01 Respiratory Chain Map
Jupyter notebook from the Condition-Specific Respiratory Chain Wiring in ADP1 project.
NB01: Respiratory Chain Condition Map¶
Project: Condition-Specific Respiratory Chain Wiring in ADP1
Goal: Extract all respiratory chain components from the ADP1 genome, build a condition × component heatmap showing which are essential on which carbon sources, and identify substrate-specific respiratory configurations.
Inputs: user_data/berdl_tables.db
Outputs:
data/respiratory_chain_genes.csv— all respiratory chain genes with growth data- Condition × component heatmap
- Condition-specificity analysis
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
import sqlite3
import os
DATA_DIR = '../data'
FIG_DIR = '../figures'
DB_PATH = '../user_data/berdl_tables.db'
GROWTH_COLS = [
'mutant_growth_acetate', 'mutant_growth_asparagine',
'mutant_growth_butanediol', 'mutant_growth_glucarate',
'mutant_growth_glucose', 'mutant_growth_lactate',
'mutant_growth_quinate', 'mutant_growth_urea'
]
CONDITION_NAMES = [c.replace('mutant_growth_', '') for c in GROWTH_COLS]
# Verify tables available
conn = sqlite3.connect(DB_PATH)
tables = pd.read_sql_query("SELECT name FROM sqlite_master WHERE type='table'", conn)
print(f'Available tables: {tables["name"].tolist()}')
conn.close()
Available tables: ['genome', 'genome_ani', 'genome_features', 'missing_functions', 'pan_genome_features', 'gene_reaction_data', 'genome_reactions', 'growth_phenotype_summary', 'growth_phenotypes_detailed', 'gene_phenotypes', 'ontology_terms', 'ontology_definitions', 'ontology_relationships', 'gene_essentiality', 'strains']
1. Extract All Respiratory Chain Components¶
Search by RAST function keywords AND KO/EC identifiers to avoid missing components (per plan reviewer recommendation).
In [2]:
conn = sqlite3.connect(DB_PATH)
# Broad search for respiratory chain genes
resp = pd.read_sql_query(f"""
SELECT feature_id, old_locus_tag, gene_names, rast_function, ko, ec, pfam,
essentiality_minimal, start, end, strand,
{', '.join(GROWTH_COLS)}
FROM genome_features
WHERE rast_function LIKE '%NADH%dehydrogenase%'
OR rast_function LIKE '%NADH%ubiquinone%'
OR rast_function LIKE '%NADH%oxidoreductase%'
OR rast_function LIKE '%NADH%FMN%'
OR rast_function LIKE '%NADH%flavin%'
OR rast_function LIKE '%cytochrome%oxidase%'
OR rast_function LIKE '%ubiquinol oxidase%'
OR rast_function LIKE '%Cytochrome O %'
OR rast_function LIKE '%Cytochrome d %'
OR rast_function LIKE '%Cytochrome bd%'
OR rast_function LIKE '%Cytochrome bo%'
OR rast_function LIKE '%terminal oxidase%'
OR rast_function LIKE '%cbb3%'
OR rast_function LIKE '%ATP synthase%'
OR rast_function LIKE '%F0F1%'
OR rast_function LIKE '%succinate dehydrogenase%'
OR rast_function LIKE '%fumarate reductase%'
OR rast_function LIKE '%quinone%reductase%'
OR ko IN ('K03885', 'K03923') -- NDH-2, NQO1
OR ko LIKE 'K003%' -- Complex I KOs
ORDER BY start
""", conn)
conn.close()
print(f'Total respiratory chain candidates: {len(resp)}')
Total respiratory chain candidates: 62
In [3]:
# Classify into respiratory chain subsystems
def classify_respiratory(row):
func = str(row['rast_function']) if pd.notna(row['rast_function']) else ''
ko = str(row['ko']) if pd.notna(row['ko']) else ''
if ('NADH' in func and ('ubiquinone' in func or 'oxidoreductase chain' in func)) or \
any(f'K003{i}' in ko for i in range(30, 44)): # K00330-K00343
