import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path

pd.set_option('display.max_colwidth', 80)
pd.set_option('display.width', 180)
sns.set_context('notebook')
sns.set_style('whitegrid')

PROJ = Path('/home/aparkin/BERIL-research-observatory/projects/caulobacter_fur_lipida_loss')
DATA_IN = Path('/home/aparkin/data/kr-caulobacter-envelope/clean')
DATA_OUT = PROJ / 'data'
FIG = PROJ / 'figures'
DATA_OUT.mkdir(exist_ok=True, parents=True)
FIG.mkdir(exist_ok=True, parents=True)

sx     = pd.read_csv(DATA_IN / 'strain_crosswalk.csv')
qc     = pd.read_csv(DATA_IN / 'sample_qc.csv')
feat   = pd.read_csv(DATA_IN / 'dim_feature.csv')
cpm    = pd.read_csv(DATA_IN / 'fact_expression_cpm.csv')
diff   = pd.read_csv(DATA_IN / 'fact_differential.csv')
prot   = pd.read_csv(DATA_IN / 'om_proteome.csv')
chvi_qy = pd.read_csv(DATA_IN / 'chvi_regulon_quintero_yanes_2022.csv')
chvi_st = pd.read_csv(DATA_IN / 'chvi_overexpression_regulon_stein_2021.csv')

print('Loaded:')
for name, df in [('strain_crosswalk', sx), ('sample_qc', qc), ('dim_feature', feat),
                 ('cpm', cpm), ('diff', diff), ('om_proteome', prot),
                 ('chvi_qy', chvi_qy), ('chvi_st', chvi_st)]:
    print(f'  {name:25s}  {df.shape}')

Loaded:
  strain_crosswalk           (3, 6)
  sample_qc                  (9, 10)
  dim_feature                (4020, 4)
  cpm                        (35613, 5)
  diff                       (11871, 8)
  om_proteome                (795, 27)
  chvi_qy                    (594, 8)
  chvi_st                    (162, 8)

print('Strain crosswalk:'); display(sx)
print('\nLibrary QC (mapping percentages):')
display(qc[['sample','strain','replicate','total_reads','pct_mapped','pct_cds_of_mapped']])

Strain crosswalk:

Library QC (mapping percentages):

def deg_counts(df, lfc_thr, p_col, p_thr):
    sig = df[(df['logFC'].abs() > lfc_thr) & (df[p_col] < p_thr)]
    return (sig
            .groupby(['contrast','feature_type'])
            .agg(n_up=('logFC', lambda x: (x>0).sum()),
                 n_dn=('logFC', lambda x: (x<0).sum()))
            .reset_index())

print('|logFC|>1 AND raw p<0.05 (author cutoff):')
display(deg_counts(diff, 1, 'pvalue', 0.05))

print('\n|logFC|>1 AND FDR<0.05 (stricter):')
display(deg_counts(diff, 1, 'fdr', 0.05))

print('\n|logFC|>1.4 AND FDR<0.05 (hand-curated cutoff from "IFCI>1.4" sheet):')
display(deg_counts(diff, 1.4, 'fdr', 0.05))

|logFC|>1 AND raw p<0.05 (author cutoff):

|logFC|>1 AND FDR<0.05 (stricter):

|logFC|>1.4 AND FDR<0.05 (hand-curated cutoff from "IFCI>1.4" sheet):

SPHINGO = ['CCNA_01212','CCNA_01213','CCNA_01214','CCNA_01217','CCNA_01218','CCNA_01219',
           'CCNA_01220','CCNA_01221','CCNA_01222','CCNA_01223','CCNA_01226','CCNA_03113']
SPHINGO_NAME = {
    'CCNA_01212':'bcerS','CCNA_01213':'lptG2 (Uchendu)','CCNA_01214':'lptF2 (Uchendu)',
    'CCNA_01217':'sphingo_01217','CCNA_01218':'sphk','CCNA_01219':'sphingo_01219',
    'CCNA_01220':'spt','CCNA_01221':'acp','CCNA_01222':'cerR','CCNA_01223':'acps',
    'CCNA_01226':'lptC2 (Uchendu)','CCNA_03113':'ctpA / LpxE-like',
}

# annotation in dim_feature
sph_feat = feat[feat['locustag'].isin(SPHINGO)][['locustag','gene','description','feature_type']]
sph_feat['role'] = sph_feat['locustag'].map(SPHINGO_NAME)
print('Annotation (dim_feature):')
display(sph_feat.sort_values('locustag'))

Annotation (dim_feature):

# Mean CPM per strain
mean_cpm_strain = (cpm[cpm['locustag'].isin(SPHINGO)]
                   .groupby(['locustag','strain'])['cpm'].mean()
                   .unstack('strain'))
mean_cpm_strain = mean_cpm_strain.reindex(SPHINGO)
mean_cpm_strain['role'] = mean_cpm_strain.index.map(SPHINGO_NAME)
mean_cpm_strain = mean_cpm_strain[['role'] + [c for c in mean_cpm_strain.columns if c != 'role']]
print('Mean TMM-CPM per strain (sphingolipid + transport locus):')
display(mean_cpm_strain.round(1))

# DE: 4584-vs-4580 (Fur+SspB effect) and 4599-vs-4584 (lpxC effect)
sph_de = (diff[diff['locustag'].isin(SPHINGO) &
              diff['contrast'].isin(['4584_vs_4580','4599_vs_4584'])]
         .pivot_table(index='locustag', columns='contrast',
                      values=['logFC','fdr'], aggfunc='first'))
sph_de.columns = ['_'.join(c) for c in sph_de.columns]
sph_de = sph_de.reindex(SPHINGO)
sph_de.insert(0, 'role', sph_de.index.map(SPHINGO_NAME))
print('\nDE in 4584_vs_4580 (Fur+SspB) and 4599_vs_4584 (lpxC), sphingolipid locus:')
display(sph_de.round(2))

sph_de.to_csv(DATA_OUT / '00_sphingolipid_locus_de.csv')
mean_cpm_strain.to_csv(DATA_OUT / '00_sphingolipid_locus_cpm.csv')

Mean TMM-CPM per strain (sphingolipid + transport locus):

DE in 4584_vs_4580 (Fur+SspB) and 4599_vs_4584 (lpxC), sphingolipid locus:

