosis marker) staining, TUNEL assay, total DNA fragmentation or caspase 3 protein cleavage between phf8+/Y and phf8-/Y cells (Fig. 4A–4C, 4E). In contrast, Annexin V staining (Fig. 4A) and TUNEL assay (Fig. 4B) showed significant decreases in the number of apoptotic cells in phf8-/Y ESCs at differentiation days 3 and 5 compared with those in phf8+/Y cells. Genomic DNA fragmentation with a pattern typical for apoptosis was detected in phf8+/Y cells at differentiation days 3 and 5, but it was reduced in phf8-/Y cells at the same time points (Fig. 4C). Moreover, approximately 35% of Annexin V+ cells were present in FLK-1+/phf8+/Y cells at differentiation day 4, whereas only 9% of the cells were Annexin V+ in FLK-1+/phf8-/Y cells (Fig. 4D). The ratio of TUNEL+ to either NESTIN+ (ectoderm) or SOX17+ (endoderm) cells did not differ between the phf8+/Y and phf8-/Y cells (Supporting Information Fig. S3C, S3D). In addition, phf8+/Y ESCs at differentiation days 3 and 5 increased the caspase 3 cleavage (Fig. 4E, upper and lower left panels) and the ratio of cleaved caspase 3 to total caspase 3 protein (Fig. 4E, lower right panel). Unexpectedly, the ratio of cleaved caspase 3 to total caspase 3 in phf8-/Y ESCs did not significantly differ from that observed in phf8+/Y ones. Consistently, a significant enhancement of the downstream target PARP1 cleavage [38, 39] was observed at differentiation days 3 and 5, but it was comparable between the phf8+/Y andphf8-/Y cells (Fig. 4F). These data suggest that the cell death associated with phf8 function does not operate through the conventional caspase 3-mediated apoptosis.

Figure 4. Plant homeo domain finger protein 8 (PHF8) regulates apoptosis during the early mouse embryonic stem cells (mESC) differentiation. (A): Left, representative flow cytometry plots showing Annexin V (x-axis), and PI (y-axis) staining in ECSs at differentiation day 0 (n = 4), day 3 (n = 3) and day 5 (n = 7). Right, the quantification of flow cytometry data. (B): Flow cytometry detection of apoptotic responses of TUNEL positive cells at differentiation day 0 (n = 3), day 3 (n = 4), and day 5 (n = 4). (C): DNA laddering analysis at differentiation days 0, 3, and 5. n = 6 each. (D): Cells double stained with FLK-1 and Annexin V were analyzed by flow cytometry at differentiation day 4. n = 3. (E): Western blot analysis of caspase 3 during the mESC differentiation. β-actin was used as a loading control. n = 4. (F): Western blot analysis of PARP1 expression during the differentiation. β-actin was used as a loading control. n = 4. Data are presented as mean ± SEM *, p < .05; ***, p < .001 compared with the corresponding phf8+/Yvalue; #, p < .05; ##, p < .01 compared with the corresponding d0 value.

pmaip1 is a Direct Target Gene of PHF8 in the Early Differentiating ESCs

To understand how PHF8 might regulate apoptosis during early ESC differentiation, we compared the profiles of apoptosis-related gene transcripts in undifferentiated and early differentiating phf8+/Y and phf8-/Y ESCs using gene expression microarrays. Among the apoptosis-related genes, the transcript to pmaip1, a proapoptotic Bcl-2 family member crucial in fine-turning the cell death decision [21, 40-42], was markedly increased during early differentiation of phf8+/Y cells but it was reduced in phf8-/Y cells at differentiation days 1 and 3.5 (Fig. 5A). These expression patterns were confirmed by qRT-PCR during cardiac differentiation (Fig. 5B), and the results were consistent with the apoptotic pattern observed during the early ESC differentiation (Fig. 4A–4C). In addition, qRT-PCR analysis showed that the expression of pmaip1 did not show any significant difference between the phf8+/Y and phf8-/Y cells during the induced ectodermal (Supporting Information Fig. S3E) or endodermal (Supporting Information Fig. S3F) differentiation.

Figure 5. pmaip1 is a direct target gene of plant homeo domain finger protein 8 (PHF8) in mouse embryonic stem cells (mESCs). (A): Microarray gene expression heat map depicting the expression of apoptosis-related genes at differentiation days 0, 1 and 3.5 in phf8+/Y and phf8-/Y ESCs. The expression values in log2 scale were calculated and presented on the heat map with red representing highly abundant transcripts and green representing poorly abundant transcripts. n = 3. (B): qRT-PCR analysis of pmaip1 during the ESC differentiation. (C): ChIP assay of PHF8 around the TSS of pmaip1 in phf8+/Y andphf8-/Y ESCs at differentiation days 0 and 3. n = 4 each. (D): Western blot analysis of H3K9me2 and H3 in phf8+/Y and phf8-/Y ESCs during the differentiation. H3 was used as a loading control. n = 9. (E): H3K9me2 staining inphf8+/Y and phf8-/Y embryoid bodies (EBs) at differentiation day 1. Scale bars = 25 μm. (F): ChIP assay of H3K9me2 around the TSS of pmaip1 in phf8+/Y andphf8-/Y ESCs at differentiation day 3. n = 4. Data are presented as mean ± SEM. *, p < .05; **, p < .01; ***, p < .001 compared with the corresponding phf8+/Y value or d0.

A direct link between the PHF8 and pmaip1 was then confirmed by ChIP analysis. We detected the endogenous binding of PHF8 at the transcription start site (TSS, from −45 bp to 104 bp) of pmaip1 in phf8+/Y ESCs and determined that binding was enhanced at differentiation day 3. The binding of PHF8 was not detectable above the IgG control levels in phf8-/Y cells (Fig. 5C). Global methylation (H3K9me2 normalized to H3) was unchanged at differentiation days 3 and 5, but it was significantly enhanced at differentiation day 1 in phf8-/Y cells (Fig. 5D). The augmentation of H3K9me2 methylation in phf8-/Y ESCs was then confirmed by immunostaining at differentiation day 1 (Fig.5E). An increase in the repressive mark of H3K9me2 was also observed at the TSS of pmaip1 in the early differentiating phf8-/Y ESCs (Fig. 5F), indicating that the PHF8 demethylase activity is actively involved in the regulation of pmaip1 gene.

Transient Knockdown of pmaip1 Decreases Apoptosis and Promotes Mesodermal and Cardiac Differentiation

To clarify the role of pmaip1 in mESC differentiation, we transfected specific siRNAs against pmaip1 (si-Pmaip1) into phf8+/Y ESCs followed by EB formation. The negative control siRNA (si-NC) did not alter pmaip1 transcript levels compared with untreated (NT) cells, while si-Pmaip1 significantly inhibited pmaip1 transcripts by 74%-76% at differentiation days 0 and 1 (Fig. 6A-a). si-Pmaip1 cells had fewer TUNEL+ cells compared with the NT and si-NC cells at differentiation day 3 in both phf8+/Y and phf8-/Y cells (Fig. 6A-b). We then examined whether the pmaip1 knockdown influences mesodermal and early cardiac differentiation. As shown in Figure 6B, the apoptosis of FLK-1+ cells was significantly decreased in si-Pmaip1 mESCs (Fig. 6B). The expression of T and gsc as well as nrp1 and flk-1 were increased in si-Pmaip1 cells compared with those in NT and si-NC cells at differentiation day 3. In addition, the expression of cardiac transcript factors mef2c and tbx5 was up-regulated at differentiation day 5, and myh6 andtnnt2 were up-regulated at differentiation day 9 (Fig. 6C). We also transfected si-Pmaip1 into phf8-/Y ESCs. The expression of pmaip1 was downregulated at differentiation day 0 and day 1 in phf8-/Y ESCs with si-Pmaip1 (Supporting Information as Fig. S4A-a), accompanied by a decrease in TUNEL+ cells compared with NT and si-NC (Fig. 6A-b), while Annexin V remained unchanged (Supporting Information Fig. S4A-b). The expression of nrp1 and flk1 did not significantly change in phf8-/Y ESCs with si-Pmaip1 at differentiation day 3, while mef2c was upregulated at differentiation day 5, and myh6 was upregulated at differentiation day 9 (Supporting Information as Fig. S4B). These results suggest that downregulation of pmaip1 in phf8-/Y ESCs may not lead to as robust of a phenotype as it did in phf8+/Y ESCs. This difference is likely due to the level ofpmaip1 during early differentiation of phf8-/Y ESCs was already decreased to a low level similar to that observed in the undifferentiated cells (Fig. 5B). Taken together, these data demonstrate that the decreased apoptosis via down-regulation of pmaip1 contributes, at least partially, to the phf8-/Y-facilitated mesodermal and cardiomyocyte commitment.

