Conserved sequence

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A multiple sequence alignment of five mammalian histone H1 proteins Sequences are the amino acids for residues 120-180 of the proteins. Residues that are conserved across all sequences are highlighted in grey. Below each site (i.e., position) of the protein sequence alignment is a key denoting conserved sites (*), sites with conservative replacements (:), sites with semi-conservative replacements (.), and sites with non-conservative replacements ( ). Histone Alignment.png
A multiple sequence alignment of five mammalian histone H1 proteins
Sequences are the amino acids for residues 120-180 of the proteins. Residues that are conserved across all sequences are highlighted in grey. Below each site (i.e., position) of the protein sequence alignment is a key denoting conserved sites (*), sites with conservative replacements (:), sites with semi-conservative replacements (.), and sites with non-conservative replacements ( ).

In evolutionary biology, conserved sequences are identical or similar sequences in nucleic acids (DNA and RNA) or proteins across species (orthologous sequences), or within a genome (paralogous sequences), or between donor and receptor taxa (xenologous sequences). Conservation indicates that a sequence has been maintained by natural selection.

Contents

A highly conserved sequence is one that has remained relatively unchanged far back up the phylogenetic tree, and hence far back in geological time. Examples of highly conserved sequences include the RNA components of ribosomes present in all domains of life, the homeobox sequences widespread amongst eukaryotes, and the tmRNA in bacteria. The study of sequence conservation overlaps with the fields of genomics, proteomics, evolutionary biology, phylogenetics, bioinformatics and mathematics.

History

The discovery of the role of DNA in heredity, and observations by Frederick Sanger of variation between animal insulins in 1949, [2] prompted early molecular biologists to study taxonomy from a molecular perspective. [3] [4] Studies in the 1960s used DNA hybridization and protein cross-reactivity techniques to measure similarity between known orthologous proteins, such as hemoglobin [5] and cytochrome c. [6] In 1965, Émile Zuckerkandl and Linus Pauling introduced the concept of the molecular clock, [7] proposing that steady rates of amino acid replacement could be used to estimate the time since two organisms diverged. While initial phylogenies closely matched the fossil record, observations that some genes appeared to evolve at different rates led to the development of theories of molecular evolution. [3] [4] Margaret Dayhoff's 1966 comparison of ferredoxin sequences showed that natural selection would act to conserve and optimise protein sequences essential to life. [8]

Mechanisms

Over many generations, nucleic acid sequences in the genome of an evolutionary lineage can gradually change over time due to random mutations and deletions. [9] [10] Sequences may also recombine or be deleted due to chromosomal rearrangements. Conserved sequences are sequences which persist in the genome despite such forces, and have slower rates of mutation than the background mutation rate. [11]

Conservation can occur in coding and non-coding nucleic acid sequences. Highly conserved DNA sequences are thought to have functional value, although the role for many highly conserved non-coding DNA sequences is poorly understood. [12] [13] The extent to which a sequence is conserved can be affected by varying selection pressures, its robustness to mutation, population size and genetic drift. Many functional sequences are also modular, containing regions which may be subject to independent selection pressures, such as protein domains. [14]

Coding sequence

In coding sequences, the nucleic acid and amino acid sequence may be conserved to different extents, as the degeneracy of the genetic code means that synonymous mutations in a coding sequence do not affect the amino acid sequence of its protein product. [15]

Amino acid sequences can be conserved to maintain the structure or function of a protein or domain. Conserved proteins undergo fewer amino acid replacements, or are more likely to substitute amino acids with similar biochemical properties. [16] Within a sequence, amino acids that are important for folding, structural stability, or that form a binding site may be more highly conserved. [17] [18]

The nucleic acid sequence of a protein coding gene may also be conserved by other selective pressures. The codon usage bias in some organisms may restrict the types of synonymous mutations in a sequence. Nucleic acid sequences that cause secondary structure in the mRNA of a coding gene may be selected against, as some structures may negatively affect translation, or conserved where the mRNA also acts as a functional non-coding RNA. [19] [20]

Non-coding

Non-coding sequences important for gene regulation, such as the binding or recognition sites of ribosomes and transcription factors, may be conserved within a genome. For example, the promoter of a conserved gene or operon may also be conserved. As with proteins, nucleic acids that are important for the structure and function of non-coding RNA (ncRNA) can also be conserved. However, sequence conservation in ncRNAs is generally poor compared to protein-coding sequences, and base pairs that contribute to structure or function are often conserved instead. [21] [22]

