Define genome, proteome, and transcriptome — Hinglish notes

6.1.1 · Biology › Genomics

Overview

Genome, proteome, aur transcriptome — ye teeno ek cell ki fundamental molecular inventories hain, har ek gene expression ke alag stage par genetic information ko capture karta hai. In concepts ko samajhna genomics, systems biology, aur personalized medicine ke liye bahut zaroori hai.

Core Concepts

Mathematical Relationship (Information Flow)

Formula Genome se Proteome tak

Chalo quantitative relationships derive karte hain:

Step 1: Genome size $G$ define karo (total base pairs) $G = G_{genic} + G_{intergenic}$ jahan $G_{genic}$ wo DNA hai jo genes ne occupy kiya hua hai (protein-coding AUR non-coding RNA genes, unke introns samet), aur $G_{intergenic}$ genes ke beech bacha hua regulatory/repetitive DNA hai.

YE alag kyun karte hain? Protein-coding genes AUR non-coding RNA genes (rRNA, tRNA, miRNA, lncRNA) dono functional transcripts produce karte hain. Sirf $G_{genic}$ directly transcribe hota hai. Note karo ki protein-coding exon fraction (wo sequence jo actually protein specify karti hai) human genome ka sirf ~1.5% hai — full genic fraction se kaafi chhota, kyunki zyaatar genic DNA introns hai.

Step 2: Genes ki number $N_{g}$ ka genic DNA se relation: $N_{g} \approx \frac{G _{genic}}{L _{avg}}$ jahan $L_{avg}$ average full gene length hai (~27,000 bp humans ke liye, introns samet).

YE full gene length use kyun kar rahe hain (1.5% exon figure nahi)? Internally consistent rehne ke liye, numerator aur denominator dono ko same cheez refer karni chahiye: poore genes (introns + exons). Hum exonic 1.5% ko full gene length se DIVIDE nahi karte — wo do alag measures ko mix kar dega. (Cross-check: $20, 000 \times 27, 000 \approx 5.4 \times 1 0^{8}$ bp ≈ genome ka 17% genic hai, jo known estimates se match karta hai.)

Step 3: Time $t$ par transcriptome size $T$ : $T (t) = \sum_{i = 1}^{N_{g}} n_{i} (t) \cdot L_{i}$ jahan:

$n_{i} (t)$ = time $t$ par gene $i$ ke transcript copies ki number
$L_{i}$ = mature transcript $i$ ki length (splicing ke baad)

YE form kyun? Har gene apne expression level AUR apne mature transcript length ke hisaab se proportionally contribute karta hai. Ek highly expressed gene with 1000 copies total RNA mein zyada contribute karta hai ek lowly expressed gene with 10 copies se.

Step 4: Proteome size $P$ (distinct protein species ki number): $P \geq N_{g}^{coding} \cdot S_{avg} \cdot M_{avg}$ jahan:

$N_{g}^{coding}$ = protein-coding genes ki number (~20,000 humans mein)
$S_{avg}$ = har gene ke average splice isoforms (~3-5 humans mein)
$M_{avg}$ = har protein ke average distinct co-existing modification states. Zyaatar proteins ke liye ye chhota hota hai (roughly 1–3 dominant states); sirf minority of heavily regulated proteins mein kaafi combinatorial states hoti hain.

YE inequality kyun? Ye ek lower bound hai. Protein complexes aur rare highly-modified proteins aur diversity add karte hain jo in average multipliers se capture nahi hoti.

Worked example Worked Example 1: Prokaryote (

E. coli) Given: E. coli genome = 4.6 × 10⁶ bp, ~4,300 genes Find: Theoretical transcriptome size agar saare genes equally 10 copies each par express hon.

Solution:

Step 1: Average gene length
L_avg = 4.6 × 10⁶ bp / 4,300 genes 
     ≈ 1,070 bp per gene

WHY this step? We need transcript length. In prokaryotes, 
genes have no introns and the genome is gene-dense, so 
gene length ≈ mRNA length.

Step 2: Total transcriptome size
T = Σ(copies × length) for all genes
 = 4,300 genes × 10 copies × 1,070 bp
 = 4.6 × 10⁷ bp = 46 Mb of RNA

WHY? Each gene contributes (copies × length) to total RNA.
Step 3: Compare to genome
T/G = 46 Mb / 4.6 Mb = 10×

Interpretation: Transcriptome total nucleotides mein genome se 10× bada hai kyunki har gene multiple times copy hoti hai.

Worked example Worked Example 2: Human Liver Cell vs Neuron

Given: Dono cells ke identical genomes hain (3.2 Gb, ~20,000 genes) Scenario:

Liver cell: 5,000 genes active, detoxification ke liye enzymes express kar rahi hai
Neuron: 8,000 genes active, synaptic proteins express kar raha hai

Find: Identical genomes ke bawajood proteomes alag kyun hain?

