1

A REPORT

ON

A COMPLETE METABOLIC AND STRUCTURAL STUDY OF miRNA

RESPONSIBLE FOR AUTISM

BY

BISWA PRASANNA MISHRA 2008B1A1609H

UNDER THE SUPERVISION OF

Dr. Savitha Govardhan

Assistant Professor

BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE (BITS) PILANI, HYDERABAD

APRIL, 2012

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A REPORT

ON

A COMPLETE METABOLIC AND STRUCTURAL STUDY OF miRNA

RESPONSIBLE FOR AUTISM

BY

BISWA PRASANNA MISHRA 2008B1A1609H MSc. (BIO) & B.E.(CHEM)

Submitted in partial fulfilment of the

Computer Projects BITS C331

UNDER THE SUPERVISION OF

Dr. Savitha Govardhan

Assistant Professor

DEPARTMENT OF BIOLOGICAL SCIENCES

BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI-HYDERABAD

CAMPUS

April, 2012

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CERTIFICATE

This is to certify that the report entitled, “A COMPLETE METABOLIC AND STRUCTURAL

STUDY OF miRNA RESPONSIBLE FOR AUTISM” and submitted by Biswa Prasanna Mishra,

ID No. 2008B1A1609H, in partial fulfillment of the requirements of BITS C331 Computer

Projects embodies the work done by him/her under my supervision.

Signature of the supervisor

Name:

Designation:

Date:

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Acknowledgements

I would like to thank Dr. Savitha Govardhan, my COP Instructor, Department of Biological

Sciences, who guided me through the course of this project and for giving valuable suggestions

regarding the same.

I am greatly indebted to Ms. Priyanka Purkayastha, RS in Department of Biological Sciences for

her continued guidance and assistance during this project. I am also thankful to other Research

Scholars for their support.

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Abstract

Autism is a disorder of neural development characterized by impaired social

interaction and communication, and by restricted and repetitive behavior. Autism affects

information processing in the brain by altering how nerve cells and their synapses connect and

organize; how this occurs is not well understood. It is one of three recognized disorders in the

autism spectrum (ASDs), the other two being Asperger syndrome, and Pervasive Developmental

Disorder-Not Otherwise Specified (commonly abbreviated as PDD-NOS). Autism can be caused

by various sources, one of them being miRNA, the one being studied in this project. We try to

know the causal miRNA species and then try to do a detailed structural study and validation. The

structural study would include using various tools and internet servers to predict the secondary

and tertiary structures of the miRNA sequence and their go for validation using MolProbity.

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Table of Contents

Acknowledgements………………………………………………………..4

Abstract……………………………………………………………………5

Autism

Characteristics………………………………………………………8

Causes……………………………………………………………….8

Mechanism…………………………………………………………10

Screening…...................................................................................15

Diagnosis…………………………………………………………...15

MicroRNA………………………………………………………………...17

miRNA and disease….............................................................................24

Nucleic Acid Secondary Structure………………………………………..26

Taxonomy of Algorithms………………………………………………....28

Secondary Structure Elements…………………………………………....30

Secondary Structure Visualization…………………………………….....32

Identifying Secondary Structure Constraints…………………………….37

Generating Tertiary Structures…………………………………………....40

Visualizing Tertiary Structure……………………………………………..42

Alignment…………………………………………………………………..45

Validation…………………………………………………………………..46

Results and Discussion…………………………………………………….47

References…………………………………………………………………..48

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Introduction: Autism is a disorder of neural development characterized by impaired social

interaction and communication, and by restricted and repetitive behavior. These signs all begin

before a child is three years old. Autism affects information processing in the brain by altering

how nerve cells and their synapses connect and organize; how this occurs is not well

understood. It is one of three recognized disorders in theautism spectrum (ASDs), the other two

being Asperger syndrome, which lacks delays in cognitive development and language,

and Pervasive Developmental Disorder-Not Otherwise Specified (commonly abbreviated as

PDD-NOS), which is diagnosed when the full set of criteria for autism or Asperger syndrome are

not met.

Autism has a strong genetic basis, although the genetics of autism are complex and it is unclear

whether ASD is explained more by rare mutations, or by rare combinations of common genetic

variants. In rare cases, autism is strongly associated with agents that cause birth defects

Controversies surround other proposed environmental causes, such as heavy metals, pesticides or

childhood vaccines; the vaccine hypotheses are biologically implausible and lack convincing

scientific evidence. The prevalence of autism is about 1–2 per 1,000 people worldwide; however,

the Centers for Disease Control and Prevention (CDC) reports approximately 9 per 1,000

children in the United States are diagnosed with ASD. The number of people diagnosed with

autism has increased dramatically since the 1980s, partly due to changes in diagnostic practice;

the question of whether actual prevalence has increased is unresolved.

Parents usually notice signs in the first two years of their child's life. The signs usually develop

gradually, but some autistic children first develop more normally and

then regress. Early behavioral or cognitive intervention can help autistic children gain self-care,

social, and communication skills. Although there is no known cure, there have been reported

cases of children who recovered. Not many children with autism live independently after

reaching adulthood, though some become successful. An autistic culture has developed, with

some individuals seeking a cure and others believing autism should be accepted as a difference

and not treated as a disorder.

Characteristics: Autism is a highly variable neurodevelopmental disorder that first appears

during infancy or childhood, and generally follows a steady course without remission. Overt

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symptoms gradually begin after the age of six months, become established by age two or three

years, and tend to continue through adulthood, although often in more muted form. It is

distinguished not by a single symptom, but by a characteristic triad of symptoms: impairments in

social interaction; impairments in communication; and restricted interests and repetitive

behavior. Other aspects, such as atypical eating, are also common but are not essential for

diagnosis. Autism's individual symptoms occur in the general population and appear not to

associate highly, without a sharp line separating pathologically severe from common traits.

Causes: It has long been presumed that there is a common cause at the genetic, cognitive, and

neural levels for autism's characteristic triad of symptoms. However, there is increasing

suspicion that autism is instead a complex disorder whose core aspects have distinct causes that

often co-occur.

Deletion (1), duplication (2) and inversion (3) are all chromosome abnormalities that have been

implicated in autism.

Autism has a strong genetic basis, although the genetics of autism are complex and it is unclear

whether ASD is explained more by raremutations with major effects, or by rare multigene

interactions of common genetic variants. Complexity arises due to interactions among multiple

genes, the environment, and epigenetic factors which do not change DNA but are heritable and

influence gene expression. Studies of twins suggest that heritability is 0.7 for autism and as high

as 0.9 for ASD, and siblings of those with autism are about 25 times more likely to be autistic

than the general population. However, most of the mutations that increase autism risk have not

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been identified. Typically, autism cannot be traced to a Mendelian (single-gene) mutation or to a

single chromosome abnormality, and none of the genetic syndromes associated with ASDs have

been shown to selectively cause ASD. Numerous candidate genes have been located, with only

small effects attributable to any particular gene. The large number of autistic individuals with

unaffected family members may result from copy number variations—

spontaneous deletions or duplications in genetic material during meiosis. Hence, a substantial

fraction of autism cases may be traceable to genetic causes that are highly heritable but not

inherited: that is, the mutation that causes the autism is not present in the parental genome.