Identification

Conserved sequences are typically identified by bioinformatics approaches based on sequence alignment. Advances in high-throughput DNA sequencing and protein mass spectrometry has substantially increased the availability of protein sequences and whole genomes for comparison since the early 2000s. [23] [24]

Conserved sequences may be identified by homology search, using tools such as BLAST, HMMER, OrthologR, [25] and Infernal. [26] Homology search tools may take an individual nucleic acid or protein sequence as input, or use statistical models generated from multiple sequence alignments of known related sequences. Statistical models such as profile-HMMs, and RNA covariance models which also incorporate structural information, [27] can be helpful when searching for more distantly related sequences. Input sequences are then aligned against a database of sequences from related individuals or other species. The resulting alignments are then scored based on the number of matching amino acids or bases, and the number of gaps or deletions generated by the alignment. Acceptable conservative substitutions may be identified using substitution matrices such as PAM and BLOSUM. Highly scoring alignments are assumed to be from homologous sequences. The conservation of a sequence may then be inferred by detection of highly similar homologs over a broad phylogenetic range. [28]

Multiple sequence alignment

A sequence logo for the LexA-binding motif of gram-positive bacteria. As the adenosine at position 5 is highly conserved, it appears larger than other characters. LexA gram positive bacteria sequence logo.png
A sequence logo for the LexA-binding motif of gram-positive bacteria. As the adenosine at position 5 is highly conserved, it appears larger than other characters.

Multiple sequence alignments can be used to visualise conserved sequences. The CLUSTAL format includes a plain-text key to annotate conserved columns of the alignment, denoting conserved sequence (*), conservative mutations (:), semi-conservative mutations (.), and non-conservative mutations ( ) [30] Sequence logos can also show conserved sequence by representing the proportions of characters at each point in the alignment by height. [29]

Genome alignment

This image from the ECR browser shows the result of aligning different vertebrate genomes to the human genome at the conserved OTX2 gene. Top: Gene annotations of exons and introns of the OTX2 gene. For each genome, sequence similarity (%) compared to the human genome is plotted. Tracks show the zebrafish, dog, chicken, western clawed frog, opossum, mouse, rhesus macaque and chimpanzee genomes. The peaks show regions of high sequence similarity across all genomes, showing that this sequence is highly conserved. ECR browser showing conserved OTX2 gene in vertebrates.png
This image from the ECR browser shows the result of aligning different vertebrate genomes to the human genome at the conserved OTX2 gene. Top: Gene annotations of exons and introns of the OTX2 gene. For each genome, sequence similarity (%) compared to the human genome is plotted. Tracks show the zebrafish, dog, chicken, western clawed frog, opossum, mouse, rhesus macaque and chimpanzee genomes. The peaks show regions of high sequence similarity across all genomes, showing that this sequence is highly conserved.

Whole genome alignments (WGAs) may also be used to identify highly conserved regions across species. Currently the accuracy and scalability of WGA tools remains limited due to the computational complexity of dealing with rearrangements, repeat regions and the large size of many eukaryotic genomes. [32] However, WGAs of 30 or more closely related bacteria (prokaryotes) are now increasingly feasible. [33] [34]

Scoring systems

Other approaches use measurements of conservation based on statistical tests that attempt to identify sequences which mutate differently to an expected background (neutral) mutation rate.

The GERP (Genomic Evolutionary Rate Profiling) framework scores conservation of genetic sequences across species. This approach estimates the rate of neutral mutation in a set of species from a multiple sequence alignment, and then identifies regions of the sequence that exhibit fewer mutations than expected. These regions are then assigned scores based on the difference between the observed mutation rate and expected background mutation rate. A high GERP score then indicates a highly conserved sequence. [35] [36]

LIST [37] [38] (Local Identity and Shared Taxa) is based on the assumption that variations observed in species closely related to human are more significant when assessing conservation compared to those in distantly related species. Thus, LIST utilizes the local alignment identity around each position to identify relevant sequences in the multiple sequence alignment (MSA) and then it estimates conservation based on the taxonomy distances of these sequences to human. Unlike other tools, LIST ignores the count/frequency of variations in the MSA.