Figure 6. Plant homeo domain finger protein 8 (PHF8) regulates the mesodermal and cardiac differentiation through pmaip1. (A-a): qRT-PCR analysis of thepmaip1 expression in phf8+/Y ESCs after being transiently transfected with si-NC or si-Pmaip1. n = 4. (A-b): Apoptosis cells were quantified by flow cytometry analysis of TUNEL assay at differentiation day 3 in phf8+/Y and phf8-/Y ESCs after transient transfection with si-NC or si-Pmaip1. n = 3. (B): Cells double stained with FLK-1 and Annexin V were analyzed by flow cytometry at differentiation day 4. n = 4. (C): qRT-PCR analysis of the expression of T, gsc, flk-1, nrp1, tbx5, mef2c, myh6, and tnnt2 in phf8+/Y ESCs after transient transfection with si-NC or si-Pmaip1. n = 5. (D): Flow cytometry detection of TUNEL positive cells at differentiation day 3, Annexin V positive cells and double stained FLK-1 and Annexin V at differentiation day 4 in phf8-/Y, phf8-NC-/+, phf8-pmaip1-/+, and phf8-hPHF8-/+ mouse embryonic stem cells (mESCs). n = 4. (E): qRT-PCR analysis of the expression of T, gsc, flk-1, nrp1, tbx5, mef2c, myh6, and tnnt2 in phf8-/Y, phf8-NC-/+, phf8-pmaip1-/+, and phf8-hPHF8-/+ mESCs. n = 3. Data are presented as mean ± SEM. *, p < .05; **, p < .01; ***, p < .001 compared with the corresponding phf8+/Y or phf8-/Y value.

Overexpression of pmaip1 or hPHF8 in phf8-/Y ESCs Increases Apoptosis and Weakens Mesodermal and Cardiac Differentiation

To further determine whether PHF8 contributes to mesoderm and cardiac cell commitment through the regulation of apoptosis via targeting pmaip1, we rescued the expression of pmaip1 and phf8 in phf8-/Y ESCs by generating pmaip1-ovexpressing phf8-/Y mESCs (phf8-pmaip1-/+ mESCs) and hPHF8-overexpressing phf8-/Y mESCs (phf8-hPHF8-/+ mESCs). The qRT-PCR analysis confirmed that the expression of hPHF8 or pmaip1 was significantly upregulated in the respective overexpressing cell lines (Supporting Information Fig. S4C). The expression of pmaip1 in undifferentiated phf8-/Y mESCs was not affected by hPHF8 overexpression. However, Pmaip1 transcripts increased by differentiation day 3 in overexpressing cells (Supporting Information Fig. S4D), indicating that PHF8 does regulate the expression of pmaip1 during differentiation. Both TUNEL and Annexin V analysis revealed significant increases of apoptosis in phf8-pmaip1-/+ and phf8-hPHF8-/+ mESCs compared with the phf8-/Y andphf8-NC-/+ mESCs at differentiation day 3 or day 4, accompanied by a higher apoptosis ratio in FLK-1+ cells (Fig. 6D). Moreover, the expression of T and gsc as well as nrp1and flk-1 were significantly decreased in phf8-pmaip1-/+ and phf8-hPHF8-/+ mESCs at differentiation day 3, followed by a down-regulation of mef2c and tbx5 at differentiation day 5, and myh6 and tnnt2 at differentiation day 9 (Fig. 6E). In addition, TUNEL analysis showed no changes in the apoptotic responses either through knockout or overexpression of phf8 compared with the corresponding wild-type cells or phf8+/Y cells during induced ectodermal differentiation (Supporting information Fig. S4E). These data are consistent with a regulatory role of phf8 on mesodermal and cardiac differentiation through targeting of pmaip1


This is the first study to unravel a regulatory role of histone demethylase in the differentiation of ESCs through the control of apoptosis and subsequent effects on cell lineage commitment. The role of PHF8 in the regulation of ESC differentiation to the mesodermal lineage and cardiac differentiation is supported by selective changes in RNA markers for mesodermal lineages, and an increase in cardiomyocyte progenitors and cardiomyocytes (Figs. 1C, 2C). Moreover, deletion of phf8 specifically inhibits apoptosis of Flk-1+ mesodermal cells with a concomitant reduction in Annexin V+ staining (Fig. 4D) and cardiac differentiation (Fig. 2B–E), while the ratio of TUNEL+ to either NESTIN+(ectodermal cells) or SOX17+ (endodermal cells) cells does not differ between the phf8+/Y and phf8-/Y lines (Supporting Information Fig. S3C, S3D). Consistently, the proportion of early apoptotic cells (Annexin V+) in pmaip1-knockdown (Fig. 6B) is also decreased, while pmaip1-overexpression or hPHF8-overexpression in phf8-/Y cells increase the proportion of TUNEL+ and Annexin V+ cells simultaneously with a reduction in mesodermal and cardiac differentiation (Fig. 6D, 6E). These findings indicate that the PHF8 functions, at least partially, through regulation of apoptosis.

It is well known that the regulation of apoptosis is of critical importance for proper ESC differentiation and embryo development [8, 43]. ESC differentiation is regulated by apoptosis induced by MAPK activation [7] and IP3R3-regulated Ca2+ release [5]. Previously only histone 3 lysine 4 methyltransferase MLL2 had been shown to activate the antiapoptotic gene bcl2 to inhibit apoptosis during ESC differentiation [44]. The data presented, here, extends and reveals the importance of epigenetic controls in the activation of proapoptotic gene associated with ESC differentiation.

Mesodermal and cardiac differentiation have been shown to be regulated by the histone demethylase ubiquitously transcribed tetratricopeptide repeat, X chromosome (UTX)[13, 45] and jumonji domain–containing protein 3 (JMJD3) [12] through transcriptional activation of mesodermal and cardiac genes. These findings together with those presented in this paper support the critical role of histone demethylases in lineage commitment through regulatory mechanisms that control the expression of core lineage specific transcription factors and apoptotic genes. The decrease in apoptosis through deletion of phf8 can be attributed to the maintenance of repressive H3K9me2 mark on the TSS of pmaip1 after phf8 deletion, resulting in a ∼70% downregulation of pmaip1 at differentiation day 3 in the phf8-/Y cells (Fig. 5B). The pro-apoptotic gene pmaip1 is, therefore, epigenetically regulated by the histone demethylase, which subsequently affects the mesodermal and cardiac differentiation.

PMAIP1 is a Bcl2 homology domain 3 (BH3)-only protein that acts as an important mediator of apoptosis [46]. Its expression is regulated transcriptionally by various transcription factors and, when present, it acts to promote cell death in a variety of ways [21] including caspase 3 dependent [47] and independent apoptosis [48] and autophagy [40]. Here, we find that PHF8 and its regulation on the pmaip1 promote DNA fragmentation and cell death most likely through a caspase 3-independent pathway. This conclusion is based on the observation that neither the ratio of cleaved caspase 3 to total caspase 3 [49, 50] nor PARP1, a downstream target of caspase 3, is significantly affected. While this may be explained as the inhibitor of apoptosis proteins can counteract the function of caspase 3 [51, 52], the exact mechanisms we observed here need to be further explored.

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Genomics and epigenetics link to DNA structure

Larry H. Bernstein, MD, FCAP, Curator



Sequence and Epigenetic Factors Determine Overall DNA Structure

Atomic-level simulations show electrostatic forces between each atom. [Alek Aksimentiev, University of Illinois at Urbana-Champaign]


The traditionally held hypothesis about the highly ordered organization of DNA describes the interaction of various proteins with DNA sequences to mediate the dynamic structure of the molecule. However, recent evidence has emerged that stretches of homologous DNA sequences can associate preferentially with one another, even in the absence of proteins.

Researchers at the University of Illinois Center for the Physics of Living Cells, Johns Hopkins University, and Ulsan National Institute of Science and Technology (UNIST) in South Korea found that DNA molecules interact directly with one another in ways that are dependent on the sequence of the DNA and epigenetic factors, such as methylation.

The researchers described evidence they found for sequence-dependent attractive interactions between double-stranded DNA molecules that neither involve intermolecular strand exchange nor are mediated by DNA-binding proteins.

“DNA molecules tend to repel each other in water, but in the presence of special types of cations, they can attract each other just like nuclei pulling each other by sharing electrons in between,” explained lead study author Hajin Kim, Ph.D., assistant professor of biophysics at UNIST. “Our study suggests that the attractive force strongly depends on the nucleic acid sequence and also the epigenetic modifications.”

The investigators used atomic-level supercomputer simulations to measure the forces between a pair of double-stranded DNA helices and proposed that the distribution of methyl groups on the DNA was the key to regulating this sequence-dependent attraction. To verify their findings experimentally, the scientists were able to observe a single pair of DNA molecules within nanoscale bubbles.

“Here we combine molecular dynamics simulations with single-molecule fluorescence resonance energy transfer experiments to examine the interactions between duplex DNA in the presence of spermine, a biological polycation,” the authors wrote. “We find that AT-rich DNA duplexes associate more strongly than GC-rich duplexes, regardless of the sequence homology. Methyl groups of thymine act as a steric block, relocating spermine from major grooves to interhelical regions, thereby increasing DNA–DNA attraction.”

The findings from this study were published recently in Nature Communications in an article entitled “Direct Evidence for Sequence-Dependent Attraction Between Double-Stranded DNA Controlled by Methylation.”

After conducting numerous further simulations, the research team concluded that direct DNA–DNA interactions could play a central role in how chromosomes are organized in the cell and which ones are expanded or folded up compactly, ultimately determining functions of different cell types or regulating the cell cycle.

“Biophysics is a fascinating subject that explores the fundamental principles behind a variety of biological processes and life phenomena,” Dr. Kim noted. “Our study requires cross-disciplinary efforts from physicists, biologists, chemists, and engineering scientists and we pursue the diversity of scientific disciplines within the group.”