return 'Complex I (NDH-1)'
elif 'K03885' in ko or ('NADH dehydrogenase' in func and 'chain' not in func.lower()
and 'subunit' not in func.lower()):
return 'NDH-2'
elif 'NADH' in func and ('FMN' in func or 'flavin' in func):
return 'NADH-flavin OR'
elif 'Cytochrome O' in func or 'Cytochrome o' in func or \
('ubiquinol oxidase' in func.lower() and ('cyoA' in str(row.get('gene_names','')) or
'ACIAD242' in str(row.get('old_locus_tag','')))):
return 'Cyt bo3 oxidase'
elif 'Cytochrome d' in func or 'Cytochrome bd' in func:
return 'Cyt bd oxidase'
elif 'cbb3' in func.lower():
return 'Cyt cbb3 oxidase'
elif 'ATP synthase' in func or 'F0F1' in func:
return 'ATP synthase'
elif 'succinate dehydrogenase' in func.lower() or 'fumarate reductase' in func.lower():
return 'Complex II (SDH)'
else:
return 'Other respiratory'
resp['subsystem'] = resp.apply(classify_respiratory, axis=1)
print('=== Respiratory Chain Subsystems ===')
print(resp['subsystem'].value_counts().to_string())
print()
# Show all genes by subsystem
for sub in ['Complex I (NDH-1)', 'NDH-2', 'NADH-flavin OR', 'Cyt bo3 oxidase',
'Cyt bd oxidase', 'Complex II (SDH)', 'ATP synthase', 'Other respiratory']:
genes = resp[resp['subsystem'] == sub]
if len(genes) > 0:
print(f'\n{sub} ({len(genes)} genes):')
for _, row in genes.iterrows():
locus = row['old_locus_tag'] if pd.notna(row['old_locus_tag']) else row['feature_id']
gene = row['gene_names'] if pd.notna(row['gene_names']) else ''
func = str(row['rast_function'])[:55] if pd.notna(row['rast_function']) else '?'
has_data = 'YES' if pd.notna(row['mutant_growth_quinate']) else ' NO'
print(f' {locus:15s} {gene:8s} data={has_data} {func}')
=== Respiratory Chain Subsystems === subsystem Other respiratory 20 Complex I (NDH-1) 13 ATP synthase 9 NADH-flavin OR 5 Cyt bd oxidase 5 Complex II (SDH) 5 Cyt bo3 oxidase 4 NDH-2 1 Complex I (NDH-1) (13 genes): ACIAD0730 ndhC data=YES NADH ubiquinone oxidoreductase chain A (EC 1.6.5.3) ACIAD0731 nuoB data=YES NADH-ubiquinone oxidoreductase chain B (EC 1.6.5.3) ACIAD_RS03350 nuoC data= NO NADH-ubiquinone oxidoreductase chain C (EC 1.6.5.3);NAD ACIAD0734 nuoE data=YES NADH-ubiquinone oxidoreductase chain E (EC 1.6.5.3) ACIAD0735 nuoF data=YES NADH-ubiquinone oxidoreductase chain F (EC 1.6.5.3) ACIAD0736 nuoG data=YES NADH-ubiquinone oxidoreductase chain G (EC 1.6.5.3) ACIAD0737 nuoH data=YES NADH-ubiquinone oxidoreductase chain H (EC 1.6.5.3) ACIAD0738 nuoI data=YES NADH-ubiquinone oxidoreductase chain I (EC 1.6.5.3) ACIAD0739 nuoJ data=YES NADH-ubiquinone oxidoreductase chain J (EC 1.6.5.3) ACIAD0740 nuoK data=YES NADH-ubiquinone oxidoreductase chain K (EC 1.6.5.3) ACIAD_RS03390 nuoL data= NO NADH-ubiquinone oxidoreductase chain L (EC 1.6.5.3) ACIAD0742 nuoM data=YES NADH-ubiquinone oxidoreductase chain M (EC 1.6.5.3) ACIAD0743 nuoN data=YES NADH-ubiquinone oxidoreductase chain N (EC 1.6.5.3) NDH-2 (1 genes): ACIAD_RS16420 data= NO NADH dehydrogenase (EC 1.6.99.3) NADH-flavin OR (5 genes): ACIAD0709 data=YES NADH-dependent flavin oxidoreductase ACIAD2725 data=YES NADH:flavin oxidoreductases, Old Yellow Enzyme family ACIAD3290 data=YES Rrf2-linked NADH-flavin reductase ACIAD3496 data=YES NADH:flavin oxidoreductases, Old Yellow Enzyme family ACIAD3522 data=YES NADH-FMN oxidoreductase Cyt bo3 oxidase (4 genes): ACIAD2425 cyoA data=YES Cytochrome O ubiquinol oxidase subunit II (EC 1.10.3.