Abstract
Trypanosomatids, which include major pathogens of humans and livestock, are flagellated protozoa for which cell cycle controls and the underlying mechanisms are not completely understood. Here, we describe a genome-wide RNA-interference library screen for cell cycle defects in Trypanosoma brucei. We induced massive parallel knockdown, sorted the perturbed population using high-throughput flow cytometry, deep-sequenced RNAi-targets from each stage and digitally reconstructed cell cycle profiles at a genomic scale; also enabling data visualisation using an online tool (https://tryp-cycle.pages.dev/). Analysis of several hundred genes that impact cell cycle progression reveals >100 flagellar component knockdowns linked to genome endoreduplication, evidence for metabolic control of the G1-S transition, surface antigen regulatory mRNA-binding protein knockdowns linked to G2M accumulation, and a putative nucleoredoxin required for both mitochondrial genome segregation and for mitosis. The outputs provide comprehensive functional genomic evidence for the known and novel machineries, pathways and regulators that coordinate trypanosome cell cycle progression.
Similar content being viewed by others
Introduction
The canonical eukaryotic cell cycle encompasses discrete phases: G1 (gap 1), when the cell prepares for DNA replication; S (synthesis) phase, when nuclear DNA replication takes place; G2 (gap 2), when the cell prepares for mitosis; and M (mitosis) when the replicated DNA is segregated and the nucleus divides1. Mitosis is followed by cytokinesis (cell division), generating two daughter cells2. Rate-limiting mechanisms that facilitate quality control are relieved at discrete points. Thus, anomalies occurring during cell cycle progression can result in cell cycle delay or arrest, to allow the cell to resolve the anomaly; in cell death, if the anomaly cannot be resolved or, among other outcomes, in carcinogenesis. Therefore, progression through the cell cycle is typically under strict checkpoint control; the G1-S, intra S phase, G2-M and spindle checkpoints control the onset of S phase, S phase progression, the onset of M phase and M phase progression, respectively3. These processes have been extensively studied, particularly because cell cycle defects are common triggers for carcinogenesis4. However, our understanding of the evolution and mechanisms of eukaryotic cell cycle progression control derives primarily from studies on the opisthokonts (including animals and fungi), with relatively fewer studies on divergent eukaryotes, such as the trypanosomatids1.
The trypanosomatids are flagellated protozoa and include parasites that cause a range of neglected tropical diseases that have major impacts on human and animal health. The African trypanosome, Trypanosoma brucei, is transmitted by tsetse flies and causes both human and animal diseases, sleeping sickness and nagana, respectively, across sub-Saharan Africa5. T. brucei has emerged as a highly tractable experimental system, both as a parasite and as a model organism6. For example, the T. brucei flagellum7 serves as a model for studies on human ciliopathies8,9,10,11. Divergent features, shared with other pathogenic trypanosomatids, such as Trypanosoma cruzi and Leishmania, include glycolysis compartmentalised within glycosomes12, a single mitochondrion with a complex mitochondrial DNA structure known as the kinetoplast13 and polycistronic transcription of almost every gene14. Widespread, and constitutive, polycistronic transcription in trypanosomatids places major emphasis on post-transcriptional controls by mRNA binding proteins (RBPs) and post-translational controls, involving protein phosphorylation, for example.
Studies that focus on cell cycle controls in T. brucei have revealed features conserved with other well-studied eukaryotes, but also divergent features13,15. Notably, the available evidence suggests that certain cell cycle checkpoints are absent. For example, cytokinesis can occur independent of either mitosis or nuclear DNA synthesis in the insect stage of T. brucei16. Moreover, functions previously thought to be fulfilled by highly conserved proteins employ lineage-specific or highly divergent proteins in trypanosomatids. The kinetochore complex, which directs chromosome segregation, is trypanosomatid-specific, for example17, while the origin recognition complex (ORC), involved in DNA replication initiation, is highly divergent18. In terms of high-throughput studies, transcriptome19 and proteome20 monitoring during the T. brucei cell cycle revealed hundreds of regulated mRNAs and proteins, while phosphoproteomic analysis revealed dynamic phosphorylation of several RBPs21. Divergence presents substantial outstanding challenges, however, since many T. brucei genes have not yet been assigned a specific function, and many cell cycle regulators likely remain to be identified. High-throughput, genome-scale functional genetic screens can be used to simultaneously assess every gene in a genome for a role in a particular process. We developed RNA Interference Target sequencing (RIT-seq) for T. brucei22 and previously generated genome-scale fitness profiles, facilitating essentiality predictions and the prioritisation of potential drug targets23.
Here, we describe a genome-scale RIT-seq screen to identify cell cycle controls and regulators in bloodstream form African trypanosomes. Following induction of knockdown, cells were sorted according to their DNA content using fluorescence-activated cell sorting (FACS). The sorted populations were the G1, S and G2M cell cycle stages as well as perturbed cell populations with either less DNA than typically found in G1 or more DNA than typically found in G2M. RIT-seq analysis was carried out for each sorted population and cell cycle profiles were digitally reconstructed for each knockdown using sequencing read-counts. This genome-wide screen reveals the protein complexes, pathways and signalling factors required for progressive steps through the trypanosome cell cycle. For example, glycolytic enzymes are shown to be required for G1/S progression, CMG (Cdc45-MCM-GINS) complex components are shown to be required for DNA replication, proteasome and kinetochore complex components are shown to be required for G2M progression, and flagellar components as well as cytokinesis initiation factors are shown to be required for cytokinesis. Two hits were selected for further validation, one of which is shown to be a cell cycle regulated putative nucleoredoxin involved in coordinating segregation of the mitochondrial kinetoplast and the nuclear genome.
Results
A genome-wide conditional knockdown screen for cell cycle progression defects
Bloodstream form T. brucei are readily grown in cell culture, with exponential proliferation and a doubling time of approximately 6.5 h. The T. brucei nuclear genome is typically diploid such that G1 cells have a 2 C genome content; C represents the haploid DNA content. Cells progressing through nuclear S phase, and replicating their DNA, have a genome content between 2 C and 4 C, while cells that have completed DNA replication (G2M) have a 4 C DNA content (Fig. 1a). Mitosis and cytokinesis then produce two daughter cells with a 2 C DNA content. Some perturbations yield defects involving a ‘short-circuit’, whereby S phase, mitosis and/or cytokinesis are skipped, producing sub-2C cells or over-replicated, polyploid (>4 C) cells (Fig. 1a). Polyploid cells arise due to endoreduplication, additional rounds of DNA replication without cytokinesis, either with24 or without25,26 mitosis, yielding cells with multiple nuclei or with polyploid nuclei, respectively.
a Schematic representation of the bloodstream form T. brucei cell cycle, also showing aberrant sub-2C and >4 C (multiple nuclei) phenotypes; anuclear zoids are not shown as they are undetectable by RIT-seq. b The schematic illustrates the RIT-seq screen; massive parallel induction of RNAi with tetracycline (Tet), followed by flow cytometry and RIT-seq, allowing for reconstruction of cell cycle profiles, using mapped reads from each knockdown. Each read-mapping profile encompasses the gene of interest and associated untranslated regions present in the cognate mRNA. The library data represents the uninduced and unsorted population. GeneIDs, Tb927.7.3160 for example, are indicated without the common ‘Tb927.’ component.
We devised a high-throughput RNA interference (RNAi) target sequencing (RIT-seq) screen to identify cell cycle controls and regulators at a genomic scale. Key features of RIT-seq screening include: first, use of a high-complexity T. brucei RNAi library comprising, in this case, approximately one million clones; second, massive parallel tetracycline-inducible expression of cognate dsRNA; and third, deep sequencing, mapping and counting of mapped reads derived from RNAi target fragments22. Each clone in the library has one of approximately 100,000 different RNAi target fragments (250–1500 bp) between head-to-head inducible T7-phage promoters. This is achieved by targeting each cassette to a specific, single chromosomal locus that supports robust and reproducible inducible expression22,27. Inducibly expressed long dsRNA is then processed to siRNA by the native RNAi machinery28. Complexity and depth of genome coverage in the library are critical, in that similar phenotypes produced by multiple clones with distinct RNAi target fragments against the same gene provide cross-validation. Improvements in reference genome annotation29, next generation sequencing technology and sequence data analysis tools (see Methods) have also greatly facilitated quantitative phenotypic analysis using short-read sequence data.
Briefly, we induced massive parallel knockdown in an asynchronous T. brucei bloodstream form RNAi library for 24 h, fixed the cells, stained their DNA with propidium iodide (PI) and then used fluorescence-activated cell sorting (FACS) to divide the perturbed cell population into; sub-diploid (<2 C), G1 (2 C), S (between 2 C and 4 C), G2M (4 C) and over-replicated (>4 C) pools (Supplementary Fig. 1). Fixation and staining with the fluorescent DNA intercalating dye were pre-optimised for high-throughput sorting (see Materials and Methods). Approximately 10 million cells were collected for each of the G1, S and G2M pools and samples from these pools were checked post-sorting to assess their purity (Fig. 1b, Supplementary Fig. 1). For the perturbed and less abundant <2 C and >4 C pools, less than one million cells were collected; these pools were retained in their entirety for RIT-seq analysis.