Several lines of evidence point to synaptic dysfunction as a cause of autism. Some rare mutations

may lead to autism by disrupting some synaptic pathways, such as those involved with cell

adhesion. Gene replacement studies in mice suggest that autistic symptoms are closely related to

later developmental steps that depend on activity in synapses and on activity-dependent changes.

All known teratogens (agents that cause birth defects) related to the risk of autism appear to act

during the first eight weeks from conception, and though this does not exclude the possibility

that autism can be initiated or affected later, it is strong evidence that autism arises very early in

development.

Although evidence for other environmental causes is anecdotal and has not been confirmed by

reliable studies, extensive searches are underway. Environmental factors that have been claimed

to contribute to or exacerbate autism, or may be important in future research, include certain

foods, infectious disease, heavy metals, solvents, diesel exhaust, PCBs,

phthalates and phenols used in plastic products, pesticides, brominated flame

retardants, alcohol, smoking, illicit drugs, vaccines, and prenatal stress, although no links have

been found, and some have been completely disproven.

Parents may first become aware of autistic symptoms in their child around the time of a routine

vaccination. This has led to unsupported theories blaming vaccine "overload", a vaccine

preservative, or the MMR vaccine for causing autism. The latter theory was supported by a

litigation-funded study that has since been shown to have been "an elaborate fraud". Although

these theories lack convincing scientific evidence and are biologically implausible, parental

concern about a potential vaccine link with autism has led to lower rates of childhood

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immunizations, outbreaks of previously controlled childhood diseases in some countries, and the

preventable deaths of several children.

Mechanism: Autism's symptoms result from maturation-related changes in various systems of

the brain. How autism occurs is not well understood. Its mechanism can be divided into two

areas: the pathophysiology of brain structures and processes associated with autism, and

the neuropsychological linkages between brain structures and behaviors. The behaviors appear to

have multiple pathophysiologies.

Pathophysiology

Autism affects the amygdala, cerebellum, and many other parts of the brain.[

Unlike many other brain disorders, such as Parkinson's, autism does not have a clear unifying

mechanism at either the molecular, cellular, or systems level; it is not known whether autism is a

few disorders caused by mutations converging on a few common molecular pathways, or is (like

intellectual disability) a large set of disorders with diverse mechanisms. Autism appears to result

from developmental factors that affect many or all functional brain systems, and to disturb the

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timing of brain development more than the final product. Neuroanatomicalstudies and the

associations with teratogens strongly suggest that autism's mechanism includes alteration of

brain development soon after conception. This anomaly appears to start a cascade of

pathological events in the brain that are significantly influenced by environmental factors. Just

after birth, the brains of autistic children tend to grow faster than usual, followed by normal or

relatively slower growth in childhood. It is not known whether early overgrowth occurs in all

autistic children. It seems to be most prominent in brain areas underlying the development of

higher cognitive specialization. Hypotheses for the cellular and molecular bases of pathological

early overgrowth include the following:

An excess of neurons that causes local over connectivity in key brain regions.

Disturbed neuronal migration during early gestation.

Unbalanced excitatory–inhibitory networks.

Abnormal formation of synapses and dendritic spines, for example, by modulation of

the neurexin–neuroligin cell-adhesion system, or by poorly regulated synthesis of synaptic

proteins. Disrupted synaptic development may also contribute to epilepsy, which may

explain why the two conditions are associated.

Interactions between the immune system and the nervous system begin early during

the embryonic stage of life, and successful neurodevelopment depends on a balanced immune

response. Aberrant immune activity during critical periods of neurodevelopment is possibly part

of the mechanism of some forms of ASD. Although some abnormalities in the immune system

have been found in specific subgroups of autistic individuals, it is not known whether these

abnormalities are relevant to or secondary to autism's disease processes. Asautoantibodies are

found. in conditions other than ASD, and are not always present in ASD, the relationship

between immune disturbances and autism remains unclear and controversial.

The relationship of neurochemicals to autism is not well understood; several have been

investigated, with the most evidence for the role of serotonin and of genetic differences in its

transport. The role of group I metabotropic glutamate receptors (mGluR) in the pathogenesis

of fragile X syndrome, the most common identified genetic cause of autism, has led to interest in

the possible implications for future autism research into this pathway. Some data suggest an

increase in several growth hormones; other data argue for diminished growth factors. Also,

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some inborn errors of metabolism are associated with autism, but probably account for less than

5% of cases.

The mirror neuron system (MNS) theory of autism hypothesizes that distortion in the

development of the MNS interferes with imitation and leads to autism's core features of social

impairment and communication difficulties. The MNS operates when an animal performs an

action or observes another animal perform the same action. The MNS may contribute to an

individual's understanding of other people by enabling the modeling of their behavior via

embodied simulation of their actions, intentions, and emotions. Several studies have tested this

hypothesis by demonstrating structural abnormalities in MNS regions of individuals with ASD,

delay in the activation in the core circuit for imitation in individuals with Asperger syndrome,

and a correlation between reduced MNS activity and severity of the syndrome in children with

ASD. However, individuals with autism also have abnormal brain activation in many circuits

outside the MNSand the MNS theory does not explain the normal performance of autistic

children on imitation tasks that involve a goal or object.

Autistic individuals tend to use different areas of the brain (yellow) for a movement task

compared to a control group (blue).[

ASD-related patterns of low function and aberrant activation in the brain differ depending on

whether the brain is doing social or nonsocial tasks. In autism there is evidence for reduced

functional connectivity of the default network, a large-scale brain network involved in social and

emotional processing, with intact connectivity of the task-positive network, used in sustained

attention and goal-directed thinking. In people with autism the two networks are not negatively

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correlated in time, suggesting an imbalance in toggling between the two networks, possibly

reflecting a disturbance of self-referential thought. A 2008 brain-imaging study found a specific

pattern of signals in the cingulate cortex which differs in individuals with ASD.

The underconnectivity theory of autism hypothesizes that autism is marked by underfunctioning

high-level neural connections and synchronization, along with an excess of low-level

processes. Evidence for this theory has been found in functional neuroimaging studies on

autistic individuals and by a brainwave study that suggested that adults with ASD have local

overconnectivity in the cortex and weak functional connections between thefrontal lobe and the

rest of the cortex. Other evidence suggests the underconnectivity is mainly within

each hemisphere of the cortex and that autism is a disorder of the association cortex.

From studies based on event-related potentials, transient changes to the brain's electrical activity

in response to stimuli, there is considerable evidence for differences in autistic individuals with

respect to attention, orientiation to auditory and visual stimuli, novelty detection, language and

face processing, and information storage; several studies have found a preference for nonsocial

stimuli. For example, magnetoencephalography studies have found evidence in autistic children

of delayed responses in the brain's processing of auditory signals.