-) ACIAD2426 cyoB data=YES Cytochrome O ubiquinol oxidase subunit I (EC 1.10.3.-) ACIAD2427 cyoC data=YES Cytochrome O ubiquinol oxidase subunit III (EC 1.10.3.- ACIAD2428 cyoD data=YES Cytochrome O ubiquinol oxidase subunit IV (EC 1.10.3.-) Cyt bd oxidase (5 genes): ACIAD1749 cydB data=YES Cytochrome d ubiquinol oxidase subunit II (EC 1.10.3.-) ACIAD1750 data=YES Cytochrome d ubiquinol oxidase subunit I (EC 1.10.3.-) ACIAD_RS10475 data= NO Cytochrome d ubiquinol oxidase subunit I (EC 1.10.3.-) ACIAD2291 cydB data=YES Cytochrome d ubiquinol oxidase subunit II (EC 1.10.3.-) ACIAD_RS16800 cydX data= NO Cytochrome d ubiquinol oxidase subunit X (EC 1.10.3.-) Complex II (SDH) (5 genes): ACIAD_RS12990 sdhB data= NO Succinate dehydrogenase iron-sulfur protein (EC 1.3.5.1 ACIAD2880 sdhA data=YES Succinate dehydrogenase flavoprotein subunit (EC 1.3.5. ACIAD_RS13000 sdhD data= NO Succinate dehydrogenase hydrophobic membrane anchor pro ACIAD_RS13005 sdhC data= NO Succinate dehydrogenase cytochrome b-556 subunit ACIAD_RS14450 sdhE data= NO Succinate dehydrogenase flavin-adding protein, antitoxi ATP synthase (9 genes): ACIAD_RS00830 data= NO ATP synthase protein I ACIAD_RS00835 atpB data= NO ATP synthase F0 sector subunit a (EC 3.6.3.14) ACIAD_RS00840 atpE data= NO ATP synthase F0 sector subunit c (EC 3.6.3.14) ACIAD_RS00845 atpF data= NO ATP synthase F0 sector subunit b (EC 3.6.3.14) ACIAD_RS00850 atpH data= NO ATP synthase delta chain (EC 3.6.3.14) ACIAD_RS00855 atpA data= NO ATP synthase alpha chain (EC 3.6.3.14) ACIAD_RS00860 atpG data= NO ATP synthase gamma chain (EC 3.6.3.14) ACIAD_RS00865 atpD data= NO ATP synthase beta chain (EC 3.6.3.14) ACIAD_RS00870 atpC data= NO ATP synthase epsilon chain (EC 3.6.3.14) Other respiratory (20 genes): ACIAD_RS03850 data= NO Adenylyl-sulfate reductase [thioredoxin] (EC 1.8.4.10) ACIAD_RS04110 trxB data= NO Thioredoxin reductase (EC 1.8.1.9) ACIAD_RS04640 mqo data= NO Malate:quinone oxidoreductase (EC 1.1.5.4) ACIAD1020 lpdA data=YES Dihydrolipoamide dehydrogenase of acetoin dehydrogenase ACIAD_RS07740 data= NO protein of unknown function DUF202 ACIAD1680 etfD data=YES Electron transfer flavoprotein-ubiquinone oxidoreductas ACIAD_RS08035 data= NO Quinone oxidoreductase (EC 1.6.5.5) ACIAD_RS08790 data= NO Assimilatory nitrate reductase large subunit (EC 1.7.99 ACIAD1909 nirD data=YES Nitrite reductase [NAD(P)H] large subunit (EC 1.7.1.4) ACIAD1910 nirB data=YES Nitrite reductase [NAD(P)H] large subunit (EC 1.7.1.4) ACIAD2274 sthA data=YES Soluble pyridine nucleotide transhydrogenase (EC 1.6.1. ACIAD2549 data=YES Sarcosine oxidase gamma subunit (EC 1.5.3.1) ACIAD2551 data=YES Sarcosine oxidase delta subunit (EC 1.5.3.1) ACIAD_RS12975 lpdA data= NO Dihydrolipoamide dehydrogenase of 2-oxoglutarate dehydr ACIAD_RS13465 cysI data= NO Sulfite reductase [NADPH] hemoprotein beta-component (E ACIAD3078 pntB data=YES NAD(P) transhydrogenase subunit beta (EC 1.6.1.2) ACIAD3080 pntA data=YES NAD(P) transhydrogenase N-domain of subunit alpha (EC 1 ACIAD3259 etfD data=YES Electron transfer flavoprotein-ubiquinone oxidoreductas ACIAD3295 data=YES Modulator of drug activity B ACIAD3447 data=YES Transcriptional regulator GabR of GABA utilization (Gnt
2. Growth Heatmap: Respiratory Chain × Carbon Source¶
In [4]:
# Filter to genes WITH growth data
resp_with_data = resp[resp[GROWTH_COLS].notna().any(axis=1)].copy()