# Heatmap of log2(CPM+1) per replicate
sph_cpm = cpm[cpm['locustag'].isin(SPHINGO)].copy()
sph_cpm['log2cpm'] = np.log2(sph_cpm['cpm'] + 1)
mat = sph_cpm.pivot_table(index='locustag', columns='sample', values='log2cpm').reindex(SPHINGO)
sample_order = sorted(mat.columns, key=lambda s: (int(s.split('_')[0]), s))
mat = mat[sample_order]
yticks = [f'{lt}  ({SPHINGO_NAME[lt]})' for lt in mat.index]

fig, ax = plt.subplots(figsize=(9, 6))
sns.heatmap(mat, cmap='viridis', annot=mat.round(1), fmt='.1f', cbar_kws={'label':'log2(CPM+1)'},
            yticklabels=yticks, xticklabels=sample_order, ax=ax)
ax.set_title('Sphingolipid biosynthesis + Uchendu 2026 transporters + CtpA — log2(CPM+1) per replicate')
ax.set_xlabel('Library')
ax.set_ylabel('Locus (role)')
plt.tight_layout()
plt.savefig(FIG / '00_sphingolipid_locus_heatmap.png', dpi=140, bbox_inches='tight')
plt.show()

# heuristic search — Lpt naming in Caulobacter NA1000 annotation may not match E. coli exactly
mask = (feat['gene'].fillna('').str.match(r'^lpt[a-zA-Z]?$', case=False) |
        feat['description'].fillna('').str.contains(r'\bLpt[A-G]\b|lipopolysaccharide.{0,30}(transport|export|assembly)', case=False, regex=True))
lpt_candidates = feat[mask][['locustag','gene','description','feature_type']]
print(f'Lpt candidates (heuristic): {len(lpt_candidates)}')
display(lpt_candidates.sort_values('gene'))

Lpt candidates (heuristic): 6

/tmp/ipykernel_46895/2721181456.py:3: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
  feat['description'].fillna('').str.contains(r'\bLpt[A-G]\b|lipopolysaccharide.{0,30}(transport|export|assembly)', case=False, regex=True))

if len(lpt_candidates) > 0:
    lpt_loci = lpt_candidates['locustag'].tolist()
    lpt_cpm = (cpm[cpm['locustag'].isin(lpt_loci)]
               .groupby(['locustag','strain'])['cpm'].mean()
               .unstack('strain'))
    lpt_cpm = lpt_cpm.merge(lpt_candidates[['locustag','gene','description']].set_index('locustag'),
                             left_index=True, right_index=True)
    lpt_de = (diff[diff['locustag'].isin(lpt_loci) &
                  diff['contrast'].isin(['4584_vs_4580','4599_vs_4584'])]
              .pivot_table(index='locustag', columns='contrast', values=['logFC','fdr'], aggfunc='first'))
    lpt_de.columns = ['_'.join(c) for c in lpt_de.columns]
    print('Canonical Lpt apparatus — mean CPM:')
    display(lpt_cpm.round(1))
    print('\nCanonical Lpt apparatus — DE:')
    display(lpt_de.round(2))
    lpt_cpm.to_csv(DATA_OUT / '00_lpt_apparatus_cpm.csv')
    lpt_de.to_csv(DATA_OUT / '00_lpt_apparatus_de.csv')
else:
    print('No canonical Lpt candidates by gene name / description — need protein-homology lookup (defer to NB01).')

Canonical Lpt apparatus — mean CPM:

Canonical Lpt apparatus — DE:

# Normalize Stein regulon to CCNA where possible; drop the 24 gene-symbol-only rows for set ops
stein_ccna = chvi_st[chvi_st['locus_or_gene'].fillna('').str.startswith('CCNA_')]['locus_or_gene'].tolist()
stein_set = set(stein_ccna)
print(f'Stein 2021 ChvI overexpression set (CCNA-keyed): {len(stein_set)} of {len(chvi_st)} total')

qy_induced = set(chvi_qy[chvi_qy['direction']=='down_in_delta_chvI']['gene_id'])
qy_repressed = set(chvi_qy[chvi_qy['direction']=='up_in_delta_chvI']['gene_id'])
print(f'Quintero-Yanes 2022 ChvI-induced set: {len(qy_induced)}')
print(f'Quintero-Yanes 2022 ChvI-repressed set: {len(qy_repressed)}')

# Our up-DEG sets at |logFC|>1, FDR<0.05
def deg_set(contrast, direction='up', lfc_thr=1.0, fdr_thr=0.05):
    sub = diff[(diff['contrast']==contrast) &
               (diff['logFC'].abs() > lfc_thr) &
               (diff['fdr'] < fdr_thr)]
    if direction == 'up':
        return set(sub[sub['logFC']>0]['locustag'])
    return set(sub[sub['logFC']<0]['locustag'])

up_4584 = deg_set('4584_vs_4580', 'up')
dn_4584 = deg_set('4584_vs_4580', 'dn')
up_4599_v_4584 = deg_set('4599_vs_4584', 'up')
dn_4599_v_4584 = deg_set('4599_vs_4584', 'dn')

print(f'\nUp in 4584_vs_4580 (Fur+SspB released): {len(up_4584)}')
print(f'Down in 4584_vs_4580: {len(dn_4584)}')
print(f'Up in 4599_vs_4584 (added Δlpxc): {len(up_4599_v_4584)}')
print(f'Down in 4599_vs_4584: {len(dn_4599_v_4584)}')

Stein 2021 ChvI overexpression set (CCNA-keyed): 137 of 162 total
Quintero-Yanes 2022 ChvI-induced set: 301
Quintero-Yanes 2022 ChvI-repressed set: 293

Up in 4584_vs_4580 (Fur+SspB released): 119
Down in 4584_vs_4580: 34
Up in 4599_vs_4584 (added Δlpxc): 235
Down in 4599_vs_4584: 95

from scipy.stats import hypergeom

# universe: tested CDS+ncRNA loci in fact_differential
universe = set(diff['locustag'].unique())
N = len(universe)
print(f'Universe (tested loci): N={N}')

def enrich(deg, regulon, label):
    deg = deg & universe
    regulon = regulon & universe
    overlap = deg & regulon
    K, n = len(regulon), len(deg)
    k = len(overlap)
    # P(X >= k) under hypergeometric
    p = hypergeom.sf(k-1, N, K, n) if k > 0 else 1.0
    expected = K * n / N if N else 0
    return dict(label=label, regulon_size=K, deg_size=n, overlap=k, expected=round(expected,1),
                fold=round((k / expected) if expected else float('nan'), 2),
                p=p)

rows = []
for deg, deg_name in [(up_4584, 'Up in 4584_vs_4580'),
                       (dn_4584, 'Down in 4584_vs_4580'),
                       (up_4599_v_4584, 'Up in 4599_vs_4584'),
                       (dn_4599_v_4584, 'Down in 4599_vs_4584')]:
    rows.append(enrich(deg, stein_set, f'{deg_name} ∩ Stein ChvI-induced'))
    rows.append(enrich(deg, qy_induced, f'{deg_name} ∩ QY ChvI-induced'))
    rows.append(enrich(deg, qy_repressed, f'{deg_name} ∩ QY ChvI-repressed'))

enrich_df = pd.DataFrame(rows)
enrich_df['p'] = enrich_df['p'].apply(lambda x: f'{x:.2e}')
print('\nHypergeometric enrichment of ChvI regulons in our DEG sets:')
display(enrich_df)
enrich_df.to_csv(DATA_OUT / '00_chvi_enrichment.csv', index=False)

