Saturn at equinox, photographed by Cassini in August 2009
A gas giant is a giant planet composed mainly of hydrogen and helium. Jupiter and Saturn are the gas giants of the Solar System. The term "gas giant" was originally synonymous with "giant planet". However, in the 1990s, it became known that Uranus and Neptune are really a distinct class of giant planets, being composed mainly of heavier volatile substances (which are referred to as "ices"). For this reason, Uranus and Neptune are now often classified in the separate category of ice giants.
Jupiter and Saturn consist mostly of elements such as hydrogen
and helium, with heavier elements making up between 3 and 13 percent of
their mass. They are thought to consist of an outer layer of compressed molecular hydrogen surrounding a layer of liquid metallic hydrogen,
with probably a molten rocky core inside. The outermost portion of
their hydrogen atmosphere contains many layers of visible clouds that
are mostly composed of water (despite earlier consensus that there was
no water anywhere in the Solar System besides Earth) and ammonia.
The layer of metallic hydrogen located in the mid-interior makes up the
bulk of every gas giant and is referred to as "metallic" because the
very large atmospheric pressure turns hydrogen into an electrical
conductor. The gas giants' cores are thought to consist of heavier
elements at such high temperatures (20,000 K [19,700 °C; 35,500 °F])
and pressures that their properties are not yet completely understood.
The placement of the solar system's gas giants can be explained by the grand tack hypothesis.
The defining differences between a very low-mass brown dwarf (which can have a mass as low as roughly 13 times that of Jupiter) and a gas giant are debated. One school of thought is based on formation; the other, on the physics of the interior. Part of the debate concerns whether brown dwarfs must, by definition, have experienced nuclear fusion at some point in their history.
Terminology
The term gas giant was coined in 1952 by the science fiction writer James Blish and was originally used to refer to all giant planets.
It is, arguably, something of a misnomer because throughout most of the
volume of all giant planets, the pressure is so high that matter is not
in gaseous form. Other than solids in the core and the upper layers of the atmosphere, all matter is above the critical point, where there is no distinction between liquids and gases.
The term has nevertheless caught on, because planetary scientists
typically use "rock", "gas", and "ice" as shorthands for classes of
elements and compounds commonly found as planetary constituents,
irrespective of what phase
the matter may appear in. In the outer Solar System, hydrogen and
helium are referred to as "gases"; water, methane, and ammonia as
"ices"; and silicates and metals as "rocks". In this terminology, since
Uranus and Neptune are primarily composed of ices, not gas, they are
more commonly called ice giants and distinct from the gas giants.
Theoretically, gas giants can be divided into five distinct classes
according to their modeled physical atmospheric properties, and hence
their appearance: ammonia clouds (I), water clouds (II), cloudless
(III), alkali-metal clouds (IV), and silicate clouds (V). Jupiter and
Saturn are both class I. Hot Jupiters are class IV or V.
Extrasolar
Artist's impression of the formation of a gas giant around the star HD 100546A gas giant exoplanet [right] with the density of a marshmallow has been detected in orbit around a cool red dwarf star [left] by the NASA-funded NEID radial-velocity instrument on the 3.5-meter WIYN Telescope at Kitt Peak National Observatory.
Cold gas giants
A cold hydrogen-rich gas giant more massive than Jupiter but less than about 500 ME (1.6MJ) will only be slightly larger in volume than Jupiter. For masses above 500 ME, gravity will cause the planet to shrink (see degenerate matter).
Kelvin–Helmholtz heating can cause a gas giant to radiate more energy than it receives from its host star.
Although the words "gas" and "giant" are often combined, hydrogen
planets need not be as large as the familiar gas giants from the Solar
System. However, smaller gas planets and planets closer to their star
will lose atmospheric mass more quickly via hydrodynamic escape than larger planets and planets farther out.
A gas dwarf could be defined as a planet with a rocky core that
has accumulated a thick envelope of hydrogen, helium and other
volatiles, having as result a total radius between 1.7 and 3.9
Earth-radii.
The smallest known extrasolar planet that is likely a "gas planet" is Kepler-138d, which has the same mass as Earth but is 60% larger and therefore has a density that indicates a thick gas envelope.
A low-mass gas planet can still have a radius resembling that of a gas giant if it has the right temperature.
Precipitation and meteorological phenomena
Jovian weather
Heat that is funneled upward by local storms is a major driver of the weather on gas giants. Much, if not all, of the deep heat escaping the interior flows up through towering thunderstorms. These disturbances develop into small eddies that eventually form storms such as the Great Red Spot on Jupiter. On Earth and Jupiter, lightning and the hydrologic cycle are intimately linked together to create intense thunderstorms. During a terrestrial thunderstorm, condensation releases heat that pushes rising air upward.
This "moist convection" engine can segregate electrical charges into
different parts of a cloud; the reuniting of those charges is lightning. Therefore, we can use lightning to signal to us where convection is happening. Although Jupiter has no ocean or wet ground, moist convection seems to function similarly compared to Earth.
Jupiter's Red Spot
The Great Red Spot (GRS) is a high-pressure system located in Jupiter's southern hemisphere. The GRS is a powerful anticyclone, swirling at about 430 to 680 kilometers per hour counterclockwise around the center. The Spot has become known for its ferocity, even feeding on smaller Jovian storms.
Tholins are brown organic compounds found within the surface of various
planets that are formed by exposure to UV irradiation. The tholins that
exist on Jupiter's surface get sucked up into the atmosphere by storms
and circulation; it is hypothesized that those tholins that become
ejected from the regolith get stuck in Jupiter's GRS, causing it to be
red.
Helium rain on Saturn and Jupiter
Condensation
of helium creates liquid helium rain on gas giants. On Saturn, this
helium condensation occurs at certain pressures and temperatures when
helium does not mix in with the liquid metallic hydrogen present on the
planet.
Regions on Saturn where helium is insoluble allow the denser helium to
form droplets and act as a source of energy, both through the release of
latent heat and by descending deeper into the center of the planet.
This phase separation leads to helium droplets that fall as rain
through the liquid metallic hydrogen until they reach a warmer region
where they dissolve in the hydrogen.
Since Jupiter and Saturn have different total masses, the thermodynamic
conditions in the planetary interior could be such that this
condensation process is more prevalent in Saturn than in Jupiter.
Helium condensation could be responsible for Saturn's excess luminosity
as well as the helium depletion in the atmosphere of both Jupiter and
Saturn.
Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell.
Transcriptomics technologies provide a broad account of which cellular
processes are active and which are dormant.
A major challenge in molecular biology is to understand how a single
genome gives rise to a variety of cells. Another is how gene expression
is regulated.
The first attempts to study whole transcriptomes began in the
early 1990s. Subsequent technological advances since the late 1990s have
repeatedly transformed the field and made transcriptomics a widespread
discipline in biological sciences. There are two key contemporary
techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA-Seq, which uses high-throughput sequencing
to record all transcripts. As the technology improved, the volume of
data produced by each transcriptome experiment increased. As a result,
data analysis methods have steadily been adapted to more accurately and
efficiently analyse increasingly large volumes of data. Transcriptome
databases getting bigger and more useful as transcriptomes continue to
be collected and shared by researchers. It would be almost impossible to
interpret the information contained in a transcriptome without the
knowledge of previous experiments.
Measuring the expression of an organism's genes in different tissues or conditions, or at different times, gives information on how genes are regulated and reveals details of an organism's biology. It can also be used to infer the functions of previously unannotated
genes. Transcriptome analysis has enabled the study of how gene
expression changes in different organisms and has been instrumental in
the understanding of human disease.
An analysis of gene expression in its entirety allows detection of
broad coordinated trends which cannot be discerned by more targeted assays.
History
Transcriptomics method use over time. Published papers referring to RNA-Seq (black), RNA microarray (red), expressed sequence tag (blue), digital differential display (green), and serial/cap analysis of gene expression (yellow) since 1990.
Transcriptomics has been characterised by the development of new
techniques which have redefined what is possible every decade or so and
rendered previous technologies obsolete. The first attempt at capturing a
partial human transcriptome was published in 1991 and reported 609 mRNA sequences from the human brain. In 2008, two human transcriptomes, composed of millions of transcript-derived sequences covering 16,000 genes, were published, and by 2015 transcriptomes had been published for hundreds of individuals. Transcriptomes of different disease states, tissues, or even single cells are now routinely generated.
This explosion in transcriptomics has been driven by the rapid
development of new technologies with improved sensitivity and economy.
Before transcriptomics
Studies of individual transcripts were being performed several decades before any transcriptomics approaches were available. Libraries of silkmoth mRNA transcripts were collected and converted to complementary DNA (cDNA) for storage using reverse transcriptase in the late 1970s. In the 1980s, low-throughput sequencing using the Sanger method was used to sequence random transcripts, producing expressed sequence tags (ESTs). The Sanger method of sequencing was predominant until the advent of high-throughput methods such as sequencing by synthesis (Solexa/Illumina). ESTs came to prominence during the 1990s as an efficient method to determine the gene content of an organism without sequencing the entire genome. Amounts of individual transcripts were quantified using Northern blotting, nylon membrane arrays, and later reverse transcriptase quantitative PCR (RT-qPCR) methods, but these methods are laborious and can only capture a tiny subsection of a transcriptome.