In the genetic area, relations have been found between autism and schizophrenia based on

duplications and deletions of chromosomes; research showed that schizophrenia and autism are

significantly more common in combination with 1q21.1 deletion syndrome. Research on

autism/schizophrenia relations for chromosome 15 (15q13.3), chromosome 16 (16p13.1) and

chromosome 17 (17p12) are inconclusive.

Neuropsychology

Two major categories of cognitive theories have been proposed about the links between autistic

brains and behavior.

The first category focuses on deficits in social cognition. The empathizing–systemizing

theory postulates that autistic individuals can systemize—that is, they can develop internal rules

of operation to handle events inside the brain—but are less effective at empathizing by handling

events generated by other agents. An extension, the extreme male brain theory, hypothesizes that

autism is an extreme case of the male brain, defined psychometrically as individuals in whom

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systemizing is better than empathizing; this extension is controversial, as many studies

contradict the idea that baby boys and girls respond differently to people and objects.

These theories are somewhat related to the earlier theory of mind approach, which hypothesizes

that autistic behavior arises from an inability to ascribe mental states to oneself and others. The

theory of mind hypothesis is supported by autistic children's atypical responses to the Sally–

Anne test for reasoning about others' motivations,[103]

and the mirror neuron system theory of

autism described in Pathophysiology maps well to the hypothesis. However, most studies have

found no evidence of impairment in autistic individuals' ability to understand other people's basic

intentions or goals; instead, data suggests that impairments are found in understanding more

complex social emotions or in considering others' viewpoints.

The second category focuses on nonsocial or general processing. Executive

dysfunction hypothesizes that autistic behavior results in part from deficits in working memory,

planning, inhibition, and other forms of executive function. Tests of core executive processes

such as eye movement tasks indicate improvement from late childhood to adolescence, but

performance never reaches typical adult levels. Strength of the theory is predicting stereotyped

behavior and narrow interests; two weaknesses are that executive function is hard to measure and

that executive function deficits have not been found in young autistic children.

Weak central coherence theory hypothesizes that a limited ability to see the big picture underlies

the central disturbance in autism. One strength of this theory is predicting special talents and

peaks in performance in autistic people. A related theory—enhanced perceptual functioning—

focuses more on the superiority of locally oriented and perceptualoperations in autistic

individuals. These theories map well from the underconnectivity theory of autism.

Neither category is satisfactory on its own; social cognition theories poorly address autism's rigid

and repetitive behaviors, while the nonsocial theories have difficulty explaining social

impairment and communication difficulties. A combined theory based on multiple deficits may

prove to be more useful.

Screening: About half of parents of children with ASD notice their child's unusual behaviors by

age 18 months, and about four-fifths notice by age 24 months.

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No babbling by 12 months.

No gesturing (pointing, waving bye-bye, etc.) by 12 months.

No single words by 16 months.

No two-word spontaneous (not just echolalic) phrases by 24 months.

Any loss of any language or social skills, at any age.

US and Japanese practice is to screen all children for ASD at 18 and 24 months, using autism-

specific formal screening tests. In contrast, in the UK, children whose families or doctors

recognize possible signs of autism are screened. It is not known which approach is more

effective. Screening tools include the Modified Checklist for Autism in Toddlers (M-CHAT), the

Early Screening of Autistic Traits Questionnaire, and the First Year Inventory; initial data on M-

CHAT and its predecessor CHAT on children aged 18–30 months suggests that it is best used in

a clinical setting and that it has low sensitivity (many false-negatives) but good specificity (few

false-positives). It may be more accurate to precede these tests with a broadband screener that

does not distinguish ASD from other developmental disorders. Screening tools designed for one

culture's norms for behaviors like eye contact may be inappropriate for a different culture.

Although genetic screening for autism is generally still impractical, it can be considered in some

cases, such as children with neurological symptoms and dysmorphic features.

Diagnosis: Diagnosis is based on behavior, not cause or mechanism. Autism is defined in

the DSM-IV-TR as exhibiting at least six symptoms total, including at least two symptoms of

qualitative impairment in social interaction, at least one symptom of qualitative impairment in

communication, and at least one symptom of restricted and repetitive behavior. Sample

symptoms include lack of social or emotional reciprocity, stereotyped and repetitive use of

language or idiosyncratic language, and persistent preoccupation with parts of objects. Onset

must be prior to age three years, with delays or abnormal functioning in either social interaction,

language as used in social communication, or symbolic or imaginative play. The disturbance

must not be better accounted for by Rett syndrome or childhood disintegrative disorder. ICD-

10 uses essentially the same definition.

Several diagnostic instruments are available. Two are commonly used in autism research:

the Autism Diagnostic Interview-Revised (ADI-R) is a semistructured parent interview, and the

Autism Diagnostic Observation Schedule (ADOS) uses observation and interaction with the

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child. The Childhood Autism Rating Scale (CARS) is used widely in clinical environments to

assess severity of autism based on observation of children.

A pediatrician commonly performs a preliminary investigation by taking developmental history

and physically examining the child. If warranted, diagnosis and evaluations are conducted with

help from ASD specialists, observing and assessing cognitive, communication, family, and other

factors using standardized tools, and taking into account any associated medical conditions. A

pediatric neuropsychologist is often asked to assess behavior and cognitive skills, both to aid

diagnosis and to help recommend educational interventions. Adifferential diagnosis for ASD at

this stage might also consider mental retardation, hearing impairment, and a specific language

impairment such as Landau–Kleffner syndrome. The presence of autism can make it harder to

diagnose coexisting psychiatric disorders such as depression.

Clinical genetics evaluations are often done once ASD is diagnosed, particularly when other

symptoms already suggest a genetic cause. Although genetic technology allows clinical

geneticists to link an estimated 40% of cases to genetic causes, consensus guidelines in the US

and UK are limited to high-resolution chromosome and fragile X testing. Agenotype-first model

of diagnosis has been proposed, which would routinely assess the genome's copy number

variations. As new genetic tests are developed several ethical, legal, and social issues will

emerge. Commercial availability of tests may precede adequate understanding of how to use test

results, given the complexity of autism's genetics. Metabolicand neuroimaging tests are

sometimes helpful, but are not routine.

ASD can sometimes be diagnosed by age 14 months, although diagnosis becomes increasingly

stable over the first three years of life: for example, a one-year-old who meets diagnostic criteria

for ASD is less likely than a three-year-old to continue to do so a few years later. In the UK the

National Autism Plan for Children recommends at most 30 weeks from first concern to

completed diagnosis and assessment, though few cases are handled that quickly in practice. A

2009 US study found the average age of formal ASD diagnosis was 5.7 years, far above

recommendations, and that 27% of children remained undiagnosed at age 8 years. Although the

symptoms of autism and ASD begin early in childhood, they are sometimes missed; years later,

adults may seek diagnoses to help them or their friends and family understand themselves, to

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help their employers make adjustments, or in some locations to claim disability living allowances

or other benefits.