Universe (tested loci): N=3957

Hypergeometric enrichment of ChvI regulons in our DEG sets:

SUPPRESSORS = ['CCNA_00497','CCNA_01068','CCNA_01553','CCNA_03733']
sup_feat = feat[feat['locustag'].isin(SUPPRESSORS)][['locustag','gene','description']]
print('Annotation (suppressor loci):')
display(sup_feat)

sup_cpm = (cpm[cpm['locustag'].isin(SUPPRESSORS)]
           .groupby(['locustag','strain'])['cpm'].mean()
           .unstack('strain').reindex(SUPPRESSORS))
print('\nMean CPM per strain:')
display(sup_cpm.round(1))

sup_de = (diff[diff['locustag'].isin(SUPPRESSORS)]
          .pivot_table(index='locustag', columns='contrast', values=['logFC','fdr'], aggfunc='first'))
sup_de.columns = ['_'.join(c) for c in sup_de.columns]
print('\nDE behavior:')
display(sup_de.round(2))
sup_de.to_csv(DATA_OUT / '00_zik_suppressors_de.csv')

Annotation (suppressor loci):

Mean CPM per strain:

DE behavior:

def topn(contrast, n=25):
    sub = diff[(diff['contrast']==contrast) & (diff['fdr'] < 0.05)].copy()
    sub = sub.merge(feat[['locustag','gene','description']], on='locustag', how='left')
    sub_up = sub[sub['logFC']>0].nlargest(n, 'logFC')[['locustag','gene','description','logFC','fdr','feature_type']]
    sub_dn = sub[sub['logFC']<0].nsmallest(n, 'logFC')[['locustag','gene','description','logFC','fdr','feature_type']]
    return sub_up, sub_dn

up25, dn25 = topn('4599_vs_4584', 25)
print(f'TOP 25 UP in 4599_vs_4584 (lipid-A-loss response):')
display(up25.reset_index(drop=True))
print(f'\nTOP 25 DOWN in 4599_vs_4584:')
display(dn25.reset_index(drop=True))
up25.to_csv(DATA_OUT / '00_top25_up_4599_vs_4584.csv', index=False)
dn25.to_csv(DATA_OUT / '00_top25_dn_4599_vs_4584.csv', index=False)

TOP 25 UP in 4599_vs_4584 (lipid-A-loss response):

TOP 25 DOWN in 4599_vs_4584:

# Same for 4584-vs-4580 (Fur+SspB release signature)
up25b, dn25b = topn('4584_vs_4580', 25)
print(f'TOP 25 UP in 4584_vs_4580 (Fur+SspB release):')
display(up25b.reset_index(drop=True))
print(f'\nTOP 25 DOWN in 4584_vs_4580:')
display(dn25b.reset_index(drop=True))
up25b.to_csv(DATA_OUT / '00_top25_up_4584_vs_4580.csv', index=False)
dn25b.to_csv(DATA_OUT / '00_top25_dn_4584_vs_4580.csv', index=False)

TOP 25 UP in 4584_vs_4580 (Fur+SspB release):

TOP 25 DOWN in 4584_vs_4580:

# How many proteins detected per strain?
det_counts = {'4580 (rsaA)': prot['abundance_4580'].notna().sum(),
              '4659 (=4584, +fur sspB)': prot['abundance_4659'].notna().sum(),
              '4672 (=4599, +lpxC)': prot['abundance_4672'].notna().sum()}
print('OM proteins detected (non-NA abundance):')
for k,v in det_counts.items():
    print(f'  {k:30s}  {v}')

# Sphingolipid pathway in OM proteome
sph_prot = prot[prot['ccna_locus'].isin(SPHINGO)]
print(f'\nSphingolipid locus in OM proteome: {len(sph_prot)} hits')
display(sph_prot[['ccna_locus','gene_symbol','description','abundance_4580','abundance_4659','abundance_4672']])

# Zik suppressors in OM
sup_prot = prot[prot['ccna_locus'].isin(SUPPRESSORS)]
print(f'\nZik suppressor loci in OM proteome: {len(sup_prot)} hits')
display(sup_prot[['ccna_locus','gene_symbol','description','abundance_4580','abundance_4659','abundance_4672']])

# Lpt by gene name
if len(lpt_candidates) > 0:
    lpt_prot = prot[prot['ccna_locus'].isin(lpt_candidates['locustag'])]
    print(f'\nCanonical Lpt candidates in OM proteome: {len(lpt_prot)} hits')
    display(lpt_prot[['ccna_locus','gene_symbol','description','abundance_4580','abundance_4659','abundance_4672']])

OM proteins detected (non-NA abundance):
  4580 (rsaA)                     468
  4659 (=4584, +fur sspB)         516
  4672 (=4599, +lpxC)             582

Sphingolipid locus in OM proteome: 4 hits

Zik suppressor loci in OM proteome: 1 hits

Canonical Lpt candidates in OM proteome: 4 hits

# Strain abundance shift for proteins detected in all 3 strains
all3 = prot.dropna(subset=['abundance_4580','abundance_4659','abundance_4672']).copy()
all3['log2_4659_over_4580'] = np.log2(all3['abundance_4659'] / all3['abundance_4580'])
all3['log2_4672_over_4659'] = np.log2(all3['abundance_4672'] / all3['abundance_4659'])
print(f'Proteins detected in all 3 strains: {len(all3)}')

# Top 15 up/down by 4659/4580 (Fur+SspB-rsaA effect at the OM)
print('\nTOP 15 UP in 4659 (Fur+SspB release) vs 4580:')
display(all3.nlargest(15, 'log2_4659_over_4580')[['ccna_locus','gene_symbol','description','log2_4659_over_4580']])
print('\nTOP 15 UP in 4672 (added Δlpxc) vs 4659:')
display(all3.nlargest(15, 'log2_4672_over_4659')[['ccna_locus','gene_symbol','description','log2_4672_over_4659']])
print('\nTOP 15 DOWN in 4672 vs 4659:')
display(all3.nsmallest(15, 'log2_4672_over_4659')[['ccna_locus','gene_symbol','description','log2_4672_over_4659']])

all3.to_csv(DATA_OUT / '00_om_proteome_strain_shifts.csv', index=False)

Proteins detected in all 3 strains: 429

TOP 15 UP in 4659 (Fur+SspB release) vs 4580:

TOP 15 UP in 4672 (added Δlpxc) vs 4659:

TOP 15 DOWN in 4672 vs 4659:

print('=== EVIDENCE SUMMARY ===\n')

