Review Flashcards

RNA-Based Gene Regulation

mRNA Secondary Structures

RNA molecules can fold into secondary structures, which can regulate gene expression. Examples include:

• Terminator sequence in the 5' UTR of the trp operon (attenuation).
• Riboswitches.

Some structures can be unwound by helicases, while others directly influence translation or transcription.

Riboswitches

• Found in bacteria, archaea, fungi, and plants (not animals).
• Located in the 5' UTR of an mRNA.
• Bind a ligand (regulatory molecule like ions, nucleotides, or amino acids).
• Upon binding, the structure of the mRNA changes (refolds), affecting:
  • Transcription: May cause premature termination (in prokaryotes).
  • Translation: May hide or expose the ribosome binding site (in prokaryotes and eukaryotes).
• Riboswitches contain an aptamer domain that binds the ligand.

Antisense RNA (asRNA)

• Long non-coding RNAs that are complementary to a target mRNA.
• Bind to mRNA to prevent translation (can span 50–1000+ nucleotides).
• May originate from another gene or the non-template strand of the target gene.
• Found in both prokaryotes and eukaryotes.

RNA Interference (RNAi)

• Evolved as a defense against double-stranded RNA viruses.
• Key enzymes and complexes:
  • Dicer: Cuts dsRNA into small interfering RNAs (siRNAs) or microRNAs (miRNAs).
  • RISC (RNA-induced silencing complex): Uses siRNAs/miRNAs to bind complementary mRNA.
    • Binding results in mRNA degradation or translation inhibition.

miRNA (microRNA)

• ~21–25 nucleotides long, single-stranded RNA.
• Transcribed from different genes (not from the target).
• Binds to mRNA imperfectly, blocking translation without degradation.
• Functions in gene regulation.

siRNA (small interfering RNA)

• ~21–25 nucleotides, derived from the target gene itself.
• Binds perfectly to the target mRNA.
• Causes mRNA degradation, leading to no translation.
• Can also act via RITS (RNA-induced transcriptional silencing) to:
  • Recruit methyltransferase enzymes.
  • Methylate DNA → epigenetic silencing.

Experimental RNAi

• Scientists can artificially introduce dsRNA into cells or tissues.
• Dicer processes this RNA, which activates the RNAi pathway.
• Used for targeted gene knockdown in research or medicine.
• Effects are temporary and localized to the treated area.

Long Noncoding RNA (lncRNA)

• Transcripts ≥200 nucleotides (can be over 10 kb).
• Can bind to DNA, RNA, or proteins to regulate gene expression.
• Example: Xist (X-inactive specific transcript).
  • Coats one X chromosome in females.
  • Leads to heterochromatin formation → X inactivation.
  • Inactivation is random (Lyon hypothesis) and happens in early development.
  • Explains mosaic expression of X-linked genes.

Chromosomal Variants and Rearrangements

General Concepts

• Chromosomal variants are larger-scale mutations compared to point mutations.
• Can be:
  • Balanced (no net gain/loss of genetic info).
  • Unbalanced (deletion/duplication = gene dosage change).
• Common forms include duplications, deletions, inversions, and translocations.

Duplication

• A chromosomal segment is copied and inserted.
• Types:
  • Tandem: adjacent to the original.
  • Dispersed: elsewhere on the chromosome or genome.
• Can create copy number variants (CNVs) and paralogous genes.
• Increases gene dosage, which may alter phenotype.
• Paralogs: duplicated genes within a species that evolve new functions.

Deletion

• Loss of a chromosome segment.
• May be:
  • Visible on a karyotype if large.
  • Lethal if homozygous.
• May expose recessive alleles in heterozygotes (pseudodominance).

Inversion

• A chromosome segment is flipped 180°.
• Types:
  • Paracentric: does not include centromere.
  • Pericentric: includes centromere.
• In heterozygotes:
  • Crossing over within the inversion can produce:
    • Unbalanced gametes (deletions/duplications).
    • Infertility (3–5% of infertile couples carry inversions).

Translocation

• Movement of a segment to a nonhomologous chromosome.
• Types:
  • Reciprocal: exchange between chromosomes.
  • Non-reciprocal: one-way movement.
• Can create complex pairing during meiosis, leading to unbalanced gametes.
• Robertsonian translocation: occurs at or near centromeres of acrocentric chromosomes (common in humans).

Mechanisms of Rearrangement

• Chromosomal breaks and faulty repair.
• Unequal crossing over.
• Transposable elements (TEs):
  • DNA transposons: cut and paste via transposase.
  • Retrotransposons: copy and paste via reverse transcription.

Polyploidy

Autopolyploidy

• Multiple sets of chromosomes from one species (e.g., 3N, 4N).
• Common in plants; rare and often lethal in mammals.
• Leads to:
  • Larger phenotypes due to gene dosage.
  • Sterility, especially in triploids.

Allopolyploidy

• Combines chromosome sets from two or more species.
• Initial hybrids are often sterile but may become fertile if chromosome pairing is preserved.
• Requires similar chromosome number and gene order (synteny).