Underdiagnosis and overdiagnosis are problems in marginal cases, and much of the recent

increase in the number of reported ASD cases is likely due to changes in diagnostic practices.

The increasing popularity of drug treatment options and the expansion of benefits has given

providers incentives to diagnose ASD, resulting in some overdiagnosis of children with uncertain

symptoms. Conversely, the cost of screening and diagnosis and the challenge of obtaining

payment can inhibit or delay diagnosis. It is particularly hard to diagnose autism among

the visually impaired, partly because some of its diagnostic criteria depend on vision, and partly

because autistic symptoms overlap with those of common blindness syndromes or blindisms.

MICRO RNA

The stem-loop secondary structure of a pre-microRNA from Brassica oleracea.

A microRNA (abbreviated miRNA) is a short ribonucleic acid (RNA) molecule found

in eukaryotic cells. A microRNA molecule has very fewnucleotides (an average of 22) compared

with other RNAs.

miRNAs are post-transcriptional regulators that bind to complementary sequences on

target messenger RNA transcripts (mRNAs), usually resulting in translational repression or

target degradation and gene silencing. The human genome may encode over 1000 miRNAs,

which may target about 60% of mammalian genes and are abundant in many human cell types.

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miRNAs show very different characteristics between plants and metazoans. In plants the miRNA

complementarity to its mRNA target is nearly perfect, with no or few mismatched bases. In

metazoans, on the other hand, miRNA complementarity typically encompasses the 5' bases 2-7

of the microRNA, the microRNA seed region, and one miRNA can target many different sites on

the same mRNA or on many different mRNAs. Another difference is the location of target sites

on mRNAs. In metazoans, the miRNA target sites are in the three prime untranslated

regions (3'UTR) of the mRNA. This is how microRNA may target several mRNAs. In plants,

targets can be located in the 3' UTR but are more often in the coding region itself. MiRNAs are

well conserved in eukaryotic organisms and are thought to be a vital and evolutionarily ancient

component of genetic regulation.

The first miRNAs were characterized in the early 1990s.However, miRNAs were not recognized

as a distinct class of biological regulators with conserved functions until the early 2000s. Since

then, miRNA research has revealed multiple roles in negative regulation (transcript degradation

and sequestering, translational suppression) and possible involvement in positive regulation

(transcriptional and translational activation). By affecting gene regulation, miRNAs are likely to

be involved in most biological processes. Different sets of expressed miRNAs are found in

different cell types and tissues.

Aberrant expression of miRNAs has been implicated in numerous disease states, and miRNA-

based therapies are under investigation.

Nomenclature: Under a standard nomenclature system, names are assigned to experimentally

confirmed miRNAs before publication of their discovery. The prefix "mir" is followed by a dash

and a number, the latter often indicating order of naming. For example, mir-123 was named and

likely discovered prior to mir-456. The uncapitalized "mir-" refers to the pre-miRNA, while a

capitalized "miR-" refers to the mature form. miRNAs with nearly identical sequences bar one or

two nucleotides are annotated with an additional lower case letter. For example, miR-123a would

be closely related to miR-123b. Pre-miRNAs that lead to 100% identical mature miRNAs but

that are located at different places in the genome are indicated with an additional dash-number

suffix. For example, the pre-miRNAs hsa-mir-194-1 and hsa-mir-194-2 lead to an identical

mature miRNA (hsa-miR-194) but are located in different regions of the genome. Species of

origin is designated with a three-letter prefix, e.g., hsa-miR-123 is a human (Homo

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sapiens)miRNA and oar-miR-123 is a sheep (Ovis aries) miRNA. Other common prefixes

include 'v' for viral (miRNA encoded by a viral genome) and 'd' for Drosophila miRNA (a fruit

fly commonly studied in genetic research). When two mature microRNAs originate from

opposite arms of the same pre-miRNA, they are denoted with a -3p or -5p suffix. (In the past,

this distinction was also made with 's' (sense) and 'as' (antisense)). When relative expression

levels are known, an asterisk following the name indicates an miRNA expressed at low levels

relative to the miRNA in the opposite arm of a hairpin. For example, miR-123 and miR-123*

would share a pre-miRNA hairpin, but more miR-123 would be found in the cell.

Biogenesis:

MicroRNAs are produced from either their own genes or from introns.

The majority of the characterized miRNA genes are intergenic or oriented antisense to

neighboring genes and are therefore suspected to be transcribed as independent units. However,

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in some cases microRNA gene is transcriped together with its host gene and this provides a mean

for coupled regulation of miRNA and protein-coding gene As much as 40% of miRNA genes

may lie in the introns of protein and non-protein coding genes or even in exons of long

nonprotein-coding transcripts. These are usually, though not exclusively, found in a sense

orientation, and thus usually are regulated together with their host genes. Other miRNA genes

showing a common promoter include the 42-48% of all miRNAs originating from polycistronic

units containing multiple discrete loops from which mature miRNAs are processed, although this

does not necessarily mean the mature miRNAs of a family will be homologous in structure and

function. The promoters mentioned have been shown to have some similarities in their motifs to

promoters of other genes transcribed by RNA polymerase II such as protein coding genes. The

DNA template is not the final word on mature miRNA production: 6% of human miRNAs show

RNA editing (IsomiRs), the site-specific modification of RNA sequences to yield products

different from those encoded by their DNA. This increases the diversity and scope of miRNA

action beyond that implicated from the genome alone.

Transcription

miRNA genes are usually transcribed by RNA polymerase II (Pol II). The polymerase often

binds to a promoter found near the DNA sequence encoding what will become the hairpin loop

of the pre-miRNA. The resulting transcript is capped with a specially-modified nucleotide at the

5’ end, polyadenylated with multipleadenosines (a poly(A) tail), and spliced. Animal miRNAs

are initially transcribed as part of one arm of an ∼80 nucleotide RNA stem-loop that in turn

forms part of a several hundred nucleotides long miRNA precursor termed a primary miRNA

(pri-miRNA)s. When a stem-loop precursor is found in the 3’ UTR, a transcript may serve as a

pri-miRNA and a mRNA. RNA polymerase III (Pol III) transcribes some miRNAs, especially

those with upstream Alu sequences, transfer RNAs (tRNAs), and mammalian wide interspersed

repeat (MWIR) promoter units.

Nuclear processing

A single pri-miRNA may contain from one to six miRNA precursors. These hairpin loop

structures are composed of about 70 nucleotides each. Each hairpin is flanked by sequences

necessary for efficient processing. The double-stranded RNA structure of the hairpins in a pri-

miRNA is recognized by a nuclear protein known as DiGeorge Syndrome Critical Region

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8(DGCR8 or "Pasha" in invertebrates), named for its association with DiGeorge Syndrome.