Consequently, the manner in which a transcriptome as a whole is
expressed and regulated remained unknown until higher-throughput
techniques were developed.
Early attempts
The word "transcriptome" was first used in the 1990s. In 1995, one of the earliest sequencing-based transcriptomic methods was developed, serial analysis of gene expression (SAGE), which worked by Sanger sequencing of concatenated random transcript fragments.
Transcripts were quantified by matching the fragments to known genes. A
variant of SAGE using high-throughput sequencing techniques, called
digital gene expression analysis, was also briefly used.
However, these methods were largely overtaken by high throughput
sequencing of entire transcripts, which provided additional information
on transcript structure such as splice variants.
The dominant contemporary techniques, microarrays and RNA-Seq, were developed in the mid-1990s and 2000s. Microarrays that measure the abundances of a defined set of transcripts via their hybridisation to an array of complementaryprobes were first published in 1995.
Microarray technology allowed the assay of thousands of transcripts
simultaneously and at a greatly reduced cost per gene and labour saving. Both spotted oligonucleotide arrays and Affymetrix high-density arrays were the method of choice for transcriptional profiling until the late 2000s. Over this period, a range of microarrays were produced to cover known genes in model
or economically important organisms. Advances in design and manufacture
of arrays improved the specificity of probes and allowed more genes to
be tested on a single array. Advances in fluorescence detection increased the sensitivity and measurement accuracy for low abundance transcripts.
RNA-Seq is accomplished by reverse transcribing RNA in vitro and sequencing the resulting cDNAs.
Transcript abundance is derived from the number of counts from each
transcript. The technique has therefore been heavily influenced by the
development of high-throughput sequencing technologies. Massively parallel signature sequencing (MPSS) was an early example based on generating 16–20 bp sequences via a complex series of hybridisations, and was used in 2004 to validate the expression of ten thousand genes in Arabidopsis thaliana. The earliest RNA-Seq work was published in 2006 with one hundred thousand transcripts sequenced using 454 technology.
This was sufficient coverage to quantify relative transcript abundance.
RNA-Seq began to increase in popularity after 2008 when new Solexa/Illumina technologies allowed one billion transcript sequences to be recorded. This yield now allows for the quantification and comparison of human transcriptomes.
Data gathering
Generating data on RNA transcripts can be achieved via either of two main principles: sequencing of individual transcripts (ESTs, or RNA-Seq) or hybridisation of transcripts to an ordered array of nucleotide probes (microarrays).
Isolation of RNA
All
transcriptomic methods require RNA to first be isolated from the
experimental organism before transcripts can be recorded. Although
biological systems are incredibly diverse, RNA extraction techniques are broadly similar and involve mechanical disruption of cells or tissues, disruption of RNase with chaotropic salts, disruption of macromolecules and nucleotide complexes, separation of RNA from undesired biomolecules including DNA, and concentration of the RNA via precipitation from solution or elution from a solid matrix. Isolated RNA may additionally be treated with DNase to digest any traces of DNA. It is necessary to enrich messenger RNA as total RNA extracts are typically 98% ribosomal RNA. Enrichment for transcripts can be performed by poly-A affinity methods or by depletion of ribosomal RNA using sequence-specific probes. Degraded RNA may affect downstream results; for example, mRNA enrichment from degraded samples will result in the depletion of 5’ mRNA ends and an uneven signal across the length of a transcript. Snap-freezing of tissue prior to RNA isolation is typical, and care is taken to reduce exposure to RNase enzymes once isolation is complete.
Expressed sequence tags
An expressed sequence tag (EST) is a short nucleotide sequence generated from a single RNA transcript. RNA is first copied as complementary DNA (cDNA) by a reverse transcriptase enzyme before the resultant cDNA is sequenced.
Because ESTs can be collected without prior knowledge of the organism
from which they come, they can be made from mixtures of organisms or
environmental samples. Although higher-throughput methods are now used, EST libraries commonly provided sequence information for early microarray designs; for example, a barley microarray was designed from 350,000 previously sequenced ESTs.
Serial and cap analysis of gene expression (SAGE/CAGE)
Summary of SAGE. Within the organisms, genes are transcribed and spliced (in eukaryotes) to produce mature mRNA transcripts (red). The mRNA is extracted from the organism, and reverse transcriptase is used to copy the mRNA into stable double-stranded–cDNA (ds-cDNA; blue). In SAGE, the ds-cDNA is digested by restriction enzymes
(at location ‘X’ and ‘X’+11) to produce 11-nucleotide "tag" fragments.
These tags are concatenated and sequenced using long-read Sanger sequencing (different shades of blue indicate tags from different genes). The sequences are deconvoluted to find the frequency of each tag. The tag frequency can be used to report on transcription of the gene that the tag came from.
Serial analysis of gene expression
(SAGE) was a development of EST methodology to increase the throughput
of the tags generated and allow some quantitation of transcript
abundance. cDNA is generated from the RNA but is then digested into 11 bp "tag" fragments using restriction enzymes that cut DNA at a specific sequence, and 11 base pairs along from that sequence. These cDNA tags are then joined head-to-tail into long strands (>500 bp) and sequenced using low-throughput, but long read-length methods such as Sanger sequencing. The sequences are then divided back into their original 11 bp tags using computer software in a process called deconvolution. If a high-quality reference genome
is available, these tags may be matched to their corresponding gene in
the genome. If a reference genome is unavailable, the tags can be
directly used as diagnostic markers if found to be differentially expressed in a disease state.
The cap analysis gene expression (CAGE) method is a variant of SAGE that sequences tags from the 5’ end of an mRNA transcript only. Therefore, the transcriptional start site of genes can be identified when the tags are aligned to a reference genome. Identifying gene start sites is of use for promoter analysis and for the cloning of full-length cDNAs.
SAGE and CAGE methods produce information on more genes than was
possible when sequencing single ESTs, but sample preparation and data
analysis are typically more labour-intensive.
Microarrays
Summary of DNA Microarrays.
Within the organisms, genes are transcribed and spliced (in eukaryotes)
to produce mature mRNA transcripts (red). The mRNA is extracted from
the organism and reverse transcriptase is used to copy the mRNA into
stable ds-cDNA (blue). In microarrays, the ds-cDNA is fragmented and
fluorescently labelled (orange). The labelled fragments bind to an
ordered array of complementary oligonucleotides, and measurement of fluorescent intensity
across the array indicates the abundance of a predetermined set of
sequences. These sequences are typically specifically chosen to report
on genes of interest within the organism's genome.
Principles and advances
Microarrays usually consist of a grid of short nucleotide oligomers, known as "probes", typically arranged on a glass slide. Transcript abundance is determined by hybridisation of fluorescently labelled transcripts to these probes. The fluorescence intensity at each probe location on the array indicates the transcript abundance for that probe sequence.
Groups of probes designed to measure the same transcript (i.e.,
hybridizing a specific transcript in different positions) are usually
referred to as "probesets".
Microarrays require some genomic knowledge from the organism of interest, for example, in the form of an annotatedgenome sequence, or a library of ESTs that can be used to generate the probes for the array.
Methods
Microarrays
for transcriptomics typically fall into one of two broad categories:
low-density spotted arrays or high-density short probe arrays.
Transcript abundance is inferred from the intensity of fluorescence
derived from fluorophore-tagged transcripts that bind to the array.
Spotted low-density arrays typically feature picolitre drops of a range of purified cDNAs arrayed on the surface of a glass slide. These probes are longer than those of high-density arrays and cannot identify alternative splicing events. Spotted arrays use two different fluorophores to label the test and control samples, and the ratio of fluorescence is used to calculate a relative measure of abundance. High-density arrays use a single fluorescent label, and each sample is hybridised and detected individually. High-density arrays were popularised by the Affymetrix GeneChip array, where each transcript is quantified by several short 25-mer probes that together assay one gene.
NimbleGen arrays were a high-density array produced by a maskless-photochemistry
method, which permitted flexible manufacture of arrays in small or
large numbers. These arrays had 100,000s of 45 to 85-mer probes and were
hybridised with a one-colour labelled sample for expression analysis. Some designs incorporated up to 12 independent arrays per slide.
RNA-Seq
Summary of RNA-Seq.
Within the organisms, genes are transcribed and spliced (in eukaryotes)
to produce mature mRNA transcripts (red). The mRNA is extracted from
the organism, fragmented, and copied into stable ds-cDNA (blue). The
ds-cDNA is sequenced using high-throughput, short-read sequencing methods. These sequences can then be aligned
to a reference genome sequence to reconstruct which genome regions were
being transcribed. This data can be used to annotate where expressed
genes are, their relative expression levels, and any alternative splice
variants.
Principles and advances
RNA-Seq refers to the combination of a high-throughput sequencing methodology with computational methods to capture and quantify transcripts present in an RNA extract.