# H1: ChvI overlap
ch_enriched = pd.read_csv(DATA_OUT / '00_chvi_enrichment.csv')
ch_4584_up_qy = ch_enriched[ch_enriched['label']=='Up in 4584_vs_4580 ∩ QY ChvI-induced'].iloc[0]
ch_4599_up_qy = ch_enriched[ch_enriched['label']=='Up in 4599_vs_4584 ∩ QY ChvI-induced'].iloc[0]
print('H1 — ChvI overlap with our DEGs (QY ChvI-induced set):')
print(f'  Up in 4584_vs_4580 (Fur+SspB release):  fold={ch_4584_up_qy["fold"]}, p={ch_4584_up_qy["p"]}, overlap={ch_4584_up_qy["overlap"]}/{ch_4584_up_qy["regulon_size"]}')
print(f'  Up in 4599_vs_4584 (added Δlpxc):       fold={ch_4599_up_qy["fold"]}, p={ch_4599_up_qy["p"]}, overlap={ch_4599_up_qy["overlap"]}/{ch_4599_up_qy["regulon_size"]}')
print('  Interpretation: If enrichment is in 4584_vs_4580, ChvI is a PERMISSIVE condition (induced before Δlpxc).')
print('                  If only in 4599_vs_4584, ChvI is a DOWNSTREAM CONSEQUENCE of lipid-A loss.')

# H3: sphingolipid locus
sph_de_df = pd.read_csv(DATA_OUT / '00_sphingolipid_locus_de.csv')
print('\nH3 — Sphingolipid locus DE (logFC; positive = induced):')
print(sph_de_df[['locustag','role','logFC_4584_vs_4580','logFC_4599_vs_4584']].round(2).to_string(index=False))

# How many sphingolipid loci significantly up in each contrast?
sph_up_4584 = (sph_de_df['logFC_4584_vs_4580'] > 0) & (sph_de_df['fdr_4584_vs_4580'] < 0.05)
sph_up_4599v4584 = (sph_de_df['logFC_4599_vs_4584'] > 0) & (sph_de_df['fdr_4599_vs_4584'] < 0.05)
print(f'\nSphingolipid loci significantly UP (FDR<0.05):')
print(f'  in 4584_vs_4580 (Fur+SspB):    {sph_up_4584.sum()}/{len(sph_de_df)}')
print(f'  in 4599_vs_4584 (added Δlpxc): {sph_up_4599v4584.sum()}/{len(sph_de_df)}')

print('\n=== End summary. See tables above and figures/, data/ for full output. ===')

=== EVIDENCE SUMMARY ===

H1 — ChvI overlap with our DEGs (QY ChvI-induced set):
  Up in 4584_vs_4580 (Fur+SspB release):  fold=3.18, p=1.82e-08, overlap=28/293
  Up in 4599_vs_4584 (added Δlpxc):       fold=2.87, p=1.4e-12, overlap=50/293
  Interpretation: If enrichment is in 4584_vs_4580, ChvI is a PERMISSIVE condition (induced before Δlpxc).
                  If only in 4599_vs_4584, ChvI is a DOWNSTREAM CONSEQUENCE of lipid-A loss.

H3 — Sphingolipid locus DE (logFC; positive = induced):
  locustag             role  logFC_4584_vs_4580  logFC_4599_vs_4584
CCNA_01212            bcerS               -0.04               -0.09
CCNA_01213  lptG2 (Uchendu)               -0.25               -0.68
CCNA_01214  lptF2 (Uchendu)               -0.06               -0.51
CCNA_01217    sphingo_01217               -0.33               -0.24
CCNA_01218             sphk               -0.40               -0.23
CCNA_01219    sphingo_01219               -0.20                0.02
CCNA_01220              spt               -0.64               -0.41
CCNA_01221              acp               -0.48                0.02
CCNA_01222             cerR               -0.20               -0.06
CCNA_01223             acps               -0.16               -0.30
CCNA_01226  lptC2 (Uchendu)               -0.02               -0.60
CCNA_03113 ctpA / LpxE-like                0.18                0.58

Sphingolipid loci significantly UP (FDR<0.05):
  in 4584_vs_4580 (Fur+SspB):    0/12
  in 4599_vs_4584 (added Δlpxc): 0/12

=== End summary. See tables above and figures/, data/ for full output. ===

Hypothesis	What this notebook checks
H1 — ChvG-ChvI as cooperator vs consequence	Overlap of 4584-vs-4580 and 4599-vs-4584 DEGs with the published Stein 2021 + Quintero-Yanes 2022 ChvI regulons
H2 — Critical Fur regulon subset	Top up/down in 4584-vs-4580 (the "Fur-released + SspB-released" axis); known Fur-target families (TBDTs, ferritin, oxidative stress)
H3 — Sphingolipid substitution + transport induction	Expression and DE of the sphingolipid biosynthesis + Uchendu IM-transporter locus (CCNA_01212–01226) and CtpA/LpxE-like CCNA_03113
Zik 2022 suppressor loci	Behavior of CCNA_00497, CCNA_01068 (wbqA), CCNA_01553, CCNA_03733
Canonical LPS transport apparatus	Is the LptA/B/D/E/F/G apparatus maintained in 4599 (no LPS) — supporting Uchendu's "shared component" model?

	rnaseq_strain_id	proteome_strain_id	genotype	deletions	role	notes
0	4580	4580	rsaA::gent	rsaA	baseline (S-layer deletion)	rsaA (S-layer) deleted in all strains so loss of LpxC/lipid A does not shed ...
1	4584	4659	fur::hyg sspB::spec rsaA::gent	fur;sspB;rsaA	fur/sspB mutant	RNA-seq strain 4584 == proteome/OM strain 4659 (regrown single colony, re-sa...
2	4599	4672	lpxC::tet fur::hyg sspB::spec rsaA::gent	lpxC;fur;sspB;rsaA	lpxC (lipid A) mutant	RNA-seq strain 4599 == proteome/OM strain 4672. Adds lpxC (lipid A / LPS bio...

	sample	strain	replicate	total_reads	pct_mapped	pct_cds_of_mapped
0	4580_626	4580	626	24333200	96.904	40.011
1	4580_629	4580	629	26363720	96.885	40.211
2	4580_630	4580	630	22752160	97.066	40.737
3	4584_626	4584	626	23609828	96.806	40.311
4	4584_629	4584	629	22755604	96.700	40.565
5	4584_630	4584	630	18906400	96.932	40.150
6	4599_626	4599	626	21137554	96.968	39.644
7	4599_629	4599	629	21519736	96.577	39.348
8	4599_630	4599	630	20040108	96.747	38.867