Dr. Kim concluded by stating that “in our lab, we try to unravel the mysteries within human cells based on the principles of physics and the mechanisms of biology. In the long run, we are seeking for ways to prevent chronic illnesses and diseases associated with aging.”


Direct evidence for sequence-dependent attraction between double-stranded DNA controlled by methylation

Jejoong Yoo, Hajin Kim, Aleksei Aksimentiev, and Taekjip Ha
Nature Communications 7 11045 (2016)    DOI:10.1038/ncomms11045BibTex

Although proteins mediate highly ordered DNA organization in vivo, theoretical studies suggest that homologous DNA duplexes can preferentially associate with one another even in the absence of proteins. Here we combine molecular dynamics simulations with single-molecule fluorescence resonance energy transfer experiments to examine the interactions between duplex DNA in the presence of spermine, a biological polycation. We find that AT-rich DNA duplexes associate more strongly than GC-rich duplexes, regardless of the sequence homology. Methyl groups of thymine acts as a steric block, relocating spermine from major grooves to interhelical regions, thereby increasing DNA–DNA attraction. Indeed, methylation of cytosines makes attraction between GC-rich DNA as strong as that between AT-rich DNA. Recent genome-wide chromosome organization studies showed that remote contact frequencies are higher for AT-rich and methylated DNA, suggesting that direct DNA–DNA interactions that we report here may play a role in the chromosome organization and gene regulation.

Formation of a DNA double helix occurs through Watson–Crick pairing mediated by the complementary hydrogen bond patterns of the two DNA strands and base stacking. Interactions between double-stranded (ds)DNA molecules in typical experimental conditions containing mono- and divalent cations are repulsive1, but can turn attractive in the presence of high-valence cations2. Theoretical studies have identified the ion–ion correlation effect as a possible microscopic mechanism of the DNA condensation phenomena3, 4, 5. Theoretical investigations have also suggested that sequence-specific attractive forces might exist between two homologous fragments of dsDNA6, and this ‘homology recognition’ hypothesis was supported by in vitro atomic force microscopy7 and in vivo point mutation assays8. However, the systems used in these measurements were too complex to rule out other possible causes such as Watson–Crick strand exchange between partially melted DNA or protein-mediated association of DNA.

Here we present direct evidence for sequence-dependent attractive interactions between dsDNA molecules that neither involve intermolecular strand exchange nor are mediated by proteins. Further, we find that the sequence-dependent attraction is controlled not by homology—contradictory to the ‘homology recognition’ hypothesis6—but by a methylation pattern. Unlike the previous in vitro study that used monovalent (Na+) or divalent (Mg2+) cations7, we presumed that for the sequence-dependent attractive interactions to operate polyamines would have to be present. Polyamine is a biological polycation present at a millimolar concentration in most eukaryotic cells and essential for cell growth and proliferation9, 10. Polyamines are also known to condense DNA in a concentration-dependent manner2, 11. In this study, we use spermine4+(Sm4+) that contains four positively charged amine groups per molecule.

Sequence dependence of DNA–DNA forces

To characterize the molecular mechanisms of DNA–DNA attraction mediated by polyamines, we performed molecular dynamics (MD) simulations where two effectively infinite parallel dsDNA molecules, 20 base pairs (bp) each in a periodic unit cell, were restrained to maintain a prescribed inter-DNA distance; the DNA molecules were free to rotate about their axes. The two DNA molecules were submerged in 100mM aqueous solution of NaCl that also contained 20 Sm4+molecules; thus, the total charge of Sm4+, 80 e, was equal in magnitude to the total charge of DNA (2 × 2 × 20 e, two unit charges per base pair; Fig. 1a). Repeating such simulations at various inter-DNA distances and applying weighted histogram analysis12 yielded the change in the interaction free energy (ΔG) as a function of the DNA–DNA distance (Fig. 1b,c). In a broad agreement with previous experimental findings13, ΔG had a minimum, ΔGmin, at the inter-DNA distance of 25−30Å for all sequences examined, indeed showing that two duplex DNA molecules can attract each other. The free energy of inter-duplex attraction was at least an order of magnitude smaller than the Watson–Crick interaction free energy of the same length DNA duplex. A minimum of ΔG was not observed in the absence of polyamines, for example, when divalent or monovalent ions were used instead14, 15.

Figure 1: Polyamine-mediated DNA sequence recognition observed in MD simulations and smFRET experiments.
Polyamine-mediated DNA sequence recognition observed in MD simulations and smFRET experiments.

(a) Set-up of MD simulations. A pair of parallel 20-bp dsDNA duplexes is surrounded by aqueous solution (semi-transparent surface) containing 20 Sm4+ molecules (which compensates exactly the charge of DNA) and 100mM NaCl. Under periodic boundary conditions, the DNA molecules are effectively infinite. A harmonic potential (not shown) is applied to maintain the prescribed distance between the dsDNA molecules. (b,c) Interaction free energy of the two DNA helices as a function of the DNA–DNA distance for repeat-sequence DNA fragments (b) and DNA homopolymers (c). (d) Schematic of experimental design. A pair of 120-bp dsDNA labelled with a Cy3/Cy5 FRET pair was encapsulated in a ~200-nm diameter lipid vesicle; the vesicles were immobilized on a quartz slide through biotin–neutravidin binding. Sm4+ molecules added after immobilization penetrated into the porous vesicles. The fluorescence signals were measured using a total internal reflection microscope. (e) Typical fluorescence signals indicative of DNA–DNA binding. Brief jumps in the FRET signal indicate binding events. (f) The fraction of traces exhibiting binding events at different Sm4+ concentrations for AT-rich, GC-rich, AT nonhomologous and CpG-methylated DNA pairs. The sequence of the CpG-methylated DNA specifies the methylation sites (CG sequence, orange), restriction sites (BstUI, triangle) and primer region (underlined). The degree of attractive interaction for the AT nonhomologous and CpG-methylated DNA pairs was similar to that of the AT-rich pair. All measurements were done at [NaCl]=50mM and T=25°C. (g) Design of the hybrid DNA constructs: 40-bp AT-rich and 40-bp GC-rich regions were flanked by 20-bp common primers. The two labelling configurations permit distinguishing parallel from anti-parallel orientation of the DNA. (h) The fraction of traces exhibiting binding events as a function of NaCl concentration at fixed concentration of Sm4+ (1mM). The fraction is significantly higher for parallel orientation of the DNA fragments.

Unexpectedly, we found that DNA sequence has a profound impact on the strength of attractive interaction. The absolute value of ΔG at minimum relative to the value at maximum separation, |ΔGmin|, showed a clearly rank-ordered dependence on the DNA sequence: |ΔGmin| of (A)20>|ΔGmin| of (AT)10>|ΔGmin| of (GC)10>|ΔGmin| of (G)20. Two trends can be noted. First, AT-rich sequences attract each other more strongly than GC-rich sequences16. For example, |ΔGmin| of (AT)10 (1.5kcalmol−1 per turn) is about twice |ΔGmin| of (GC)10 (0.8kcalmol−1 per turn) (Fig. 1b). Second, duplexes having identical AT content but different partitioning of the nucleotides between the strands (that is, (A)20 versus (AT)10 or (G)20 versus (GC)10) exhibit statistically significant differences (~0.3kcalmol−1 per turn) in the value of |ΔGmin|.

To validate the findings of MD simulations, we performed single-molecule fluorescence resonance energy transfer (smFRET)17 experiments of vesicle-encapsulated DNA molecules. Equimolar mixture of donor- and acceptor-labelled 120-bp dsDNA molecules was encapsulated in sub-micron size, porous lipid vesicles18 so that we could observe and quantitate rare binding events between a pair of dsDNA molecules without triggering large-scale DNA condensation2. Our DNA constructs were long enough to ensure dsDNA–dsDNA binding that is stable on the timescale of an smFRET measurement, but shorter than the DNA’s persistence length (~150bp (ref. 19)) to avoid intramolecular condensation20. The vesicles were immobilized on a polymer-passivated surface, and fluorescence signals from individual vesicles containing one donor and one acceptor were selectively analysed (Fig. 1d). Binding of two dsDNA molecules brings their fluorescent labels in close proximity, increasing the FRET efficiency (Fig. 1e).

FRET signals from individual vesicles were diverse. Sporadic binding events were observed in some vesicles, while others exhibited stable binding; traces indicative of frequent conformational transitions were also observed (Supplementary Fig. 1A). Such diverse behaviours could be expected from non-specific interactions of two large biomolecules having structural degrees of freedom. No binding events were observed in the absence of Sm4+ (Supplementary Fig. 1B) or when no DNA molecules were present. To quantitatively assess the propensity of forming a bound state, we chose to use the fraction of single-molecule traces that showed any binding events within the observation time of 2min (Methods). This binding fraction for the pair of AT-rich dsDNAs (AT1, 100% AT in the middle 80-bp section of the 120-bp construct) reached a maximum at ~2mM Sm4+(Fig. 1f), which is consistent with the results of previous experimental studies2, 3. In accordance with the prediction of our MD simulations, GC-rich dsDNAs (GC1, 75% GC in the middle 80bp) showed much lower binding fraction at all Sm4+ concentrations (Fig. 1b,c). Regardless of the DNA sequence, the binding fraction reduced back to zero at high Sm4+ concentrations, likely due to the resolubilization of now positively charged DNA–Sm4+ complexes2, 3, 13.