The nucleotide sequences generated are typically around 100 bp in
length, but can range from 30 bp to over 10,000 bp depending on the
sequencing method used. RNA-Seq leverages deep sampling
of the transcriptome with many short fragments from a transcriptome to
allow computational reconstruction of the original RNA transcript by aligning reads to a reference genome or to each other (de novo assembly). Both low-abundance and high-abundance RNAs can be quantified in an RNA-Seq experiment (dynamic range of 5 orders of magnitude)—a
key advantage over microarray transcriptomes. In addition, input RNA
amounts are much lower for RNA-Seq (nanogram quantity) compared to
microarrays (microgram quantity), which allow examination of the
transcriptome even at a single-cell resolution when combined with
amplification of cDNA.
Theoretically, there is no upper limit of quantification in RNA-Seq,
and background noise is very low for 100 bp reads in non-repetitive
regions.
RNA-Seq may be used to identify genes within a genome,
or identify which genes are active at a particular point in time, and
read counts can be used to accurately model the relative gene expression
level. RNA-Seq methodology has constantly improved, primarily through
the development of DNA sequencing technologies to increase throughput,
accuracy, and read length. Since the first descriptions in 2006 and 2008, RNA-Seq has been rapidly adopted and overtook microarrays as the dominant transcriptomics technique in 2015.
The quest for transcriptome data at the level of individual cells
has driven advances in RNA-Seq library preparation methods, resulting
in dramatic advances in sensitivity. Single-cell transcriptomes are now well described and have even been extended to in situ RNA-Seq where transcriptomes of individual cells are directly interrogated in fixed tissues.
Methods
RNA-Seq was established in concert with the rapid development of a range of high-throughput DNA sequencing technologies.
However, before the extracted RNA transcripts are sequenced, several
key processing steps are performed. Methods differ in the use of
transcript enrichment, fragmentation, amplification, single or
paired-end sequencing, and whether to preserve strand information.
The sensitivity of an RNA-Seq experiment can be increased by
enriching classes of RNA that are of interest and depleting known
abundant RNAs. The mRNA molecules can be separated using
oligonucleotides probes which bind their poly-A tails. Alternatively, ribo-depletion can be used to specifically remove abundant but uninformative ribosomal RNAs (rRNAs) by hybridisation to probes tailored to the taxon's
specific rRNA sequences (e.g. mammal rRNA, plant rRNA). However,
ribo-depletion can also introduce some bias via non-specific depletion
of off-target transcripts. Small RNAs, such as micro RNAs, can be purified based on their size by gel electrophoresis and extraction.
During preparation for sequencing, cDNA copies of transcripts may be amplified by PCR to enrich for fragments that contain the expected 5’ and 3’ adapter sequences. Amplification is also used to allow sequencing of very low input amounts of RNA, down to as little as 50 pg in extreme applications. Spike-in controls of known RNAs can be used for quality control assessment to check library preparation and sequencing, in terms of GC-content, fragment length, as well as the bias due to fragment position within a transcript. Unique molecular identifiers
(UMIs) are short random sequences that are used to individually tag
sequence fragments during library preparation so that every tagged
fragment is unique.
UMIs provide an absolute scale for quantification, the opportunity to
correct for subsequent amplification bias introduced during library
construction, and accurately estimate the initial sample size. UMIs are
particularly well-suited to single-cell RNA-Seq transcriptomics, where
the amount of input RNA is restricted and extended amplification of the
sample is required.
Once the transcript molecules have been prepared they can be
sequenced in just one direction (single-end) or both directions
(paired-end). A single-end sequence is usually quicker to produce,
cheaper than paired-end sequencing and sufficient for quantification of
gene expression levels. Paired-end sequencing produces more robust
alignments/assemblies, which is beneficial for gene annotation and
transcript isoform discovery. Strand-specific RNA-Seq methods preserve the strand information of a sequenced transcript.
Without strand information, reads can be aligned to a gene locus but do
not inform in which direction the gene is transcribed. Stranded-RNA-Seq
is useful for deciphering transcription for genes that overlap in different directions and to make more robust gene predictions in non-model organisms.
Sequencing technology platforms commonly used for RNA-Seq
Legend: NCBI SRA – National center for biotechnology information sequence read archive.
Currently RNA-Seq relies on copying RNA molecules into cDNA
molecules prior to sequencing; therefore, the subsequent platforms are
the same for transcriptomic and genomic data. Consequently, the
development of DNA sequencing technologies has been a defining feature
of RNA-Seq. Direct sequencing of RNA using nanopore sequencing represents a current state-of-the-art RNA-Seq technique. Nanopore sequencing of RNA can detect modified bases that would be otherwise masked when sequencing cDNA and also eliminates amplification steps that can otherwise introduce bias.
The sensitivity and accuracy of an RNA-Seq experiment are dependent on the number of reads obtained from each sample.
A large number of reads are needed to ensure sufficient coverage of the
transcriptome, enabling detection of low abundance transcripts.
Experimental design is further complicated by sequencing technologies
with a limited output range, the variable efficiency of sequence
creation, and variable sequence quality. Added to those considerations
is that every species has a different number of genes
and therefore requires a tailored sequence yield for an effective
transcriptome. Early studies determined suitable thresholds empirically,
but as the technology matured suitable coverage was predicted
computationally by transcriptome saturation. Somewhat
counter-intuitively, the most effective way to improve detection of
differential expression in low expression genes is to add more biological replicates rather than adding more reads. The current benchmarks recommended by the Encyclopedia of DNA Elements
(ENCODE) Project are for 70-fold exome coverage for standard RNA-Seq
and up to 500-fold exome coverage to detect rare transcripts and
isoforms.
Data analysis
Transcriptomics
methods are highly parallel and require significant computation to
produce meaningful data for both microarray and RNA-Seq experiments. Microarray data is recorded as high-resolution images, requiring feature detection and spectral analysis.
Microarray raw image files are each about 750 MB in size, while the
processed intensities are around 60 MB in size. Multiple short probes
matching a single transcript can reveal details about the intron-exon
structure, requiring statistical models to determine the authenticity
of the resulting signal. RNA-Seq studies produce billions of short DNA
sequences, which must be aligned to reference genomes composed of millions to billions of base pairs. De novo assembly of reads within a dataset requires the construction of highly complex sequence graphs. RNA-Seq operations are highly repetitious and benefit from parallelised computation
but modern algorithms mean consumer computing hardware is sufficient
for simple transcriptomics experiments that do not require de novo assembly of reads. A human transcriptome could be accurately captured using RNA-Seq with 30 million 100 bp sequences per sample. This example would require approximately 1.8 gigabytes of disk space per sample when stored in a compressed fastq format.
Processed count data for each gene would be much smaller, equivalent to
processed microarray intensities. Sequence data may be stored in public
repositories, such as the Sequence Read Archive (SRA). RNA-Seq datasets can be uploaded via the Gene Expression Omnibus.
Image processing
Microarray and sequencing flow cell.
Microarrays and RNA-seq rely on image analysis in different ways. In a
microarray chip, each spot on a chip is a defined oligonucleotide probe,
and fluorescence intensity directly detects the abundance of a specific
sequence (Affymetrix). In a high-throughput sequencing flow cell, spots
are sequenced one nucleotide at a time, with the colour at each round
indicating the next nucleotide in the sequence (Illumina Hiseq). Other
variations of these techniques use more or fewer colour channels.
Microarray image processing must correctly identify the regular grid of features within an image and independently quantify the fluorescence intensity for each feature. Image artefacts
must be additionally identified and removed from the overall analysis.
Fluorescence intensities directly indicate the abundance of each
sequence, since the sequence of each probe on the array is already
known.
The first steps of RNA-seq also include similar image processing;
however, conversion of images to sequence data is typically handled
automatically by the instrument software. The Illumina
sequencing-by-synthesis method results in an array of clusters
distributed over the surface of a flow cell.
The flow cell is imaged up to four times during each sequencing cycle,
with tens to hundreds of cycles in total. Flow cell clusters are
analogous to microarray spots and must be correctly identified during
the early stages of the sequencing process. In Roche’s pyrosequencing
method, the intensity of emitted light determines the number of
consecutive nucleotides in a homopolymer repeat. There are many variants
on these methods, each with a different error profile for the resulting
data.
RNA-Seq data analysis
RNA-Seq
experiments generate a large volume of raw sequence reads which have to
be processed to yield useful information. Data analysis usually
requires a combination of bioinformatics software tools (see also List of RNA-Seq bioinformatics tools)
that vary according to the experimental design and goals. The process
can be broken down into four stages: quality control, alignment,
quantification, and differential expression. Most popular RNA-Seq programs are run from a command-line interface, either in a Unix environment or within the R/Bioconductor statistical environment.
Quality control
Sequence
reads are not perfect, so the accuracy of each base in the sequence
needs to be estimated for downstream analyses. Raw data is examined to
ensure: quality scores for base calls are high, the GC content matches
the expected distribution, short sequence motifs (k-mers) are not over-represented, and the read duplication rate is acceptably low. Several software options exist for sequence quality analysis, including FastQC and FaQCs. Abnormalities may be removed (trimming) or tagged for special treatment during later processes.