Aminode [39] combines multiple alignments with phylogenetic analysis to analyze changes in homologous proteins and produce a plot that indicates the local rates of evolutionary changes. This approach identifies the Evolutionarily Constrained Regions in a protein, which are segments that are subject to purifying selection and are typically critical for normal protein function.

Other approaches such as PhyloP and PhyloHMM incorporate statistical phylogenetics methods to compare probability distributions of substitution rates, which allows the detection of both conservation and accelerated mutation. First, a background probability distribution is generated of the number of substitutions expected to occur for a column in a multiple sequence alignment, based on a phylogenetic tree. The estimated evolutionary relationships between the species of interest are used to calculate the significance of any substitutions (i.e. a substitution between two closely related species may be less likely to occur than distantly related ones, and therefore more significant). To detect conservation, a probability distribution is calculated for a subset of the multiple sequence alignment, and compared to the background distribution using a statistical test such as a likelihood-ratio test or score test. P-values generated from comparing the two distributions are then used to identify conserved regions. PhyloHMM uses hidden Markov models to generate probability distributions. The PhyloP software package compares probability distributions using a likelihood-ratio test or score test, as well as using a GERP-like scoring system. [40] [41] [42]

Extreme conservation

Ultra-conserved elements

Ultra-conserved elements or UCEs are sequences that are highly similar or identical across multiple taxonomic groupings. These were first discovered in vertebrates, [43] and have subsequently been identified within widely-differing taxa. [44] While the origin and function of UCEs are poorly understood, [45] they have been used to investigate deep-time divergences in amniotes, [46] insects, [47] and between animals and plants. [48]

Universally conserved genes

The most highly conserved genes are those that can be found in all organisms. These consist mainly of the ncRNAs and proteins required for transcription and translation, which are assumed to have been conserved from the last universal common ancestor of all life. [49]

Genes or gene families that have been found to be universally conserved include GTP-binding elongation factors, Methionine aminopeptidase 2, Serine hydroxymethyltransferase, and ATP transporters. [50] Components of the transcription machinery, such as RNA polymerase and helicases, and of the translation machinery, such as ribosomal RNAs, tRNAs and ribosomal proteins are also universally conserved. [51]

Applications

Phylogenetics and taxonomy

Sets of conserved sequences are often used for generating phylogenetic trees, as it can be assumed that organisms with similar sequences are closely related. [52] The choice of sequences may vary depending on the taxonomic scope of the study. For example, the most highly conserved genes such as the 16S RNA and other ribosomal sequences are useful for reconstructing deep phylogenetic relationships and identifying bacterial phyla in metagenomics studies. [53] [54] Sequences that are conserved within a clade but undergo some mutations, such as housekeeping genes, can be used to study species relationships. [55] [56] [57] The internal transcribed spacer (ITS) region, which is required for spacing conserved rRNA genes but undergoes rapid evolution, is commonly used to classify fungi and strains of rapidly evolving bacteria. [58] [59] [60] [61]

Medical research

As highly conserved sequences often have important biological functions, they can be useful a starting point for identifying the cause of genetic diseases. Many congenital metabolic disorders and Lysosomal storage diseases are the result of changes to individual conserved genes, resulting in missing or faulty enzymes that are the underlying cause of the symptoms of the disease. Genetic diseases may be predicted by identifying sequences that are conserved between humans and lab organisms such as mice [62] or fruit flies, [63] and studying the effects of knock-outs of these genes. [64] Genome-wide association studies can also be used to identify variation in conserved sequences associated with disease or health outcomes. More than two dozen novel potential susceptibility loci have been discovered for Alzehimer's disease. [65] [66]

Functional annotation

Identifying conserved sequences can be used to discover and predict functional sequences such as genes. [67] Conserved sequences with a known function, such as protein domains, can also be used to predict the function of a sequence. Databases of conserved protein domains such as Pfam and the Conserved Domain Database can be used to annotate functional domains in predicted protein coding genes. [68]

See also

Related Research Articles

<span class="mw-page-title-main">Human genome</span> Complete set of nucleic acid sequences for humans