RIT-seq was carried out for both the uninduced and induced, unsorted library controls, and for each of the five induced and sorted pools of cells as described in the Methods section. Briefly, we extracted genomic DNA from each sample, amplified DNA fragments containing each RNAi target fragment in PCR reactions (Supplementary Fig. 1) and used the amplified products to generate Illumina sequencing libraries. Analysis of sequencing reads mapped to the reference genome yielded counts for both total reads as well as reads containing the barcode (GTGAGGCCTCGCGA) that flanks each RNAi target fragment; the presence of the barcode confirmed that reads were derived from a specific RNAi target fragment and not from elsewhere in the genome. We derived counts of reads mapped to each of >7200 non-redundant gene sequences in the uninduced and induced, unsorted library controls and in each of the five sorted samples. We selected the 24 h timepoint, equivalent to approximately 3.5 population doubling times, for the current analysis. We found that reads for 23.4% of genes were diminished by >3-fold following 72 h of knockdown in our prior RIT-seq study23, while reads for only 0.6% of genes dropped by >3-fold following 24 h of knockdown in the unsorted control samples analysed here (see Supplementary Fig. 2a, b). Thus, 24 h should have allowed sufficient time for the development of robust inducible phenotypes and also captured perturbed cells before they were critically diminished due to loss-of-fitness. An unanticipated feature that emerged from this analysis of prior RIT-seq data was that knockdown of proteins associated with DNA replication typically failed to register a major loss-of-fitness (Supplementary Fig. 2a, b). This suggested that a reduced rate of DNA replication can be tolerated, albeit extending S phase (see below) but having relatively little impact on viability. Each sorted sample library yielded between 23 and 37 million mapped read-pairs; <2 C = 37 M, G1 = 35 M, S = 30 M, G2M = 23 M, > 4 C = 25 M; this set of five samples yielded data for >7000 genes which equates to >35,000 RNAi data-points (Supplementary data 1).
The RIT-seq digital data for individual genes following knockdown provided a measure of abundance in each pool and were, therefore, used to digitally reconstruct cell cycle profiles for individual gene knockdowns (Fig. 1b). We expected to observe accumulation of particular knockdowns in specific cell cycle phase pools, thereby reflecting specific defects. This was indeed the case, and some examples are shown to illustrate; no major defect, G2M overrepresented or >4 C overrepresented, following knockdown (Fig. 1b). These outputs suggest that loss of a cytoplasmic dynein heavy chain (7.3160) does not perturb cell cycle distribution; that the proteasome is required to complete G2M (see below); and that knockdown of a flagellar axonemal dynein heavy chain (11.11220) results in endoreduplication in the absence of cytokinesis; dyneins are cytoskeletal motor proteins that either move along microtubules or drive microtubule sliding, to produce a flagellar beat, for example30.
Validation and identification of genes linked to cell cycle defects
The T. brucei core genome comprises a non-redundant set of over 7200 protein-coding sequences, for which we were now able to digitally reconstruct cell cycle profiles following knockdown. First, we examined knockdowns reporting an overrepresentation of >4 C cells, indicating endoreduplication, which yielded 284 genes (Fig. 2a, left-hand panel; Supplementary data 1). The >4 C phenotype was previously observed following α-tubulin knockdown in a landmark study that first described RNAi in T. brucei24 and, indeed, we observed pronounced overrepresentation of >4 C cells for both adjacent α-tubulin and β-tubulin gene knockdowns (Fig. 2a, middle and right-hand panel). We then examined knockdowns reporting an overrepresentation of <2 C cells, indicating a reduced DNA content, which yielded 10 hits (Fig. 2b, left-hand panel; Supplementary data 1). Haploid cells were previously observed following DOT1A knockdown31 and, consistent with the previous report, we observed pronounced overrepresentation of <2 C cells for the DOT1A gene knockdown (Fig. 2b, middle and right-hand panel); we are not aware of other knockdowns reported to yield a similar phenotype. Indeed, other ‘<2 C hits’ mostly encode small hypothetical proteins, seven of which are 73 ± 11% shorter than the average, consistent with low read-count and under-sampling for these hits (Supplementary Fig. 2c). The remaining two hits are a histone chaperone (ASF1B) and a glycolytic enzyme (PFK). Together, these results provided initial validation for the >4 C and <2 C components of the screen.
a The plot on the left shows knockdowns overrepresented in the >4 C experiment in red; those with reads in the >4 C pool that exceeded the mean fold-change value by >1.75 times the SD, equivalent to >1.117-fold the sum of reads in the G1, S phase and G2M samples combined. The read-mapping profile and read-counts for α/β-tubulin are shown to the right. b The plot on the left shows knockdowns overrepresented in the sub-2C experiment in orange; those with reads in the sub-2C pool that exceeded the mean fold-change value by >1.75 times the SD, equivalent to >4-fold the sum of reads in the G1, S phase and G2M samples combined. The read-mapping profile and read-counts for DOT1A are shown to the right. c The RadViz plot shows knockdowns that registered >25% overrepresented read-counts in the G1 (purple), S phase (green), or G2M (blue) categories. d Read-mapping profiles and relative read-counts for example hits. PCNA, proliferating cell nuclear antigen; PPL2, PrimPol-like 2. e The Venn diagram shows the distribution of knockdowns overrepresented in each arm of the screen.
Next, we turned our attention to knockdowns reporting an overrepresentation of G1, S phase or G2M cells. The pools of knockdowns that registered >25% overrepresented read counts in each of these categories are highlighted in the RadViz plot in Fig. 2c (also see Supplementary data 1) and data for an example from each category are shown in Fig. 2d; the glycolytic enzyme, aldolase, reported 104% increase in G1 cells (further details below); the proliferating cell nuclear antigen (PCNA), a DNA sliding clamp that is a central component of the replication machinery32, reported 25% increase in S phase cells and 13% increase in G2M cells, consistent with prior analysis33; and PrimPol-like 2 (PPL2), a post-replication translesion polymerase, reported 65% increase in G2M cells, also consistent with prior analysis34. These results provided initial validation for the G1, S phase and G2M components of the screen. The full dataset can be searched and browsed using an interactive, open access, online data visualization tool (see Supplementary Fig. 3; https://tryp-cycle.pages.dev/).
Overall, the five components of the screen yielded 1198 genes that registered a cell cycle defect, based on the thresholds applied above. This is 16.6% of the 7205 genes analysed, and the distributions of these genes among the five arms of the screen are shown in the Venn diagram in Fig. 2e. Since we predicted that knockdowns associated with a cell cycle defect were more likely to also register a growth defect, we compared these datasets to prior RIT-seq fitness profiling data23. All groups of genes that registered cell cycle defects, except for the small <2 C set, were significantly enriched for genes that previously registered a loss-of-fitness phenotype following knockdown in bloodstream form cells (χ2 test; <2 C, p = 0.93; G1, p = 0.04; S phase, p = 1.3−4; G2M, p = 1.3−23; >4 C, p = 9.4−213), consistent with loss-of-fitness as a common outcome following a cell cycle progression defect. Taken together, the analyses above provided validation for the RIT-seq based cell cycle phenotyping approach and yielded >1000 candidate proteins that impact progression through specific steps of the T. brucei cell cycle.
Cytokinesis defects associated with endoreduplication
In bloodstream form T. brucei, defective >4 C cells can arise due to endoreduplication without cytokinesis, either with24 or without25,26 mitosis. Endoreduplication defects were previously observed following knockdown of α-tubulin24 or flagellar proteins7,35; consistent with the view that flagellar beat is required for cytokinesis in bloodstream form T. brucei. As shown above, dynein heavy chain (see Fig. 1b), α-tubulin and β-tubulin (see Fig. 2a) knockdowns were amongst 284 knockdowns overrepresented in the endoreduplicated pool in our screen. Gene Ontology (GO) annotations, which provide structured descriptions of gene products in terms of functions, processes, and compartments, were assessed to further profile this cohort of knockdowns. Terms overrepresented in association with an endoreduplication defect included ‘dynein’, ‘intraflagellar transport’ (IFT), ‘axoneme’ and ‘cytoskeleton’, and also ‘chaperonin T-complex’, ‘cytokinesis’ and ‘cell cycle’ (Fig. 3a). The violin plot in Fig. 3b shows specific enrichment of IFT and dynein knockdowns in association with endoreduplication. Exocyst components, primarily involved in exocytosis36, were included as a control cohort since none of the exocyst components registered enrichment in the >4 C pool, nor in any other experimental pool analysed here (see below). Enrichment of individual chaperonin T-complex components, dyneins, and IFT factors in the >4 C pool is illustrated in Fig. 3c. The chaperonin T-complex is involved in tubulin and actin folding37 and, notably, actin knockdown was also associated with endoreduplication (Supplementary Fig. 4).
a The bar-graph shows enriched Gene Ontology terms associated with the >4 C hits, those that exceed the mean fold-change value in this set by >1.75 times the SD. P-values are shown on the right. b The violin plot shows relative >4 C read-counts for cohorts of genes and reflects data distribution. Open circles indicate median values and the vertical bars indicate 95% confidence intervals. Significantly overrepresented cohorts are indicated in red. c The plots show overrepresentation of T-complex, dynein and intraflagellar transport (IFT) factors in red in the >4 C experiment. d The heatmaps show relative representation in all five sorted pools for the above and additional cohorts of knockdowns; blue, most overrepresented. e Example read-mapping profiles for hits overrepresented in the >4 C pool. f Example read-mapping profiles for ciliopathy-associated hits overrepresented in the >4 C pool. CMF Component of Motile Flagella, CFAP Cilia and Flagella Associated Protein.