Alignment
In order to link sequence read abundance to the expression of a particular gene, transcript sequences are aligned to a reference genome or de novo aligned to one another if no reference is available. The key challenges for alignment software
include sufficient speed to permit billions of short sequences to be
aligned in a meaningful timeframe, flexibility to recognise and deal
with intron splicing of eukaryotic mRNA, and correct assignment of reads
that map to multiple locations. Software advances have greatly
addressed these issues, and increases in sequencing read length reduce
the chance of ambiguous read alignments. A list of currently available
high-throughput sequence aligners is maintained by the EBI.
Alignment of primary transcript mRNA sequences derived from eukaryotes to a reference genome requires specialised handling of intron sequences, which are absent from mature mRNA. Short read aligners perform an additional round of alignments specifically designed to identify splice junctions,
informed by canonical splice site sequences and known intron splice
site information. Identification of intron splice junctions prevents
reads from being misaligned across splice junctions or erroneously
discarded, allowing more reads to be aligned to the reference genome and
improving the accuracy of gene expression estimates. Since gene regulation may occur at the mRNA isoform
level, splice-aware alignments also permit detection of isoform
abundance changes that would otherwise be lost in a bulked analysis.
De novo assembly can be used to align reads to one another
to construct full-length transcript sequences without use of a
reference genome. Challenges particular to de novo
assembly include larger computational requirements compared to a
reference-based transcriptome, additional validation of gene variants or
fragments, and additional annotation of assembled transcripts. The
first metrics used to describe transcriptome assemblies, such as N50, have been shown to be misleading and improved evaluation methods are now available. Annotation-based metrics are better assessments of assembly completeness, such as contig reciprocal best hit count. Once assembled de novo, the assembly can be used as a reference for subsequent sequence alignment methods and quantitative gene expression analysis.
RNA-Seq de novo assembly software
Software
Released
Last updated
Computational efficiency
Strengths and weaknesses
Velvet-Oases
2008
2011
Low, single-threaded, high RAM requirement
The original short read assembler. It is now largely superseded.
SOAPdenovo-trans
2011
2014
Moderate, multi-thread, medium RAM requirement
An early example of a short read assembler. It has been updated for transcriptome assembly.
Trans-ABySS
2010
2016
Moderate, multi-thread, medium RAM requirement
Suited to short reads, can handle complex transcriptomes, and an MPI-parallel version is available for computing clusters.
Trinity
2011
2017
Moderate, multi-thread, medium RAM requirement
Suited to short reads. It can handle complex transcriptomes but is memory intensive.
miraEST
1999
2016
Moderate, multi-thread, medium RAM requirement
Can process repetitive sequences, combine different sequencing formats, and a wide range of sequence platforms are accepted.
Newbler
2004
2012
Low, single-thread, high RAM requirement
Specialised to accommodate the homo-polymer sequencing errors typical of Roche 454 sequencers.
CLC genomics workbench
2008
2014
High, multi-thread, low RAM requirement
Has a graphical user interface, can combine diverse sequencing
technologies, has no transcriptome-specific features, and a licence must
be purchased before use.
SPAdes
2012
2017
High, multi-thread, low RAM requirement
Used for transcriptomics experiments on single cells.
RSEM
2011
2017
High, multi-thread, low RAM requirement
Can estimate frequency of alternatively spliced transcripts. User friendly.
StringTie
2015
2019
High, multi-thread, low RAM requirement
Can use a combination of reference-guided and de novo assembly methods to identify transcripts.
Legend: RAM – random access memory; MPI – message passing interface; EST – expressed sequence tag.
Quantification
Heatmap identification of gene co-expression patterns across different samples.
Each column contains the measurements for gene expression change for a
single sample. Relative gene expression is indicated by colour:
high-expression (red), median-expression (white) and low-expression
(blue). Genes and samples with similar expression profiles can be
automatically grouped (left and top trees). Samples may be different
individuals, tissues, environments or health conditions. In this
example, expression of gene set 1 is high and expression of gene set 2
is low in samples 1, 2, and 3.
Quantification of sequence alignments may be performed at the gene, exon, or transcript level. Typical outputs include a table of read counts for each feature supplied to the software; for example, for genes in a general feature format file. Gene and exon read counts may be calculated quite easily using HTSeq, for example.
Quantitation at the transcript level is more complicated and requires
probabilistic methods to estimate transcript isoform abundance from
short read information; for example, using cufflinks software.
Reads that align equally well to multiple locations must be identified
and either removed, aligned to one of the possible locations, or aligned
to the most probable location.
Some quantification methods can circumvent the need for an exact
alignment of a read to a reference sequence altogether. The kallisto
software method combines pseudoalignment and quantification into a
single step that runs 2 orders of magnitude faster than contemporary
methods such as those used by tophat/cufflinks software, with less
computational burden.
Differential expression
Once quantitative counts of each transcript are available, differential gene expression is measured by normalising, modelling, and statistically analysing the data. Most tools will read a table of genes and read counts as their input, but some programs, such as cuffdiff, will accept binary alignment map
format read alignments as input. The final outputs of these analyses
are gene lists with associated pair-wise tests for differential
expression between treatments and the probability estimates of those
differences.
RNA-Seq differential gene expression software
Software
Environment
Specialisation
Cuffdiff2
Unix-based
Transcript analysis that tracks alternative splicing of mRNA
EdgeR
R/Bioconductor
Any count-based genomic data
DEseq2
R/Bioconductor
Flexible data types, low replication
Limma/Voom
R/Bioconductor
Microarray or RNA-Seq data, flexible experiment design
Ballgown
R/Bioconductor
Efficient and sensitive transcript discovery, flexible.
Legend: mRNA - messenger RNA.
Validation
Transcriptomic analyses may be validated using an independent technique, for example, quantitative PCR (qPCR), which is recognisable and statistically assessable. Gene expression is measured against defined standards both for the gene of interest and control
genes. The measurement by qPCR is similar to that obtained by RNA-Seq
wherein a value can be calculated for the concentration of a target
region in a given sample. qPCR is, however, restricted to amplicons smaller than 300 bp, usually toward the 3’ end of the coding region, avoiding the 3’UTR. If validation of transcript isoforms is required, an inspection of RNA-Seq read alignments should indicate where qPCR primers
might be placed for maximum discrimination. The measurement of multiple
control genes along with the genes of interest produces a stable
reference within a biological context. qPCR validation of RNA-Seq data has generally shown that different RNA-Seq methods are highly correlated.
Functional validation of key genes is an important consideration
for post transcriptome planning. Observed gene expression patterns may
be functionally linked to a phenotype by an independent knock-down/rescue study in the organism of interest.
Applications
Diagnostics and disease profiling
Transcriptomic strategies have seen broad application across diverse areas of biomedical research, including disease diagnosis and profiling. RNA-Seq approaches have allowed for the large-scale identification of transcriptional start sites, uncovered alternative promoter usage, and novel splicing alterations. These regulatory elements are important in human disease and, therefore, defining such variants is crucial to the interpretation of disease-association studies. RNA-Seq can also identify disease-associated single nucleotide polymorphisms (SNPs), allele-specific expression, and gene fusions, which contributes to the understanding of disease causal variants.
Retrotransposons are transposable elements which proliferate within eukaryotic genomes through a process involving reverse transcription.
RNA-Seq can provide information about the transcription of endogenous
retrotransposons that may influence the transcription of neighboring
genes by various epigenetic mechanisms that lead to disease. Similarly, the potential for using RNA-Seq to understand immune-related disease is expanding rapidly due to the ability to dissect immune cell populations and to sequence T cell and B cell receptor repertoires from patients.
Transcriptomic analysis has predominantly focused on either the
host or the pathogen. Dual RNA-Seq has been applied to simultaneously
profile RNA expression in both the pathogen and host throughout the
infection process. This technique enables the study of the dynamic
response and interspecies gene regulatory networks
in both interaction partners from initial contact through to invasion
and the final persistence of the pathogen or clearance by the host
immune system.
Responses to environment
Transcriptomics allows identification of genes and pathways that respond to and counteract biotic and abiotic environmental stresses.
The non-targeted nature of transcriptomics allows the identification of
novel transcriptional networks in complex systems. For example,
comparative analysis of a range of chickpea lines at different developmental stages identified distinct transcriptional profiles associated with drought and salinity stresses, including identifying the role of transcript isoforms of AP2-EREBP. Investigation of gene expression during biofilm formation by the fungal pathogen Candida albicans revealed a co-regulated set of genes critical for biofilm establishment and maintenance.
The use of transcriptomics is also important to investigate responses in the marine environment. In marine ecology, "stress" and "adaptation" have been among the most common research topics, especially related to anthropogenic stress, such as global change and pollution. Most of the studies in this area have been done in animals, although invertebrates have been underrepresented. One issue still is a deficiency in functional genetic studies, which hamper gene annotations, especially for non-model species, and can lead to vague conclusions on the effects of responses studied.
