A platform for robust quantitative functional genomics reveals temporal fitness landscapes in African trypanosomes
Simon D’ArchivioiD, Lorenzo BrusiniiD, Sandra TrindadeiD, Luísa M. FigueiredoiD, Catarina GadelhaiD, Bill WicksteadiD
Abstract
Genome-scale phenotypic screening is transforming our understanding of host-pathogen interactions, but accurate quantitation of mutant fitness and generation of libraries in strains that capture complex disease characteristics remains challenging. Here we introduce Direct RNAi Fragment Sequencing (DRiF-Seq), a sequencing-optimized high-throughput RNA-interference platform that enables robust, clone-resolved quantification of fitness effects in African trypanosomes. Combined with an a posteriori noise estimation approach, DRiF-Seq provides reproducible estimates of fitness magnitude, timing, and statistical confidence for individual mutants and genes. Application to pleomorphic bloodstream-form Trypanosoma brucei enables the first genome-scale quantitative profiling in a mammalian infection model, creating a framework for investigating parasite biology at scale in host. DRiF-Seq demonstrates that over half of core genes have a fitness cost when targetted by RNAi, accurately resolves fitness landscapes within molecular complexes, identifies characteristic temporal signatures of gene disruption, and detects biologically meaningful differences between closely related parasite strains that predict differential drug sensitivity. Comparative analyses with genome-wide datasets from Toxoplasma and Plasmodium further demonstrate conserved and lineage-specific determinants of parasite fitness across eukaryotes. Together, DRiF-Seq provides a sensitive and transferable framework for quantitative functional genomics, a community resource for understanding parasite biology and a system for exploitation of high-complexity mutant screens in host.
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In-depth summary
Claude Sonnet 4.6 · xhigh effort · 8 Nov 2026 · from the full text of version 2 (23 Jul 2026) · CC BY 4.0
DRiF-Seq, a sequencing-optimized RNAi platform with a posteriori noise estimation, quantifies clone-resolved fitness genome-wide in pleomorphic Trypanosoma brucei; over half of core genes carry fitness costs.
Earlier RIT-seq screens were weakly quantitative: 951 of 2859 significant genes appeared to gain fitness on RNAi, implying many false positives and sensitivity possibly as low as 33% at Day 3. DRiF-Seq redesigns the fragment library, amplification and statistics to give reliable fitness estimates.
Key results
ShownFitness estimates are reproducible between independent libraries, and individual RNAi clones are distinguishable by their sequenced fragment ends.
Gene-wise effect-size correlation ρ=0.74 between independent libraries; ρ=0.90 for the average independent mutant vs combined abundance; only 40 identical opposite-orientation pairs among 363,958 fragments, all in repeats (MAPQ ≤ 1). · Direct-RNAi Fragment Sequencing; Robust fitness change estimation by a posteriori…
ShownMore than half of core genes cause a fitness loss on RNAi in bloodstream-form EATRO1125, with very few apparent gains, and most defects appear early.
3601 core genes (44% of 8170 targeted) have loss-of-fitness at fdr ≤ 0.01; >5000 genes at 10% FDR; 1427 core genes significant at 24h; only 12 core genes show gain-of-fitness at day 6, vs the RIT-seq excess of apparent gains. · Genome-scale DRiF-Seq in quorum-sensing…
ShownQuantitative fitness maps resolve hierarchies within complexes, flag dispensable subunits, and reveal that shorter RNAi fragments work better.
AP-1 > AP-3 > AP-4 fitness cost, with AP-4 showing no detectable effect; one of 36 Complex I/II subunits (160 mutants) had LoF across 18 generations; ~300 bp fragments gave greater LoF than longer fragments in the same gene, ~1.5 fewer generations over 6 days (p = 10^-79; 1280 genes). · Mapping fitness landscapes within molecular…; Figure legends
ShownFitness profiles separate two closely related strains, and the differential sensitivity predicted by the screen was confirmed with an actin inhibitor.