The human genome is a complete set of nucleic acid sequences for humans, encoded as DNA within the 23 chromosome pairs in cell nuclei and in a small DNA molecule found within individual mitochondria. These are usually treated separately as the nuclear genome and the mitochondrial genome. Human genomes include both protein-coding DNA sequences and various types of DNA that does not encode proteins. The latter is a diverse category that includes DNA coding for non-translated RNA, such as that for ribosomal RNA, transfer RNA, ribozymes, small nuclear RNAs, and several types of regulatory RNAs. It also includes promoters and their associated gene-regulatory elements, DNA playing structural and replicatory roles, such as scaffolding regions, telomeres, centromeres, and origins of replication, plus large numbers of transposable elements, inserted viral DNA, non-functional pseudogenes and simple, highly repetitive sequences. Introns make up a large percentage of non-coding DNA. Some of this non-coding DNA is non-functional junk DNA, such as pseudogenes, but there is no firm consensus on the total amount of junk DNA.

<span class="mw-page-title-main">Sequence alignment</span> Process in bioinformatics that identifies equivalent sites within molecular sequences

In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences such as calculating the distance cost between strings in a natural language, or to display financial data.

Molecular evolution is the process of change in the sequence composition of cellular molecules such as DNA, RNA, and proteins across generations. The field of molecular evolution uses principles of evolutionary biology and population genetics to explain patterns in these changes. Major topics in molecular evolution concern the rates and impacts of single nucleotide changes, neutral evolution vs. natural selection, origins of new genes, the genetic nature of complex traits, the genetic basis of speciation, the evolution of development, and ways that evolutionary forces influence genomic and phenotypic changes.

The coding region of a gene, also known as the coding sequence (CDS), is the portion of a gene's DNA or RNA that codes for a protein. Studying the length, composition, regulation, splicing, structures, and functions of coding regions compared to non-coding regions over different species and time periods can provide a significant amount of important information regarding gene organization and evolution of prokaryotes and eukaryotes. This can further assist in mapping the human genome and developing gene therapy.

<span class="mw-page-title-main">Pseudogene</span> Functionless relative of a gene

Pseudogenes are nonfunctional segments of DNA that resemble functional genes. Most arise as superfluous copies of functional genes, either directly by gene duplication or indirectly by reverse transcription of an mRNA transcript. Pseudogenes are usually identified when genome sequence analysis finds gene-like sequences that lack regulatory sequences needed for transcription or translation, or whose coding sequences are obviously defective due to frameshifts or premature stop codons. Pseudogenes are a type of junk DNA.

<span class="mw-page-title-main">Nucleic acid sequence</span> Succession of nucleotides in a nucleic acid

A nucleic acid sequence is a succession of bases within the nucleotides forming alleles within a DNA or RNA (GACU) molecule. This succession is denoted by a series of a set of five different letters that indicate the order of the nucleotides. By convention, sequences are usually presented from the 5' end to the 3' end. For DNA, with its double helix, there are two possible directions for the notated sequence; of these two, the sense strand is used. Because nucleic acids are normally linear (unbranched) polymers, specifying the sequence is equivalent to defining the covalent structure of the entire molecule. For this reason, the nucleic acid sequence is also termed the primary structure.

An Alu element is a short stretch of DNA originally characterized by the action of the Arthrobacter luteus (Alu) restriction endonuclease. Alu elements are the most abundant transposable elements, containing over one million copies dispersed throughout the human genome. Alu elements were thought to be selfish or parasitic DNA, because their sole known function is self reproduction. However, they are likely to play a role in evolution and have been used as genetic markers. They are derived from the small cytoplasmic 7SL RNA, a component of the signal recognition particle. Alu elements are highly conserved within primate genomes and originated in the genome of an ancestor of Supraprimates.

<span class="mw-page-title-main">Ribonuclease H</span> Enzyme family

Ribonuclease H is a family of non-sequence-specific endonuclease enzymes that catalyze the cleavage of RNA in an RNA/DNA substrate via a hydrolytic mechanism. Members of the RNase H family can be found in nearly all organisms, from bacteria to archaea to eukaryotes.

<span class="mw-page-title-main">Aminoacyl tRNA synthetase</span> Class of enzymes

An aminoacyl-tRNA synthetase, also called tRNA-ligase, is an enzyme that attaches the appropriate amino acid onto its corresponding tRNA. It does so by catalyzing the transesterification of a specific cognate amino acid or its precursor to one of all its compatible cognate tRNAs to form an aminoacyl-tRNA. In humans, the 20 different types of aa-tRNA are made by the 20 different aminoacyl-tRNA synthetases, one for each amino acid of the genetic code.