Gene function annotation
All transcriptomic techniques have been particularly useful in identifying the functions of genes and identifying those responsible for particular phenotypes. Transcriptomics of Arabidopsisecotypes that hyperaccumulate metals correlated genes involved in metal uptake, tolerance, and homeostasis with the phenotype.
Integration of RNA-Seq datasets across different tissues has been used
to improve annotation of gene functions in commercially important
organisms (e.g. cucumber) or threatened species (e.g. koala).
Assembly of RNA-Seq reads is not dependent on a reference genome
and so is ideal for gene expression studies of non-model organisms with
non-existing or poorly developed genomic resources. For example, a
database of SNPs used in Douglas fir breeding programs was created by de novo transcriptome analysis in the absence of a sequenced genome.
Similarly, genes that function in the development of cardiac, muscle,
and nervous tissue in lobsters were identified by comparing the
transcriptomes of the various tissue types without use of a genome
sequence. RNA-Seq can also be used to identify previously unknown protein coding regions in existing sequenced genomes.
Non-coding RNA
Transcriptomics
is most commonly applied to the mRNA content of the cell. However, the
same techniques are equally applicable to non-coding RNAs (ncRNAs) that
are not translated into a protein, but instead have direct functions
(e.g. roles in protein translation, DNA replication, RNA splicing, and transcriptional regulation). Many of these ncRNAs affect disease states, including cancer, cardiovascular, and neurological diseases.
Transcriptome databases
Transcriptomics
studies generate large amounts of data that have potential applications
far beyond the original aims of an experiment. As such, raw or
processed data may be deposited in public databases
to ensure their utility for the broader scientific community. For
example, as of 2018, the Gene Expression Omnibus contained millions of
experiments.
First transcriptomics database to accept data from any source. Introduced MIAME and MINSEQE community standards that define necessary experiment metadata to ensure effective interpretation and repeatability.
Imports datasets from the Gene Expression Omnibus and accepts direct
submissions. Processed data and experiment metadata is stored at
ArrayExpress, while the raw sequence reads are held at the ENA. Complies
with MIAME and MINSEQE standards.
Tissue-specific gene expression database for animals and plants.
Displays secondary analyses and visualisation, such as functional
enrichment of Gene Ontology terms, InterPro domains, or pathways. Links to protein abundance data where available.
Contains manual curations of public transcriptome datasets, focusing
on medical and plant biology data. Individual experiments are
normalised across the full database to allow comparison of gene
expression across diverse experiments. Full functionality requires
licence purchase, with free access to a limited functionality.
Human, mouse, and rat transcriptomes from 40 different organs. Gene expression visualised as heatmaps projected onto 3D representations of anatomical structures.
Legend: NCBI – National Center for Biotechnology Information;
EBI – European Bioinformatics Institute; DDBJ – DNA Data Bank of Japan;
ENA – European Nucleotide Archive; MIAME – Minimum Information About a
Microarray Experiment; MINSEQE – Minimum Information about a
high-throughput nucleotide SEQuencing Experiment.
In genetics, gene expression is the most fundamental level at which the genotype gives rise to the phenotype, i.e. observable trait. The genetic information stored in DNA
represents the genotype, whereas the phenotype results from the
"interpretation" of that information. Such phenotypes are often
displayed by the synthesis of proteins that control the organism's
structure and development, or that act as enzymes catalyzing specific metabolic pathways.
The production of a RNA copy from a DNA strand is called transcription, and is performed by RNA polymerases, which add one ribonucleotide at a time to a growing RNA strand as per the complementarity law of the nucleotide bases. This RNA is complementary to the template 3′ → 5′ DNA strand, with the exception that thymines (T) are replaced with uracils (U) in the RNA and possible errors.
In bacteria, transcription is carried out by a single type of RNA polymerase, which needs to bind a DNA sequence called a Pribnow box with the help of the sigma factor
protein (σ factor) to start transcription. In eukaryotes, transcription
is performed in the nucleus by three types of RNA polymerases, each of
which needs a special DNA sequence called the promoter and a set of DNA-binding proteins—transcription factors—to initiate the process (see regulation of transcription below). RNA polymerase I is responsible for transcription of ribosomal RNA (rRNA) genes. RNA polymerase II (Pol II) transcribes all protein-coding genes but also some non-coding RNAs (e.g., snRNAs, snoRNAs or long non-coding RNAs). RNA polymerase III transcribes 5S rRNA, transfer RNA (tRNA) genes, and some small non-coding RNAs (e.g., 7SK). Transcription ends when the polymerase encounters a sequence called the terminator.
While transcription of prokaryotic protein-coding genes creates messenger RNA (mRNA) that is ready for translation into protein, transcription of eukaryotic genes leaves a primary transcript
of RNA (pre-RNA), which first has to undergo a series of modifications
to become a mature RNA. Types and steps involved in the maturation
processes vary between coding and non-coding preRNAs; i.e. even though preRNA molecules for both mRNA and tRNA undergo splicing, the steps and machinery involved are different. The processing of non-coding RNA is described below (non-coding RNA maturation).
The processing of pre-mRNA include 5′ capping, which is set of enzymatic reactions that add 7-methylguanosine (m7G) to the 5′ end of pre-mRNA and thus protect the RNA from degradation by exonucleases. The m7G cap is then bound by cap binding complex heterodimer (CBP20/CBP80), which aids in mRNA export to cytoplasm and also protect the RNA from decapping.
Another modification is 3′ cleavage and polyadenylation.
They occur if polyadenylation signal sequence (5′- AAUAAA-3′) is
present in pre-mRNA, which is usually between protein-coding sequence
and terminator.
The pre-mRNA is first cleaved and then a series of ~200 adenines (A)
are added to form poly(A) tail, which protects the RNA from degradation. The poly(A) tail is bound by multiple poly(A)-binding proteins (PABPs) necessary for mRNA export and translation re-initiation.[8] In the inverse process of deadenylation, poly(A) tails are shortened by the CCR4-Not 3′-5′ exonuclease, which often leads to full transcript decay.
Illustration
of exons and introns in pre-mRNA and the formation of mature mRNA by
splicing. The UTRs (in green) are non-coding parts of exons at the ends
of the mRNA.
A very important modification of eukaryotic pre-mRNA is RNA splicing. The majority of eukaryotic pre-mRNAs consist of alternating segments called exons and introns. During the process of splicing, an RNA-protein catalytical complex known as spliceosome catalyzes two transesterification reactions, which remove an intron and release it in form of lariat structure, and then splice neighbouring exons together. In certain cases, some introns or exons can be either removed or retained in mature mRNA. This so-called alternative splicing
creates series of different transcripts originating from a single gene.
Because these transcripts can be potentially translated into different
proteins, splicing extends the complexity of eukaryotic gene expression
and the size of a species proteome.
Extensive RNA processing may be an evolutionary advantage
made possible by the nucleus of eukaryotes. In prokaryotes,
transcription and translation happen together, whilst in eukaryotes, the
nuclear membrane separates the two processes, giving time for RNA processing to occur.
In most organisms non-coding genes (ncRNA)
are transcribed as precursors that undergo further processing. In the
case of ribosomal RNAs (rRNA), they are often transcribed as a pre-rRNA
that contains one or more rRNAs. The pre-rRNA is cleaved and modified (2′-O-methylation and pseudouridine
formation) at specific sites by approximately 150 different small
nucleolus-restricted RNA species, called snoRNAs. SnoRNAs associate with
proteins, forming snoRNPs. While snoRNA part basepair with the target
RNA and thus position the modification at a precise site, the protein
part performs the catalytical reaction. In eukaryotes, in particular a
snoRNP called RNase, MRP cleaves the 45S pre-rRNA into the 28S, 5.8S, and 18S rRNAs. The rRNA and RNA processing factors form large aggregates called the nucleolus.
In the case of transfer RNA (tRNA), for example, the 5′ sequence is removed by RNase P, whereas the 3′ end is removed by the tRNase Z enzyme and the non-templated 3′ CCA tail is added by a nucleotidyl transferase. In the case of micro RNA (miRNA),
miRNAs are first transcribed as primary transcripts or pri-miRNA with a
cap and poly-A tail and processed to short, 70-nucleotide stem-loop
structures known as pre-miRNA in the cell nucleus by the enzymes Drosha and Pasha. After being exported, it is then processed to mature miRNAs in the cytoplasm by interaction with the endonuclease Dicer, which also initiates the formation of the RNA-induced silencing complex (RISC), composed of the Argonaute protein.
Even snRNAs and snoRNAs themselves undergo series of modification before they become part of functional RNP complex. This is done either in the nucleoplasm or in the specialized compartments called Cajal bodies. Their bases are methylated or pseudouridinilated by a group of small Cajal body-specific RNAs (scaRNAs), which are structurally similar to snoRNAs.
In eukaryotes most mature RNA must be exported to the cytoplasm from the nucleus. While some RNAs function in the nucleus, many RNAs are transported through the nuclear pores and into the cytosol.