Locus	Gene / role
CCNA_01212	bcerS — bacterial ceramide synthase
CCNA_01213	lptG2 — sphingolipid IM transporter (Uchendu 2026)
CCNA_01214	lptF2 — sphingolipid IM transporter (Uchendu 2026)
CCNA_01217	sphingolipid biosynthesis (required for CHIR-090 tolerance, Zik 2022)
CCNA_01218	sphingosine kinase
CCNA_01219	sphingolipid biosynthesis (required, Zik 2022)
CCNA_01220	spt — serine palmitoyltransferase
CCNA_01221	acp — acyl carrier protein
CCNA_01222	cerR — ceramide reductase
CCNA_01223	acps — ACP synthetase
CCNA_01226	lptC2 — sphingolipid IM transporter (Caulobacter-specific, Uchendu 2026)
CCNA_03113	ctpA — C-terminal processing protease; LpxE-like (may substitute for LpxF)

strain	role	4580	4584	4599
locustag
CCNA_01212	bcerS	186.6	181.3	170.8
CCNA_01213	lptG2 (Uchendu)	264.7	221.8	138.1
CCNA_01214	lptF2 (Uchendu)	191.1	183.0	128.2
CCNA_01217	sphingo_01217	418.5	333.2	281.4
CCNA_01218	sphk	247.0	187.7	160.2
CCNA_01219	sphingo_01219	71.8	62.4	63.2
CCNA_01220	spt	1397.8	898.0	674.0
CCNA_01221	acp	185.9	133.3	134.9
CCNA_01222	cerR	75.1	65.3	62.6
CCNA_01223	acps	194.8	173.9	141.4
CCNA_01226	lptC2 (Uchendu)	25.4	25.1	16.6
CCNA_03113	ctpA / LpxE-like	37.5	42.5	63.4

00 Orientation

00 — Phase A Orientation: data shape, sphingolipid locus, ChvI overlap¶

Purpose¶

1. Strain and library layout (sanity)¶

2. DEG counts per contrast¶

3. Sphingolipid biosynthesis + transport locus (H3 — primary test)¶

4. Canonical LPS transport apparatus — is it maintained in 4599?¶

5. ChvI envelope-stress regulon overlap (H1)¶

6. Zik 2022 suppressor loci behavior¶

7. Top up/down in 4599-vs-4584 — the added-Δlpxc signature¶

8. OM proteome — sphingolipid pathway, Lpt apparatus, suppressor loci¶

9. Summary — what the data says before formalizing the plan¶

Outputs¶

	contrast	feature_type	n_up	n_dn
0	4584_vs_4580	CDS	113	34
1	4584_vs_4580	ncRNA	16	3
2	4599_vs_4580	CDS	412	177
3	4599_vs_4580	ncRNA	23	14
4	4599_vs_4584	CDS	231	92
5	4599_vs_4584	ncRNA	13	5

	locustag	gene	description	feature_type	role
1176	CCNA_01212	NaN	dATP pyrophosphohydrolase	CDS	bcerS
1177	CCNA_01213	NaN	YjgP/YjgQ family membrane permease	CDS	lptG2 (Uchendu)
1178	CCNA_01214	NaN	YjgP/YjgQ family membrane permease	CDS	lptF2 (Uchendu)
1181	CCNA_01217	NaN	phosphatidylglycerophosphate synthase	CDS	sphingo_01217
1182	CCNA_01218	NaN	sphingosine kinase/diacylglycerol kinase-related protein	CDS	sphk
1183	CCNA_01219	NaN	putative cytosolic protein	CDS	sphingo_01219
1184	CCNA_01220	ccbF	BioF-family ceramide biosynthesis protein CcbF	CDS	spt
1185	CCNA_01221	NaN	acyl carrier protein	CDS	acp
1186	CCNA_01222	NaN	NADH-ubiquinone oxidoreductase	CDS	cerR
1187	CCNA_01223	NaN	acyl-CoA synthetase	CDS	acps
1190	CCNA_01226	NaN	OstA family protein	CDS	lptC2 (Uchendu)
3018	CCNA_03113	NaN	membrane-associated phospholipid phosphatase	CDS	ctpA / LpxE-like

	locustag	gene	description	feature_type
303	CCNA_00307	NaN	phospholipid-lipopolysaccharide ABC transporter	CDS
1704	CCNA_01760	NaN	LPS assembly outer membrane protein LptD	CDS
1705	CCNA_01761	NaN	LPS export ABC transporter permease LptG	CDS
1706	CCNA_01762	NaN	LPS export ABC transporter permease LptF	CDS
3605	CCNA_03716	NaN	LptC-related lipopolysaccharide assembly protein	CDS
3753	CCNA_03866	NaN	LptE superfamily protein	CDS

	4580	4584	4599	gene	description
locustag
CCNA_00307	132.4	139.8	259.9	NaN	phospholipid-lipopolysaccharide ABC transporter
CCNA_01760	432.5	436.7	486.8	NaN	LPS assembly outer membrane protein LptD
CCNA_01761	172.7	153.7	176.3	NaN	LPS export ABC transporter permease LptG
CCNA_01762	262.9	233.0	258.1	NaN	LPS export ABC transporter permease LptF
CCNA_03716	64.7	83.7	123.1	NaN	LptC-related lipopolysaccharide assembly protein
CCNA_03866	195.0	228.6	211.7	NaN	LptE superfamily protein

	fdr_4584_vs_4580	fdr_4599_vs_4584	logFC_4584_vs_4580	logFC_4599_vs_4584
locustag
CCNA_00307	0.88	0.01	0.08	0.89
CCNA_01760	0.96	0.20	0.01	0.16
CCNA_01761	0.47	0.21	-0.17	0.20
CCNA_01762	0.46	0.35	-0.17	0.15
CCNA_03716	0.08	0.00	0.37	0.56
CCNA_03866	0.16	0.35	0.23	-0.11

	label	regulon_size	deg_size	overlap	expected	fold	p
0	Up in 4584_vs_4580 ∩ Stein ChvI-induced	133	119	19	4.0	4.75	8.05e-09
1	Up in 4584_vs_4580 ∩ QY ChvI-induced	293	119	28	8.8	3.18	1.82e-08
2	Up in 4584_vs_4580 ∩ QY ChvI-repressed	291	119	7	8.8	0.80	7.84e-01
3	Down in 4584_vs_4580 ∩ Stein ChvI-induced	133	34	2	1.1	1.75	3.18e-01
4	Down in 4584_vs_4580 ∩ QY ChvI-induced	293	34	1	2.5	0.40	9.28e-01
5	Down in 4584_vs_4580 ∩ QY ChvI-repressed	291	34	2	2.5	0.80	7.26e-01
6	Up in 4599_vs_4584 ∩ Stein ChvI-induced	133	235	36	7.9	4.56	1.44e-15
7	Up in 4599_vs_4584 ∩ QY ChvI-induced	293	235	50	17.4	2.87	1.40e-12
8	Up in 4599_vs_4584 ∩ QY ChvI-repressed	291	235	16	17.3	0.93	6.67e-01
9	Down in 4599_vs_4584 ∩ Stein ChvI-induced	133	95	2	3.2	0.63	8.36e-01
10	Down in 4599_vs_4584 ∩ QY ChvI-induced	293	95	2	7.0	0.28	9.95e-01
11	Down in 4599_vs_4584 ∩ QY ChvI-repressed	291	95	9	7.0	1.29	2.62e-01