17 GO terms enriched for greater LoF in EATRO1125 and 5 in Lister 427 (adjusted p ≤ 0.01); EATRO1125 ~3x more sensitive to latrunculin A; same-fragment knockdown lines replicated direction and magnitude for 4 and 6 genes. · DRiF-Seq reveals biological differences at…; Figure legends
ShownLoss-of-fitness timing is gene-set specific, so temporal profiles add information beyond effect size.
3806 genes (fdr ≤ 0.01 by both metrics) split by k-means (k=6, gap statistic) into 5 early-to-late clusters plus a constant-low cluster; 11 gain-of-fitness genes; nucleosome/chromatin terms skew early, ribosomal processing late. · Kinetics of fitness loss map to biological…; Temporal analysis of fitness cost profiles
Methods, data and reproducibility
Randomly sheared ~400 bp genomic fragments are cloned into an inducible RNAi vector integrated at a minichromosomal locus. Paired-end sequencing across induction time points is analysed by a posteriori noise estimation (censored Gaussian fit on MA-plot, Z scores, Benjamini-Hochberg FDR).
- Lister 427 DRiF-Seq RNAi libraries in 427-TTS cells, induced RNAi time series · Trypanosoma brucei (Lister 427) · n = 2 libraries, ~300,000 mutants each; 363,958 mappable fragments
- EATRO1125 (AnTat-TTS) pleomorphic RNAi library, gene-level fitness profiles · Trypanosoma brucei (EATRO1125) · n = ~260,000 mutants; 14211 genes
- Reanalysis of prior RIT-Seq data in Lister 427 with apNE · Trypanosoma brucei · n = 7435 non-redundant TREU927 genes
- Reanalysis of genome-wide Toxoplasma CRISPR screen and comparison with Plasmodium knockout growth data · Toxoplasma gondii; Plasmodium berghei · n = 8158 genes (Toxoplasma); 2578 knockouts (Plasmodium)
- Bloodstream-form EATRO1125 RNA-Seq (RPKM) used for expression comparison · Trypanosoma brucei · ENA study ERP125374
- Mouse infection to test AnTat-TTS parasitaemia waves and virulence · mouse (C57BL/6J, male, 8 weeks) · n = infected with 2,000 parasites
- Methods & tools
- DRiF-Seq, a posteriori noise estimation (apNE), bcl2fastq2 v2.20, trim_galore v0.6.10, bowtie2 v2.5.2, TriTrypDB v48/v58, OrthoMCL-DB v6, NbClust (gap statistic), Monte Carlo permutation GO enrichment, DESeq / DEGSeq (RIT-Seq comparison), NextSeq 500 sequencing, CellTiter-Glo luminescence assay
- Data & accessions
- https://tritrypdb.org/
- Reagents & environment
- bcl2fastq2 v2.20; fastx_trimmer v0.0.14; trim_galore v0.6.10; bowtie2 v2.5.2; TriTrypDB v48 (mapping) and v58 (orthologs, GO); OrthoMCL-DB v6; R package NbClust.
- Reproducibility gaps
- No repository for the custom fragment-counting script or apNE implementation is given; processed counts and effect estimates are in supplementary data files; no accession for the new sequencing data is stated.
Limitations (5)
- RNAi knockdown may not reduce RNA enough to reveal fitness effects for some essential genes, so the fraction of genes with fitness cost may be underestimated. · stated by the authors
- Kinetic fingerprints of loss-of-fitness are not discriminatory enough to place unknown genes into specific processes or locations. · stated by the authors
- The cause of stronger RNAi effects in EATRO1125 than Lister 427 is unknown (more active RNAi or another factor), and CDS vs 3'-UTR differences may reflect annotation confidence or alternative splicing. · stated by the authors
- Fitness was assessed only in culture below quorum-sensing density, so genes from earlier quorum-sensing screens were not recovered; screening in host is presented only as a future extension. · noted in this summary
- Strain differences rest on two strains with one inhibitor (latrunculin A) as the sole pharmacological validation, and EATRO1125 reads were cross-mapped to the Lister 427 assembly. · noted in this summary
Who should read:Read this if you work on pooled RNAi or CRISPR fitness screens, parasite functional genomics, or noise models for amplicon-count data without extensive replication.
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