The heat-map in Fig. 3d shows the data for all five sorted pools for the cohorts described above and for additional cohorts of knockdowns enriched in the >4 C pool; these include additional dynein chains, radial spoke proteins, extra-axonemal paraflagellar rod (PFR) proteins, as well as nucleoporins. The gallery in Fig. 3e shows examples of RIT-seq read-mapping profiles for twenty-six individual genes that register >4 C enrichment following knockdown. In addition to the categories above, these include the inner arm dynein 5-138, FAZ proteins which mediate attachment of the flagellum to the cell body39; all four cytokinesis initiation factors CIF1-440, and chromosomal passenger complex components, including CPC1 and the aurora B kinase, AUK1. AUK1 and CPC1 are spindle-associated and regulate mitosis and cytokinesis26,41. Notably, endoreduplication was reported previously following AUK1 knockdown in bloodstream form T. brucei42 and this is the kinase with the most pronounced overrepresentation in our >4 C dataset. The next >4 C overrepresented kinase is the CMGC/RCK (Tb927.3.690), knockdown of which previously yielded a striking cytokinesis defect43.
Additional examples of genes registering >4 C overrepresentation include the centriole cartwheel protein SAS644, the cleavage furrow-localizing protein FRW145, the basal body—axoneme transition zone protein TZP12546 and the basal body protein BBP24847. One hundred additional examples are shown in Supplementary Fig. 4, including intermediate and light chain dyneins, other flagellum-associated factors, radial spoke proteins, components of motile flagella, flagellum attachment and transition zone proteins, kinesins48,49, nucleoporins50, and many previously uncharacterised hypothetical proteins. Some other notable examples include the microtubule-severing katanin KAT8051, the dynein regulatory factor trypanin52, the AIR9 microtubule associated protein53, CAP51V54 and importin, IMP155.
Orthologues of several T. brucei flagellar proteins have previously been linked to debilitating human ciliopathies, such that the trypanosome flagellum is exploited as a model for studies on these defects7. Defects in intraflagellar dynein transport are associated with respiratory infections, for example9. Orthologues of DNAH (10.5350 and 11.8160, Fig. 3e) are linked to male infertility, while additional ciliopathy-associated orthologues which register overrepresentation in the >4 C pool are shown in Fig. 3f and Supplementary Fig. 4. These include orthologues of proteins linked to primary ciliary dyskinesia (DNAH5, DNAH11, RSPH4 and DNAI1)11; male infertility (PF16, PACRGA, CFAP43 and CMF7/TbCFAP44)7,10; and cone-rod dystrophies, as well as other ocular defects (CMF17, CMF39 and CMF46)8.
From analysis of knockdowns overrepresented in the >4 C pool, we conclude that RIT-seq screening provided comprehensive genome-scale identification of cytokinesis defects in bloodstream form T. brucei. Endoreduplication appears to be a common outcome following a cytokinesis defect. Amongst hundreds of genes required for progression through cytokinesis, flagellar proteins featured prominently, including the majority of dynein chains and intraflagellar transport factors. Many of these factors are essential for viability and include potential druggable targets in trypanosomatids, as well as orthologues of proteins associated with ciliopathies.
Defects producing sub-diploid cells
A DNA replication or mitosis defect followed by cytokinesis may result in generation of cells that retain nuclear DNA with a sub-2C DNA content. We emphasise retention of nuclear DNA here because T. brucei cells lacking nuclear DNA, referred to as zoids, have been reported previously as a result of asymmetrical cell division. Zoids are typically observed when DNA replication or mitosis are perturbed in insect stage cells16,25,56. The zoid phenotype is typically either absent or less pronounced in the developmentally distinct bloodstream form cells57 that we analysed here. Nevertheless, any zoids present in the <2 C pool will not have been detected using RIT-seq, since detection relies upon the presence of a nuclear RNAi target fragment.
Ten knockdowns were overrepresented in the <2 C RIT-seq screening dataset (Supplementary data 1), including the previously identified histone methyltransferase, DOT1A (Fig. 2b). DOT1A is responsible for dimethylation of histone H3K76, and DOT1A knockdown results in mitosis and cytokinesis without DNA replication, generating cells with a haploid DNA content 31. Our data suggest that few additional knockdowns yield a similar phenotype in bloodstream form T. brucei.
A profile of G1, S phase and G2M defects
We next analysed knockdowns overrepresented in the G1, S phase or G2M pools. Several hundred knockdowns registered >25% overrepresented read counts in each of these categories (Fig. 2c, e). GO annotations within each cohort revealed a number of enriched terms (Fig. 4a). Overrepresented knockdowns were associated with glycolysis, mRNA binding and the mitochondrion in the G1 pool, with DNA replication in the S phase pool and with a broadly similar profile to that seen for the >4 C set in the G2M pool.
a The bar-graphs show enriched Gene Ontology terms associated with the G1, S phase or G2M hits, those that register >25% overrepresented read-count in each of these categories. P-values are shown on the right. b The violin plots show relative G1, S phase or G2M read-counts for cohorts of genes and reflect data distribution. Open circles indicate median values and the vertical bars indicate 95% confidence intervals. Overrepresented cohorts are indicated in purple, green and blue, respectively. IFT intraflagellar transport. c The heatmaps show relative representation in all five sorted pools for the above and additional cohorts of knockdowns; blue, most overrepresented. MCM minichromosome maintenance, PSP1 DNA polymerase suppressor 1.
The violin plots in Fig. 4b show specific enrichment of individual knockdowns for glycolytic enzymes and a subset of mRNA binding proteins in the G1 pool, for DNA replication factors in the S phase pool, and proteasome components and a subset of kinetochore components in the G2M pool (Fig. 4b). Overlap between knockdowns that accumulate in both the G2M and >4 C pools likely reflects cytokinesis defects with cells accumulating both before and after endoreduplication; compare G2M and >4 C data for IFT factors and dyneins in Fig. 4b and Fig. 3b, for example. Other mitosis or cytokinesis-perturbed phenotypes are likely not associated with substantial endoreduplication; see the kinetochore and proteasome cohorts in Fig. 4b, for example. Once again, the exocyst provided a control cohort with no components registering enrichment in the G1, S phase or G2M pools following knockdown (Fig. 4b).
The heat-map in Fig. 4c shows the data for all five sorted pools for the cohorts described above and for additional knockdowns enriched in the G1 or S phase (tRNA synthetases), S phase (core histones) or G2M pools (PSP1, DNA polymerase suppressor 1), or not enriched in any pool. These latter sets provide further controls that do not appear to have substantial impacts on cell cycle progression, including the mitochondrial RNA editing accessory complex MRB158 and the mitochondrial ATP synthase complex V59. Thus, we identify protein complexes, pathways and regulatory factors that are specifically required for progressive steps through the trypanosome cell cycle.
Pathways and protein complexes associated with G1, S phase and G2M defects
We next explored some of the cohorts of hits described above in more detail. Glycolytic enzymes are particularly prominent amongst knockdowns that accumulate in the G1 pool, and we illustrate the RIT-seq profiling data for these enzymes in Fig. 5a. Seven of eleven glycolytic enzyme knockdowns register >25% overrepresentation in the G1 pool; hexokinase, phosphofructokinase, aldolase (see Fig. 2c), triosephosphate isomerase, glyceraldehyde 3-phosphate dehydrogenase, phosphoglycerate kinase C and pyruvate kinase. Glycolysis operates in peroxisome-like organelles known as glycosomes in trypanosomes and is thought to be the single source of ATP in bloodstream form cells12. Glycolysis also provides metabolic intermediates that support nucleotide production. Notably, mammalian cell proliferation is accompanied by activation of glycolysis, and the Warburg effect relates to this phenomenon in oncology60,61. Indeed, hexokinase regulates the G1/S checkpoint in tumour cells62. The results are also consistent with the observation that T. brucei accumulate in G1 or G0 under growth-limiting conditions63 or during differentiation to the non-dividing stumpy form64, possibly reflecting a role for glucose sensing in differentiation65. Notably, glycolytic enzymes are downregulated 6.7 + /−5.2-fold in stumpy-form cells66. We conclude that, as in other organisms67, there is metabolic control of the cell cycle and a nutrient sensitive restriction point in T. brucei, with glycolysis playing a role in the G1 to S phase transition and possibly also the G1/G0 transition.
a The RadViz plot shows glycolytic enzyme knockdowns. Those that registered >25% overrepresented read-counts in the G1 category are indicated. Black data-points indicate other genes from each cohort. Grey data-points indicate all other genes. The read-mapping profiles and relative read-counts in the lower panel show example hits. b As in a but for DNA replication initiation factor knockdowns that registered >25% overrepresented read-counts, primarily in the S phase category. c As in a but for proteasome component knockdowns that registered >25% overrepresented read-counts, primarily in the G2M category. d As in a but for kinetochore component knockdowns that registered >25% overrepresented read-counts, primarily in the G2M category.