Export of RNAs requires association with specific proteins known as
exportins. Specific exportin molecules are responsible for the export of
a given RNA type. mRNA transport also requires the correct association
with Exon Junction Complex
(EJC), which ensures that correct processing of the mRNA is completed
before export. In some cases RNAs are additionally transported to a
specific part of the cytoplasm, such as a synapse; they are then towed by motor proteins that bind through linker proteins to specific sequences (called "zipcodes") on the RNA.
For some non-coding RNA, the mature RNA is the final gene product.
In the case of messenger RNA (mRNA) the RNA is an information carrier
coding for the synthesis of one or more proteins. mRNA carrying a single
protein sequence (common in eukaryotes) is monocistronic whilst mRNA carrying multiple protein sequences (common in prokaryotes) is known as polycistronic.
During
the translation, tRNA charged with amino acid enters the ribosome and
aligns with the correct mRNA triplet. Ribosome then adds amino acid to
growing protein chain.
Every mRNA consists of three parts: a 5′ untranslated region (5′UTR), a protein-coding region or open reading frame (ORF), and a 3′ untranslated region (3′UTR). The coding region carries information for protein synthesis encoded by the genetic code to form triplets. Each triplet of nucleotides of the coding region is called a codon
and corresponds to a binding site complementary to an anticodon triplet
in transfer RNA. Transfer RNAs with the same anticodon sequence always
carry an identical type of amino acid. Amino acids are then chained together by the ribosome
according to the order of triplets in the coding region. The ribosome
helps transfer RNA to bind to messenger RNA and takes the amino acid
from each transfer RNA and makes a structure-less protein out of it. Each mRNA molecule is translated into many protein molecules, on average ~2800 in mammals.
In prokaryotes translation generally occurs at the point of
transcription (co-transcriptionally), often using a messenger RNA that
is still in the process of being created. In eukaryotes translation can
occur in a variety of regions of the cell depending on where the protein
being written is supposed to be. Major locations are the cytoplasm for soluble cytoplasmic proteins and the membrane of the endoplasmic reticulum for proteins that are for export from the cell or insertion into a cell membrane.
Proteins that are supposed to be produced at the endoplasmic reticulum
are recognised part-way through the translation process. This is
governed by the signal recognition particle—a protein that binds to the ribosome and directs it to the endoplasmic reticulum when it finds a signal peptide on the growing (nascent) amino acid chain.
Each protein exists as an unfolded polypeptide or random coil when translated from a sequence of mRNA into a linear chain of amino acids.
This polypeptide lacks any developed three-dimensional structure (the
left hand side of the neighboring figure). The polypeptide then folds
into its characteristic and functional three-dimensional structure from a random coil.
Amino acids interact with each other to produce a well-defined
three-dimensional structure, the folded protein (the right hand side of
the figure) known as the native state. The resulting three-dimensional structure is determined by the amino acid sequence (Anfinsen's dogma).
The correct three-dimensional structure is essential to function, although some parts of functional proteins may remain unfolded. Failure to fold into the intended shape usually produces inactive proteins with different properties including toxic prions. Several neurodegenerative and other diseases are believed to result from the accumulation of misfolded proteins. Many allergies are caused by the folding of the proteins, for the immune system does not produce antibodies for certain protein structures.
Enzymes called chaperones assist the newly formed protein to attain (fold into) the 3-dimensional structure it needs to function. Similarly, RNA chaperones help RNAs attain their functional shapes. Assisting protein folding is one of the main roles of the endoplasmic reticulum in eukaryotes.
Translocation
Secretory
proteins of eukaryotes or prokaryotes must be translocated to enter the
secretory pathway. Newly synthesized proteins are directed to the
eukaryotic Sec61 or prokaryotic SecYEG translocation channel by signal peptides. The efficiency of protein secretion in eukaryotes is very dependent on the signal peptide which has been used.
Protein transport
Many proteins are destined for other parts of the cell than the cytosol and a wide range of signalling sequences or (signal peptides) are used to direct proteins to where they are supposed to be. In prokaryotes this is normally a simple process due to limited compartmentalisation of the cell.
However, in eukaryotes there is a great variety of different targeting
processes to ensure the protein arrives at the correct organelle.
Not all proteins remain within the cell and many are exported, for example, digestive enzymes, hormones and extracellular matrix
proteins. In eukaryotes the export pathway is well developed and the
main mechanism for the export of these proteins is translocation to the
endoplasmic reticulum, followed by transport via the Golgi apparatus.
The patchy colours of a tortoiseshell cat are the result of different levels of expression of pigmentation genes in different areas of the skin.
Regulation of gene expression is the control of the amount and timing
of appearance of the functional product of a gene. Control of
expression is vital to allow a cell to produce the gene products it
needs when it needs them; in turn, this gives cells the flexibility to
adapt to a variable environment, external signals, damage to the cell,
and other stimuli. More generally, gene regulation gives the cell
control over all structure and function, and is the basis for cellular differentiation, morphogenesis and the versatility and adaptability of any organism.
Numerous terms are used to describe types of genes depending on how they are regulated; these include:
A constitutive gene is a gene that is transcribed continually as opposed to a facultative gene, which is only transcribed when needed.
A housekeeping gene
is a gene that is required to maintain basic cellular function and so
is typically expressed in all cell types of an organism. Examples
include actin, GAPDH and ubiquitin.
Some housekeeping genes are transcribed at a relatively constant rate
and these genes can be used as a reference point in experiments to
measure the expression rates of other genes.
A facultative gene is a gene only transcribed when needed as opposed to a constitutive gene.
An inducible gene is a gene whose expression is either responsive to environmental change or dependent on the position in the cell cycle.
Any step of gene expression may be modulated, from the DNA-RNA transcription step to post-translational modification
of a protein. The stability of the final gene product, whether it is
RNA or protein, also contributes to the expression level of the gene—an
unstable product results in a low expression level. In general gene
expression is regulated through changes in the number and type of interactions between molecules that collectively influence transcription of DNA and translation of RNA.
Some simple examples of where gene expression is important are:
When
lactose is present in a prokaryote, it acts as an inducer and
inactivates the repressor so that the genes for lactose metabolism can
be transcribed.
Regulation of transcription
can be broken down into three main routes of influence; genetic (direct
interaction of a control factor with the gene), modulation interaction
of a control factor with the transcription machinery and epigenetic
(non-sequence changes in DNA structure that influence transcription).
The lambda repressor transcription factor (green) binds as a dimer to major groove of DNA target (red and blue) and disables initiation of transcription. From PDB: 1LMB.
Direct interaction with DNA is the simplest and the most direct method by which a protein changes transcription levels. Genes often have several protein binding sites around the coding region with the specific function of regulating transcription. There are many classes of regulatory DNA binding sites known as enhancers, insulators and silencers. The mechanisms for regulating transcription are varied, from blocking key binding sites on the DNA for RNA polymerase to acting as an activator and promoting transcription by assisting RNA polymerase binding.
The activity of transcription factors is further modulated by
intracellular signals causing protein post-translational modification
including phosphorylation, acetylation, or glycosylation.
These changes influence a transcription factor's ability to bind,
directly or indirectly, to promoter DNA, to recruit RNA polymerase, or
to favor elongation of a newly synthesized RNA molecule.
The nuclear membrane in eukaryotes allows further regulation of
transcription factors by the duration of their presence in the nucleus,
which is regulated by reversible changes in their structure and by
binding of other proteins. Environmental stimuli or endocrine signals may cause modification of regulatory proteins eliciting cascades of intracellular signals, which result in regulation of gene expression.
It has become apparent that there is a significant influence of non-DNA-sequence specific effects on transcription. These effects are referred to as epigenetic and involve the higher order structure of DNA, non-sequence specific DNA binding proteins and chemical modification of DNA. In general epigenetic effects alter the accessibility of DNA to proteins and so modulate transcription.
In eukaryotes, DNA is organized in form of nucleosomes. Note how the DNA (blue and green) is tightly wrapped around the protein core made of histoneoctamer (ribbon coils), restricting access to the DNA. From PDB: 1KX5.
In eukaryotes the structure of chromatin, controlled by the histone code, regulates access to DNA with significant impacts on the expression of genes in euchromatin and heterochromatin areas.
Enhancers, transcription factors, mediator complex and DNA loops in mammalian transcription
Regulation of transcription in mammals. An active enhancer regulatory region is enabled to interact with the promoter region of its target gene by formation of a chromosome loop. This can initiate messenger RNA (mRNA) synthesis by RNA polymerase II (RNAP II) bound to the promoter at the transcription start site
of the gene. The loop is stabilized by one architectural protein
anchored to the enhancer and one anchored to the promoter and these
proteins are joined to form a dimer (red zigzags). Specific regulatory transcription factors
bind to DNA sequence motifs on the enhancer. General transcription
factors bind to the promoter. When a transcription factor is activated
by a signal (here indicated as phosphorylation
shown by a small red star on a transcription factor on the enhancer)
the enhancer is activated and can now activate its target promoter. The
active enhancer is transcribed on each strand of DNA in opposite
directions by bound RNAP IIs. Mediator (a complex consisting of about 26
proteins in an interacting structure) communicates regulatory signals
from the enhancer DNA-bound transcription factors to the promoter.