	locustag	gene	description
488	CCNA_00497	NaN	smooth LPS biosynthesis glycosyltransferase
1037	CCNA_01068	NaN	glycosyltransferase
1503	CCNA_01553	NaN	WcaJ-family glycosyltransferase
3621	CCNA_03733	NaN	mannose-1-phosphate guanylyltransferase

strain	4580	4584	4599
locustag
CCNA_00497	381.1	312.9	260.1
CCNA_01068	114.9	105.3	89.4
CCNA_01553	58.7	47.6	52.2
CCNA_03733	185.7	185.2	200.4

	fdr_4584_vs_4580	fdr_4599_vs_4580	fdr_4599_vs_4584	logFC_4584_vs_4580	logFC_4599_vs_4580	logFC_4599_vs_4584
locustag
CCNA_00497	0.18	0.00	0.09	-0.28	-0.55	-0.27
CCNA_01068	0.54	0.01	0.10	-0.13	-0.36	-0.24
CCNA_01553	0.16	0.26	0.40	-0.30	-0.17	0.13
CCNA_03733	0.99	0.41	0.41	-0.00	0.11	0.11

	locustag	gene	description	logFC	fdr	feature_type
0	CCNA_02517	NaN	RcnB family protein	4.517696	0.000025	CDS
1	CCNA_02205	NaN	RcnB family protein	4.420999	0.000009	CDS
2	CCNA_03327	NaN	hypothetical protein	3.884544	0.000082	CDS
3	CCNA_02378	NaN	SIMPL family protein	3.874556	0.000076	CDS
4	CCNA_03169	NaN	SH3 domain protein	3.868726	0.000159	CDS
5	CCNA_03487	NaN	zonular occludens toxin, zot-like protein	3.857214	0.000009	CDS
6	CCNA_R0184	NaN	NaN	3.773363	0.000015	ncRNA
7	CCNA_01361	NaN	PepSY superfamily protein	3.768517	0.000033	CDS
8	CCNA_01075	NaN	beta/gamma crystallin family protein	3.611622	0.000003	CDS
9	CCNA_01362	urpR	two-component response regulator UrpR	3.377279	0.000006	CDS
10	CCNA_02146	NaN	hypothetical protein	3.341338	0.000003	CDS
11	CCNA_02381	NaN	hypothetical protein	3.308646	0.000051	CDS
12	CCNA_02977	sigU	RNA polymerase ECF-type sigma factor sigU	3.126731	0.000007	CDS
13	CCNA_01386	NaN	hypothetical protein	3.114447	0.000006	CDS
14	CCNA_01076	NaN	beta/gamma crystallin family protein	2.964401	0.000003	CDS
15	CCNA_03820	NaN	LolA-family outer membrane lipoprotein carrier protein	2.885226	0.000004	CDS
16	CCNA_00282	NaN	conserved hypothetical lipoprotein	2.807265	0.001185	CDS
17	CCNA_R0181	NaN	NaN	2.802786	0.000015	ncRNA
18	CCNA_01601	NaN	hypothetical protein	2.783237	0.000016	CDS
19	CCNA_02597	NaN	hypothetical protein	2.762144	0.000003	CDS
20	CCNA_01348	NaN	hypothetical protein	2.761522	0.000003	CDS
21	CCNA_00882	osrP	stress regulated protein OsrP	2.692501	0.020488	CDS
22	CCNA_00732	NaN	GumN superfamily protein	2.661901	0.000006	CDS
23	CCNA_03726	NaN	dTDP-4-amino-4,6-dideoxygalactose transaminase, WecE family	2.654336	0.000002	CDS
24	CCNA_01237	NaN	hypothetical protein	2.565525	0.001144	CDS

	locustag	gene	description	logFC	fdr	feature_type
0	CCNA_02788	NaN	Xre-family transcriptional regulator	-2.398584	0.000105	CDS
1	CCNA_00427	NaN	hypothetical protein	-2.394932	0.005727	CDS
2	CCNA_R0148	NaN	NaN	-1.881834	0.009458	ncRNA
3	CCNA_03915	NaN	hypothetical protein	-1.861848	0.006196	CDS
4	CCNA_03218	NaN	periplasmic multidrug efflux lipoprotein precursor	-1.826563	0.001444	CDS
5	CCNA_02638	NaN	CLC voltage-gated chloride channel	-1.712824	0.000757	CDS
6	CCNA_03229	NaN	FAD dependent oxidoreductase	-1.699446	0.000232	CDS
7	CCNA_03230	glnA2	glutamine synthetase	-1.644673	0.000172	CDS
8	CCNA_02282	NaN	VanZ family protein	-1.556039	0.003228	CDS
9	CCNA_02529	dprA	DNA recombination-mediator protein A, SMF family	-1.540242	0.028716	CDS
10	CCNA_02374	NaN	transposase	-1.536406	0.027536	CDS
11	CCNA_00600	NaN	hypothetical protein	-1.524819	0.018800	CDS
12	CCNA_00613	NaN	cyanophycinase	-1.520347	0.003040	CDS
13	CCNA_03914	NaN	hypothetical protein	-1.486018	0.049911	CDS
14	CCNA_02292	NaN	acetyltransferase	-1.468352	0.004537	CDS
15	CCNA_03627	NaN	ATP phosphoribosyltransferase regulatory subunit	-1.463889	0.000746	CDS
16	CCNA_02722	hfaE	holdfast attachment protein HfaE	-1.432275	0.001484	CDS
17	CCNA_03481	NaN	DNA integration/recombination/inversion protein	-1.375272	0.002595	CDS
18	CCNA_02300	ftsX	cell division protein FtsX	-1.372005	0.009931	CDS
19	CCNA_R0122	NaN	NaN	-1.369317	0.047738	ncRNA
20	CCNA_03249	NaN	TonB-dependent receptor	-1.366080	0.000102	CDS
21	CCNA_00404	NaN	hypothetical protein	-1.362548	0.015151	CDS
22	CCNA_03501	NaN	threonylcarbamoyl-AMP synthase	-1.345996	0.006185	CDS
23	CCNA_02427	NaN	septum formation protein Maf	-1.345154	0.000518	CDS
24	CCNA_02789	NaN	Xre-family transcriptional regulator	-1.340084	0.005107	CDS