DNA replication initiation factors are particularly prominent amongst knockdowns that accumulate in S phase and we illustrate the RIT-seq profiling data for these factors in Fig. 5b. Five knockdowns that register >25% overrepresentation in the S phase pool are components of the eukaryotic replicative helicase, the CMG (Cdc45-MCM-GINS) complex. At the core of this complex is the minichromosome maintenance complex (MCM2-7), a helicase that unwinds the duplex DNA ahead of the moving replication fork68. Identification of CMG complex components suggests that each of these subunits is required for timely progression through S phase.
Proteasome activity promotes mitosis in T. brucei69 and, consistent with this, proteasome components are particularly prominent amongst knockdowns that accumulate in G2M; we illustrate the RIT-seq profiling data for this protein complex in Fig. 5c. Sixteen of 28 proteasome component knockdowns register >25% overrepresentation in the G2M pool. This output is consistent with the view that the T. brucei proteasome is responsible for degrading cell cycle regulators, such as poly-ubiquitinated cyclins, some of which are known to control cell cycle checkpoints in T. brucei. Candidate targets in T. brucei include: CIF1, AUK170, cyclin 6 (CYC6), degradation of which is required for mitosis71; cyclin-like CFB2, required for cytokinesis72; and cyclin 2 (CYC2) or cyclin 3 (CYC3), which have short half-lives and a candidate destruction box motif in the case of CYC373.
Kinetochore components17 are also amongst knockdowns that accumulate in G2M and we illustrate the RIT-seq profiling data for this protein complex in Fig. 5d. Although knockdown of KKT2, a putative kinase, registered overrepresentation in the S phase pool, KKT1, KKT7 and KKT10/CLK1 knockdowns registered >25% overrepresentation in the G2M pool, suggesting that these particular kinetochore components, which all display temporal patterns of phosphorylation from S phase to G2M21, are required for progression through mitosis. Notably, KKT10 is a kinase responsible for phosphorylation of KKT7, which is required for the metaphase to anaphase transition74; as well as for the phosphorylation of KKT1 and KKT2, in turn required for kinetochore assembly75,76. These findings are consistent with the view that kinetochore components control a non-canonical spindle checkpoint in trypanosomes74.
RBPs, kinases and hypothetical proteins associated with G1, S phase and G2M defects
Widespread polycistronic transcription in trypanosomatids places great emphasis on post-transcriptional controls and, consistent with this, knockdowns overrepresented in the G1, S phase and G2M pools revealed many putative mRNA binding proteins (RBPs) and kinases. Indeed, RBPs are significantly enriched amongst knockdowns that registered G1, S phase or G2M cell cycle defects (χ2 test, p = 7−5). We show the RIT-seq profiling data for eleven RBP knockdowns that register >25% overrepresentation in these pools (Fig. 6a). These include knockdowns for RBP10 and RBP29 enriched in G1; RBP10, in particular, has been characterised in some detail and promotes the bloodstream form state77. ZC3H1178, ZC3H41 and ZC3H2879 knockdowns were enriched in G1, S phase and G2M, respectively, while knockdowns of CFB2, MKT1 or PBP1, all recently linked to variant surface glycoprotein expression control80,81, were enriched in G2M. Indeed, based on the outputs of the current screen, we prioritised these latter three RBPs for follow-up analysis in a separate study; all three were thereby validated as G2M hits80. Thus, the RIT-seq cell cycle screen implicated a number of specific RBPs in post-transcriptional control of cell cycle progression through modulation of mRNA stability and/or translation.
a The RadViz plot shows mRNA binding protein knockdowns (RBPs). Those that registered >25% overrepresented read-counts in the G1, S phase or G2M categories are indicated, in purple, green and blue, respectively. The read-mapping profiles and relative read-counts in the other panels show example hits. b As in a but for protein kinase knockdowns, with selected kinases indicated. c As in a but for hypothetical (conserved) protein knockdowns.
We show data for protein kinases above, linked to enriched >4 C (Fig. 3d), S phase or G2M (Fig. 5d) phenotypes, and now show the RIT-seq profiling data for five additional protein kinase knockdowns that register >25% overrepresentation in the G1, S phase or G2M pools (Fig. 6b). These include knockdowns for CRK7, linked to accumulation in G1; MAPK5, linked to accumulation in S phase and polo-like kinase (PLK) and cdc2-related kinase 3 (CRK3), linked to accumulation in G2M. PLK was previously shown to control cell morphology, furrow ingression and cytokinesis82,83,84, while CRK3 was shown to play a role in G2M progression in bloodstream form T. brucei43,85. Overall correspondence was also excellent with a prior kinome-wide RNAi screen43. For example, eight among nine kinases linked to a mitosis defect in that screen also reported an (21 ± 12%) increase in the G2M pool in the current screen.
Finally, we analysed genes encoding proteins annotated as hypothetical (conserved). Despite excellent progress in genome annotation, 35% of non-redundant genes in T. brucei retain this annotation, amounting to >2500 genes. We show data for several hypothetical protein knockdowns above, linked to the enriched >4 C phenotype (Supplementary Fig. 4), and we here identify >300 additional hypothetical protein knockdowns that register >25% overrepresentation in the G1, S phase or G2M pools. RIT-seq profiling data are shown for five examples in Fig. 6c and for several additional examples in Supplementary Fig. 5. Amongst other examples of knockdowns shown in Supplementary Fig. 5, are alternative oxidase86, linked to G1 enrichment; kinesins linked to G2M enrichment, including both chromosomal passenger complex kinesins (KIN-A and KIN-B)26 and KIN-G; CYC625,87, centrin 388 and, finally, both components of the histone chaperone FACT (facilitates chromatin transcription) complex89 Spt16 and Pob3, linked to G2M enrichment. Notably, the FACT complex has been linked to centromere function in human cells90.
Cell cycle regulated proteins linked to cell cycle progression defects
Factors required for cell cycle progression may themselves be cell cycle regulated. To identify some of these factors, we compared our current dataset with quantitative transcriptome19, proteome20 and phosphoproteome21 cell cycle profiling data (Supplementary data 1). An initial survey of all 1,198 genes that registered a cell cycle defect here (see Fig. 2e) revealed significant enrichment of cell cycle regulated mRNAs (overlap = 114 of 484, χ2 p = 3.2−4), and proteins displaying cell cycle regulated phosphorylation (overlap = 112 of 547, χ2 p = 0.025). This, despite the fact that the transcriptome and (phospho)proteome datasets were derived from insect stage T. brucei, such that regulation may differ in the bloodstream T. brucei cells used for RIT-seq analysis here.
In terms of specific cell cycle regulated proteins20 required for specific cell cycle progression steps, multiple glycolytic enzymes upregulated in G1 were linked to accumulation in the G1 pool following knockdown (χ2 p = 7.9−11). In addition, proteins upregulated in G2 and M were linked to accumulation in the G2M (χ2 p = 1.8−8) or >4 C pools (χ2 p = 8.9−9) following knockdown, including kinetochore and chromosomal passenger complex components, respectively. Some specific transcripts required for cell cycle progression may be upregulated prior to peak demand for the encoded protein, and we found evidence to support this view. For example, transcripts upregulated in late G1 or in S phase were enriched amongst those knockdowns linked to accumulation in the G2M pool (χ2 p = 3.3−3 and p = 0.011, respectively); both components of the FACT complex, upregulated in G1, for example (see Supplementary Fig. 5). Similarly, S phase and G2M upregulated transcripts, including those encoding multiple flagellum-associated proteins, were enriched amongst knockdowns linked to accumulation in the >4 C pool (χ2 p = 4.6−18 and p = 2.4−5 respectively).