Enhancers
are genome regions that regulate genes. Enhancers control
cell-type-specific gene expression programs, most often by looping
through long distances to come in physical proximity with the promoters
of their target genes.
Multiple enhancers, each often tens or hundred of thousands of
nucleotides distant from their target genes, loop to their target gene
promoters and coordinate with each other to control gene expression.
The illustration shows an enhancer looping around to come into
proximity with the promoter of a target gene. The loop is stabilized by a
dimer of a connector protein (e.g. dimer of CTCF or YY1).
One member of the dimer is anchored to its binding motif on the
enhancer and the other member is anchored to its binding motif on the
promoter (represented by the red zigzags in the illustration). Several cell function-specific transcription factors (among the about 1,600 transcription factors in a human cell) generally bind to specific motifs on an enhancer.
A small combination of these enhancer-bound transcription factors, when
brought close to a promoter by a DNA loop, govern transcription level
of the target gene. Mediator (a complex usually consisting of about 26
proteins in an interacting structure) communicates regulatory signals
from enhancer DNA-bound transcription factors directly to the RNA
polymerase II (pol II) enzyme bound to the promoter.
Enhancers, when active, are generally transcribed from both
strands of DNA with RNA polymerases acting in two different directions,
producing two eRNAs as illustrated in the figure.
An inactive enhancer may be bound by an inactive transcription factor.
Phosphorylation of the transcription factor may activate it and that
activated transcription factor may then activate the enhancer to which
it is bound (see small red star representing phosphorylation of
transcription factor bound to enhancer in the illustration). An activated enhancer begins transcription of its RNA before activating transcription of messenger RNA from its target gene.
DNA methylation and demethylation in transcriptional regulation
DNA methylation is the addition of a methyl group to the DNA that happens at cytosine.
The image shows a cytosine single ring base and a methyl group added on
to the 5 carbon. In mammals, DNA methylation occurs almost exclusively
at a cytosine that is followed by a guanine.
DNA methylation is a widespread mechanism for epigenetic influence on gene expression and is seen in bacteria and eukaryotes
and has roles in heritable transcription silencing and transcription
regulation. Methylation most often occurs on a cytosine (see Figure).
Methylation of cytosine primarily occurs in dinucleotide sequences where
a cytosine is followed by a guanine, a CpG site. The number of CpG sites in the human genome is about 28 million. Depending on the type of cell, about 70% of the CpG sites have a methylated cytosine.
Methylation of cytosine in DNA has a major role in regulating
gene expression. Methylation of CpGs in a promoter region of a gene
usually represses gene transcription while methylation of CpGs in the body of a gene increases expression. TET enzymes play a central role in demethylation of methylated cytosines. Demethylation of CpGs in a gene promoter by TET enzyme activity increases transcription of the gene.
The identified areas of the human brain are involved in memory formation.
In a rat, contextual fear conditioning (CFC) is a painful learning experience. Just one episode of CFC can result in a life-long fearful memory. After an episode of CFC, cytosine methylation is altered in the promoter regions of about 9.17% of all genes in the hippocampus neuron DNA of a rat. The hippocampus
is where new memories are initially stored. After CFC about 500 genes
have increased transcription (often due to demethylation of CpG sites in
a promoter region) and about 1,000 genes have decreased transcription
(often due to newly formed 5-methylcytosine at CpG sites in a promoter
region). The pattern of induced and repressed genes within neurons
appears to provide a molecular basis for forming the first transient
memory of this training event in the hippocampus of the rat brain.
Some specific mechanisms guiding new DNA methylations and new DNA demethylations in the hippocampus during memory establishment have been established. One mechanism includes guiding the short isoform of the TET1DNA demethylation enzyme, TET1s, to about 600 locations on the genome. The guidance is performed by association of TET1s with EGR1
protein, a transcription factor important in memory formation.
Bringing TET1s to these locations initiates DNA demethylation at those
sites, up-regulating associated genes. A second mechanism involves
DNMT3A2, a splice-isoform of DNA methyltransferase
DNMT3A, which adds methyl groups to cytosines in DNA. This isoform is
induced by synaptic activity, and its location of action appears to be
determined by histone post-translational modifications (a histone code). The resulting new messenger RNAs are then transported by messenger RNP
particles (neuronal granules) to synapses of the neurons, where they
can be translated into proteins affecting the activities of synapses.
In particular, the brain-derived neurotrophic factor gene (BDNF) is known as a "learning gene". After CFC there was upregulation of BDNF
gene expression, related to decreased CpG methylation of certain
internal promoters of the gene, and this was correlated with learning.
The majority of gene promoters contain a CpG island with numerous CpG sites. When many of a gene's promoter CpG sites are methylated the gene becomes silenced. Colorectal cancers typically have 3 to 6 driver mutations and 33 to 66 hitchhiker or passenger mutations.
However, transcriptional silencing may be of more importance than
mutation in causing progression to cancer. For example, in colorectal
cancers about 600 to 800 genes are transcriptionally silenced by CpG
island methylation (see regulation of transcription in cancer). Transcriptional repression in cancer can also occur by other epigenetic mechanisms, such as altered expression of microRNAs. In breast cancer, transcriptional repression of BRCA1 may occur more frequently by over-transcribed microRNA-182 than by hypermethylation of the BRCA1 promoter (see Low expression of BRCA1 in breast and ovarian cancers).
In eukaryotes, where export of RNA is required before translation is
possible, nuclear export is thought to provide additional control over
gene expression. All transport in and out of the nucleus is via the nuclear pore and transport is controlled by a wide range of importin and exportin proteins.
Expression of a gene coding for a protein is only possible if the
messenger RNA carrying the code survives long enough to be translated. In a typical cell, an RNA molecule is only stable if specifically protected from degradation.
RNA degradation has particular importance in regulation of expression
in eukaryotic cells where mRNA has to travel significant distances
before being translated. In eukaryotes, RNA is stabilised by certain post-transcriptional modifications, particularly the 5′ cap and poly-adenylated tail.
Intentional degradation of mRNA is used not just as a defence
mechanism from foreign RNA (normally from viruses) but also as a route
of mRNA destabilisation. If an mRNA molecule has a complementary sequence to a small interfering RNA then it is targeted for destruction via the RNA interference pathway.
Three prime untranslated regions (3′UTRs) of messenger RNAs
(mRNAs) often contain regulatory sequences that post-transcriptionally
influence gene expression. Such 3′-UTRs often contain both binding sites
for microRNAs (miRNAs) as well as for regulatory proteins.
By binding to specific sites within the 3′-UTR, miRNAs can decrease
gene expression of various mRNAs by either inhibiting translation or
directly causing degradation of the transcript. The 3′-UTR also may have silencer regions that bind repressor proteins that inhibit the expression of a mRNA.
The 3′-UTR often contains microRNA response elements (MREs).
MREs are sequences to which miRNAs bind. These are prevalent motifs
within 3′-UTRs. Among all regulatory motifs within the 3′-UTRs (e.g.
including silencer regions), MREs make up about half of the motifs.
As of 2014, the miRBase web site, an archive of miRNAsequences
and annotations, listed 28,645 entries in 233 biologic species. Of
these, 1,881 miRNAs were in annotated human miRNA loci. miRNAs were
predicted to have an average of about four hundred target mRNAs (affecting expression of several hundred genes). Friedman et al.
estimate that >45,000 miRNA target sites within human mRNA 3′UTRs
are conserved above background levels, and >60% of human
protein-coding genes have been under selective pressure to maintain
pairing to miRNAs.
Direct experiments show that a single miRNA can reduce the stability of hundreds of unique mRNAs.
Other experiments show that a single miRNA may repress the production
of hundreds of proteins, but that this repression often is relatively
mild (less than 2-fold).
The effects of miRNA dysregulation of gene expression seem to be important in cancer. For instance, in gastrointestinal cancers, nine miRNAs have been identified as epigenetically altered and effective in down regulating DNA repair enzymes.
The effects of miRNA dysregulation of gene expression also seem
to be important in neuropsychiatric disorders, such as schizophrenia,
bipolar disorder, major depression, Parkinson's disease, Alzheimer's
disease and autism spectrum disorders.
Translational regulation
Neomycin
is an example of a small molecule that reduces expression of all
protein genes inevitably leading to cell death; it thus acts as an antibiotic.
Direct regulation of translation is less prevalent than control of transcription or mRNA stability but is occasionally used. Inhibition of protein translation is a major target for toxins and antibiotics, so they can kill a cell by overriding its normal gene expression control. Protein synthesis inhibitors include the antibiotic neomycin and the toxin ricin.
Post-translational modifications (PTMs) are covalent
modifications to proteins. Like RNA splicing, they help to
significantly diversify the proteome. These modifications are usually
catalyzed by enzymes. Additionally, processes like covalent additions to
amino acid side chain residues can often be reversed by other enzymes.
However, some, like the proteolytic cleavage of the protein backbone, are irreversible.
PTMs play many important roles in the cell. For example, phosphorylation is primarily involved in activating and deactivating proteins and in signaling pathways.
