Paul Gardner Flashcards

Question

why do we want to induce error during SELEX?

Answer 1

you want a lot of variants within that population not just original copy idea is using SELEX to generate RNAs with novel properties

Answer 2

use SELEX approach to select for things that bind fluorogens which are small-molecule dyes that fluoresce by binding aptamers (e.g. RNA) has allowed discovery of lots of fluorescent RNAs that fluoresce at diff wavelengths and fold into specific structures with lots of non-canonical bping to bind specific fluorogens can sequence population throughout SELEX to see evolutionary changes e.g. what is conserved as it goes on

Answer 3

one SELEX-like approach called directed evolution used same principles; induce mutations in protein-coding gene, insert into bacteria, select for those with desirable properties, repeat more rounds of this to evolve novel proteins

Answer 4

can be essential for function

Answer 5

RNA structures are module meaning they can be decomposed into subcomponents e.g. loops, stems, bulges - these are what we use for computational modelling

Answer 6

if not involved in watson-crick bp you put a dot and if it is you put a bracket

Answer 7

canonical - C-G, A-U (3/4 of RNA bp are canonical) non-canonical - G-U

Answer 8

under certain conditions every base pair can form i.e G will pair with A, C with C etc.

Answer 9

each nucleotide has three possible edges where each nt interaction can occur - watson-crick edge (most common) - sugar edge - hoogsteen edge these interactions can also occur in cis (normal bp) or trans (both sugars same direction) this means we have 18 possible pairing relationships based purely on geometry i.e. lots of diverse nt interactions

Answer 10

base stacking - RNA backbone is negatively charged so bases stack like coins in a roll non-canonical interactions underrated in their contribution to RNA structure

Answer 11

methods range in effort required and accuracy of result; more effort generally means more accuracy can predict secondary structure from: 1 - computational prediction e.g. free energy minimisation 2 - indirect experimental evidence e.g. chemical/ enzymatic probing 3 - direct evolutionary evidence e.g. comparative analysis 4 - direct structural evidence e.g. x-ray crysto, NMR, C-EM 1 and 2 more accessible, faster and generate more models 3 and 4 more effort but give more information and more accurate models

Answer 12

algorithm that maximises basepairs to find minimal free energy structures; total energy can be computed by summing energies for each structural component (e.g. stacks, loops) - all you have to do is decompose into structural components and enter sequence calculates most stable secondary structure that corresponds to that sequence; the more negative gibbs free energy the more stable energies can be looked up in tables derived from melting experiments; ground state (0) assumed to be completely unfolded i.e. no bping

Answer 13

accuracy is low and this method sucks because: energy parameters estimated from non-biological conditions and models extrapolated from limited experiments fails to account for a variety of things influencing RNA folding e.g. crowding of cellular environment, PTMs, folding kinetics, co-transcriptional folding, transcriptional pausing also you end up getting a lot of v different structures with similar energy values but this method is easy

Answer 14

often RNA structure conserved better than RNA sequence conserved RNA structure indicated from covarying base-pairs (cause negative selection preserves variation that maintains base-pairs) - identify these with deep alignments alignments can be pasted into RNAalifold which converts covaration measures to pseudoenergies; gives bonuses to stacks supported by covariation and penalises variation that is inconsistent with pairing; combines this with MFEs for each sequence and gives consensus secondary structure prediction

Answer 15

alphafold is an AI model that uses similar approach for protein predictions - build deep sequence alignments - find covarying sites - predict global structure there is currently no alphafold for RNA cause limited solved RNA structures and also RNA folding more complex cause six torsion angles (protein has 2)

Answer 16

structure-dependent modification of RNAs can impede polymerases use reagent that covalently modifies either paired or paired bases map fragments to full-length RNAs to infer features of RNA structure; can tell if the nt is paired or unpaired based on what reagent used info can then be used to improve or constrain MFE structure predictions

Answer 17

e.g. X-ray crysto, NMR, cryo-EM these are ideal as RNA is challenging; flexible ribose+phosphate backbone, weak long-range tertiary interactions, alternative conformations and multiple functional states

Answer 18

up to 90% significant GWAS results lie in non-coding regions; roughly half of these map to introns large scale screens for disease association often don't study non-coding SNPs as coding variants are enriched and can test for function much easier

Answer 19

proteins are generally single copy but many ncRNAs are multicopy with multiple paralogs or pseudogenes for most nuclear genes both parental copies expressed because of this ncRNAs robust to variation due to redundancy; hard to knockout w frameshift etc. cause another copy covers for it exception is 24 mitochondrial RNA genes as maternally inherited so genes single copy

Answer 20

mitochondrial transfer RNA genes especially susceptible as single copy and maternally inherited i.e. no redundant copies 22 mt-tRNAs and >350 mutations in these reported; phenotype can be complex; same mutation often results in v diff diseases and vice versa diseases associated with variants in mitochondrial genes tend to affect intensive processes e.g. muscle and brain function

Answer 21

if you a carrier of a mitochondrial syndrome and wanna have kids can do this take donor eggs, take out nucleus leaving maternal mitchondria, put your own nucleus in and use IVF to produce embryo

Answer 22

required for maturing rRNAs; guide covalent modifications of target rRNAs and snRNAs; some may regulate splicing events two main classes; H/ACA box and C/D box snoRNAs; called this cause carry motifs motifs: H - ANANNA; C - AUGAUGA; D - CUGA i.e. H(ANANNA) which is a hinge and an ACA tail evolutionarily conserved sites imply important interactions

Answer 23

some have been well characterised in terms of function (people have figured out their targets) some are orphans i.e. no known targets about 17% C/D box and 16% H/ACA box snoRNAs are orphans including SNORD116

Answer 24

C/D box orphan that has strong link with prader-willi syndrome which results from a paternal deletion on chromosome 15 i.e. imprinted locus; characterised by weak muscles and developmental issues in newborns; constant hunger in adults and physical deformities SNORD116 has 29 tandomly repeated copies i.e. multicopy but all located on same part of genome hence why deletion takes them out --> PWS function and targets of SNORD116 unknown; recent research may have found a SNORD116 target; potentially influences expression of 200 mRNAs

Answer 25

target different splice sites and comprised of slightly different RNAs (minor has U11 and U12 while major has U1 and U2, both have U5); there is a lot of homology between them tho diff stages of formation referred to as diff complexes; key one is B complex (tri-snRNP) which is critical for function; forms on the mRNA at site of intron being removed

Answer 26

autosomal recessive disorder characterised by developmental issues caused by mutations in single copy RNU4ATAC gene encoding U4atac/U6atac snRNA leading to decreased formation of tri-snRNP complex resulting in small splicing defect and retention of introns usually removed by minor spliceosome U4atac and U6atac have long region of complementarity and bind each other to form part of tri-snRNP complex - most variants linked to MOPDI affect formation of stem loop severe effect; death by age 3

Answer 27

non-synonymous changes in proteins are enriched and easier to test can be difficult to discover mechanisms of ncRNA function large numbers of paralogs and pseudogenised copies of ncRNAs make identifying variants difficult exome sequencing (protein-coding exons) has dominated large-scale efforts to connect genetic variation w disease