Some proteins displayed both cell cycle regulated expression and phosphorylation patterns that were consistent with their roles in cell cycle progression. These included putative RBPs of the DNA polymerase suppressor 1 (PSP1) family, which display mRNA upregulation in G1, protein upregulation in S phase, cell cycle regulated phosphorylation and, following knockdown, accumulation in G2M (see Figs. 4c and 6a). The kinetochore components, KKT1 and KKT7, and also CRK3, all display mRNA upregulation in S phase, protein upregulation in G2 and M, cell cycle regulated phosphorylation and, following knockdown, accumulation in G2M (see Figs. 5d and 6b); KKT10 and CYC6 report a similar profile (see Fig. 5d), except for the mRNA regulation component. The cytokinesis initiation factors, CIF1 and CIF2, display mRNA upregulation in S phase, protein upregulation in G2 and M, cell cycle regulated phosphorylation and, following knockdown, accumulation in the endoreduplicated pool (see Fig. 3e). Finally, the chromosomal passenger complex components, CPC1 and AUK1, as well as furrow localized FRW1, report mRNA and protein upregulation in G2M and, following knockdown, accumulation in the endoreduplicated pool (see Fig. 3e). Thus, several regulators linked to specific cell cycle progression defects by RIT-seq profiling, are themselves cell cycle regulated.
A putative nucleoredoxin controls kinetoplast segregation and mitosis
The current RIT-seq screen identified many novel candidate cell cycle regulators, two of which, both associated with significant loss-of-fitness in our prior RIT-seq screen23, were investigated in more detail. First, Tb927.10.970 was associated with pronounced endoreduplication following knockdown (Fig. 7a). The predicted protein contains a tetratricopeptide repeat motif, a cluster of phosphorylation sites, one of which, T706, has been reported to be cell cycle regulated, peaking in late G2M21, and a string of putative calmodulin-binding IQ domains (Fig. 7b). Tb927.10.970 was shown to localise to the paraflagellar rod in insect stage T. brucei (www.tryptag.org91,92). To validate Tb927.10.970 as a >4 C hit in bloodstream-form trypanosomes, we assembled a pair of independent inducible RNAi knockdown strains. Analysis of cell growth revealed a severe loss-of-fitness following knockdown, confirmed by qRT-PCR (Fig. 7c). Flow cytometry then confirmed endoreduplication, with prominent peaks detected representing 8 C and 16 C cells following knockdown (Fig. 7d, left-hand panel), while examination of these cells by microscopy revealed multiple nuclei, indicating endoreduplication with continued mitosis (Fig. 7d, right-hand panel).
a Mapped reads and reconstructed cell cycle profile following 10.970 knockdown. b Schematic map of 10.970. TPR tetratricopeptide repeat motif, P phosphorylation, IQ motifs, putative calmodulin-binding domains. c The plot on the left reveals efficient tetracycline (tet)-induced knockdown of 10.970 after 24 h. Mean values and 3 technical replicates are indicated, with the p-value derived using an unpaired t-test. The growth curve on the right reveals a severe growth defect following knockdown. n = 2 biologically independent clones and the line indicates mean values. Source data are provided as a Source Data file. d Flow cytometry analysis revealed endoreduplication following 24 h of 10.970 knockdown, based on propidium iodide staining. The gates indicate the various cell cycle stages, based on uninduced (- tet) cells. The panel on the right shows representative cells with DNA stained with DAPI (4′,6-diamidino-2-phenylindole) before and after knockdown. N nucleus, K kinetoplast.
We next turned our attention to Tb927.10.3970, annotated ‘hypothetical protein, conserved’, and associated with increased DNA content following knockdown (Fig. 8a). The predicted Tb927.10.3970 protein contains three cell cycle regulated phosphorylation sites21 and a thioredoxin-like domain (Fig. 8b). This protein was shown to be cell cycle regulated based on proteomic analysis and to localise to the nucleus in insect stage T. brucei20. To explore the role of Tb927.10.3970 in bloodstream-form trypanosomes, we assembled a pair of independent inducible RNAi knockdown strains. Analysis of cell growth revealed a severe loss-of-fitness following knockdown, confirmed by monitoring the expression of epitope-tagged 10.3970 (Fig. 8c). Flow cytometry confirmed increased DNA content following knockdown, and also revealed increased cell size (Fig. 8d). Examination of these cells by microscopy allowed us to assess both the nuclei and kinetoplasts (mitochondrial genomes), revealing a major increase in the proportion of cells with a single nucleus and a single rounded kinetoplast following knockdown, an arrangement typically characteristic of G1 cells (Fig. 8e). Quantitative analysis of these compartments revealed a pronounced increase in DNA content in both genomes following knockdown (Fig. 8f), indicating that both kinetoplast segregation and mitosis failed, while genome replication was able to proceed to varying degrees, generating enlarged kinetoplasts and polyploid nuclei. Finally, we assessed the subcellular localisation of epitope-tagged 10.3970 during the bloodstream form cell cycle. Quantitative immunofluorescence microscopy revealed a pattern that was also observed in insect stage cells (www.tryptag.org20,91,92). 10.3970 displayed a nuclear localisation, which increased in intensity during the cell cycle, and then dropped precipitously in post-mitotic cells (Fig. 8g). We conclude that 10.3970 encodes a putative nucleoredoxin that is cell cycle regulated and required for both mitochondrial and nuclear genome segregation and cytokinesis.
a Mapped reads and reconstructed cell cycle profile following 10.3970 knockdown. b Schematic map of the putative nucleoredoxin encoded by 10.3970. TRX thioredoxin-like domain. c The protein blots on the left reveal efficient tetracycline (tet)-induced knockdown of N-terminal myc-tagged 10.3970 for one representative clone after 24 h. EF1α serves as a loading control. The growth curve on the right reveals a severe growth defect following knockdown. n = 2 biologically independent clones and the line indicates mean values. d Flow cytometry analysis revealed increased DNA content following 24 h of 10.3970 knockdown, based on propidium iodide staining on the left, and increased cell size, based on the forward scatter profile on the right. The gates on the profile on the left indicate the various cell cycle stages, based on uninduced (- tet) cells. e Quantification of cells with a single nucleus and a single rounded kinetoplast (1 N: 1Kr) before and after 24 h of 10.3970 knockdown, based on DAPI-staining and microscopy. n = 2 biologically independent clones and the lines indicate mean values; n = 80 cells in each case. f The boxplots show quantification of DAPI intensity of nuclei (left) and kinetoplasts (right) in cells with a single nucleus and a single rounded kinetoplast, before and after 24 h of 10.3970 knockdown; n = 100 in each case. The data are from two independent clones and are shown as jittered dots, the box indicates the IQR, whiskers show the range of values within 1.5*IQR, the horizontal lines indicate the median and the notches represent 95% confidence intervals. The inset on the right shows representative cells before and after knockdown. N, nucleus; K, kinetoplast. g The representative immunofluorescence microscopy images show N-terminal GFP-tagged 10.3970 (green) at different cell cycle stages, as defined by the nucleus (N) and kinetoplast (K) configuration observed by staining DNA with DAPI. Intensity of the GFP10.3970 signal was quantified and is shown in the graph on the right. n = 26, 21, 5 and 6 cells for G1, S phase, G2M and post-M, respectively. The lines indicate the median. Source data for c and e–g are provided as a Source Data file.
Discussion
Despite intense interest and study13,15, many cell cycle regulators in trypanosomatids remain to be identified and much remains to be learned about cell cycle control and progression in these parasites. DNA staining followed by flow cytometry is a widely used approach for quantifying cellular DNA content and to analyse cell cycle distribution across otherwise asynchronous populations. Here, we combined genome scale loss-of-function genetic screening with DNA staining and flow cytometry in bloodstream form African trypanosomes and identify hundreds of genes required for progression through specific stages of the cell cycle.
Functional annotation of the trypanosomatid genomes will continue to benefit from novel high-throughput functional analyses, and RNAi-mediated knockdown has proven to be a powerful approach for T. brucei. RIT-seq profiling provides data for almost every gene and, using this approach, we previously described genome-scale loss-of-fitness data23. Amongst 3117 knockdowns that scored a significant loss-of-fitness in bloodstream-form cells in that screen (42% of all genes analysed) were genes encoding all 18 intraflagellar transport complex subunits (χ2 p = 1−6), 12 of 13 dynein heavy-chains (χ2 p = 4−4), all 8 TCP-1 chaperone components (χ2 p = 1−3), 27 of 30 nucleoporins (χ2 p = 2−7), all eleven glycolytic enzymes (χ2 p = 2−4) and 30 of 31 proteasome subunits (χ2 p = 2−9). This set also included 18 of 19 kinetochore proteins (χ2 p = 6−6), only later identified as components of this essential complex17. With the current study, we now link many of these genes, and many more, to specific cell cycle defects following RNAi knockdown. A large number of flagellar protein knockdowns, in particular, yielded cells with excess DNA, indicating that DNA replication often continues following failure to complete cytokinesis, due to defects that occur during cytokinesis itself or earlier in the cell cycle in some cases. We identified pathways and protein complexes that impact cell cycle progression, such as glycolysis (G1/S transition) and the proteasome (G2/M transition). We also identify many mRNA binding proteins and protein kinases implicated in control of cell cycle progression. Notably, we link multiple known potential and promising drug targets to cell cycle progression defects, such as glycolytic enzymes93, the proteasome94, kinetochore kinases75,95 and other kinases96.