Because the donor and acceptor fluorophores were attached to the same sequence of DNA, it remained possible that the sequence homology between the donor-labelled DNA and the acceptor-labelled DNA was necessary for their interaction6. To test this possibility, we designed another AT-rich DNA construct AT2 by scrambling the central 80-bp section of AT1 to remove the sequence homology (Supplementary Table 1). The fraction of binding traces for this nonhomologous pair of donor-labelled AT1 and acceptor-labelled AT2 was comparable to that for the homologous AT-rich pair (donor-labelled AT1 and acceptor-labelled AT1) at all Sm4+ concentrations tested (Fig. 1f). Furthermore, this data set rules out the possibility that the higher binding fraction observed experimentally for the AT-rich constructs was caused by inter-duplex Watson–Crick base pairing of the partially melted constructs.

Next, we designed a DNA construct named ATGC, containing, in its middle section, a 40-bp AT-rich segment followed by a 40-bp GC-rich segment (Fig. 1g). By attaching the acceptor to the end of either the AT-rich or GC-rich segments, we could compare the likelihood of observing the parallel binding mode that brings the two AT-rich segments together and the anti-parallel binding mode. Measurements at 1mM Sm4+ and 25 or 50mM NaCl indicated a preference for the parallel binding mode by ~30% (Fig. 1h). Therefore, AT content can modulate DNA–DNA interactions even in a complex sequence context. Note that increasing the concentration of NaCl while keeping the concentration of Sm4+ constant enhances competition between Na+ and Sm4+ counterions, which reduces the concentration of Sm4+ near DNA and hence the frequency of dsDNA–dsDNA binding events (Supplementary Fig. 2).

Methylation determines the strength of DNA–DNA attraction

Analysis of the MD simulations revealed the molecular mechanism of the polyamine-mediated sequence-dependent attraction (Fig. 2). In the case of the AT-rich fragments, the bulky methyl group of thymine base blocks Sm4+ binding to the N7 nitrogen atom of adenine, which is the cation-binding hotspot21, 22. As a result, Sm4+ is not found in the major grooves of the AT-rich duplexes and resides mostly near the DNA backbone (Fig. 2a,d). Such relocated Sm4+ molecules bridge the two DNA duplexes better, accounting for the stronger attraction16, 23, 24, 25. In contrast, significant amount of Sm4+ is adsorbed to the major groove of the GC-rich helices that lacks cation-blocking methyl group (Fig. 2b,e).

Figure 2: Molecular mechanism of polyamine-mediated DNA sequence recognition.
Molecular mechanism of polyamine-mediated DNA sequence recognition.

(ac) Representative configurations of Sm4+ molecules at the DNA–DNA distance of 28Å for the (AT)10–(AT)10 (a), (GC)10–(GC)10 (b) and (GmC)10–(GmC)10 (c) DNA pairs. The backbone and bases of DNA are shown as ribbon and molecular bond, respectively; Sm4+ molecules are shown as molecular bonds. Spheres indicate the location of the N7 atoms and the methyl groups. (df) The average distributions of cations for the three sequence pairs featured in ac. Top: density of Sm4+ nitrogen atoms (d=28Å) averaged over the corresponding MD trajectory and the z axis. White circles (20Å in diameter) indicate the location of the DNA helices. Bottom: the average density of Sm4+ nitrogen (blue), DNA phosphate (black) and sodium (red) atoms projected onto the DNA–DNA distance axis (x axis). The plot was obtained by averaging the corresponding heat map data over y=[−10, 10] Å. See Supplementary Figs 4 and 5 for the cation distributions at d=30, 32, 34 and 36Å.

If indeed the extra methyl group in thymine, which is not found in cytosine, is responsible for stronger DNA–DNA interactions, we can predict that cytosine methylation, which occurs naturally in many eukaryotic organisms and is an essential epigenetic regulation mechanism26, would also increase the strength of DNA–DNA attraction. MD simulations showed that the GC-rich helices containing methylated cytosines (mC) lose the adsorbed Sm4+ (Fig. 2c,f) and that |ΔGmin| of (GC)10 increases on methylation of cytosines to become similar to |ΔGmin| of (AT)10 (Fig. 1b).

To experimentally assess the effect of cytosine methylation, we designed another GC-rich construct GC2 that had the same GC content as GC1 but a higher density of CpG sites (Supplementary Table 1). The CpG sites were then fully methylated using M. SssI methyltransferase (Supplementary Fig. 3; Methods). As predicted from the MD simulations, methylation of the GC-rich constructs increased the binding fraction to the level of the AT-rich constructs (Fig. 1f).

The sequence dependence of |ΔGmin| and its relation to the Sm4+ adsorption patterns can be rationalized by examining the number of Sm4+ molecules shared by the dsDNA molecules (Fig. 3a). An Sm4+ cation adsorbed to the major groove of one dsDNA is separated from the other dsDNA by at least 10Å, contributing much less to the effective DNA–DNA attractive force than a cation positioned between the helices, that is, the ‘bridging’ Sm4+ (ref. 23). An adsorbed Sm4+ also repels other Sm4+ molecules due to like-charge repulsion, lowering the concentration of bridging Sm4+. To demonstrate that the concentration of bridging Sm4+ controls the strength of DNA–DNA attraction, we computed the number of bridging Sm4+ molecules, Nspm (Fig. 3b). Indeed, the number of bridging Sm4+ molecules ranks in the same order as |ΔGmin|: Nspm of (A)20>Nspm of (AT)10Nspm of (GmC)10>Nspm of (GC)10>Nspm of (G)20. Thus, the number density of nucleotides carrying a methyl group (T and mC) is the primary determinant of the strength of attractive interaction between two dsDNA molecules. At the same time, the spatial arrangement of the methyl group carrying nucleotides can affect the interaction strength as well (Fig. 3c). The number of methyl groups and their distribution in the (AT)10 and (GmC)10 duplex DNA are identical, and so are their interaction free energies, |ΔGmin| of (AT)10Gmin| of (GmC)10. For AT-rich DNA sequences, clustering of the methyl groups repels Sm4+ from the major groove more efficiently than when the same number of methyl groups is distributed along the DNA (Fig. 3b). Hence, |ΔGmin| of (A)20>|ΔGmin| of (AT)10. For GC-rich DNA sequences, clustering of the cation-binding sites (N7 nitrogen) attracts more Sm4+ than when such sites are distributed along the DNA (Fig. 3b), hence |ΔGmin| is larger for (GC)10 than for (G)20.

Figure 3: Methylation modulates the interaction free energy of two dsDNA molecules by altering the number of bridging Sm4+.
Methylation modulates the interaction free energy of two dsDNA molecules by altering the number of bridging Sm4+.

(a) Typical spatial arrangement of Sm4+ molecules around a pair of DNA helices. The phosphates groups of DNA and the amine groups of Sm4+ are shown as red and blue spheres, respectively. ‘Bridging’ Sm4+molecules reside between the DNA helices. Orange rectangles illustrate the volume used for counting the number of bridging Sm4+ molecules. (b) The number of bridging amine groups as a function of the inter-DNA distance. The total number of Sm4+ nitrogen atoms was computed by averaging over the corresponding MD trajectory and the 10Å (x axis) by 20Å (y axis) rectangle prism volume (a) centred between the DNA molecules. (c) Schematic representation of the dependence of the interaction free energy of two DNA molecules on their nucleotide sequence. The number and spatial arrangement of nucleotides carrying a methyl group (T or mC) determine the interaction free energy of two dsDNA molecules.

Genome-wide investigations of chromosome conformations using the Hi–C technique revealed that AT-rich loci form tight clusters in human nucleus27, 28. Gene or chromosome inactivation is often accompanied by increased methylation of DNA29 and compaction of facultative heterochromatin regions30. The consistency between those phenomena and our findings suggest the possibility that the polyamine-mediated sequence-dependent DNA–DNA interaction might play a role in chromosome folding and epigenetic regulation of gene expression.

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  8. Gladyshev, E. & Kleckner, N. Direct recognition of homology between double helices of DNA in Neurospora crassa. Nat. Commun. 5, 3509 (2014).
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Periodic table of protein complexes

Larry H. Bernstein, MD, FCAP, Curator



Periodic Table of Protein Complexes

New tool helps to visualise, understand and predict how proteins combine to drive biological processes

A new ‘periodic table’ of protein complexes has been developed that provides a unified way to classify and visualise protein complexes, providing a valuable tool for biotechnology and the engineering of novel complexes.

This study also provides insights into evolutionary distribution of different types of existing protein complexes.

The Periodic Table of Protein Complexes offers a new way of looking at the enormous variety of structures that proteins can build in nature, which ones might be discovered next, and predicting how entirely novel structures could be engineered. Created by an interdisciplinary team led by researchers at the Wellcome Genome Campus and the University of Cambridge, the Table provides a valuable tool for research into evolution and protein engineering.

Almost every biological process depends on proteins interacting and assembling into complexes in a specific way, and many diseases are associated with problems in complex assembly. The principles underpinning this organisation are not yet fully understood, but by defining the fundamental steps in the evolution of protein complexes, the new ‘periodic table’ presents a systematic, ordered view on protein assembly, providing a visual tool for understanding biological function.