PTMs are involved in transcriptional regulation: an important function
of acetylation and methylation is histone tail modification, which
alters how accessible DNA is for transcription. They can also be seen in the immune system, where glycosylation plays a key role. One type of PTM can initiate another type of PTM, as can be seen in how ubiquitination tags proteins for degradation through proteolysis.
Proteolysis, other than being involved in breaking down proteins, is
also important in activating and deactivating them, and in regulating
biological processes such as DNA transcription and cell death.
Measurement
Schematic karyogram of a human, showing an overview of the expression of the human genome using G banding, which is a method that includes Giemsa staining, wherein the lighter staining regions are generally more transcriptionally active, whereas darker regions are more inactive.
Measuring gene expression is an important part of many life sciences,
as the ability to quantify the level at which a particular gene is
expressed within a cell, tissue or organism can provide a lot of
valuable information. For example, measuring gene expression can:
Identify viral infection of a cell (viral protein expression).
Determine an individual's susceptibility to cancer (oncogene expression).
Similarly, the analysis of the location of protein expression is a
powerful tool, and this can be done on an organismal or cellular scale.
Investigation of localization is particularly important for the study of
development
in multicellular organisms and as an indicator of protein function in
single cells. Ideally, measurement of expression is done by detecting
the final gene product (for many genes, this is the protein); however,
it is often easier to detect one of the precursors, typically mRNA and to infer gene-expression levels from these measurements.
mRNA quantification
Levels of mRNA can be quantitatively measured by northern blotting, which provides size and sequence information about the mRNA molecules. A sample of RNA is separated on an agarose gel and hybridized to a radioactively labeled RNA probe that is complementary to the target sequence. The radiolabeled RNA is then detected by an autoradiograph.
Because the use of radioactive reagents makes the procedure
time-consuming and potentially dangerous, alternative labeling and
detection methods, such as digoxigenin and biotin chemistries, have been
developed.
Perceived disadvantages of Northern blotting are that large quantities
of RNA are required and that quantification may not be completely
accurate, as it involves measuring band strength in an image of a gel.
On the other hand, the additional mRNA size information from the
Northern blot allows the discrimination of alternately spliced
transcripts.
Another approach for measuring mRNA abundance is RT-qPCR. In this technique, reverse transcription is followed by quantitative PCR. Reverse transcription first generates a DNA template from the mRNA; this single-stranded template is called cDNA. The cDNA template is then amplified in the quantitative step, during which the fluorescence emitted by labeled hybridization probes or intercalating dyes changes as the DNA amplification process progresses.
With a carefully constructed standard curve, qPCR can produce an
absolute measurement of the number of copies of original mRNA, typically
in units of copies per nanolitre of homogenized tissue or copies per
cell.
qPCR is very sensitive (detection of a single mRNA molecule is
theoretically possible), but can be expensive depending on the type of
reporter used; fluorescently labeled oligonucleotide probes are more
expensive than non-specific intercalating fluorescent dyes.
For expression profiling, or high-throughput analysis of many genes within a sample, quantitative PCR may be performed for hundreds of genes simultaneously in the case of low-density arrays. A second approach is the hybridization microarray.
A single array or "chip" may contain probes to determine transcript
levels for every known gene in the genome of one or more organisms. Alternatively, "tag based" technologies like Serial analysis of gene expression (SAGE) and RNA-Seq, which can provide a relative measure of the cellular concentration of different mRNAs, can be used.
An advantage of tag-based methods is the "open architecture", allowing
for the exact measurement of any transcript, with a known or unknown
sequence. Next-generation sequencing (NGS) such as RNA-Seq
is another approach, producing vast quantities of sequence data that
can be matched to a reference genome. Although NGS is comparatively
time-consuming, expensive, and resource-intensive, it can identify single-nucleotide polymorphisms,
splice-variants, and novel genes, and can also be used to profile
expression in organisms for which little or no sequence information is
available.
RNA profiles in Wikipedia
The RNA expression profile of the GLUT4 Transporter (one of the main glucose transporters found in the human body)
Profiles like these are found for almost all proteins listed in Wikipedia. They are generated by organizations such as the Genomics Institute of the Novartis Research Foundation and the European Bioinformatics Institute.
Additional information can be found by searching their databases (for
an example of the GLUT4 transporter pictured here, see citation).
These profiles indicate the level of DNA expression (and hence RNA
produced) of a certain protein in a certain tissue, and are color-coded
accordingly in the images located in the Protein Box on the right side
of each Wikipedia page.
Protein quantification
For
genes encoding proteins, the expression level can be directly assessed
by a number of methods with some clear analogies to the techniques for
mRNA quantification.
One of the most commonly used methods is to perform a Western blot against the protein of interest. This gives information on the size of the protein in addition to its identity. A sample (often cellular lysate) is separated on a polyacrylamide gel, transferred to a membrane and then probed with an antibody to the protein of interest. The antibody can either be conjugated to a fluorophore or to horseradish peroxidase
for imaging and/or quantification. The gel-based nature of this assay
makes quantification less accurate, but it has the advantage of being
able to identify later modifications to the protein, for example
proteolysis or ubiquitination, from changes in size.
mRNA-protein correlation
While
transcription directly reflects gene expression, the copy number of
mRNA molecules does not directly correlate with the number of protein
molecules translated from mRNA. Quantification of both protein and mRNA
permits a correlation of the two levels. Regulation on each step of gene
expression can impact the correlation, as shown for regulation of
translation or protein stability. Post-translational factors, such as protein transport in highly polar cells, can influence the measured mRNA-protein correlation as well.
In situ-hybridization of Drosophilaembryos at different developmental stages for the mRNA responsible for the expression of hunchback. High intensity of blue color marks places with high hunchback mRNA quantity.
Analysis of expression is not limited to quantification; localization
can also be determined. mRNA can be detected with a suitably labelled
complementary mRNA strand and protein can be detected via labelled
antibodies. The probed sample is then observed by microscopy to identify
where the mRNA or protein is.
The three-dimensional structure of green fluorescent protein.
The residues in the centre of the "barrel" are responsible for
production of green light after exposing to higher energetic blue light.
From PDB: 1EMA.
By replacing the gene with a new version fused to a green fluorescent protein marker or similar, expression may be directly quantified in live cells. This is done by imaging using a fluorescence microscope.
It is very difficult to clone a GFP-fused protein into its native
location in the genome without affecting expression levels, so this
method often cannot be used to measure endogenous gene expression. It
is, however, widely used to measure the expression of a gene
artificially introduced into the cell, for example via an expression vector.
By fusing a target protein to a fluorescent reporter, the protein's
behavior, including its cellular localization and expression level, can
be significantly changed.
The enzyme-linked immunosorbent assay works by using antibodies immobilised on a microtiter plate
to capture proteins of interest from samples added to the well. Using a
detection antibody conjugated to an enzyme or fluorophore the quantity
of bound protein can be accurately measured by fluorometric or colourimetric
detection. The detection process is very similar to that of a Western
blot, but by avoiding the gel steps more accurate quantification can be
achieved.
An expression system is a system specifically designed for the
production of a gene product of choice. This is normally a protein
although may also be RNA, such as tRNA or a ribozyme. An expression system consists of a gene, normally encoded by DNA, and the molecular machinery required to transcribe the DNA into mRNA and translate the mRNA into protein
using the reagents provided. In the broadest sense this includes every
living cell but the term is more normally used to refer to expression as
a laboratory tool. An expression system is therefore often artificial
in some manner. Expression systems are, however, a fundamentally natural
process. Viruses are an excellent example where they replicate by using
the host cell as an expression system for the viral proteins and
genome.
In
addition to these biological tools, certain naturally observed
configurations of DNA (genes, promoters, enhancers, repressors) and the
associated machinery itself are referred to as an expression system.
This term is normally used in the case where a gene or set of genes is
switched on under well defined conditions, for example, the simple
repressor switch expression system in Lambda phage and the lac operator
system in bacteria. Several natural expression systems are directly
used or modified and used for artificial expression systems such as the Tet-on and Tet-off expression system.
Genes have sometimes been regarded as nodes in a network, with inputs being proteins such as transcription factors,
and outputs being the level of gene expression. The node itself
performs a function, and the operation of these functions have been
interpreted as performing a kind of information processing within cells
and determines cellular behavior.
Gene networks can also be constructed without formulating an
explicit causal model. This is often the case when assembling networks
from large expression data sets. Covariation and correlation of expression is computed across a large sample of cases and measurements (often transcriptome or proteome
data). The source of variation can be either experimental or natural
(observational). There are several ways to construct gene expression
networks, but one common approach is to compute a matrix of all
pair-wise correlations of expression across conditions, time points, or
individuals and convert the matrix (after thresholding at some cut-off
value) into a graphical representation in which nodes represent genes,
transcripts, or proteins and edges connecting these nodes represent the
strength of association (see GeneNetwork GeneNetwork 2).
Techniques and tools
The
following experimental techniques are used to measure gene expression
and are listed in roughly chronological order, starting with the older,
more established technologies. They are divided into two groups based on
their degree of multiplexity.