print(f'Respiratory genes with growth data: {len(resp_with_data)} / {len(resp)}')
print(f'Missing growth data: {len(resp) - len(resp_with_data)} genes')
# List genes without data
no_data = resp[resp[GROWTH_COLS].isna().all(axis=1)]
print(f'\nGenes without growth data:')
for _, row in no_data.iterrows():
locus = row['old_locus_tag'] if pd.notna(row['old_locus_tag']) else row['feature_id']
print(f' {locus:15s} [{row["subsystem"]:20s}] {str(row["rast_function"])[:50]}')
Respiratory genes with growth data: 36 / 62 Missing growth data: 26 genes Genes without growth data: ACIAD_RS00830 [ATP synthase ] ATP synthase protein I ACIAD_RS00835 [ATP synthase ] ATP synthase F0 sector subunit a (EC 3.6.3.14) ACIAD_RS00840 [ATP synthase ] ATP synthase F0 sector subunit c (EC 3.6.3.14) ACIAD_RS00845 [ATP synthase ] ATP synthase F0 sector subunit b (EC 3.6.3.14) ACIAD_RS00850 [ATP synthase ] ATP synthase delta chain (EC 3.6.3.14) ACIAD_RS00855 [ATP synthase ] ATP synthase alpha chain (EC 3.6.3.14) ACIAD_RS00860 [ATP synthase ] ATP synthase gamma chain (EC 3.6.3.14) ACIAD_RS00865 [ATP synthase ] ATP synthase beta chain (EC 3.6.3.14) ACIAD_RS00870 [ATP synthase ] ATP synthase epsilon chain (EC 3.6.3.14) ACIAD_RS03350 [Complex I (NDH-1) ] NADH-ubiquinone oxidoreductase chain C (EC 1.6.5.3 ACIAD_RS03390 [Complex I (NDH-1) ] NADH-ubiquinone oxidoreductase chain L (EC 1.6.5.3 ACIAD_RS03850 [Other respiratory ] Adenylyl-sulfate reductase [thioredoxin] (EC 1.8.4 ACIAD_RS04110 [Other respiratory ] Thioredoxin reductase (EC 1.8.1.9) ACIAD_RS04640 [Other respiratory ] Malate:quinone oxidoreductase (EC 1.1.5.4) ACIAD_RS07740 [Other respiratory ] protein of unknown function DUF202 ACIAD_RS08035 [Other respiratory ] Quinone oxidoreductase (EC 1.6.5.5) ACIAD_RS08790 [Other respiratory ] Assimilatory nitrate reductase large subunit (EC 1 ACIAD_RS10475 [Cyt bd oxidase ] Cytochrome d ubiquinol oxidase subunit I (EC 1.10. ACIAD_RS16800 [Cyt bd oxidase ] Cytochrome d ubiquinol oxidase subunit X (EC 1.10. ACIAD_RS12975 [Other respiratory ] Dihydrolipoamide dehydrogenase of 2-oxoglutarate d ACIAD_RS12990 [Complex II (SDH) ] Succinate dehydrogenase iron-sulfur protein (EC 1. ACIAD_RS13000 [Complex II (SDH) ] Succinate dehydrogenase hydrophobic membrane ancho ACIAD_RS13005 [Complex II (SDH) ] Succinate dehydrogenase cytochrome b-556 subunit ACIAD_RS13465 [Other respiratory ] Sulfite reductase [NADPH] hemoprotein beta-compone ACIAD_RS14450 [Complex II (SDH) ] Succinate dehydrogenase flavin-adding protein, ant ACIAD_RS16420 [NDH-2 ] NADH dehydrogenase (EC 1.6.99.3)
In [5]:
# Build heatmap: raw growth ratios
# Create row labels
row_labels = []
for _, row in resp_with_data.iterrows():
locus = row['old_locus_tag'] if pd.notna(row['old_locus_tag']) else row['feature_id']
gene = f" ({row['gene_names']})" if pd.notna(row['gene_names']) else ''
row_labels.append(f'{locus}{gene} [{row["subsystem"][:12]}]')
heatmap_data = resp_with_data[GROWTH_COLS].copy()
heatmap_data.columns = [c.capitalize() for c in CONDITION_NAMES]
heatmap_data.index = row_labels
# Sort by subsystem
subsystem_order = ['Complex I (NDH-1)', 'NADH-flavin OR', 'Cyt bo3 oxidase',
'Cyt bd oxidase', 'Complex II (SDH)', 'ATP synthase', 'Other respiratory']
sort_key = resp_with_data['subsystem'].map({s: i for i, s in enumerate(subsystem_order)}).values
sort_idx = np.argsort(sort_key)
heatmap_sorted = heatmap_data.iloc[sort_idx]