DGCR8 associates with the enzyme Drosha, a protein that cuts RNA, to form the

"Microprocessor" complex. In this complex, DGCR8 orients the catalytic RNase III domain of

Drosha to liberate hairpins from pri-miRNAs by cleaving RNA about eleven nucleotides from

the hairpin base (two helical RNA turns into the stem). The product resulting has a two-

nucleotide overhang at its 3’ end; it has 3' hydroxyl and 5' phosphate groups. It is often termed as

a pre-miRNA (precursor-miRNA).

pre-miRNAs that are spliced directly out of introns, bypassing the Microprocessor complex, are

known as "Mirtrons." Originally thought to exist only in Drosophila and C. elegans, mirtrons

have now been found in mammals.

Perhaps as many as 16% of pri-miRNAs may be altered through nuclear RNA editing. Most

commonly, enzymes known as adenosine deaminases acting on RNA (ADARs)

catalyze adenosine to inosine (A to I) transitions. RNA editing can halt nuclear processing (for

example, of pri-miR-142, leading to degradation by the ribonuclease Tudor-SN) and alter

downstream processes including cytoplasmic miRNA processing and target specificity (e.g., by

changing the seed region of miR-376 in the central nervous system).

Nuclear export

pre-miRNA hairpins are exported from the nucleus in a process involving the nucleocytoplasmic

shuttle Exportin-5. This protein, a member of the karyopherin family, recognizes a two-

nucleotide overhang left by the RNase III enzyme Drosha at the 3' end of the pre-miRNA

hairpin. Exportin-5-mediated transport to the cytoplasm is energy-dependent, using GTP bound

to the Ran protein.

Cytoplasmic processing

In cytoplasm, the pre-miRNA hairpin is cleaved by the RNase III enzyme Dicer.[50]

This

endoribonuclease interacts with the 3' end of the hairpin and cuts away the loop joining the 3' and

5' arms, yielding an imperfect miRNA:miRNA* duplex about 22 nucleotides in length. Overall

hairpin length and loop size influence the efficiency of Dicer processing, and the imperfect

nature of the miRNA:miRNA* pairing also affects cleavage. Although either strand of the

duplex may potentially act as a functional miRNA, only one strand is usually incorporated into

the RNA-induced silencing complex (RISC) where the miRNA and its mRNA target interact.

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Biogenesis in plants

miRNA biogenesis in plants differs from metazoan biogenesis mainly in the steps of nuclear

processing and export. Instead of being cleaved by two different enzymes, once inside and once

outside the nucleus, both cleavages of the plant miRNA is performed by a Dicer homolog,

called Dicer-like1 (DL1). DL1 is only expressed in the nucleus of plant cells, which indicates

that both reactions take place inside the nucleus. Before plant miRNA:miRNA* duplexes are

transported out of the nucleus its 3' overhangs are methylated by a RNA

methyltransferaseprotein called Hua-Enhancer1 (HEN1). The duplex is then transported out of

the nucleus to the cytoplasm by a protein called Hasty (HST), an Exportin 5 homolog, where

they disassemble and the mature miRNA is incorporated into the RISC.

The RNA-induced silencing complex

The mature miRNA is part of an active RNA-induced silencing complex (RISC) containing

Dicer and many associated proteins. RISC is also known as a microRNA ribonucleoprotein

complex (miRNP); RISC with incorporated miRNA is sometimes referred to as "miRISC."

Dicer processing of the pre-miRNA is thought to be coupled with unwinding of the duplex.

Generally, only one strand is incorporated into the miRISC, selected on the basis of its

thermodynamic instability and weaker base-pairing relative to the other strand. The position of

the stem-loop may also influence strand choice. The other strand, called the passenger strand

due to its lower levels in the steady state, is denoted with an asterisk (*) and is normally

degraded. In some cases, both strands of the duplex are viable and become functional miRNA

that target different mRNA populations.

Members of the Argonaute (Ago) protein family are central to RISC function. Argonautes are

needed for miRNA-induced silencing and contain two conserved RNA binding domains: a PAZ

domain that can bind the single stranded 3’ end of the mature miRNA and a PIWI domain that

structurally resembles ribonuclease-H and functions to interact with the 5’ end of the guide

strand. They bind the mature miRNA and orient it for interaction with a target mRNA. Some

argonautes, for example human Ago2, cleave target transcripts directly; argonautes may also

recruit additional proteins to achieve translational repression. The human genome encodes eight

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argonaute proteins divided by sequence similarities into two families: AGO (with four members

present in all mammalian cells and called E1F2C/hAgo in humans), and PIWI (found in the germ

line and hematopoietic stem cells).

Additional RISC components include TRBP [human immunodeficiency virus (HIV)

transactivating response RNA (TAR) binding protein], PACT (protein activator of the interferon

induced protein kinase (PACT), the SMN complex, fragile X mental retardation protein (FMRP),

and Tudor staphylococcal nuclease-domain-containing protein (Tudor-SN).

Cellular functions: The function of miRNAs appears to be in gene regulation. For that purpose,

a miRNA is complementary to a part of one or more messenger RNAs (mRNAs). Animal

miRNAs are usually complementary to a site in the 3' UTR whereas plant miRNAs are usually

complementary to coding regions of mRNAs. Perfect or near perfect base pairing with the target

RNA promotes cleavage of the RNA. This is the primary mode of plant miRNAs. In animals

miRNAs more often have only partly the right sequence of nucleotides to bond with the target

mRNA. The match-ups are imperfect. Animal miRNAs inhibit protein translation of the target

mRNA (this exists in plants as well but is less common). MicroRNAs that are partially

complementary to a target can also speed up deadenylation, causing mRNAs to be degraded

sooner. For partially complementary microRNAs to recognise their targets, nucleotides 2–7 of

the miRNA (its 'seed region') still have to be perfectly complementary. miRNAs occasionally

also cause histone modification and DNA methylation of promoter sites, which affects the

expression of target genes.

Unlike plant microRNAs, the animal microRNAs target a diverse set of genes. However, genes

involved in functions common to all cells, such as gene expression, have relatively fewer

microRNA target sites and seem to be under selection to avoid targeting by microRNAs.

dsRNA can also activate gene expression, a mechanism that has been termed "small RNA-

induced gene activation" or RNAa. dsRNAs targeting gene promoters can induce potent

transcriptional activation of associated genes. This was demonstrated in human cells using

synthetic dsRNAs termed small activating RNAs (saRNAs), but has also been demonstrated for

endogenous microRNA.

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Interactions between microRNAs and complementary sequences on genes and

even pseudogenes that share sequence homology are thought to be a back channel of

communication regulating expression levels between paralogous genes. Given the name

"competing endogenous RNAs" (ceRNAs), these microRNAs bind to "microRNA response

elements" on genes and pseudogenes and may provide another explanation for the persistence of

non-coding ("junk") DNA.

miRNA and Disease

miRNA and inherited diseases

A mutation in the seed region of miR-96 causes hereditary progressive hearing loss. A mutation

in the seed region of miR-184 causes hereditary keratoconus with anterior polar cataract.