<span class="mw-page-title-main">Sequence homology</span> Shared ancestry between DNA, RNA or protein sequences

Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal gene transfer event (xenologs).

<span class="mw-page-title-main">Pfam</span> Database of protein families

Pfam is a database of protein families that includes their annotations and multiple sequence alignments generated using hidden Markov models. The most recent version, Pfam 36.0, was released in September 2023 and contains 20,795 families.

The Kozak consensus sequence is a nucleic acid motif that functions as the protein translation initiation site in most eukaryotic mRNA transcripts. Regarded as the optimum sequence for initiating translation in eukaryotes, the sequence is an integral aspect of protein regulation and overall cellular health as well as having implications in human disease. It ensures that a protein is correctly translated from the genetic message, mediating ribosome assembly and translation initiation. A wrong start site can result in non-functional proteins. As it has become more studied, expansions of the nucleotide sequence, bases of importance, and notable exceptions have arisen. The sequence was named after the scientist who discovered it, Marilyn Kozak. Kozak discovered the sequence through a detailed analysis of DNA genomic sequences.

<span class="mw-page-title-main">Untranslated region</span> Non-coding regions on either end of mRNA

In molecular genetics, an untranslated region refers to either of two sections, one on each side of a coding sequence on a strand of mRNA. If it is found on the 5' side, it is called the 5' UTR, or if it is found on the 3' side, it is called the 3' UTR. mRNA is RNA that carries information from DNA to the ribosome, the site of protein synthesis (translation) within a cell. The mRNA is initially transcribed from the corresponding DNA sequence and then translated into protein. However, several regions of the mRNA are usually not translated into protein, including the 5' and 3' UTRs.

<span class="mw-page-title-main">16S ribosomal RNA</span> RNA component

16S ribosomal RNA is the RNA component of the 30S subunit of a prokaryotic ribosome. It binds to the Shine-Dalgarno sequence and provides most of the SSU structure.

<span class="mw-page-title-main">DNA annotation</span> The process of describing the structure and function of a genome

In molecular biology and genetics, DNA annotation or genome annotation is the process of describing the structure and function of the components of a genome, by analyzing and interpreting them in order to extract their biological significance and understand the biological processes in which they participate. Among other things, it identifies the locations of genes and all the coding regions in a genome and determines what those genes do.

Periannan Senapathy is a molecular biologist, geneticist, author and entrepreneur. He is the founder, president and chief scientific officer at Genome International Corporation, a biotechnology, bioinformatics, and information technology firm based in Madison, Wisconsin, which develops computational genomics applications of next-generation DNA sequencing (NGS) and clinical decision support systems for analyzing patient genome data that aids in diagnosis and treatment of diseases.

An ultra-conserved element (UCE) was originally defined as a genome segment longer than 200 base pairs (bp) that is absolutely conserved, with no insertions or deletions and 100% identity, between orthologous regions of the human, rat, and mouse genomes. 481 ultra-conserved elements have been identified in the human genome. If ribosomal DNA are excluded, these range in size from 200 bp to 781 bp. UCRs are found on all chromosomes except for 21 and Y. A database collecting genomic information about ultra-conserved elements (UCbase) is available at http://ucbase.unimore.it.

A protein superfamily is the largest grouping (clade) of proteins for which common ancestry can be inferred. Usually this common ancestry is inferred from structural alignment and mechanistic similarity, even if no sequence similarity is evident. Sequence homology can then be deduced even if not apparent. Superfamilies typically contain several protein families which show sequence similarity within each family. The term protein clan is commonly used for protease and glycosyl hydrolases superfamilies based on the MEROPS and CAZy classification systems.

Non-coding RNAs have been discovered using both experimental and bioinformatic approaches. Bioinformatic approaches can be divided into three main categories. The first involves homology search, although these techniques are by definition unable to find new classes of ncRNAs. The second category includes algorithms designed to discover specific types of ncRNAs that have similar properties. Finally, some discovery methods are based on very general properties of RNA, and are thus able to discover entirely new kinds of ncRNAs.

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