“Evolution has given rise to a huge variety of protein complexes, and it can seem a bit chaotic. But if you break down the steps proteins take to become complexes, there are some basic rules that can explain almost all of the assemblies people have observed so far.”


Dr Joe Marsh, formerly of the Wellcome Genome Campus and now of the MRC Human Genetics Unit at the University of Edinburgh.

Different ballroom dances can be seen as an endless combination of a small number of basic steps. Similarly, the ‘dance’ of protein complex assembly can be seen as endless variations on dimerization (one doubles, and becomes two), cyclisation (one forms a ring of three or more) and subunit addition (two different proteins bind to each other). Because these happen in a fairly predictable way, it’s not as hard as you might think to predict how a novel protein would form.

“We’re bringing a lot of order into the messy world of protein complexes. Proteins can keep go through several iterations of these simple steps, adding more and more levels of complexity and resulting in a huge variety of structures. What we’ve made is a classification based on these underlying principles that helps people get a handle on the complexity.”

Dr Sebastian Ahnert of the Cavendish Laboratory at the University of Cambridge

The exceptions to the rule are interesting in their own right, as are the subject of on-going studies.

“By analysing the tens of thousands of protein complexes for which three-dimensional structures have already been experimentally determined, we could see repeating patterns in the assembly transitions that occur – and with new data from mass spectrometry we could start to see the bigger picture.”

Dr Joe Marsh

“The core work for this study is in theoretical physics and computational biology, but it couldn’t have been done without the mass spectrometry work by our colleagues at Oxford University. This is yet another excellent example of how extremely valuable interdisciplinary research can be.”

Dr Sarah Teichmann, Research Group Leader at the Wellcome Trust Sanger Institute and the European Bioinformatics Institute (EMBL-EBI)

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Serpins: A Review

Reporter: Stephen J. Williams, Ph.D.

Update of the human and mouse SERPIN gene superfamily


The serpin family comprises a structurally similar, yet functionally diverse, set of proteins. Named originally for their function as serine proteinase inhibitors, many of its members are not inhibitors but rather chaperones, involved in storage, transport, and other roles. Serpins are found in genomes of all kingdoms, with 36 human protein-coding genes and five pseudogenes. The mouse has 60 Serpin functional genes, many of which are orthologous to human SERPIN genes and some of which have expanded into multiple paralogous genes. Serpins are found in tissues throughout the body; whereas most are extracellular, there is a class of intracellular serpins. Serpins appear to have roles in inflammation, immune function, tumorigenesis, blood clotting, dementia, and cancer metastasis. Further characterization of these proteins will likely reveal potential biomarkers and therapeutic targets for disease.

Keywords: Serpins, Serine protease inhibitor, Chaperone, Blood clotting, Thrombolysis, Complement, Cell death, Metastatic cancer


Serpins represent the largest and most functionally diverse family of protease inhibitors. The name serpin originates from the first described function of this family, viz., serine proteinase inhibitors. In their native state, serpins exist as monomeric proteins. Most serpin family members inhibit serine proteinases of the chymotrypsin family [1], thereby inhibiting proteolytic cascades. However, some serpins exhibit functions unrelated to inhibition of catalytic activity, such as hormone transport and other mechanisms.

Approximately 1,500 serpin sequences have been identified; they are found in the genomes of all five kingdoms [2]. There are 36 identified human putatively functional protein-coding genes [3]. The serpin superfamily is divided into groups called clades according to their sequence similarity. Clades are classified as A–P, with clades A–I representing human serpins [4].

Serpins have well-conserved secondary structures with an exposed reactive center loop (RCL) (Figure 1), which interacts with the protease active site to inhibit protease activity [5]. The ability for serpins to undergo conformational change is crucial for their function, in which serpins act via a suicide substrate inhibitory mechanism [2,4]. Although most serpins selectively inhibit serine proteases, some inhibit cysteine proteases, such as caspases and cathespins; others perform hormone transport and blood pressure regulation [4]. Serpins play important physiological roles in hormone transport, corticosteroid binding, coagulation, and blood pressure regulation.

Figure 1

Native SERPINA1. Native SERPINA1 with labeled structural elements: β sheet a and reactive center loop (RCL); α helices in red, β sheets in turquoise, turns in green. (Adapted from PDB 1HP7).

Serpin nomenclature

Initially named for tissue location or function (Table 1), a nomenclature committee convened in 1999 with the goal of standardizing serpin gene nomenclature [4]. ‘SERPIN’ was designated as the gene symbol for humans and other species because it is well known and used in the literature and as a keyword [4]. Serpins were not named for activity or function due to the diversity of member structure and tissue distribution. In 2005, proteinase in human gene names was replaced with the term peptidase; however, ‘serpin’ remains the stem because the name was designated prior to this change. The current classification of serpins involves division into clades that are based on phylogenetic relationships (Figure 2). There are 16 clades labeled A–P. Human serpins are represented in the first nine clades (i.e., A–I), with a variety of members being in each clade. Clades are phylogenetically unique and it is important to recognize that no relationships between the clade letters are implied by their order [4]. Some serpins are classified as orphans because they do not group with any other clade. It is likely that they will form clades as new serpins are identified. An example to help illustrate the nomenclature would be α-1-antitrypsin. This was assigned to the first clade, giving it the symbol SERPINA1 with the ‘A’ referencing the clade and the ‘1’ referencing the gene number within the clade [4].

Table 1

SERPIN aliases and function

Figure 2

SERPIN phylogenetic tree. Phylogenetic tree of human and mouse serpin proteins. Protein sequences were aligned using TCOFFEE and analysed using neighbour-joining methods with 10,000 bootstrap replicates in the Phylip package.

Structure function

Serpins have a metastable structure that is required for their function. It consists of a highly conserved secondary structure with three β-sheets (A, B, and C), nine α-helices and a RCL (Figure 1), which serve as bait for target proteases [4,6]. Well-conserved throughout the serpin family, the tertiary structure of scaffold allows for a conformational change critical to protease inhibitor activity [4]. In their native state, serpins exist as monomeric proteins. A serpin molecule consists of a single 330- to 500-amino acid polypeptide chain that has conserved secondary helices and sheets. To inhibit proteolytic activity, the serpin acts as a suicide substrate for the protease [4]. This is accomplished by the RCL of the serpin interacting with the protease’s active site [6].

Serpins can exist in several forms, viz., active, latent, cleaved, delta, and polymeric. Each form is defined by the RCL, which is the moiety required for inhibitory activity. The active form (or the native state) has an exposed RCL that allows it to interact with the protease. The RCL forms an exposed extension located above the molecule. Following proteolysis, the amino acid terminus of the RCL inserts into the A β sheet forming a fourth strand. This process is called the ‘stressed (S) to relaxed (R) transition’ [3] used to inhibit proteases, resulting in the cleaved form. The cleaved form is necessary for inhibition of proteases resulting in an irreversible covalent complex with the target protease thus inactivating both the serpin and the target. Some serpins bind cofactors and/or glycosaminoglycans to maximize protease inhibition, which can vastly increase inhibitory potential [7].

The native form of serpins has low thermal stability indicating that it is not the most stable conformation; rather, native serpins are metastable. However, not all serpins undergo this transition. Serpins can transition to the latent form from the active form and back to the active form from the latent form. The latent form does not possess inhibitory activity but it can convert to the active form through denaturation and refolding [4]. Consequently, it can be considered a control mechanism in regulating homeostasis for certain serpins [3]. Alternatively, the latent state caused by a mutation can be pathological [3].

The delta form is an intermediate conformation between latent and native state where the RCL inserts into the A β sheet and one of the helices unwinds and completes hydrogen bonding of the β sheet [3]. Little is known about the function of this conformation; however, it is likely that this favors polymeric or latent conformation transition rather than native. The polymeric form has a loop sheet mechanism whereby the RCL that would be inserted into the same serpin is instead inserted into the A β sheet of another serpin forming a long chain of these molecules [3]. However, this mechanism of polymerization has recently been challenged in favor of that of a domain-swapping model [8]. Serpins are unique in that their native state (active form) is not the most kinetically stable; rather, it is ‘metastable’. By incorporating the RCL into their A β sheet, either by cleavage for inhibition of target protease or spontaneous latency, they become more stable [9]. For an excellent minireview on kinetics of serpins, see Silverman et al. [4].


Whereas serpins have highly conserved secondary and tertiary structures upon which they are grouped, they often share little amino acid sequence similarity. They do, however, share a highly conserved core, especially in the shutter domain including Ser56 and Ser53 [10], which is thought to be critical in determining tertiary structure and conformational flexibility.

Due to the numerous, yet distinct, processes regulated by serpins and their widespread functions, serpins offer a unique perspective for protein evolution. Members of the serpin family tend to group phylogenetically by species rather than by function. Therefore, evolution of the serpin family was likely driven by speciation to fill their physiological roles rather than by coevolution with the serine proteases (which group by function) [10]. Numerous serpin genes are also found in clusters on the same chromosomes, reflecting earlier gene-duplication events and potentially indicating a common precursor [11,12]. Interestingly, these genes are functionally divergent, despite their chromosomal proximity [7]. In addition, serpins have distinct patterns of introns and exons. These patterns may contain information regarding phylogenetic signals and be evolutionarily related based on relative intron positioning [13,14].