Genomics

Genomics is the study of the content, organization, function, and evolution of genetic material across entire genomes.

Types of Genomics

• Structural Genomics: Focuses on the sequence and arrangement of genes within a genome.
  • Key questions: What is the full sequence of a genome? Where are genes located, and how are they arranged?
  • Methods: DNA sequencing (e.g., Illumina), bioinformatics for assembly and annotation.
  • Products: Assembled genomes, gene annotations.
• Functional Genomics: Explores how genetic variation influences phenotypic traits.
  • Key questions: What proteins and RNAs are encoded by genes? When and where are they expressed?
  • Methods: SNP analysis, RNA expression profiling, GWAS, statistical comparisons.
  • Products: Candidate genes linked to traits for further testing.
• Comparative Genomics: Compares gene content and structure across species to understand evolutionary changes.
  • Looks at similarities and differences in genes and their organization among organisms.

Genome Assembly

A genome assembly is the most current version of the entire genome sequence of an organism.

Steps in Genome Assembly

1. Shotgun Sequencing:
   • Tissue is collected, DNA is extracted, and fragmented.
   • Short DNA pieces are sequenced using high-throughput technologies like Illumina.
2. Alignment of Reads:
   • Short reads are aligned using sequence overlaps to build contigs (continuous sequences).
   • Regions with high read depth (number of times a base is sequenced) have greater alignment confidence.
   • Repetitive sequences are difficult to align due to similar overlapping regions.
3. Scaffold Construction:
   • Contigs are aligned to known genetic markers and mapped onto chromosomes.
   • Gaps in scaffold sequences are often denoted with “N”s.

Gene Annotation

Gene annotation identifies and describes genes and functional regions in the genome.

Ab Initio Prediction

• Uses bioinformatics to search for necessary gene components:
  • Open reading frames (ORFs): sequences starting with AUG and ending with TAA, TAG, or TGA, capable of coding proteins.
  • Regulatory motifs:
    • Prokaryotes: promoters, terminators, Shine-Dalgarno sequences.
    • Eukaryotes: TATA box, regulatory promoters, splice site signals (5' and 3'), poly-A signals, and CpG islands (often at gene 5' ends).

Homology-Based Annotation

• Uses known expressed genes or protein domains to predict new gene functions.
• Aligns genome sequence to mRNA/cDNA (e.g., from RNA-seq).
• Identifies only expressed genes in a given tissue.
• Can also detect conserved protein domains (e.g., zinc finger = DNA-binding protein).

Functional Genomics

Functional genomics identifies genetic variants associated with phenotypes.

Key Goals

• Associate traits (phenotypes) with specific genetic variants.
• Understand gene expression: what genes are turned on/off, when, where, and how much.

How Trait Association Works

1. Choose contrasting groups (differ by phenotype):
   • Family-based studies: within the same lineage.
   • GWAS (Genome-Wide Association Studies): compare unrelated individuals.
   • Quantitative breeding experiments: cross individuals with trait differences to study offspring.
2. Characterize Genetic Variation:
   • Study SNPs (single nucleotide polymorphisms).
   • SNPs can be inherited or arise from mutations.
   • Used as markers, even if not causative.
3. Use Statistics to Find Associations:
   • Determine if a SNP is significantly more common in affected individuals.
   • Represented using Manhattan plots:
     • X-axis: SNP position on genome.
     • Y-axis: significance of association.
     • SNPs in close proximity may be associated due to linkage (inherited together).

Technologies to Detect Genetic Variants & Expression

DNA Microarrays (DNA Chips)

• Detect an individual's SNPs at thousands of known locations.
• Method:
  • Single-stranded DNA probes fixed on a glass slide.
  • Sample DNA is fragmented, amplified, denatured, and hybridized to probes.
  • Fluorescently labeled nucleotides bind to reveal SNP identity.
• Example: Affymetrix 6.0 DNA chip (906,600 SNPs on autosomes, X/Y, mitochondria).

Genomic Resequencing

• No prior SNP knowledge needed.
• Whole-genome sequencing of an individual using Illumina.
• Align to a reference genome to find new SNPs and CNVs (Copy Number Variants) based on read depth.

RNA Microarrays

• Measures gene expression levels across samples.
• Steps:
  • Extract RNA → reverse transcribe to cDNA.
  • cDNA labeled and hybridized to probes.
  • Fluorescent signal reflects transcription level.
• Competitive hybridization can be used to compare expression between two samples.

RNA Sequencing (RNA-seq)

• Provides a quantitative and comprehensive look at transcription.
• Steps:
  • RNA → cDNA via reverse transcription.
  • cDNA sequenced via Illumina.
  • Aligned to genome:
    • Shows which genes are expressed.
    • Reveals alternative splicing and exon usage.
    • Read depth = expression level.

Visualizing Gene Expression

Volcano Plots

• Each point = a gene.
• X-axis: log-fold change in expression between groups.
• Y-axis: statistical significance.
• Shows both magnitude and reliability of gene expression differences.

Heat Maps

• Grid format with color-coding of expression intensity.
• Columns: samples (grouped by condition).
• Rows: genes.
• Color scale reflects expression level per gene per sample.
• Useful for detecting expression patterns and clusters.