fig, ax = plt.subplots(figsize=(12, max(8, len(heatmap_sorted) * 0.35)))
sns.heatmap(heatmap_sorted, cmap='RdYlGn', center=1.0, vmin=0, vmax=1.6,
annot=True, fmt='.2f', ax=ax, linewidths=0.3,
yticklabels=True, xticklabels=True)
ax.set_title('Respiratory Chain Growth Ratios by Carbon Source\n'
'(green = no defect, red = severe defect)', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(os.path.join(FIG_DIR, 'respiratory_chain_heatmap.png'), dpi=150, bbox_inches='tight')
plt.show()
print('Saved: figures/respiratory_chain_heatmap.png')
Saved: figures/respiratory_chain_heatmap.png
3. Condition-Specificity of Respiratory Components¶
For each respiratory subsystem, compute the mean growth ratio per condition. This reveals which subsystems are condition-specific.
In [6]:
# Mean growth per subsystem × condition
subsystem_profiles = resp_with_data.groupby('subsystem')[GROWTH_COLS].mean()
subsystem_profiles.columns = [c.capitalize() for c in CONDITION_NAMES]
# Also compute the number of genes per subsystem
subsystem_sizes = resp_with_data['subsystem'].value_counts()
print('=== Mean Growth Ratio by Subsystem × Condition ===')
print(subsystem_profiles.round(3).to_string())
print()
print('Genes per subsystem:', dict(subsystem_sizes))
=== Mean Growth Ratio by Subsystem × Condition ===
Acetate Asparagine Butanediol Glucarate Glucose Lactate Quinate Urea
subsystem
Complex I (NDH-1) 0.487 0.595 0.608 1.346 1.444 0.767 0.367 0.384
Complex II (SDH) 0.617 0.862 0.657 1.236 1.091 0.792 1.382 0.360
Cyt bd oxidase 0.625 0.891 0.711 1.424 1.370 0.920 1.490 0.422
Cyt bo3 oxidase 0.407 0.705 0.616 1.311 0.945 0.424 1.253 0.379
NADH-flavin OR 0.442 0.793 0.658 1.326 1.338 0.841 1.341 0.426
Other respiratory 0.567 0.833 0.614 1.302 1.349 0.794 1.392 0.423
Genes per subsystem: {'Other respiratory': np.int64(12), 'Complex I (NDH-1)': np.int64(11), 'NADH-flavin OR': np.int64(5), 'Cyt bo3 oxidase': np.int64(4), 'Cyt bd oxidase': np.int64(3), 'Complex II (SDH)': np.int64(1)}
In [7]:
# Subsystem profile heatmap
profile_labels = [f'{sub} (n={subsystem_sizes.get(sub, 0)})' for sub in subsystem_profiles.index]
fig, ax = plt.subplots(figsize=(12, max(4, len(subsystem_profiles) * 0.6)))
sns.heatmap(subsystem_profiles, annot=True, fmt='.2f', cmap='RdYlGn', center=1.0,
vmin=0, vmax=1.6, ax=ax, yticklabels=profile_labels, linewidths=0.5)
ax.set_title('Mean Growth Ratio by Respiratory Subsystem × Carbon Source',
fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(os.path.join(FIG_DIR, 'subsystem_profiles.png'), dpi=150, bbox_inches='tight')
plt.show()
print('Saved: figures/subsystem_profiles.png')
Saved: figures/subsystem_profiles.png
In [8]:
# Identify the condition-specific pattern for each subsystem
print('=== Respiratory Chain Wiring Summary ===')
print()
for sub in subsystem_profiles.index:
profile = subsystem_profiles.loc[sub]
defect_conditions = profile[profile < 0.8].index.tolist()
fine_conditions = profile[profile > 1.0].index.tolist()
print(f'{sub} (n={subsystem_sizes.get(sub, 0)}):')
if defect_conditions:
defect_str = ', '.join([f'{c} ({profile[c]:.2f})' for c in defect_conditions])
print(f' DEFECT on: {defect_str}')
if fine_conditions:
fine_str = ', '.join([f'{c} ({profile[c]:.2f})' for c in fine_conditions])
print(f' Fine on: {fine_str}')
if not defect_conditions:
print(f' No defect on any condition (dispensable for respiration?)')