Prior cell cycle studies have often focused on trypanosome orthologues of known regulators from other eukaryotes. Since genome-scale profiling is unbiased, it presents the opportunity to uncover divergent as well as novel factors and regulators that impact cell cycle progression. Accordingly, we link many previously uncharacterised and hypothetical proteins of unknown function to specific cell cycle progression defects. Thus, we uncover mechanisms with an ancient origin in a common eukaryotic ancestor and others likely reflecting trypanosomatid-specific biology. We also compared our cell cycle profiling data with cell cycle regulated transcriptome and (phospho)proteome datasets.
The digital dataset provided in Supplementary data 1 facilitates further interrogation and further analysis of the genome-scale cell cycle RIT-seq data. We have also made the data available via an interactive, open access, online data visualization tool (https://tryp-cycle.pages.dev/), which allows data searching and browsing (see Supplementary Fig. 3). Comparison with existing and new datasets, including with high-throughput subcellular localisation data92 www.tryptag.org, should facilitate future studies. Since high-throughput genetic screens typically yield a proportion of false positive ‘hits’22, we urge some caution, however, in particular where outputs may be predominantly generated by a single RIT-seq fragment. On the other hand, there are knockdowns in the current dataset that display potentially informative cell cycle pool enrichment yet fail to surpass the thresholds applied above. Considering both of these points, we hope that the digital dataset and the online tool will serve as valuable resources. Since other important trypanosomatid parasites, including other African trypanosomes, Trypanosoma congolense, Trypanosoma vivax; American trypanosomes, Trypanosoma cruzi; and Leishmania spp. share a high degree of conservation and synteny with T. brucei spp.97 the current datasets can also assist and inform studies on these and other trypanosomatids.
Further illustrating the value of the current RIT-seq dataset, we analysed two novel hits from the screen in more detail and reveal a role for a putative nucleoredoxin in both kinetoplast and nuclear segregation. Indeed, although DNA replication continues, cells lacking this factor fail to undergo kinetoplast scission or separation98, mitosis or cytokinesis. Mitochondrial and nuclear DNA replication and segregation are coordinated during the T. brucei cell cycle13 to ensure inheritance of a single copy of each genome by each daughter cell, but the mechanism underlying coordination remains unknown. Identification of a thioredoxin-like protein required for both nuclear and kinetoplast DNA segregation suggests that redox signalling is involved in coordinating these processes. A comparable phenotype was observed following knockdown of both the mitochondrial and nuclear DNA-binding proteins, UMSBP1 (universal minicircle sequence binding protein) and UMSBP2, previously linked to replication and segregation of kinetoplast and nuclear DNA in insect stage T. brucei99,100. Notably, both DNA-binding and USMBP dimerization are redox-regulated101. Although not understood in detail, reactive cysteine thiols function as cell cycle associated redox sensors in mammalian cells102. Thus, we propose a role for the current putative nucleoredoxin in reducing thiols and potentially disrupting disulphide bonds in one or more key cell cycle regulators. This role may be conserved amongst trypanosomatids and indeed, redox signalling may play further roles in cell cycle control, since our screen linked two additional thioredoxin-like proteins to specific and distinct cell cycle defects; TRX2, a redox-regulated mitochondrial chaperone103, was linked to accumulation in G1, while the putative thioredoxin encoded by Tb927.9.12330 was linked to accumulation in G2M (Supplementary data 1). Further work will be required to explore how thiol-based redox switch104 or sensing mechanisms choreograph cell cycle progression in the trypanosomatids.
In summary, we report RNAi induced cell cycle defects at a genomic scale and identify the T. brucei genes that underlie these defects. The outputs confirm known roles in cell cycle progression and provide functional annotation for many additional genes, including many with no prior functional assignment and many that are trypanosomatid-specific. As such, the data not only improve our understanding of cell cycle progression in these important and divergent pathogens but should also accelerate further discovery. Taken together, our findings facilitate genome annotation and provide comprehensive genetic evidence for the protein complexes, pathways and regulatory factors that facilitate and coordinate progression through the trypanosome cell cycle.
Methods
T. brucei growth and manipulation
The bloodstream form T. brucei RNAi library22 was thawed in HMI-11 containing 1 μg.ml−1 of blasticidin and 0.2 μg.ml−1 of phleomycin and incubated at 37 °C in 5% CO2. After approximately 48 h, six flasks, each containing 2 × 107 cells in 150 ml of HMI-11 as above, were prepared; 1 μg.ml−1 of tetracycline was added to five of them, while one served as the non-induced control. The cells were grown under these conditions for 24 h. Bloodstream form T. brucei 2T1 cells105 were grown as above in the presence of 1 μg.ml−1 phleomycin and 1 μg.ml−1 puromycin. These cells were transfected by electroporation as described22 and selected with 2.5 μg.ml−1 hygromycin (RNAi constructs) or 10 μg.ml−1 blasticidin (myc or GFP tagging constructs). RNAi knockdown was induced with 1 μg.ml−1 tetracycline.
Flow cytometry
RNAi library samples were harvested by centrifugation for 10 min at 1000 g. Cells from each flask were then re-suspended in 25 ml of 1x PBS (pH 7.0) supplemented with 5 mM EDTA and 1 % FBS (“supplemented PBS”), centrifuged again for 10 min at 1000 g, and then re-suspended in 0.5 ml of supplemented PBS. To each cell suspension, 9.5 ml of 1 % formaldehyde in supplemented PBS was added dropwise, with regular vortexing. The cells were incubated for a further 10 min at room temperature and then washed twice in 10 ml of supplemented PBS using centrifugation as above. The cells were finally re-suspended at 2.5 × 107 per ml in supplemented PBS and were subsequently stored at 4 °C, in the dark. Fixed cells, 3 × 108 Tet-induced and 107 uninduced, were centrifuged for 10 min at 1000 g, and re-suspended in 10 ml of supplemented PBS containing 0.01% Triton X-100 (Sigma Aldrich). The cells were incubated for 30 min at room temperature, centrifuged for 10 min at 700 g and washed once in 10 ml of supplemented PBS. The cells were then re-suspended in 4 ml of supplemented PBS containing 10 μg.ml−1 of propidium iodide (Sigma Aldrich) and 100 μg.ml−1 of RNaseA (Sigma Aldrich), and incubated for 45 min at 37 °C, in the dark; cells were subsequently kept on ice and in the dark. Immediately prior to sorting, the Tet-induced cells were filtered (Filcon Cup-type filter, 50 μm mesh, BD™ Medimachine) into 5 ml polystyrene round-bottom tubes (BD Falcon). Cells were sorted using the BD InfluxTM (Becton Dickinson) cell sorter, with BD FACSortTM software, at the Flow Cytometry and Cell Sorting Facility in the School of Life Sciences, University of Dundee. The cells were sorted into pools of <2 C (~5 × 105 cells), 2 C (G1, 1 × 107 cells), 2-4 C (S, 1 × 107 cells), 4 C (G2M, 1 × 107 cells) and >4 C (~9 × 105 cells) based on their DNA content and collected into 50 ml Falcon tubes (BD Falcon); total sorting time was approx. 4 h. The 2 C, 2–4 C and 4 C sorted samples were then run on a FACS LSR Fortessa flow cytometry analyser for a post-sorting quality check. For the analysis of Tb927.10.970 or Tb927.10.3970 knockdowns, 1 × 107 cells were centrifuged for 10 min at 1000 g, washed in supplemented PBS. Cells were fixed for 10 min in 1% paraformaldehyde in supplemented PBS, washed and stored at 4 °C in supplemented PBS. Cells were pelleted for 10 min at 1000 g and permeabilised at room temperature for 30 min in supplemented PBS plus 0.01% Triton X-100. Cells were washed once in supplemented PBS followed by centrifugation for 10 min at 700 g and then stained in supplemented PBS with 10 μg.ml−1 propidium iodide and 100 μg.ml−1 RNAse A for 1 h at 37 °C. The samples were run on a BD FACSCanto (Becton Dickinson). FlowJo v10.7.1 was used for data analysis and visualisation.
RNA interference target amplification
The five pools of Tet-induced, sorted cells as well as uninduced or induced, but unsorted cells, were lysed overnight at 56 °C in 50% (v/v) of Buffer AL (Qiagen) and 0.5 mg.ml−1 of Proteinase K (Qiagen), to reverse formaldehyde crosslinking. Genomic DNA was then extracted using the DNeasy Blood and Tissue DNA extraction kit (Qiagen), according to the manufacturer’s instructions, with the exception that each sample was eluted in 50 μl of Buffer AE. The whole sample (range = 140–840 ng) was used for PCR, in a 100 μl reaction, using OneTaq (NEB), and the Lib3F (CCTCGAGGGCCAGTGAG) and Lib3R (ATCAAGCTTGGCCTGTGAG) primers and with the following programme: 94 °C for 4 min, followed by 27 cycles of 94 °C for 30 sec, 55 °C for 30 sec and 68 °C for 2 min and 10 sec, and a final extension of 68 °C for 5 min. The PCR products were then purified using the Qiaquick PCR extraction kit (Qiagen), as per the manufacturer’s instructions, and eluted in 30 μl of nuclease-free water (Ambion); two columns per sample.