The distribution of serpins in eukaryotes suggests that they arose early in eukaryotic evolution [1]. Extensive gene clustering indicates that numerous serpins in close proximity on the same chromosome may have arisen as a result of duplications from a common precursor [12]; however, the evolution of these proximal genes gave way to vastly divergent functions.

Intracellular serpins of clade B are ancestral to most extracellular serpins [15,16] and each inhibitory serpin contains a highly conserved hinge region [16] within the RCL. Clade F serpins specifically share ancestry with a sea lamprey serpin. Clade P is specific to plant serpins which form a discrete clade. At the time of divergence between Viridiplantae and fungi/Metazoa groups, there was likely only one serpin gene [16]; however, the ancestral homolog from prokaryote or fungi has not yet been identified [16].

There are eight human serpin pseudogenes listed in Table 2. SERPINA15P has been named in succession for the A clade with the parent gene SERPINA6 according to Ensembl and SERPINE2 is the parent gene for SERPINE4P, again named in sequence of the E clade. There are ten mouse pseudogenes listed (Table 3) which remain uncharacterized.

Table 2

Human SERPIN genes

Table 3

Mouse Serpin genes


Protein sequences for human serpins were accessed from Uniprot through the HUGO Gene Nomenclature Committee website ( Sequences were retrieved from the National Center for Biotechnology Information (NCBI) gene database ( referenced through the HUGO Gene Nomenclature Committee website ( for humans and MGI website ( for mouse. All sequences were aligned using the most accurate settings of T-Coffee ( and phylogenetic trees were constructed using neighbor-joining methods with 1000 replicate bootstrap in PHYLIP 3.69 ( (Figure 2). Expression data were determined using Genecards ( and alternative name information was determined using HGNC ( or MGI (

Human and mouse serpin isoforms

Clade A

Clade A serpins are classified as antitrypsin-like, extracellular proteins. They are the largest of the eight clades of extracellular serpins. The SERPINA clade has eleven human genes (1, 3–12) and two pseudogenes.

SERPINA1 is an inhibitory serpin formerly known as antitrypsin. It plays a role in the inhibition of neutrophil elastase [3,17].

SERPINA2 was initially classified as a pseudogene; however, recent evidence indicates that it produces an active transcript that encodes a protein located in the endoplasmic reticulum [18]. A study that sequenced SERPINA2 genes across multiple ethnic groups indicated that in addition to active SERPINA2 protein, there is a haplotype characterized by a partial deletion which has patterns suggestive of positive selection for loss-of-function of SERPINA2 protein. They suggest that the partial pseudogenization in humans may indicate an ongoing process of pseudogenization [19].

SERPINA3 is an inhibitory protein formerly known as antichymotrypsin. It inhibits chymotrypsin and cathepsin G [3,16]. This serpin is normally found in blood, liver, kidney, and lung.

SERPINA4 is an inhibitory protein formerly known as kallistatin (PI4), which inhibits kallikrein [20]. It is expressed in blood, liver, kidney, and heart.

SERPINA5, formerly a protein C inhibitor, inhibits active protein C. It is present in blood, kidney and liver.

SERPINA6 was formerly known as corticosteroid-binding globulin. It is a non-inhibitory protein that binds hormones, i.e., cortisol [16].

SERPINA7, formerly thyroxine-binding globulin, is involved in non-inhibitory thyroid hormone transport. It is expressed in blood, kidney, and heart.

SERPINA8 is now referred to as angiotensinogen (AGT), which is a hormone precursor. It has a distinct serpin domain (phylogenetically unrelated to other clade A members in the current analysis) and a distinct, smaller, agt domain. This particular serpin domain appears to be more closely associated with SERPINF and SERPING [21].

SERPINA9 appears to have a role in naïve B cell maintenance. Formerly called centerin, it is expressed in the plasma and liver.

SERPINA10 is an inhibitory protein responsible for inhibition of activated coagulation factors Z and XI [3]. Formerly known as protein Z-dependent proteinase inhibitor, it is expressed in blood and liver.

SERPINA11 is likely a pseudogene and is uncharacterized.

SERPINA12, formerly vaspin, inhibits kallikrein [22] and plays a role in insulin sensitivity [23]. It appears to be expressed in plasma, platelets, liver and heart.

In the mouse (Table 3), Serpina1 has been expanded to include six members, af. Serpina3 has been expanded to include nine members, ac and fn. The other clade a members are orthologous to human genes. Serpina8, now known as Agt in the mouse, is vital for the development and function of the renin-angiotensin system [24]. It is orthologous to AGT in humans.

Clade B

Clade B consists of intracellular serpins, including ov-serpins, which are ancestral to the extracellular serpins [16]. Members of this subfamily have shorter C and N termini than typical A members and also lack the secretory signal peptide sequence [4]. There are 13 human genes in clade B and one pseudogene. Serpins in clade B are important in inflammation and immune system function as well as mucous production [25]. SERPINB1, B6, B7, and B9 are involved in immune system function with roles in neutrophil and megakaryocyte development [26,27], as well as in the inhibition of the cytotoxic granule protease granzyme B [28]. SERPINB3 and its close homolog B4 are inhibitors that have roles in mucous production [29] and are expressed in epithelial tissues, such as tongue, tonsils, uterus, cervix, and vagina as well as in the upper respiratory tract and thymus [30].

Despite elusive function, SERPINB3 appears to have a role in apoptotic regulation and immunity, which implicates B3 in tumor metastasis and autoimmunity [30]. SERPINB5 has been shown to inhibit metastasis as a tumor suppressor in breast and prostate cancer [30,31]. In addition, multiple serpins in the B clade have been associated with oral squamous cell carcinoma, specifically SERPINB12, SERPINB13, SERPINB4, SERPINB3, SERPINB11, SERPINB7, and SERPINB2 [32]. Less is known about SERPINB10–B13. However, recent evidence points to a role for SERPINB13 in autoimmune diabetes progression and in inflammation [33].

SERPINB1 is an inhibitor of neutrophil elastase. It was formerly called monocyte neutrophil elastase inhibitor and is expressed ubiquitously.

SERPINB2 inhibits PLAU (uPA). It was formerly called plasminogen activator inhibitor 2 (PAI2) and is expressed in blood, kidney, and liver.

SERPINB3 is a cross-class inhibitor of cathepsin L and V [34]. Formerly referred to as squamous cell carcinoma antigen 1, it is expressed in blood, immune cells, kidney, lung, heart, and brain as well as numerous mucosal cells.

SERPINB4 was formerly known as squamous cell carcinoma antigen 2; it was discovered with SERPINB3 [25]. It is a cross-class inhibitor of cathepsin G and chymase [35] and is found in plasma, platelets, kidney, and heart, as well as saliva.

SERPINB5 is a non-inhibitory protein formerly called maspin. It is likely expressed in blood, kidney, liver, lung, as well as saliva.

SERPINB6, formerly called proteinase inhibitor 6 (PI6), is an inhibitor of granule protease, cathepsin G [36]. It is expressed ubiquitously.

SERPINB7 is involved in mesangial cell proliferation [37]. Formerly called megsin, it is expressed in blood and liver.

SERPINB8 is an inhibitory protein. Formerly called proteinase inhibitor 8 (PI8), it is expressed in blood and heart.

SERPINB9 is an inhibitory protein. Formerly called proteinase inhibitor 9 (PI9), it is expressed in blood, liver, lung, and heart.

SERPINB10 is an inhibitory protein involved in hematopoietic and myeloid development [37]. Formerly called bomapin, it expressed in blood and possibly in the brain.

SERPINB11 is a non-inhibitory serpin in human but retains trypsin inhibitory activity in mice [38]. It appears not to exhibit tissue-specific expression; however, it is expressed in HEK cells.

SERPINB12 is a trypsin inhibitor formerly known as yukopin [39]. It is expressed in blood, kidney, liver, heart, and brain.

SERPINB13, formerly known as hurpin, is expressed in blood, kidney, and saliva.

In clade b, mouse Serpinb1 has been expanded to include three members ac; Serpinb3 as well as Serpinb6 have each expanded to include four members, ad. In mice, Serpinb4 is not listed; however, it appears that SERPINB3 and SERPINB4 are equally related to Serpinb3a, Serpinb3b, Serpinb3c, and Serpinb3d, despite the initial theory that Serpinb3d is the mouse homolog of human SERPINB3 and Serpinb3c is the mouse homolog of SERPINB4. Serpinb9 has been expanded to seven members and one pseudogene. Interestingly, Serpinb11 is an active proteinase inhibitor, whereas the human ortholog is inactive.

Clade C

Serpin clade C consists of only one serpin member, SERPINC1, more commonly known as antithrombin. SERPINC1 inhibits coagulation factors IX and X [40]. It is expressed in blood, kidney, liver, lung, heart, brain, as well as saliva.

Serpinc1 gene encodes antithrombin and is orthologous to human SERPINC1.

Clade D

Clade D has one serpin member, SERPIND1, which is an extracellular protein also known as heparin cofactor II [41]. It is an inhibitor of thrombin [42] and is expressed in blood, kidney, liver, and heart.

Serpind1 encodes heparin cofactor II and is orthologous to SERPIND1.

Clade E

Clade E has three members, E1, E2, and E3, all of which are extracellular.

SERPINE1, also known as plasminogen activator inhibitor-1 (PAI1), inhibits thrombin. It is expressed in blood, liver, and heart.

