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Genomics and epigenetics link to DNA structure

Posted in Behavioral Genetics, Biological Networks, Gene Regulation and Evolution, Cancer Informatics, Cell Biology, Signaling & Cell Circuits, Cerebrovascular and Neurodegenerative Diseases, Chemical Genetics, Clinical & Translational, Clinical Genomics, Cognition, Computational Biology/Systems and Bioinformatics, Curation, Developmental biology, Disease Biology, DNA repair, Embryology, Enzyme Induction, Epigenetics and Cardiovascular Risks, Epigenetics and Environmental Factors, Exosomes, Gene Regulation and Evolution, Genome Biology, Genomic Endocrinology, Genomic Expression, Human aging, Mutagenesis, tagged AT-rich DNA duplex, DNA methylation, double stranded DNA, epigenetic modifications, Epigenetics, fluorescence resonance energy transfer, interhelical region, nanoscale, Nucleic acid sequence, sequence-dependent attraction, spermine on April 11, 2016| Leave a Comment »

Genomics and epigenetics link to DNA structure

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Sequence and Epigenetic Factors Determine Overall DNA Structure

http://www.genengnews.com/gen-news-highlights/sequence-and-epigenetic-factors-determine-overall-dna-structure/81252592/

http://www.genengnews.com/Media/images/GENHighlight/Atomiclevelsimulationsshowingelectrostaticforcesbetweeneachatom1259202113.jpg

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

http://bionano.physics.illinois.edu/sites/default/files/styles/large/public/telepathy_figures_0.png?itok=VUJIHX2_

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 phenomena^{3, 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 proliferation^{9, 10}. Polyamines are also known to condense DNA in a concentration-dependent manner^{2, 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 100 mM 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, ΔG_min, at the inter-DNA distance of 25−30 Å 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 instead^{14, 15}.

(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 100 mM 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]=50 mM 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⁺ (1 mM). The fraction is significantly higher for parallel orientation of the DNA fragments.

• Full size image (202 KB)

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, |ΔG_min|, showed a clearly rank-ordered dependence on the DNA sequence: |ΔG_min| of (A)₂₀>|ΔG_min| of (AT)₁₀>|ΔG_min| of (GC)₁₀>|ΔG_min| of (G)₂₀. Two trends can be noted. First, AT-rich sequences attract each other more strongly than GC-rich sequences¹⁶. For example, |ΔG_min| of (AT)₁₀ (1.5 kcal mol⁻¹ per turn) is about twice |ΔG_min| of (GC)₁₀ (0.8 kcal mol⁻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 (~0.3 kcal mol⁻¹ per turn) in the value of |ΔG_min|.

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 (~150 bp (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 2 min (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 ~2 mM Sm⁴⁺(Fig. 1f), which is consistent with the results of previous experimental studies^{2, 3}. In accordance with the prediction of our MD simulations, GC-rich dsDNAs (GC1, 75% GC in the middle 80 bp) 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⁺ complexes^{2, 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 1 mM Sm⁴⁺ and 25 or 50 mM 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 hotspot^{21, 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 attraction^{16, 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).

(a–c) Representative configurations of Sm⁴⁺ molecules at the DNA–DNA distance of 28 Å 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. (d–f) The average distributions of cations for the three sequence pairs featured in a–c. Top: density of Sm⁴⁺ nitrogen atoms (d=28 Å) averaged over the corresponding MD trajectory and the z axis. White circles (20 Å 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 36 Å.

• Full size image (182 KB)

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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 |ΔG_min| of (GC)₁₀ increases on methylation of cytosines to become similar to |ΔG_min| 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 |ΔG_min| 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 10 Å, 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, N_spm (Fig. 3b). Indeed, the number of bridging Sm⁴⁺ molecules ranks in the same order as |ΔG_min|: N_spm of (A)₂₀>N_spm of (AT)₁₀≈N_spm of (GmC)₁₀>N_spm of (GC)₁₀>N_spm 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, |ΔG_min| of (AT)₁₀≈|ΔG_min| 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, |ΔG_min| of (A)₂₀>|ΔG_min| 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 |ΔG_min| is larger for (GC)₁₀ than for (G)₂₀.

(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 10 Å (x axis) by 20 Å (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.

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Genome-wide investigations of chromosome conformations using the Hi–C technique revealed that AT-rich loci form tight clusters in human nucleus^{27, 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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Periodic table of protein complexes

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Bio Instrumentation in Experimental Life Sciences Research, Biochemical pathways, BioIT: BioInformatics, Biological Networks, Cell Biology, Signaling & Cell Circuits, Computational Biology/Systems and Bioinformatics, Curation, Embryology, Enzyme Induction, Gene Regulation, Lipid metabolism, Proteomics, tagged protein complexes on January 9, 2016| Leave a Comment »

Periodic table of protein complexes

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Periodic Table of Protein Complexes

http://www.technologynetworks.com/Proteomics/news.aspx?ID=186248

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 in Human Genomics

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Cell Biology, Signaling & Cell Circuits, Cytoskeleton, Developmental biology, Disease Biology, Embryology, tagged Cancer - General, immune response, neutrophil, protease, serpin on January 7, 2016| Leave a Comment »

Serpins: A Review

Reporter: Stephen J. Williams, Ph.D.

Update of the human and mouse SERPIN gene superfamily

Claire Heit,¹ Brian C Jackson,¹ Monica McAndrews,² Mathew W Wright,³ David C Thompson,⁴ Gary A Silverman,⁵ Daniel W Nebert,⁶ and Vasilis Vasiliou¹

Author information ► Article notes ► Copyright and License information ►

This article has been cited by other articles in PMC.

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Abstract

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

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Introduction

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].

Evolution

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

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Methods

Protein sequences for human serpins were accessed from Uniprot through the HUGO Gene Nomenclature Committee website (http://www.genenames.org). Sequences were retrieved from the National Center for Biotechnology Information (NCBI) gene database (http://www.ncbi.nlm.nih.gov/gene) referenced through the HUGO Gene Nomenclature Committee website (http://www.genenames.org) for humans and MGI website (http://www.informatics.jax.org) for mouse. All sequences were aligned using the most accurate settings of T-Coffee (http://tcoffee.crg.cat/) and phylogenetic trees were constructed using neighbor-joining methods with 1000 replicate bootstrap in PHYLIP 3.69 (http://evolution.genetics.washington.edu/phylip.html) (Figure 2). Expression data were determined using Genecards (http://www.genecards.org) and alternative name information was determined using HGNC (http://www.genenames.org) or MGI (http://www.informatics.jax.org).

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, a–f. Serpina3 has been expanded to include nine members, a–c and f–n. 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 a–c; Serpinb3 as well as Serpinb6 have each expanded to include four members, a–d. 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 j–p 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 (http://www.ncbi.nlm.nih.gov/snp) 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].

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Conclusions

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.

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Competing interests

The authors declare that they have no competing interests.

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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.

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Acknowledgements

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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