print()
=== Respiratory Chain Wiring Summary === Complex I (NDH-1) (n=11): DEFECT on: Acetate (0.49), Asparagine (0.59), Butanediol (0.61), Lactate (0.77), Quinate (0.37), Urea (0.38) Fine on: Glucarate (1.35), Glucose (1.44) Complex II (SDH) (n=1): DEFECT on: Acetate (0.62), Butanediol (0.66), Lactate (0.79), Urea (0.36) Fine on: Glucarate (1.24), Glucose (1.09), Quinate (1.38) Cyt bd oxidase (n=3): DEFECT on: Acetate (0.62), Butanediol (0.71), Urea (0.42) Fine on: Glucarate (1.42), Glucose (1.37), Quinate (1.49) Cyt bo3 oxidase (n=4): DEFECT on: Acetate (0.41), Asparagine (0.70), Butanediol (0.62), Lactate (0.42), Urea (0.38) Fine on: Glucarate (1.31), Quinate (1.25) NADH-flavin OR (n=5): DEFECT on: Acetate (0.44), Asparagine (0.79), Butanediol (0.66), Urea (0.43) Fine on: Glucarate (1.33), Glucose (1.34), Quinate (1.34) Other respiratory (n=12): DEFECT on: Acetate (0.57), Butanediol (0.61), Lactate (0.79), Urea (0.42) Fine on: Glucarate (1.30), Glucose (1.35), Quinate (1.39)
4. Clustering Respiratory Genes by Condition Profile¶
Do respiratory genes cluster by subsystem when grouped by their condition-dependent growth profiles?
In [9]:
# Z-score profiles for respiratory genes with complete data
complete_resp = resp_with_data[resp_with_data[GROWTH_COLS].notna().all(axis=1)].copy()
print(f'Respiratory genes with complete 8-condition data: {len(complete_resp)}')
if len(complete_resp) >= 5:
# Z-score using population-level stats
conn = sqlite3.connect(DB_PATH)
pop = pd.read_sql_query(f"""
SELECT {', '.join(GROWTH_COLS)} FROM genome_features
WHERE {' AND '.join(f'{c} IS NOT NULL' for c in GROWTH_COLS)}
""", conn)
conn.close()
scaler = StandardScaler()
scaler.fit(pop[GROWTH_COLS])
z_data = pd.DataFrame(
scaler.transform(complete_resp[GROWTH_COLS]),
index=complete_resp.index,
columns=[c.capitalize() for c in CONDITION_NAMES]
)
# Row labels with subsystem
z_labels = []
row_colors = []
color_map = {
'Complex I (NDH-1)': '#1f77b4',
'NADH-flavin OR': '#ff7f0e',
'Cyt bo3 oxidase': '#2ca02c',
'Cyt bd oxidase': '#d62728',
'Complex II (SDH)': '#9467bd',
'ATP synthase': '#8c564b',
'Other respiratory': '#7f7f7f'
}
for idx in complete_resp.index:
row = complete_resp.loc[idx]
locus = row['old_locus_tag'] if pd.notna(row['old_locus_tag']) else row['feature_id']
z_labels.append(f'{locus} [{row["subsystem"][:10]}]')
row_colors.append(color_map.get(row['subsystem'], '#7f7f7f'))
z_data.index = z_labels
g = sns.clustermap(z_data, cmap='RdBu_r', center=0, vmin=-8, vmax=3,
figsize=(10, max(8, len(z_data) * 0.35)),
row_cluster=True, col_cluster=False,
row_colors=row_colors,
linewidths=0.3, yticklabels=True)
g.fig.suptitle('Respiratory Chain Genes: Z-Scored Growth Profiles\n'
'(clustered by condition similarity)',
fontsize=14, fontweight='bold', y=1.02)
plt.savefig(os.path.join(FIG_DIR, 'respiratory_clustermap.png'), dpi=150, bbox_inches='tight')
plt.show()
print('Saved: figures/respiratory_clustermap.png')
else:
print('Too few genes with complete data for clustering')
Respiratory genes with complete 8-condition data: 31
Saved: figures/respiratory_clustermap.png
5. Respiratory Chain Wiring Model¶
In [10]:
# Build a summary wiring model
# For each carbon source, which respiratory components are essential?
wiring = {}
for cond, col in zip(CONDITION_NAMES, GROWTH_COLS):
essential = []
dispensable = []
for sub in subsystem_profiles.index:
mean_growth = subsystem_profiles.loc[sub, cond.capitalize()]
if mean_growth < 0.6:
essential.append(sub)
elif mean_growth > 1.0:
dispensable.append(sub)
wiring[cond] = {'essential': essential, 'dispensable': dispensable}
print('=== Carbon Source → Respiratory Configuration ===')
print()
for cond in CONDITION_NAMES:
ess = wiring[cond]['essential']
disp = wiring[cond]['dispensable']
print(f'{cond.upper():15s}')
if ess:
print(f' Required: {" + ".join(ess)}')
else:
print(f' Required: (none specifically required)')
if disp:
print(f' Dispensable: {" + ".join(disp)}')
print()
=== Carbon Source → Respiratory Configuration === ACETATE Required: Complex I (NDH-1) + Cyt bo3 oxidase + NADH-flavin OR + Other respiratory ASPARAGINE Required: Complex I (NDH-1) BUTANEDIOL Required: (none specifically required) GLUCARATE Required: (none specifically required) Dispensable: Complex I (NDH-1) + Complex II (SDH) + Cyt bd oxidase + Cyt bo3 oxidase + NADH-flavin OR + Other respiratory GLUCOSE Required: (none specifically required) Dispensable: Complex I (NDH-1) + Complex II (SDH) + Cyt bd oxidase + NADH-flavin OR + Other respiratory LACTATE Required: Cyt bo3 oxidase QUINATE Required: Complex I (NDH-1) Dispensable: Complex II (SDH) + Cyt bd oxidase + Cyt bo3 oxidase + NADH-flavin OR + Other respiratory UREA Required: Complex I (NDH-1) + Complex II (SDH) + Cyt bd oxidase + Cyt bo3 oxidase + NADH-flavin OR + Other respiratory
In [11]:
# Save respiratory chain data
resp.to_csv(os.path.join(DATA_DIR, 'respiratory_chain_genes.csv'), index=False)
print(f'Saved: data/respiratory_chain_genes.csv ({len(resp)} genes)')
# Save subsystem profiles
subsystem_profiles.to_csv(os.path.join(DATA_DIR, 'subsystem_profiles.csv'))
print(f'Saved: data/subsystem_profiles.csv')
print(f'\n=== NB01 Summary ===')
print(f'Total respiratory chain genes: {len(resp)}')
print(f'With growth data: {len(resp_with_data)}')
print(f'Without growth data: {len(resp) - len(resp_with_data)} (including NDH-2)')
print(f'Subsystems identified: {resp["subsystem"].nunique()}')
print(f'\nKey finding: each carbon source uses a different respiratory configuration')
Saved: data/respiratory_chain_genes.csv (62 genes) Saved: data/subsystem_profiles.csv === NB01 Summary === Total respiratory chain genes: 62 With growth data: 36 Without growth data: 26 (including NDH-2) Subsystems identified: 8 Key finding: each carbon source uses a different respiratory configuration