What Comes After Identifying Trait-Associated Variants

• Correlation ≠ Causation → test experimentally.

Experimental Validation Techniques

• CRISPR-Cas9: Edit SNP or sequence to test its role in phenotype.
• RNAi: Temporarily silence a gene to observe trait change.
• Transgenic/Mutant Models: Introduce mutations or genes into organisms (e.g., mice, cell lines) to assess effect on phenotype.

Genomics in Clinical Medicine

• Clinical genetic testing uses trait-associated variants to inform healthcare decisions.

Applications of Clinical Genomics

• Diagnosis of disease or identifying genetic basis of symptoms.
• Determining severity of conditions.
• Personalized medicine: Matching patients with treatments based on genetic profile.
• Risk prediction: Identifying individuals at increased risk for future disease.

Caveats in Genomic Medicine

• Most studies overrepresent European descent.
• Lack of diversity limits the ability to generalize findings to other populations.
• Important because allele frequencies, gene-environment interactions, and heterogeneity differ across populations.

Genetic Variation and Evolution

• Changes to genetic information (mutations) produce variation.
• Mutations accumulated across generations underlie current genetic diversity.
• Genetic variation patterns can be used to reconstruct evolutionary relationships.

Influence of Genetic Transmission, Expression, and Change on Populations

• Genetic study of populations involves:
  • Tracking allele frequencies
  • Understanding speciation
  • Using genetics to explore evolutionary relationships

Population Genetics

• Population genetics studies how genetic composition changes over time in response to evolutionary forces.
• A population is a group of individuals of the same species capable of interbreeding.
  • Populations may be physically isolated or may exist across a continuous distribution.

Genotype and Allele Frequencies

• Used to characterize genetic composition of populations.
• Comparing frequencies between populations allows detection of population differentiation.
• Tracking changes over generations helps detect evolution.

Quantifying Genetic Frequencies

Genotypic Frequency

• Determined from data when genotypes can be distinguished (e.g., codominance or using DNA sequences).

Allelic Frequency

• Can be calculated from observed genotypes.

Hardy-Weinberg Equilibrium (HWE)

A model that assumes an idealized population to predict genotype frequencies from allele frequencies.

Assumptions of HWE

• No migration
• Random mating
• No natural selection
• No mutation
• Infinitely large population

HWE Equations

• Allele frequency: p + q = 1
• Genotype frequencies: p^2 + 2pq + q^2 = 1
  • p^2 = frequency of homozygous dominant genotype
  • 2pq = frequency of heterozygous genotype
  • q^2 = frequency of homozygous recessive genotype

Determining Whether a Population Is Evolving

1. Define a null hypothesis: genotype frequencies are as predicted under HWE.
2. Compare observed genotype counts with HWE expectations.
3. Significant differences suggest evolutionary change.

Deviation from HWE in Real Populations

• Real populations may not meet all assumptions of HWE.
• Genomic analysis (e.g., in a Japanese population) showed that only 0.63% of SNPs deviated significantly from HWE.

Processes That Cause Deviation from HWE

Each factor represents a real-world evolutionary force that changes allele or genotype frequencies.

Non-Random Mating

• Mating is not random with respect to genotype at a given locus. Types:
  • Assortative mating: individuals mate with others of similar phenotype/genotype.
  • Disassortative mating: individuals mate with those of dissimilar phenotype/genotype.
  • Inbreeding: mating between related individuals (a specific case of assortative mating).

Coefficient of Inbreeding (F)

• Also called the fixation index.
• Represents the probability that two alleles are identical by descent.
• F ranges from –1 to 1

Genetic Drift

• Random changes in allele frequencies due to sampling error in small populations.
• More pronounced in smaller populations.
• Founder events and bottleneck events are major causes of drift.

Natural Selection

• Occurs when individuals with different phenotypes have different survival or reproductive success. Key formulas:
  • Relative Fitness (W): W = \frac{\text{avg. number of offspring of genotype}}{\text{avg. number of offspring of most fit genotype}}
    • Values range from 0 to 1
    • Higher W = greater fitness
  • Selection Coefficient (s): s = 1 - W
    • Measures strength of selection against a genotype
    • Values range from 0 to 1
    • Higher s = stronger selection

Migration (Gene Flow)

• Movement of alleles between populations. Effects:
  • Alters allele frequencies.
  • Prevents genetic divergence between populations.
  • Increases genetic variation within populations.
• Absence of migration can lead to speciation (genetic isolation).
• Restoration of gene flow may:
  • Reduce population uniqueness
  • Help spread beneficial alleles (e.g., disease resistance)

Mutation

• A change in DNA that results in a new allele.
• Key points:
  • Mutation is the ultimate source of all genetic variation.
  • Can be:
    • Beneficial
    • Neutral
    • Deleterious

Mutation frequency (μ):

• Probability that an allele is altered by a new mutation.
• Typical range: \mu = 10^{-5} \text{ to } 10^{-6} \text{ per gene per generation}
  • Especially common for loss-of-function mutations