	locustag	gene	description	logFC	fdr	feature_type
0	CCNA_02277	hutA	TonB-dependent outer membrane channel HutA	10.510821	6.676913e-10	CDS
1	CCNA_02275	NaN	DUF4198 domain-containing protein	9.847587	9.778952e-09	CDS
2	CCNA_02794	NaN	lasso peptide isopeptide bond-forming cyclase	8.601769	9.097781e-11	CDS
3	CCNA_R0158	NaN	NaN	8.374005	1.337285e-10	ncRNA
4	CCNA_02274	NaN	EF-Hand domain protein	8.265150	1.339354e-09	CDS
5	CCNA_03904	NaN	hypothetical protein	7.424133	9.818341e-09	CDS
6	CCNA_03365	NaN	hypothetical protein	6.854481	1.145391e-10	CDS
7	CCNA_02869	NaN	GTA-gp10 phage tail tube protein	5.714557	2.430178e-07	CDS
8	CCNA_R0183	NaN	NaN	4.509436	3.540468e-07	ncRNA
9	CCNA_R0097	NaN	NaN	4.403156	2.470957e-05	ncRNA
10	CCNA_03175	NaN	DUF86 domain-containing protein	4.390742	7.743476e-09	CDS
11	CCNA_00028	NaN	TonB-dependent receptor	4.346785	2.078017e-04	CDS
12	CCNA_02452	NaN	hypothetical protein	4.263908	4.008802e-08	CDS
13	CCNA_03157	NaN	DUF4198 domain-containing protein	3.984614	6.816612e-06	CDS
14	CCNA_03155	NaN	PepSY-associated transmembrane protein	3.935256	3.445656e-05	CDS
15	CCNA_00598	NaN	TonB-dependent receptor	3.934072	9.818341e-09	CDS
16	CCNA_03158	NaN	FAD:protein FMN transferase ApbE	3.806852	7.891681e-05	CDS
17	CCNA_03156	NaN	FlgD-Ig family periplasmic protein	3.798423	9.233978e-05	CDS
18	CCNA_03159	NaN	sulfite reductase (NADPH) flavoprotein alpha-component	3.596584	1.959441e-04	CDS
19	CCNA_00616	NaN	Lrp-family transcriptional regulator	3.454346	5.870469e-07	CDS
20	CCNA_00027	NaN	2OG-Fe(II) oxygenase	3.435592	4.559981e-05	CDS
21	CCNA_00154	NaN	hypothetical protein	3.276719	1.866684e-05	CDS
22	CCNA_00669	NaN	glycosyltransferase family 99 protein WbsX	3.220729	1.121652e-08	CDS
23	CCNA_03372	NaN	bacterioferritin-associated ferredoxin	3.089329	7.714849e-04	CDS
24	CCNA_01189	NaN	TonB-dependent receptor	3.067178	1.763360e-08	CDS

	locustag	gene	description	logFC	fdr	feature_type
0	CCNA_02188	NaN	PaaI-family thioesterase	-4.411331	7.570828e-08	CDS
1	CCNA_00055	NaN	ferric uptake regulation protein	-4.085965	4.008802e-08	CDS
2	CCNA_02189	NaN	peptidase, C13 family	-2.618966	2.325397e-05	CDS
3	CCNA_02743	NaN	TonB dependent receptor	-1.921445	4.233093e-03	CDS
4	CCNA_03644	sdhC	succinate dehydrogenase cytochrome b556 subunit	-1.734002	3.761314e-05	CDS
5	CCNA_03108	chvT	TonB-dependent outer membrane receptor ChvT	-1.730037	3.143158e-03	CDS
6	CCNA_03643	NaN	succinate dehydrogenase membrane anchor subunit	-1.718340	1.489069e-06	CDS
7	CCNA_02033	nuoA	NADH-quinone oxidoreductase chain A	-1.627552	8.007009e-06	CDS
8	CCNA_02742	NaN	hypothetical protein	-1.625199	7.018986e-05	CDS
9	CCNA_02032	nuoB	NADH-quinone oxidoreductase chain B	-1.538695	3.507305e-05	CDS
10	CCNA_02187	sspB	adapter for ClpXP proteolysis of tmRNA-tagged proteins SspB	-1.530940	8.007009e-06	CDS
11	CCNA_03642	NaN	succinate dehydrogenase flavoprotein subunit	-1.514384	1.236055e-06	CDS
12	CCNA_02031	nuoC	NADH-quinone oxidoreductase chain C	-1.424882	4.719260e-05	CDS
13	CCNA_02030	NaN	hypothetical protein	-1.413716	1.727399e-05	CDS
14	CCNA_02048	NaN	TonB-dependent receptor	-1.371913	2.190433e-03	CDS
15	CCNA_02026	NaN	CRISPR/Cas system endoribonuclease Cas6-like protein	-1.315314	4.186911e-05	CDS
16	CCNA_03961	NaN	hypothetical protein	-1.313518	4.958722e-04	CDS
17	CCNA_02022	nuoH	NADH-quinone oxidoreductase chain H	-1.236365	7.891681e-05	CDS
18	CCNA_03975	NaN	hypothetical protein	-1.234118	4.445721e-03	CDS
19	CCNA_02029	nuoD	NADH-quinone oxidoreductase chain D	-1.223333	5.747228e-05	CDS
20	CCNA_02027	nuoE	NADH-quinone oxidoreductase chain E	-1.208744	4.791568e-05	CDS
21	CCNA_02020	nuoI	NADH-quinone oxidoreductase chain I	-1.205109	4.516322e-04	CDS
22	CCNA_02025	NaN	Cytochrome bd-type quinol oxidase, subunit 2-like protein	-1.200180	8.711937e-04	CDS
23	CCNA_02910	NaN	TonB-dependent receptor	-1.146710	1.241171e-02	CDS
24	CCNA_02741	NaN	Gcw/chp-family protein	-1.141744	1.886121e-04	CDS

	ccna_locus	gene_symbol	description	abundance_4580	abundance_4659	abundance_4672
395	CCNA_01217	NaN	submitted name: Phosphatidylglycerophosphate synthase [OS=Caulobacter vibrio...	82.0	80.6	137.5
526	CCNA_01226	NaN	LPS export ABC transporter periplasmic protein LptC [OS=Caulobacter vibrioid...	90.2	67.3	142.5
592	CCNA_01223	NaN	Putative acyl-CoA synthetase CCNA_01223 [OS=Caulobacter vibrioides NA1000]	NaN	NaN	NaN
778	CCNA_01212	bcerS	Bacterial ceramide synthase [OS=Caulobacter vibrioides NA1000]	NaN	NaN	NaN