SERPINE2 is a glial-derived nexin that is important in recovery of nerve structure and function [43]. It is expressed in blood, liver, kidney, and brain.

Little is known about the function of SERPINE3.

The mouse genes in clade e (Serpine1–3) are orthologous to human SERPINE1–3.

Clade F

There are two members in SERPIN clade F.

SERPINF1 (or pigment epithelium-derived factor (PEDF)) regulates angiogenesis and is an example of a non-inhibitory serpin. It is also thought to be a neurotrophic factor [16], and appears to be expressed in blood, liver, kidney, heart, and possibly lung.

SERPINF2, also known as α-2-antiplasmin, is an inhibitor of fibrinolysis. It is found in blood, kidney, liver, and heart.

Mouse Serpinf1 and f2 genes are orthologous to the human SERPINF1 and SERPINF2 genes, respectively.

Clade G

Clade G consists of one inhibitory serpin.

SERPING1 is a complement I esterase inhibitor [44] formerly called C1 inhibitor. It is expressed in blood, liver, kidney, lung, heart, and brain.

Mouse Serping1 encodes C1 inhibitor and is orthologous to SERPING1.

Clade H

Clade H consists of one member.

SERPINH1, also known as 47-kDa heat shock protein (HSP47), does not act as a proteinase inhibitor, but rather as a chaperone for collagen [45]. It is expressed in blood, liver and heart.

Mouse Serpinh1 encodes HSP47 and is orthologous to SERPINH1. Knockouts of Serpinh1 in mice are lethal [46] and missense mutations are associated with osteogenesis imperfecta [47].

Clade I

Clade I consists of two extracellular proteins. Serpins in clade I include the following.

SERPINI1 is a neuroserpin inhibitor of PLAT (tPA), PLAU (uPA), and plasmin [48]. It is expressed in liver and possibly plasma.

SERPINI2, previously known as pancipin, has an unknown protein target but may be involved in pancreatic dysfunction [49]. It is found in platelets and plasma as well as the heart.

The genes Serpini1 and Serpini2 encode mouse neuroserpin and pancipin, respectively. These are orthologous to SERPINI1 and SERPINI2 in the human.

Clades J–P

Clades jp represent viral, nematode, horseshoe crab, blood fluke, and plant serpins [16] and will not be described further in this update.

Serpins associated with disease

Serpin polymorphisms have been associated with in many disease states, including blood clotting disorders, emphysema, cirrhosis, and dementia [15,16,50] as well as tumorigenesis and metastasis.

Mutations in SERPINA1 result in a decrease in circulating α-1-antitrypsin which is associated with emphysema and hepatocellular carcinoma [51]. Serpins are implicated in regulation of the cardiovascular system. For example, SERPINA4 depletion is related to renal and cardiovascular injury [52], SERPINA8 variations are integral to the normal function of the renin-angiotensin system and have been found to regulate blood pressure [53], and a SERPINA10 polymorphism was found to increase the risk of venous thromboembolism [54,55]. SERPINA3 deficiency is associated with emphysema [56].

Many SERPINBs are implicated in immune function and dysfunction. In many of these cases, intracellular serpins cause autoimmune antibody production, inflammation, neutropenia, and cancer metastasis [25]. SERPINC1 deficiency has been correlated with autoimmune disease, especially in patients producing antinuclear antibodies, such as those with systemic lupus erythematosus [30]. Interestingly, a SERPINA6 polymorphism has been associated with chronic fatigue syndrome [57], which is thought to be an immune disorder. SERPINA7 deficiency is associated with hyperthyroidism, and high SERPINA12 levels have been associated with insulin resistance [23].

Mutations in SERPINH1, as well as in SERPINF1, are associated with osteogenesis imperfecta [47,58].

Serpins appear to influence protein aggregation. In this respect, SERPINI1 expression has been correlated with dementia [4]. In addition, SERPINA5 accumulation has been identified in plaques in multiple sclerosis [59] and SERPINA3 polymerization may accelerate onset and severity of Alzheimer’s disease [30].

Many serpins have been implicated in cancer progression including SERPINBs (on the 18q21 locus) in oral squamous cell carcinoma [25]. Breast and prostate cancer metastases are also closely associated with SERPINB5 [60,61]. In addition, SERPINE1 appears to have a role in tumor progression [62] and metastasis [63]. Further, SERPINI2 may play a possible role in breast and pancreatic cancer metastasis [49]. Adult gliomas have significant associations with SERPINI1 [64], although its role is unknown. In addition, SERPINI1 has also been proposed as one of five biomarkers in hepatocellular carcinoma [65]. Another potential biomarker includes SERPINA9, which has been found to be strongly expressed in B cell lymphomas [66].

Mouse models of human disease

There are numerous mouse models used to study the role of SERPINs in disease. Some examples include knockout of Serpinag3 used in studying T cells in immunology [67], hepatic specific knockout of Serpinc1, which exhibits coagulopathy [68], and Agt knockout to study blood pressure regulation and the renin-angiotensin system where adipocyte-specific knockout of agt caused decreased systolic blood pressure [69]. Serpinb1 knockout mice show neutropenia [70].

Gene variants in SERPINS

A large number of human variants of serpin genes have been found. For example, NCBI’s dbSNP database ( has 621 entries for SNPs of SERPINA1 alone (accessed October 2013). In addition, several groups have developed specific databases for individual SERPIN genes. These include databases for SERPINA1[71], SERPINC3[72], and SERPING1[73]. A number of pathologies in humans have been attributed to SERPIN gene variants, and often multiple deleterious mutations are known for each gene. Although a full listing of disease-causing SERPIN mutations is beyond the scope of this review, a sample of their scope is provided here. Mutations in the SERPINA1 gene have been linked with early-onset pulmonary emphysema, neonatal hepatitis, liver cirrhosis, and sometimes panniculitis and vasculitis [74,75]. SERPINA5 mutations have been linked with increased papillary thyroid cancer risk [76], and mutations in SERPINA10 have been linked to pregnancy complications [77]. Predisposition to familial venous thromboembolic disease has been linked to mutations in SERPINC1[78,79]. Finally, SNP variants for the SERPING1 gene have been shown to be associated with hereditary angioedema [80].


Serpins are a large class of diverse proteins, which contribute to numerous physiological and pathological conditions. Identification of serpins in immunological functions, pathology due to polymerization, and cancer metastasis underscores their diverse functions and physiological and pathological importance, and gene mutations often lead to loss-of-function and pathology in affected individuals. However, there is still much to learn about the functions and evolutionary development of serpins. Because of numerous biological functions and pathological states associated with serpins, further characterization of these proteins and mechanistic information will provide insight into potential biomarker identification and therapeutic targets.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

CH carried out the sequence alignments and drafted the manuscript. BJ participated in the sequence alignment and analysis. MM reviewed mouse gene/protein data and the nomenclature for accuracy and completeness. MW reviewed human gene/protein data and nomenclature for accuracy and completeness. DT, GS and DWN reviewed and edited the manuscript. VV designed the study and reviewed data and manuscript. All authors read and approved the final manuscript.


This work was supported, in part, by the following NIH grants: R24 AA022057, NIEHS P30 ES06096, HG000330, U41HG003345 and also by a Welcome Trust grant no. 099129/Z/12/Z. Fellowship assistance for BCJ (F31 AA020728) is acknowledged. We would like to thank Konstandinos Vasiliou for his assistance.


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brown adipocyte protein CIDEA promotes lipid droplet fusion

Larry H. Bernstein, MD, FCAP, Curator





The brown adipocyte protein CIDEA promotes lipid droplet fusion via a phosphatidic acid-binding

Parker, Nicholas T Ktistakis, Ann M Dixon, Judith Klein-Seetharaman, Susan Henry, Mark Christian Dirk Dormann, Gil-Soo Han, Stephen A Jesch, George M Carman, Valerian Kagan, et al.

eLife 2015;10.7554/eLife.07485


Maintenance of energy homeostasis depends on the highly regulated storage and release of triacylglycerol primarily in adipose tissue and excessive storage is a feature of common metabolic disorders. CIDEA is a lipid droplet (LD)-protein enriched in brown adipocytes promoting the enlargement of LDs which are dynamic, ubiquitous organelles specialized for storing neutral lipids. We demonstrate an essential role in this process for an amphipathic helix in CIDEA, which facilitates embedding in the LD phospholipid monolayer and binds phosphatidic acid (PA). LD pairs are docked by CIDEA trans-complexes through contributions of the N-terminal domain and a C-terminal dimerization region. These complexes, enriched at the LD-LD contact site, interact with the cone-shaped phospholipid PA and likely increase phospholipid barrier permeability, promoting LD fusion by transference of lipids. This physiological process is essential in adipocyte differentiation as well as serving to facilitate the tight coupling of lipolysis and lipogenesis in activated brown fat.


Evolutionary pressures for survival in fluctuating environments that expose organisms to times of both feast and famine have selected for the ability to efficiently store and release energy in the form of triacyclglycerol (TAG). However, excessive or defective lipid storage is a key feature of common diseases such as diabetes, atherosclerosis and the metabolic syndrome (1). The organelles that are essential for storing and mobilizing intracellular fat are lipid droplets (LDs) (2). They constitute a unique cellular structure where a core of neutral lipids is stabilized in the hydrophilic cytosol by a phospholipid monolayer embedding LD-proteins. While most mammalian 46 cells present small LDs (<1 Pm) (3), white (unilocular) adipocytes contain a single giant LD occupying most of their cell volume. In contrast, brown (multilocular) adipocytes hold multiple LDs of lesser size, increasing the LD surface/volume ratio which facilitates the rapid consumption of lipids for adaptive thermogenesis (4).