Deletion of the miR-17~92 cluster causes skeletal and growth defects.

miRNA and cancer

Several miRNAs have been found to have links with some types of cancer. MicroRNA-21 is one

of the first microRNAs that was identified as an oncomiR.

A study of mice altered to produce excess c-Myc — a protein with mutated forms implicated in

several cancers — shows that miRNA has an effect on the development of cancer. Mice that

were engineered to produce a surplus of types of miRNA found in lymphoma cells developed the

disease within 50 days and died two weeks later. In contrast, mice without the surplus miRNA

lived over 100 days. Leukemia can be caused by the insertion of a viral genome next to the 17-92

array of microRNAs leading to increased expression of this microRNA.

Another study found that two types of miRNA inhibit the E2F1 protein, which regulates cell

proliferation. miRNA appears to bind to messenger RNA before it can be translated to proteins

that switch genes on and off.

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By measuring activity among 217 genes encoding miRNA, patterns of gene activity that can

distinguish types of cancers can be discerned. miRNA signatures may enable classification of

cancer. This will allow doctors to determine the original tissue type which spawned a cancer and

to be able to target a treatment course based on the original tissue type. miRNA profiling has

already been able to determine whether patients with chronic lymphocytic leukemia had slow

growing or aggressive forms of the cancer.

Transgenic mice that over-express or lack specific miRNAs have provided insight into the role of

small RNAs in various malignancies.

A novel miRNA-profiling based screening assay for the detection of early-stage colorectal

cancer has been developed and is currently in clinical trials. Early results showed that blood

plasma samples collected from patients with early, resectable (Stage II) colorectal cancer could

be distinguished from those of sex-and age-matched healthy volunteers. Sufficient selectivity and

specificity could be achieved using small (less than 1 mL) samples of blood. The test has

potential to be a cost-effective, non-invasive way to identify at-risk patients who should undergo

colonoscopy.

Another role for miRNA in cancers is to use their expression level as a prognostic, for example

one study on NSCLC samples found that low miR-324a levels could serve as a prognostic

indicator of poor survival, another found that either high miR-185 or low miR-133b levels

correlated with metastasis and poor survival in colorectal cancer.

miRNA and heart disease

The global role of miRNA function in the heart has been addressed by conditionally inhibiting

miRNA maturation in the murine heart, and has revealed that miRNAs play an essential role

during its development. miRNA expression profiling studies demonstrate that expression levels

of specific miRNAs change in diseased human hearts, pointing to their involvement

in cardiomyopathies. Furthermore, studies on specific miRNAs in animal models have identified

distinct roles for miRNAs both during heart development and under pathological conditions,

including the regulation of key factors important for cardiogenesis, the hypertrophic growth

response, and cardiac conductance.

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miRNA and the nervous system

miRNAs appear to regulate the nervous system. Neural miRNAs are involved at various stages

of synaptic development, including dendritogenesis (involving miR-132, miR-134 andmiR-

124), synapse formation and synapse maturation (where miR-134 and miR-138 are thought to be

involved). Some studies find altered miRNA expression inschizophrenia.

NUCLEIC ACID SECONDARY STRUCTURE

The secondary structure of a nucleic acid molecule refers to the base-pairing interactions within a

single molecule or set of interacting molecules, and can be represented as a list of bases which

are paired in a nucleic acid moleculeThe secondary structures of biological DNA's and RNA's

tend to be different: biological DNA mostly exists as fully base paired-double helices, while

biological RNA is single stranded and often forms complicated base-pairing interactions due to

its increased ability to form hydrogen bonds stemming from the extra hydroxyl group in

the ribose sugar.

In a non-biological context, secondary structure is a vital consideration in the rational design of

nucleic acid structures for DNA nanotechnology and DNA computing, since the pattern of

basepairing ultimately determines the overall structure of the molecules.

SECONDARY STRUCTURE PREDICTION

One application of bioinformatics uses predicted RNA secondary structures in searching

a genome for noncoding but functional forms of RNA. For example, microRNAs have canonical

long stem-loop structures interrupted by small internal loops. A general method of calculating

probable RNA secondary structure is dynamic programming, although this has the disadvantage

that it cannot detect pseudoknots or other cases in which base pairs are not fully nested. More

general methods are based on stochastic context-free grammars. A web server that implements a

type of dynamic programming is Mfold.

For many RNA molecules, the secondary structure is highly important to the correct function of

the RNA — often more so than the actual sequence. This fact aids in the analysis of non-coding

RNA sometimes termed "RNA genes". RNA secondary structure can be predicted with some

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accuracy by computer and many bioinformatics applications use some notion of secondary

structure in analysis of RNA.

Secondary RNA Structure

This chapter gives an overview of the secondary RNA structure prediction problem. It

starts out by formally describing the secondary structure prediction problem as suggested

by Zuker [14]. It describes the different types of algorithms and the classifications that

these algorithms fall into. The chapter explains the issues and deficiencies of the

algorithms. There is a section which describes the structural elements that the secondary

structure is made of. Finally, the chapter describes all ways in which the secondary

structure has been visualized.

Secondary Structure Formal Description

RNA secondary structure refers to the two dimensional shape that RNA would physically

fold into under natural conditions. As RNA folds back on itself it forms hydrogen bonds

at complimentary base pair locations. These hydrogen bonds formed by the pairing of

complementary Watson-Crick bases as well as the weaker wobble pair G-U are described

as canonical base pairs.

Formally, the secondary structure of RNA can be described as suggested by Zuker [14] as

follows: An RNA sequence is represented by R as R = r1, r2, r3, …., rn, where ri

is

28

called the Ith nucleotide. Each ri belongs to the set {A, C, G, U}. A secondary structure,

or folding, on R is a set S of ordered pairs, written as i,j, 1 <= i <= j <= n satisfying: 18

1. j – i > 4

2. If i,j and i’,j’ are 2 base pairs, (assuming without loss in generality that i <= i’),

then either:

a. i = i’ and j = j’ (they are the same base pair),

b. i < j < i’ < j’ (i,j precedes i’, j’), or

c. i < i’ < j’ < j (i,j includes i’,j’).

Item 2c above disallows pseudoknots which occur when two base pairs, i,j and i’,j’

satisfy the condition i < i’ < j < j’[14]. The formal description does not account for

pseudoknots for several reasons. First, the algorithms which try to predict the secondary

structure through energy minimization are not able to handle pseudoknots. Energy

minimization algorithms can not handle pseudoknots because it is beyond current

scientific understanding how to assign energy values to the structures created by

psuedoknots. Secondly, pseudoknots are not considered because the dynamic

programming based algorithms are not able to handle the loop structures created by

pseudoknots.