Solution:

Step 1: Transcriptome differences
Liver: Genes for P450 enzymes highly transcribed
      (albumin, cytochrome P450 family: 100-1000 copies each)
Neuron: Genes for neurotransmitter receptors highly transcribed
       (NMDA receptors, ion channels: 100-1000 copies each)

WHY different? Gene expression is REGULATED. Transcription 
factors activate different gene sets in each cell type.

Step 2: Proteome consequences
Liver proteome: Rich in detoxification enzymes
               ~10,000 protein species (lower splicing complexity)
Neuron proteome: Rich in signaling molecules
               ~13,000 protein species (neurons use extensive 
               alternative splicing for receptor diversity)

WHY more protein species than transcripts? Alternative splicing 
and post-translational modifications multiply one transcript
into several protein variants.

Key insight: Same genome → alag transcriptomes → alag proteomes. Gene regulation cellular identity banata hai.

Worked example Worked Example 3: Temporal Dynamics - Heat Shock Response

Scenario: E. coli ko 37°C se 42°C par shift kiya gaya

Time course (E. coli ke apne chaperones DnaK (ek Hsp70-family protein) aur GroEL (Hsp60-family) use karke):

t = 0 min (before heat):
 Heat shock genes (dnaK, groEL): 1-2 transcripts each
 Proteome: ~2,000-3,000 protein species; DnaK present 
           at a modest baseline level
t = 5 min (during heat):
 Heat shock genes: 50-100 transcripts each
 WHY? Heat activates the alternative sigma factor σ³² (RpoH), 
 which redirects RNA polymerase to heat-shock promoters
 Transcriptome has changed, but proteome lags 
 (translation takes time)
t = 15 min:
 Heat shock transcripts: still elevated
 Proteome: DnaK rises several-fold above baseline. 
 (Total protein content of one E. coli cell is only 
 ~2-4 × 10⁶ molecules, so DnaK stays well within that budget.)
 WHY the delay? Translation and accumulation are gradual
t = 60 min (return to 37°C):
 Heat shock transcripts: back to 1-2 (rapid mRNA decay)
 Proteins: still elevated (protein decays slowly)
 WHY proteins persist? Protein half-life = many minutes to hours, 
 mRNA half-life = a few minutes

Lesson: Transcriptome changes fast hote hain (minutes mein), proteome changes slow hoti hain (minutes-to-hours). Isse cellular responses mein ek temporal lag create hota hai.

Common Mistakes

Common mistake Mistake 1: "Zyada DNA = Zyada Genes = Zyada Complexity"

Galat intuition: "Humans ka genome 3.2 Gb hai, E. coli ka 4.6 Mb, toh humans mein ~700× zyada genes honge." Ye sahi kyun lagta hai: Bada genome → zyada information capacity → zyada genes (linear relationship assume ki gayi).

Haqeeqat:

Humans: 3.2 Gb → ~20,000 protein-coding genes (0.006 genes/kb)
E. coli: 4.6 Mb → ~4,300 genes (0.93 genes/kb)
Ratio: humans mein sirf ~5× zyada genes hain, 700× nahi

YE paradox kyun? C-value paradox: genome size organism complexity se correlate nahi karta. Humans mein vast non-coding regions hain (introns, repetitive DNA). E. coli gene-dense hai (~87% coding).

Fix: Complexity in cheezon se aati hai:

Alternative splicing (ek gene → kai proteins)
Regulatory complexity (gene networks)
Post-translational modifications
Non-coding RNA genes (regulatory transcripts jo protein-coding genes mein count nahi hote)

Human proteome (bahut bada) >> E. coli proteome (~4,000 species), chahe gene count similar ho.

Common mistake Mistake 2: "Transcriptome = Proteome"

Galat intuition: "Agar main saari mRNA sequence kar loon (transcriptome), toh main saare proteins jaanta hoon (proteome)." Ye sahi kyun lagta hai: Central dogma kehta hai DNA → RNA → protein, toh RNA directly protein predict karta hai.

Haqeeqat: Transcript levels aur protein levels ke beech correlation sirf ~0.4-0.6 hai (moderate).

Disconnect kyun?

Translational control: Kuch mRNAs sequester ho jaati hain aur translate nahi hoti (jaise eggs mein maternal mRNAs)
Protein stability: Ek short-lived protein (half-life 10 min) accumulate nahi hoga chahe uski mRNA abundant ho
Post-translational modifications: Ek mRNA → kai protein forms (phosphorylated, glycosylated, etc.)