	ccna_locus	gene_symbol	description	abundance_4580	abundance_4659	abundance_4672
34	CCNA_01760	lptD	LPS-assembly protein LptD [OS=Caulobacter vibrioides NA1000]	117.4	106.0	76.6
156	CCNA_03866	NaN	submitted name: LptE superfamily protein [OS=Caulobacter vibrioides NA1000]	111.3	119.3	69.4
315	CCNA_00307	NaN	submitted name: Phospholipid-lipopolysaccharide ABC transporter [OS=Caulobac...	NaN	NaN	300.0
720	CCNA_01762	NaN	submitted name: YjgP/YjgQ family membrane permease [OS=Caulobacter vibrioide...	NaN	NaN	NaN

	ccna_locus	gene_symbol	description	log2_4659_over_4580
114	CCNA_02275	NaN	submitted name: ABC transporter, periplasmic component [OS=Caulobacter vibri...	8.908393
120	CCNA_00778	bipA	Large ribosomal subunit assembly factor BipA [OS=Caulobacter vibrioides NA1000]	6.309328
6	CCNA_00028	NaN	submitted name: TonB-dependent receptor [OS=Caulobacter vibrioides NA1000]	5.567802
220	CCNA_02623	ftsZ	Cell division protein FtsZ [OS=Caulobacter vibrioides NA1000]	4.772072
211	CCNA_01770	ndk	Nucleoside diphosphate kinase [OS=Caulobacter vibrioides NA1000]	3.884523
364	CCNA_03156	NaN	submitted name: Periplasmic protein [OS=Caulobacter vibrioides NA1000]	3.843274
200	CCNA_01805	NaN	Dihydrolipoyl dehydrogenase [OS=Caulobacter vibrioides NA1000]	3.807979
240	CCNA_01086	lepA	Elongation factor 4 [OS=Caulobacter vibrioides NA1000]	3.484070
298	CCNA_03711	hpf	Ribosome hibernation promoting factor [OS=Caulobacter vibrioides NA1000]	3.428635
3	CCNA_03023	NaN	submitted name: TonB-dependent receptor [OS=Caulobacter vibrioides NA1000]	3.413302
258	CCNA_02661	NaN	submitted name: M16 family peptidase [OS=Caulobacter vibrioides NA1000]	3.378694
241	CCNA_03119	NaN	submitted name: Uncharacterized protein [OS=Caulobacter vibrioides NA1000]	3.334984
18	CCNA_00138	NaN	submitted name: TonB-dependent receptor [OS=Caulobacter vibrioides NA1000]	3.330071
687	CCNA_00442	cheAI	Chemotaxis protein CheA [OS=Caulobacter vibrioides NA1000]	3.217231
190	CCNA_02517	NaN	submitted name: Uncharacterized protein [OS=Caulobacter vibrioides NA1000]	3.108524

	ccna_locus	gene_symbol	description	log2_4672_over_4659
188	CCNA_00698	NaN	DUF3617 domain-containing protein [OS=Caulobacter vibrioides NA1000]	8.738092
725	CCNA_00369	NaN	submitted name: Magnesium and cobalt efflux protein corC-like protein [OS=Ca...	6.698457
481	CCNA_00025	NaN	submitted name: Major facilitator superfamily transporter [OS=Caulobacter vi...	6.478625
224	CCNA_01252	sdpA	submitted name: Soluble lytic murein transglycosylase SdpA [OS=Caulobacter v...	4.819918
48	CCNA_01386	NaN	17 kDa surface antigen [OS=Caulobacter vibrioides NA1000]	4.747745
314	CCNA_02986	NaN	submitted name: Uncharacterized protein [OS=Caulobacter vibrioides NA1000]	4.651249
30	CCNA_03169	NaN	submitted name: SH3 domain protein [OS=Caulobacter vibrioides NA1000]	4.515006
190	CCNA_02517	NaN	submitted name: Uncharacterized protein [OS=Caulobacter vibrioides NA1000]	4.366195
306	CCNA_03563	atpH	ATP synthase subunit delta [OS=Caulobacter vibrioides NA1000]	3.846704
233	CCNA_00800	cydA	submitted name: Cytochrome bd-type quinol oxidase, subunit 1 cydA [OS=Caulob...	3.773996
403	CCNA_03003	NaN	histidine kinase [OS=Caulobacter vibrioides NA1000]	3.684138
146	CCNA_01406	NaN	submitted name: Metal-dependent hydrolase [OS=Caulobacter vibrioides NA1000]	3.676258
386	CCNA_01592	NaN	submitted name: SN-glycerol-3-phosphate transport ATP-binding protein [OS=Ca...	3.511406
167	CCNA_03619	NaN	submitted name: Zinc metalloprotease [OS=Caulobacter vibrioides NA1000]	3.439765
241	CCNA_03119	NaN	submitted name: Uncharacterized protein [OS=Caulobacter vibrioides NA1000]	2.983113

	ccna_locus	gene_symbol	description	log2_4672_over_4659
299	CCNA_03384	rpmE	Large ribosomal subunit protein bL31 [OS=Caulobacter vibrioides NA1000]	-5.661966
115	CCNA_00357	NaN	submitted name: Secretory tripeptidyl aminopeptidase [OS=Caulobacter vibrioi...	-5.152919
694	CCNA_01792	rpmF	Large ribosomal subunit protein bL32 [OS=Caulobacter vibrioides NA1000]	-4.972080
88	CCNA_01306	rplC	Large ribosomal subunit protein uL3 [OS=Caulobacter vibrioides NA1000]	-4.327020
161	CCNA_01317	rplX	Large ribosomal subunit protein uL24 [OS=Caulobacter vibrioides NA1000]	-4.317017
323	CCNA_01325	rplO	Large ribosomal subunit protein uL15 [OS=Caulobacter vibrioides NA1000]	-4.222635
252	CCNA_01313	rplP	Large ribosomal subunit protein uL16 [OS=Caulobacter vibrioides NA1000]	-3.696686
230	CCNA_00321	rplU	Large ribosomal subunit protein bL21 [OS=Caulobacter vibrioides NA1000]	-3.685466
545	NaN	NaN	SWISS-PROT:P00766 Chymotrypsinogen A - Bos taurus (Bovine).	-3.388271
407	CCNA_01654	NaN	peptidylprolyl isomerase [OS=Caulobacter vibrioides NA1000]	-3.315687
132	CCNA_00531	rplL	Large ribosomal subunit protein bL12 [OS=Caulobacter vibrioides NA1000]	-3.292131
209	CCNA_02471	NaN	submitted name: Cobalt-zinc-cadmium resistance protein czcC [OS=Caulobacter ...	-3.253135
292	CCNA_00639	NaN	submitted name: Heme:hemopexin-binding protein [OS=Caulobacter vibrioides NA...	-3.249445
110	CCNA_01739	rplI	Large ribosomal subunit protein bL9 [OS=Caulobacter vibrioides NA1000]	-3.146274
111	CCNA_01307	rplD	Large ribosomal subunit protein uL4 [OS=Caulobacter vibrioides NA1000]	-3.055233