Taxonomy of Algorithms

There are several methods used in the laboratory in order to determine the secondary and

tertiary structure of RNA. These methods include x-ray crystallography and nuclear

magnetic resonance spectroscopy [15]. The problem with these methods is that they are

29

very expensive and time consuming to produce the secondary structure results. It would

be tremendously preferable if a computer based algorithm could be created which would

accurately calculate the secondary structure in mere seconds. This has been the focus of

many researches for the past several decades. There are many types of algorithms which

have been devised which endeavor to fulfill this goal but the algorithms can be

categorized into two main types, deterministic and stochastic [13].

Deterministic

There are a whole host of algorithms which fit into the classification of deterministic.

The one fact that is common to all algorithms of this type is that the correct next step in

the algorithm only depends on the current state of the algorithm. There is no point in the 19

algorithm at which there are several next steps that could happen with some unknown

way to choose between them. Algorithms that fall into this category are

Minimum Free Energy such as Zuker’s algorithm [14], Kinetic Folding such as

Martinez [16], 5’ – 3’ Folding [13], Partition Function [13], and Maximum Matching

such as Nussinov [8]. Kinetic Folding and 5’ – 3’ Folding are able to determine

pseudoknots.

Stochastic

The common theme between all the stochastic algorithms is that they are based on

probabilities. One such example is based on a special Monte Carlo procedure know as

Simulated Annealing [17]. The Simulated Annealing algorithm is able assign

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probabilities to for both the opening and closing of single base pairs. This allows the

algorithm to account for a wide range of secondary structures.

Issues with Algorithms

The main issue with all the computer based algorithms is that they are no more than first

order approximations of the actual secondary structure which would occur in nature. The

determination of secondary structure is by no means an exact science. Furthermore, the

structure which the algorithm calculates to be the optimal structure might not be the most

biologically correct. Many of the algorithms allow for suboptimal structures to be

calculated as well taking into account this anomaly.

Secondary Structure Elements

All RNA secondary structures are composed of several basic structural shapes which

occur naturally when RNA folds back on its self. These basic structures are usually

depicted as two dimensional pictures which indicate the positions where base pair bonds

occur. The regions where base pairs stack on top of each other and form into helical

regions are called stems or stacking pairs (See Figure 3.1 Stacking Pairs). Sections of

RNA which occur at either the start or end of the sequence that are not part of any

structure are called unstructured single strands or free ends (See Figure 3.1 Joint and 20

Free Ends). All other structures formed by RNA are variations of loop structures which

occur when a section of RNA loops around on its self and is bounded by base pairs.

Hairpin loops are loops which occur at the end of a stem and consist of three or more

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base pairs because a three base pair loop is the smallest biologically feasible loop (See

Figure 3.1 Hairpin Loop). Hairpin loops are sometime referred to as stem loops but the

hairpin name stuck because the loops resemble a hairpin. A bulge which occurs in a

single strand of a stem is referred to as a bulge loop (See Figure 3.1 Bulge). When

bulges occur on both strands of a stem an interior loop is formed (See Figure 3.1

Interior Loop). When loops occur which have three of more branches (stems) extending

out of the loop then a multi-branched loop of formed (See Figure 3.1 Multiple Loop).

The last type of loop is referred to as a pseudoknot. A pseudoknot occurs when bases

inside a loop are bonded with bases in another section of the RNA which is outside the

bounding stem of the loop. Psuedoknots occur relatively infrequently as compared to the other

RNA structure elements.

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Secondary Structure Visualizations

The human mind is not capable of comprehending problems which have large amounts of

data in strictly numerical or character formats. The RNA secondary structure problem is

one such problem where any given RNA sequence could be hundreds of bases in length

and that fact makes it necessary to devise methods to help the human mind comprehend

the data. In order to increase the comprehension of the RNA secondary structure

problem, many types of visualizations have been devised which represent the data in

alternate formats so that the largest amount of intuitive understanding can be gained from

the depictions.

String Representation

The simplest form of representing an RNA sequence is strictly by the bases which make

up the sequence (See Figure 3.2). A string is formed where the characters in the string

represent the four RNA bases. The characters in the string are positioned as to represent

the ordering of the bases in the RNA sequence.

AACGGAACCAACAUGGAUUCAUGCUUCGGCCCUGGUCGCG

RNA in String Representation

Bracketed Representation

The bracketed representation is one of the simplest ways to visualize the secondary

structure of RNA. This representation is sometimes referred to as bracket dot notation

because of the brackets and dots used in the representation [31]. The bracketed

representation consists of using the string representation of RNA on one line and then on

33

a line directly below the string representation a sequence of open or close brackets and

dots are used to represent nucleotides which are bonded as pairs (See Figure 3.3). If a

bond exists between nucleotides at position i and position j then an open bracket ‘(‘ is

used at position i and a close bracket ‘)’ is used at position j to represent the bond. If no

bond exists then a dot is placed at the nucleotide position to represent that no bond exists. 22

For every open bracket there must be a corresponding closing bracket to represent the

pairs.

AACGGAACCAACAUGGAUUCAUGCUUCGGCCCUGGUCGCG

(())(((.)(()))((((()).())(())))((..)).).

Figure 3.3. Secondary Structure in Bracketed Representation

3.5.3 Linked Graph Representation

For the linked graph representation the nucleotide bases are drawn on a line at equidistant

intervals. Arcs are then drawn which connect base pairs which have bonds [18]. This

representation makes if very easy to determine if pseudoknots exist by examining the

graph for arcs that cross one another. If any arcs cross then a psuedoknot exists.

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Planar Graph Representation

The planar graph representation is the most intuitive representation. This is the closest

approximation to what the actual two dimensional structures would look under natural

conditions. The planar graph is merely a topology therefore structures drawn in the graph

which visually seem close together may actually be distant in reality.

Tree Representation

The tree representation of RNA secondary structure not only produce a visual display of

the secondary structure but also allows mathematical properties of tree theory to be used

in the process of examining the tree. The tree graph is actually a forest where paired

35

bases correspond to internal nodes. The labels on the internal nodes are the bases which

are paired. The leaf nodes correspond to the unpaired bases whose label is a single base

[20]. One useful operation that can be performed on two forest graphs is creating a

mathematical value which represents how similar two forests are.

Circular Representation

The circular representation of secondary RNA structure can be thought of as an extension

to the Linked Graph representation where the ends of the string have been wrapped

around into a circle [8]. The circular representation uses a circle and then places the

nucleotide bases at equidistant intervals around the circle. Chords are then drawn on the

interior of the circle between base pairs that form a bond. This representation also allows

for easy visual detection of pseudoknots by examining the graph for any chords that

intersect. If any chords intersect then a pseudoknot is present. This representation was

first devised by Ruth Nussinov.