RIT-seq library preparation and sequencing
Purified PCR products were used for library preparation and sequencing at the Tayside Centre for Genomic Analysis at the University of Dundee. The PCR products were fragmented using a Covaris M220 sonicator (20% duty factor, 75 W peak/displayed power, 60 s duration – 3 × 20 sec with intermittent spin down step, 18–20 °C temperature; resulting in 250–300 bp enriched fragments), and the libraries were prepared using the Truseq Nano DNA Library Prep kit (Illumina). The samples were multiplexed, and sequenced on an Illumina NextSeq 500 platform, on a 150 cycle Output Cartridge v2, paired-end. Each library was run on 4 sequencing lanes. Base call, index deconvolution, trimming and QC were performed in BaseSpace using bcl2fastq2 Conversion Software v2.17.
RIT-seq data mapping and analysis
The sequencing data analysis pipeline was adapted from22. The FASTQ files with forward and reverse paired end reads (4 technical replicates for each samples) were concatenated and aligned to the reference genome v46 of T. brucei clone TREU927 downloaded from TriTrypDB29 using Bowtie2106, with the ‘very-sensitive-local’ pre-set alignment option. The alignments were converted to BAM format, reference sorted and indexed with SAMtools107. The quality of alignments was evaluated with Qualimap 2108 using the bamqc and rnaseq options. The Qualimap 2 output files were aggregated with MultiQC109 and inspected. The alignments were deduplicated with the Picard tools package using the MarkDuplicates function (http://broadinstitute.github.io/picard/); to minimise the potential for overrepresentation of the shortest RIT-seq fragments. Alignments with properly paired reads were extracted with SAMtool view using the -f 2 option and parsed with a custom python script to extract the paired reads containing the barcode sequence (GTGAGGCCTCGCGA) in forward or reverse complement orientation. The genome coverage of the aligned reads was extracted from the bam files using deeptools in bedGraph format110. The—scaleFactor option was used to normalise each sample with respect to the library size. Firstly, the mean value of the library size was computed from all the samples. Secondly, the mean value was divided by the library size of each sample to obtain the scaling factors. The bedGraph read-mapping files were visualized with the svist4get python package111. Read counts for protein coding sequences and associated untranslated regions (where annotated) were determined from the bam files using featureCounts112 and normalized to Transcripts Per Kilobase Million (TPM). Dimensionality reduction of the G1, S and G2M TPM values was performed with the radviz algoritm implemented in the pandas python package113. The bash script containing the analysis pipeline, a conda environment specification file for its execution, the python script to extract barcoded reads and a basic usage example are available in GitHub (https://github.com/mtinti/ritseq_cellcycle). Data were subsequently analysed using a GO-slim set and Gene Ontology tools available via tritrypdb.org and visualised using tools available at huygens.science.uva.nl/PlotsOfData.
Plasmid construction for knockdown and tagging
For tetracycline-inducible knockdown of Tb927.10.970 or Tb927.10.3970 expression, gene fragments were amplified using either the 970RiF (GATCGGGCCCGGTACCCGCCACACTGAACAACCTT) and 970RiR (GATCTCTAGAGGATCCTCCCTTTGCCGCCTTACCAC) PCR primers or the 3970RiF (GATCGGGCCCGGTACCGCGTCGGAGATGTGATCCTT) and 3970RiR (GATCTCTAGAGGATCCACAACTCGCATACACGGAGG) PCR primers and cloned in two steps in pRPaiSL 105. The resulting constructs were confirmed by sequencing and digested with AscI prior to transfection. Tb927.10.970 knockdown was assessed using Real-Time Quantitative Reverse Transcription PCR (qRT-PCR). RNA was extracted using the RNeasy Mini Kit with an on-column DNase digest step (RNase-free DNase, Qiagen). RNA (1 μg) was reverse-transcribed using a high capacity RNA to cDNA kit (Thermo Fisher Scientific) and the equivalent of 25 ng of RNA was used in each qPCR reaction, with the 970qRT_F (AGGAAGCGGAAGGAGAGGAT) and 970qRT_R (AGCGGAATTTATGCGTTCGC) primers. The reactions were performed in technical triplicates on a QuantStudio3 (Applied Biosystems) using the Luna® Universal qPCR Master Mix (NEB). TERT (Tb927.11.10190) was used as reference gene to calculate the 2^-deltaCt value for each sample. For N-terminal tagging of Tb927.10.3970, the targeting fragment was amplified using the 3970tagF (GATCTCTAGAGTAGGTGCTTCTTCCAAGC) and 3970tagR (GATCGGATCCCCGGAAGAGCACTAAGATCC) PCR primers and cloned into either 6 x myc or GFP pNATTAGx tagging constructs; versions with a blasticidin selectable marker were used105. Correct assembly was confirmed by sequencing and these constructs were linearised with SalI prior to transfection.
Protein blotting
Pelleted cells were lysed in RIPA buffer with 8 × 5 s sonication cycles at 5 µm amplitude in a Soniprep 150 Utrasonic Disintegrator (MSE) with a 23 kHz generator and centrifuged at 4 °C for 15 min at maximum speed. Proteins in the supernatant were separated using SDS-PAGE and standard blotting procedures. Protein detection was achieved using mouse α-myc primary antibody clone 9B11 (1:5000) or mouse α-EF1α primary antibody (Millipore, 1:20,000) with goat α-mouse horseradish peroxidase coupled secondary antibody (Biorad, 1:2000). Blots were developed using the enhanced chemiluminescence kit (Amersham) as per the manufacturer’s instructions.
Microscopy and immunofluorescence microscopy
Cells were fixed in 1% paraformaldehyde and attached to 12-well 5 mm slides (Thermo Scientific) by drying overnight. Following rehydration in PBS for 5 min, cells were permeabilised in 0.5% Triton X-100 for 15 min and washed 3 x in PBS. Slides were blocked with 50% FBS in PBS for 15 min and washed in PBS twice. Slides were first incubated with rabbit α-GFP primary antibody (Abcam, 1:250) then α-rabbit Alexa 488 secondary antibody (1:2000) for 1 h each followed by 3 washes in PBS. Slides were finally mounted in Vectashield with DAPI (4′,6-diamidino-2-phenylindole) and sealed under a coverslip prior to imaging. For DAPI-only staining, slides were directly mounted in Vectashield with DAPI after rehydration. Cells were imaged at 63x magnification with oil immersion under a Zeiss Axiovert 200 M microscope with Zen Pro software (Zeiss). DAPI and GFP visualisation and quantification were performed in ImageJ (Fiji) v1.53q. For quantification of signal intensity, particles were defined automatically from a binary image of the DAPI channel then kinetoplast and nucleus particles were distinguished manually.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability
The high-throughput sequencing data generated in this study have been deposited in the Short Read Archive (SRA) under accession code PRJNA641153. The mapped data can be visualised using an online tool at https://tryp-cycle.pages.dev/. Other data relating to individual genes can be found at tritrypdb.org. Source data are provided with this paper.
Code availability
Code for the RIT-seq data analysis114 (https://zenodo.org/record/7002689) and for the online visualisation tool115 (https://zenodo.org/record/7002687) are available in GitHub.
References
Harashima, H., Dissmeyer, N. & Schnittger, A. Cell cycle control across the eukaryotic kingdom. Trends Cell Biol. 23, 345–356 (2013).
Pollard, T. D. & O’Shaughnessy, B. Molecular mechanism of mytokinesis. Annu Rev. Biochem. 88, 661–689 (2019).
Poon, R. Y. Cell cycle control: A system of interlinking oscillators. Methods Mol. Biol. 1342, 3–19 (2016).
Visconti, R., Della Monica, R. & Grieco, D. Cell cycle checkpoint in cancer: a therapeutically targetable double-edged sword. J. Exp. Clin. Cancer Res. 35, 153 (2016).
Buscher, P., Cecchi, G., Jamonneau, V. & Priotto, G. Human African trypanosomiasis. Lancet 390, 2397–2409 (2017).
Matthews, K. R. 25 years of African trypanosome research: From description to molecular dissection and new drug discovery. Mol. Biochem Parasitol. 200, 30–40 (2015).
Broadhead, R. et al. Flagellar motility is required for the viability of the bloodstream trypanosome. Nature 440, 224–227 (2006).