Example:

Reticulocytes mein β-globin mRNA: high copy number
β-globin protein: bahut high levels par accumulate hoti hai
KYU? High translation rate + bahut stable protein

Ulta:

Cancer cells mein c-Myc mRNA: elevated
c-Myc protein: mRNA ke relative kaafi kam amplified
KYU? Rapid protein degradation (half-life ~20 min)

Fix: Accurate proteome data chahiye toh proteins directly measure karo (mass spectrometry). Transcriptome ek proxy hai, mirror nahi.

Active Recall Practice

Recall Feynman Technique: 12-saal ke bachche ko samjhao

Socho tumhara body trillions of tiny factories (cells) se bani hai. Har factory mein teen important cheezein hain:

Genome ek giant recipe book ki tarah hai jo factory ke vault mein rakhi hai. Isme SAARI recipes (instructions) hain har tool aur machine banane ke liye jo factory ko kabhi bhi chahiye ho sakti hai. Tumhare body ki har factory ke paas same complete recipe book hai (isliye har cell mein same DNA hoti hai).
Transcriptome un recipes ki photocopies ki tarah hai jo workers abhi ABHI use kar rahe hain. Factory vault ki book directly nahi kholti — balki workers wo recipes photocopy karte hain jo aaj chahiye hain. Ek brain factory "thinking molecules" ki recipes photocopy karti hai. Ek stomach factory "digestion molecules" ki recipes photocopy karti hai. Same recipe book, alag photocopies!
Proteome actual tools aur machines hain jo un recipes se bane hain. Ye physical cheezein hain jo kaam kar rahi hain: scissors kaat rahe hain, hammers thok rahe hain, conveyor belts chal rahe hain. Ek recipe (photocopy) ko same tool 100 baar banane ke liye use kiya ja sakta hai, ya workers tool ko modify karke alag versions bana sakte hain (jaise hammer mein handle add karna).

Key insight: Recipe book kabhi nahi badalti, lekin kaun si recipes photocopy karo aur kaun se tools banao — ye har second badalta hai based on factory ko kya karna hai. Isliye tumhari muscle cells tumhari brain cells se bilkul alag dikhti hain — same recipe book, kya banana hai iske totally different choices!

Connections

Central Dogma of Molecular Biology - genome → transcriptome → proteome ka flow
Gene Expression Regulation - cells decide karte hain kaun se genes transcribe karne hain
Alternative Splicing - mechanism jo proteome ko gene count se aage expand karta hai
Post-Translational Modifications - proteome ko aur diversify karta hai
Non-coding RNA - non-protein-coding genes ke functional transcripts
RNA-Seq Technology - transcriptome measure karne ka method
Mass Spectrometry Proteomics - proteome measure karne ka method
Systems Biology - genome, transcriptome, proteome data ko integrate karta hai
C-Value Paradox - genome size organism complexity predict nahi karta
Single-Cell Genomics - tissues ke andar transcriptome/proteome heterogeneity reveal karta hai

Diagram

#flashcards/biology

Genome kya hai? :: Kisi organism mein genetic material (DNA) ka complete set, jisme protein-coding genes, non-coding RNA genes, aur saare non-coding/regulatory sequences shamil hain

Transcriptome kya hai? :: Ek specific time par cell mein RNA transcripts (mRNA, rRNA, tRNA, non-coding RNA) ka complete set

Kya sirf protein-coding genes functional transcripts produce karte hain?

Nahi — non-coding RNA genes (rRNA, tRNA, miRNA, lncRNA) bhi functional transcripts produce karte hain

Proteome kya hai?

Ek given time par kisi cell, tissue, ya organism mein express hone wale proteins ka entire complement, jisme saare isoforms aur modifications shamil hain

Proteome genes ki number se bada kyun hota hai?

Alternative splicing ek gene se multiple mRNA isoforms banata hai, aur post-translational modifications additional protein variants create karti hain

Genome dynamic hai ya static?

Static — ye kisi organism ki saari cells mein mostly same rehta hai (mutations ke alawa)

Transcriptome aur proteome dynamic hain ya static?

Dynamic — ye cell type, developmental stage, environmental conditions, aur time ke hisaab se change hote hain

C-value paradox kya hai?

Ye observation ki genome size organism complexity se correlate nahi karti; humans mein E. coli se sirf ~5× zyada genes hain jabki ~700× zyada DNA hai

Transcriptome proteome perfectly predict kyun nahi karta?

Kyunki translational control, protein stability mein differences, post-translational modifications, aur variable translation rates hoti hain

Environmental stimulus ke baad kaun zyada jaldi badalta hai: transcriptome ya proteome?

Transcriptome (minutes mein) kyunki mRNA rapidly banta aur degrade hota hai; proteome zyada dheere badalta hai kyunki proteins generally zyada stable hote hain

E. coli kaunse heat-shock chaperones use karta hai, aur unhe kya activate karta hai?

DnaK (Hsp70-family) aur GroEL (Hsp60-family), heat stress par alternative sigma factor σ³² (RpoH) se activate hote hain