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Matrix Representation

The matrix representation is a visual representation of the dynamic programming matrix

which is created in algorithms like Nussinov’s [1]. The nucleotide bases are listed

horizontally and vertically along the edges of the matrix and then the scores from the

algorithm fill the interior of the matrix. Some representations show the trace back path

through the matrix as a color scale path.

37

Dot Plot Representation

A dot plot is a graph which is setup as a triangular array. The RNA sequence it places

along the to axis of the triangular array and a dot is placed in the graph corresponding to

pair i,j at the ith row and jth column. Dot plots are generally used for comparative

analysis because many dot plots can be superimposed on the same graph where the plot

can be easily compared.

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Mountain Plot Representation

The roots of the mountain plot were originally devised in a paper written by Paulien

Hogeweg in 1984 [22]. Later, another related paper written by Danielle Konings in 1989

furthered the mountain plot “by identifying ‘(‘, ‘)’, and ‘.’, with “up”, “down”, and

“horizontal”, respectively” [18]. The mountain plot is represented by three elements:

peaks, plateaus, and valleys. The peaks correspond to hairpin loops, the plateaus

correspond to unpaired bases and the valleys indicate either unpaired sequences between

the branches of a multi-branch loop or unpaired sequences which join components of the

secondary structure.

39

Identifying secondary structure constraints for initial RNA structures

The number of possible conformations for even small RNA molecules is immense, requiring

sampling methods to efficiently search the conformational space for prediction of native-like

structures. One strength of Fragment Assembly of RNA (FARNA) (Das and Baker 2007) is its

ability to restrict the searched conformational space by exploiting correlations between torsion

angles in RNA fragments extracted from the database of high resolution structures. Even using

FARNA, however, conformational sampling remains the computational bottleneck for accurate

structure prediction.

To reduce the size of the conformational space sampled, RSIM uses secondary structure

constraints to generate a diverse set of initial tertiary structures for refinement with Monte Carlo

simulations. For benchmarking against FARNA, the top ten suboptimal secondary structures as

predicted by the ViennaRNA thermodynamic folding algorithm (Hofacker et al. 1994) were

used. However, constraints can be generated from any secondary structure prediction program.

40

The web server CompaRNA (T Puton, K Rother, Ł Kozłowski, E Tkalińska, and J Bujnicki, in

prep.) provides a continuously updated ranking of RNA secondary structure prediction programs,

with CentroidFold (Hamada et al. 2009; Sato et al. 2009), Contrafold (Do et al. 2006), and

McQFold (Metzler and Nebel 2008) performing the best in pairwise comparison tests. MC-Fold

(Parisien and Major 2008) is not currently evaluated by the CompaRNA server but is also an

excellent choice.

Generating tertiary structures with guided fragment assembly

Programs such as RNA2D3D (Martinez et al. 2008) can rapidly generate a tertiary structure for a

given secondary structure. However, the generated tertiary structure often has steric clashes or

unrealistic loop conformations. Additionally, we sought to generate a diverse set of initial

tertiary structures, a goal not currently possible in RNA2D3D. RSIM provides an automated

method to convert a secondary structure into a series of fragment assembly steps to ensure that

each base-pairing constraint is met.

The process of converting a secondary structure into fragment assembly steps begins with the

hairpin loops in the secondary structure. For each hairpin loop, RSIM chooses a midpoint

between the first and last nucleotide in the loop. The position of this midpoint is guided by the

observation that the distance traveled by 3 nt along the axis perpendicular to the plane of the first

nucleotide has two discrete distributions (Fig. 2). One distribution is centered at 3.5 Å, while the

other is centered at 7.0 Å. These discrete step sizes set a maximum asymmetry of 2:1 or 1:2 for

the number of nucleotides 3′ and 5′ of the midpoint position. All possible positions of the

midpoint occurring at or between nucleotides that preserve this ratio are identified.

Secondary sequence of the miRNA

41

Multidimensional scaling (MDS) is a set of related statistical techniques often used

in information visualization for exploring similarities or dissimilarities in data.

42

An MDS algorithm starts with a matrix of item–item similarities, then assigns a location to each

item in N-dimensional space, where N is specified a priori. For sufficiently small N, the resulting

locations may be displayed in a graph or 3D visualisation.

Visualizing Tertiary Structures

1. After obtaining the secondary structures using MC-fold, we submit the lowest energy

structure into MC-sym to calculate the list of tertiary structures.

2. The output is in the form of a list of pdb files which are arranged in ascending order of

lowest to higher energies.

3. We choose the lowest energy pdb file and visualize it using PyMOL and Discovery

Studio 3.1

4. We see that PyMOL gives fragmented structure whereas Discovery Studio non-

fragmented structure.

43

Tertiary Structure of pre-miRNA sequence

44

Tertiary structure of miRNA sequence

45

ALIGNMENT

Alignment of pre-miRNA and miRNA

Alignment Inferences

• After alignment in PyMOL, RMSD = 16.565

• 539 atoms aligned

• Such high RMSD indicates that the structure of the miRNA had undergone a drastic

confirmational change after the splicing by dicer.

Thermodynamic Data

• Two different webservers were used to calculate secondary structures namely, MC-fold

and MFold.

• MC-fold gave a lowest energy state of -13.4 KD and Mfold gave -12.8 KD.

46

• After superimposition of pre-miRNA and miRNA-5p, the RMSD came out to be 12.6

from PyMOL, which is quite high.

Validation using MolProbity

• 1 chain(s) is/are present [1 unique chain(s)]

• A total of 21 residues are present.

• 21 nucleic acid residues are present.

• Explicit hydrogens are present.

• 385 PDBv2.3 atoms were found. They have been converted to PDBv3.

Multi-criterion visualizations

We see that there are bad overlaps, length and angle deviations. So we have to go for

molecular dynamic simulations for refinement of the structure.

47

As disease-specific miRNAs are identified, the validation of novel targets within a disease

pathway of interest may lead to novel therapeutic strategies. Therefore, it is critically important

to be able to identify and validate miRNA/mRNA target pairs. Although not perfect,

computational algorithms and ΔG analyses allow for the identification of putative

miRNA/mRNA targets. Once identified, the authenticity of a functional miRNA/mRNA target

pair can be validated by fulfilling four criteria. First, miRNA/mRNA target interaction must be

verified. Second, the predicted miRNA and mRNA target gene must be co-expressed. Third, a

given miRNA must have a predictable effect on target protein expression. That is, if a gene is a

true target of a given miRNA, its miRNA mimic will decrease the target gene expression level

while a miRNA ASO inhibitor will increase the target gene expression level. Fourth and final,

miRNA-mediated regulation of target gene expression should equate to altered biological

function.

Results and Discussion

• Tertiary structure prediction is essential for drug design.

• We can synthesize crystals depending upon the validation study and inject it into the

bloodstream which will alter the respective metabolic pathways in autism.

• If a miRNA sequence is suppressed, the drug will try activating it.

• If over–activated, drug tries to compensate by decreasing its activity.

48

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