The exploration of new approaches for the treatment of metabolic disorders has been stimulated by the rediscovery of active brown adipose tissue (BAT) in adult humans (5, 6) and by the induction of multilocular brown-like cells in white adipose tissue (WAT) (7). The multilocular morphology of brown adipocytes is a defining characteristic of these cells along with expression of genes such as Ucp1. The acquisition of a unilocular or multilocular phenotype is likely to be controlled by the regulation of LD growth. Two related proteins, CIDEA and CIDEC promote LD enlargement in adipocytes (8-10), with CIDEA being specifically found in BAT. Together with CIDEB, they form the CIDE (cell death-inducing DFF45-like effector) family of LD-proteins, which have emerged as important metabolic regulators (11).

Different mechanisms have been proposed for LD enlargement, including in situ neutral lipid synthesis, lipid uptake and LD-LD coalescence (12-14). The study of CIDE 62 proteins has revealed a critical role in the LD fusion process in which a donor LD progressively transfers its content to an acceptor LD until it is completely absorbed (15). However, the underlying mechanism by which CIDEC and CIDEA facilitate the interchange of triacylglycerol (TAG) molecules between LDs is not understood. In the present study, we have obtained a detailed picture of the different steps driving this LD enlargement process, which involves the stabilization of LD pairs, phospholipid binding, and the permeabilization of the LD monolayer to allow the transference of lipids.


CIDEA expression mimics the LD dynamics observed during the differentiation of brown adipocytes

Phases of CIDEA activity: LD targeting, LD-LD docking and LD growth

A cationic amphipathic helix in C-term drives LD targeting

The amphipathic helix is essential for LD enlargement

LD-LD docking is induced by the formation of CIDEA complexes

CIDEC differs from CIDEA in its dependence on the N-term domain

CIDEA interacts with Phosphatidic Acid

PA is required for LD enlargement


The Cidea gene is highly expressed in BAT, induced in WAT following cold exposure (46), and is widely used by researchers as a defining marker to discriminate brown or brite adipocytes from white adipocytes (7, 28). As evidence indicated a key role in the LD biology (47) we have characterized the mechanism by which CIDEA promotes LD enlargement, which involves the targeting of LDs, the docking of LD pairs and the transference of lipids between them. The lipid transfer step requires the interaction of CIDEA and PA through a cationic amphipathic helix. Independently of PA-binding, this helix is also responsible for anchoring CIDEA in the LD membrane. Finally, we demonstrate that the docking of LD pairs is driven by the formation of CIDEA complexes involving the N-term domain and a C-term interaction site.

CIDE proteins appeared during vertebrate evolution by the combination of an ancestor N-term domain and a LD-binding C-term domain (35). In spite of this, the full process of LD enlargement can be induced in yeast by the sole exogenous expression of 395 CIDEA, indicating that in contrast to SNARE-triggered vesicle fusion, LD fusion by lipid transference does not require the coordination of multiple specific proteins (48). Whereas vesicle fusion implicates an intricate restructuring of the phospholipid bilayers, LD fusion is a spontaneous process that the cell has to prevent by tightly controlling their phospholipid composition (23). However, although phospholipid-modifying enzymes have been linked with the biogenesis of LDs (49, 50), the implication of phospholipids in physiologic LD fusion processes has not been previously described.

Complete LD fusion by lipid transfer can last several hours, during which the participating LDs remain in contact. Our results indicate that both the N-term domain and a C-term dimerization site (aa 126-155) independently participate in the docking of LD pairs by forming trans interactions (Fig. 7). Certain mutations in the dimerization sites that do not eliminate the interaction result in a decrease on the TAG transference efficiency, reflected on the presence of small LDs docked to enlarged LDs. This suggests that in addition to stabilizing the LD-LD interaction, the correct conformation of the 409 CIDEA complexes is necessary for optimal TAG transfer. Furthermore, the formation of stable LD pairs is not sufficient to trigger LD fusion by lipid transfer. In fact, although LDs can be tightly packed in cultured adipocytes, no TAG transference across neighbour LDs is observed in the absence of CIDE proteins (15), showing that the phospholipid monolayer acts as a barrier impermeable to TAG. Our CG-MD simulations indicate that certain TAG molecules can escape the neutral lipid core of the LD and be integrated within the aliphatic chains of the phospholipid monolayer. This could be a transition state 416 prior to the TAG transference and our data indicates that the docking of the amphipathic helix in the LD membrane could facilitate this process. However, the infiltrated TAGs in LD membranes in the presence of mutant helices, or even in the absence of docking, suggests that this is not enough to complete the TAG transference.

To be transferred to the adjacent LD, the TAGs integrated in the hydrophobic region of the LD membrane should cross the energy barrier defined by the phospholipid polar heads, and the interaction of CIDEA with PA could play a role in this process, as suggested by the disruption of LD enlargement by the mutations preventing PA-binding (K167E/R171E/R175E) and the inhibition of CIDEA after PA depletion. The minor effects observed with more conservative substitutions in the helix, suggests that the presence of positive charges is sufficient to induce TAG transference by attracting anionic phospholipids present in the LD membrane. PA, which requirement is indicated by our PA-depletion experiments, is a cone-shaped anionic phospholipid which could locally destabilize the LD monolayer by favoring a negative membrane curvature incompatible with the spherical LD morphology (51). Interestingly, while the zwitterion PC, the main component of the monolayer, stabilizes the LD structure (23), the negatively charged PA promote their coalescence (29). This is supported by our CD-MD results which resulted in a deformation of the LD shape by the addition of PA. We propose a model in which the C-term amphipathic helix positions itself in the LD monolayer and interacts with PA molecules in its vicinity, which might include trans interactions with PA in the adjacent LD. The interaction with PA disturbs the integrity of the phospholipid barrier at the LD-LD interface, allowing the LD to LD transference of TAG molecules integrated in the LD membrane (Fig. 7). Additional alterations in the LD composition could be facilitating TAG transference, as differentiating adipocytes experience a reduction in saturated fatty acids in the LD phospholipids (52), and in their PC/PE ratio (53) which could increase the permeability of the LD membranes, and we previously observed that a change in the molecular structures of TAG results in an altered migration pattern to the LD surface (32).

During LD fusion by lipid transfer, the pressure gradient experienced by LDs favors TAG flux from small to large LDs (15). However, the implication of PA, a minor component of the LD membrane, could represent a control mechanism, as it is plausible that the cell could actively influence the TAG flux direction by differently regulating the levels of PA in large and small LDs, which could be controlled by the activity of enzymes such as AGPAT3 and LIPIN-1J (13, 30). This is a remarkable possibility, as a switch in the favored TAG flux direction could promote the acquisition of a multilocular phenotype and facilitate the browning of WAT (24). Interestingly, Cidea mRNA is the LD protein- encoding transcript that experiences the greatest increase during the cold-induced process by which multilocular BAT-like cells appear in WAT (24). Furthermore, in BAT, cold exposure instigates a profound increase in CIDEA protein levels that is independent of transcriptional regulation (54). The profound increase in CIDEA is coincident with elevated lipolysis and de novo lipogenesis that occurs in both brown and white adipose tissues after E-adrenergic receptor activation (55). It is likely that CIDEA has a central role in coupling these processes to package newly synthesized TAG in LDs for subsequent lipolysis and fatty acid oxidation. Importantly, BAT displays high levels of glycerol kinase activity (56, 57) that facilitates glycerol recycling rather than release into the blood stream, following induction of lipolysis (58), which occurs in WAT. Hence, the reported elevated glycerol released from cells depleted of CIDEA (28) is likely to be a result of decoupling lipolysis from the ability to efficiently store the products of lipogenesis in LDs and therefore producing a net increase in detected extracellular glycerol. This important role of CIDEA is supported by the marked depletion of TAG in the BAT of Cidea null mice following overnight exposure to 4 °C (28) and our findings that CIDEA-dependent LD enlargement is maintained in a lipase negative yeast strain.

Cidea and the genes that are required to facilitate high rates of lipolysis and lipogenesis are associated with the “browning” of white fat either following cold exposure (46) or in genetic models such as RIP140 knockout WAT (59). The induction of a brown- like phenotype in WAT has potential benefits in the treatment and prevention of metabolic disorders (60). Differences in the activity and regulation of CIDEC and CIDEA could also be responsible for the adoption of unilocular or multilocular phenotypes. In addition to their differential interaction with PLIN1 and 5, we have observed that CIDEC is more resilient to the deletion of the N-term than CIDEA, indicating that it may be less sensitive to regulatory posttranslational modifications of this domain. This robustness of CIDEC activity together with its potentiation by PLIN1, could facilitate the continuity of the LD enlargement in white adipocytes until the unilocular phenotype is achieved. In contrast, in brown adipocytes expressing CIDEA the process would be stopped at the multilocular stage for example due to post-translational modifications that modulate the function or stability of the protein or alteration of the PA levels in LDs.

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