Baron, D. M., Kabututu, Z. P. & Hill, K. L. Stuck in reverse: loss of LC1 in Trypanosoma brucei disrupts outer dynein arms and leads to reverse flagellar beat and backward movement. J. Cell Sci. 120, 1513–1520 (2007).
Bonnefoy, S. et al. Biallelic mutations in LRRC56, encoding a protein associated with intraflagellar transport, cause mucociliary clearance and laterality defects. Am. J. Hum. Genet. 103, 727–739 (2018).
Coutton, C. et al. Mutations in CFAP43 and CFAP44 cause male infertility and flagellum defects in Trypanosoma and human. Nat. Commun. 9, 686 (2018).
Vincensini, L., Blisnick, T. & Bastin, P. [The importance of model organisms to study cilia and flagella biology]. Biol. Aujourdhui 205, 5–28 (2011).
Allmann, S. & Bringaud, F. Glycosomes: A comprehensive view of their metabolic roles in T. brucei. Int J. Biochem. Cell Biol. 85, 85–90 (2017).
Wheeler, R. J., Gull, K. & Sunter, J. D. Coordination of the cell cycle in trypanosomes. Annu Rev. Microbiol. 73, 133–154 (2019).
Clayton, C. Regulation of gene expression in trypanosomatids: living with polycistronic transcription. Open Biol. 9, 190072 (2019).
Zhou, Q., Hu, H. & Li, Z. New insights into the molecular mechanisms of mitosis and cytokinesis in trypanosomes. Int Rev. Cell Mol. Biol. 308, 127–166 (2014).
Ploubidou, A., Robinson, D. R., Docherty, R. C., Ogbadoyi, E. O. & Gull, K. Evidence for novel cell cycle checkpoints in trypanosomes: kinetoplast segregation and cytokinesis in the absence of mitosis. J. Cell Sci. 112, 4641–4650 (1999).
Akiyoshi, B. & Gull, K. Discovery of unconventional kinetochores in kinetoplastids. Cell 156, 1247–1258 (2014).
Marques, C. A. & McCulloch, R. Conservation and variation in strategies for DNA replication of kinetoplastid nuclear genomes. Curr. Genomics 19, 98–109 (2018).
Archer, S. K., Inchaustegui, D., Queiroz, R. & Clayton, C. The cell cycle regulated transcriptome of Trypanosoma brucei. PLoS One 6, e18425 (2011).
Crozier, T. W. M. et al. Proteomic analysis of the cell cycle of procylic form Trypanosoma brucei. Mol. Cell Proteom. 17, 1184–1195 (2018).
Benz, C. & Urbaniak, M. D. Organising the cell cycle in the absence of transcriptional control: Dynamic phosphorylation co-ordinates the Trypanosoma brucei cell cycle post-transcriptionally. PLoS Pathog. 15, e1008129 (2019).
Glover, L. et al. Genome-scale RNAi screens for high-throughput phenotyping in bloodstream-form African trypanosomes. Nat. Protoc. 10, 106–133 (2015).
Alsford, S. et al. High-throughput phenotyping using parallel sequencing of RNA interference targets in the African trypanosome. Genome Res. 21, 915–924 (2011).
Ngo, H., Tschudi, C., Gull, K. & Ullu, E. Double-stranded RNA induces mRNA degradation in Trypanosoma brucei. Proc. Natl Acad. Sci. USA 95, 14687–14692 (1998).
Hammarton, T. C., Clark, J., Douglas, F., Boshart, M. & Mottram, J. C. Stage-specific differences in cell cycle control in Trypanosoma brucei revealed by RNA interference of a mitotic cyclin. J. Biol. Chem. 278, 22877–22886 (2003).
Li, Z., Umeyama, T. & Wang, C. C. The chromosomal passenger complex and a mitotic kinesin interact with the tousled-like kinase in trypanosomes to regulate mitosis and cytokinesis. PLoS One 3, e3814 (2008).
Alsford, S., Kawahara, T., Glover, L. & Horn, D. Tagging a T. brucei RRNA locus improves stable transfection efficiency and circumvents inducible expression position effects. Mol. Biochem Parasitol. 144, 142–148 (2005).
Kolev, N. G., Tschudi, C. & Ullu, E. RNA interference in protozoan parasites: achievements and challenges. Eukaryot. Cell 10, 1156–1163 (2011).
Aslett, M. et al. TriTrypDB: a functional genomic resource for the Trypanosomatidae. Nucleic Acids Res. 38, D457–D462 (2010).
Springer, A. L. et al. Silencing of a putative inner arm dynein heavy chain results in flagellar immotility in Trypanosoma brucei. Mol. Biochem Parasitol. 175, 68–75 (2011).
Gassen, A. et al. DOT1A-dependent H3K76 methylation is required for replication regulation in Trypanosoma brucei. Nucleic Acids Res. 40, 10302–10311 (2012).
Choe, K. N. & Moldovan, G. L. Forging ahead through darkness: PCNA, still the principal conductor at the replication fork. Mol. Cell 65, 380–392 (2017).
Valenciano, A. L., Ramsey, A. C. & Mackey, Z. B. Deviating the level of proliferating cell nuclear antigen in Trypanosoma brucei elicits distinct mechanisms for inhibiting proliferation and cell cycle progression. Cell Cycle 14, 674–688 (2015).
Rudd, S. G., Glover, L., Jozwiakowski, S. K., Horn, D. & Doherty, A. J. PPL2 translesion polymerase is essential for the completion of chromosomal DNA replication in the African trypanosome. Mol. Cell 52, 554–565 (2013).
Ralston, K. S., Lerner, A. G., Diener, D. R. & Hill, K. L. Flagellar motility contributes to cytokinesis in Trypanosoma brucei and is modulated by an evolutionarily conserved dynein regulatory system. Eukaryot. Cell 5, 696–711 (2006).
Boehm, C. M. et al. The trypanosome exocyst: A conserved structure revealing a new role in endocytosis. PLoS Pathog. 13, e1006063 (2017).
Wang, D. Y., Kamuda, K., Montoya, G. & Mesa, P. The TRiC/CCT chaperonin and its role in uncontrolled proliferation. Adv. Exp. Med. Biol. 1243, 21–40 (2020).
Zhang, X., Hu, H., Lun, Z. R. & Li, Z. Functional analyses of an axonemal inner-arm dynein complex in the bloodstream form of Trypanosoma brucei uncover its essential role in cytokinesis initiation. Mol. Microbiol. 112, 1718–1730 (2019).
Sunter, J. D., Varga, V., Dean, S. & Gull, K. A dynamic coordination of flagellum and cytoplasmic cytoskeleton assembly specifies cell morphogenesis in trypanosomes. J. Cell Sci. 128, 1580–1594 (2015).
Hu, H., An, T., Kurasawa, Y., Zhou, Q. & Li, Z. The trypanosome-specific proteins FPRC and CIF4 regulate cytokinesis initiation by recruiting CIF1 to the cytokinesis initiation site. J. Biol. Chem. 294, 16672–16683 (2019).
Li, Z. et al. Identification of a novel chromosomal passenger complex and its unique localization during cytokinesis in Trypanosoma brucei. PLoS One 3, e2354 (2008).
Li, Z. & Wang, C. C. Changing roles of aurora-B kinase in two life cycle stages of Trypanosoma brucei. Eukaryot. Cell 5, 1026–1035 (2006).
Jones, N. G. et al. Regulators of Trypanosoma brucei cell cycle progression and differentiation identified using a kinome-wide RNAi screen. PLoS Pathog. 10, e1003886 (2014).
Hu, H., Liu, Y., Zhou, Q., Siegel, S. & Li, Z. The centriole cartwheel protein SAS-6 in Trypanosoma brucei is required for probasal body biogenesis and flagellum assembly. Eukaryot. Cell 14, 898–907 (2015).
Zhang, X., An, T., Pham, K. T. M., Lun, Z. R. & Li, Z. Functional analyses of cytokinesis regulators in bloodstream stage Trypanosoma brucei parasites identify functions and regulations specific to the life cycle stage. mSphere 4, e00199–19 (2019).
Dean, S., Moreira-Leite, F., Varga, V. & Gull, K. Cilium transition zone proteome reveals compartmentalization and differential dynamics of ciliopathy complexes. Proc. Natl Acad. Sci. USA 113, E5135–E5143 (2016).
Dang, H. Q. et al. Proximity interactions among basal body components in Trypanosoma brucei identify novel regulators of basal body biogenesis and inheritance. mBio. 8, e02120–16 (2017).
An, T. & Li, Z. An orphan kinesin controls trypanosome morphology transitions by targeting FLAM3 to the flagellum. PLoS Pathog. 14, e1007101 (2018).
Hu, L., Hu, H. & Li, Z. A kinetoplastid-specific kinesin is required for cytokinesis and for maintenance of cell morphology in Trypanosoma brucei. Mol. Microbiol. 83, 565–578 (2012).
DeGrasse, J. A. et al. Evidence for a shared nuclear pore complex architecture that is conserved from the last common eukaryotic ancestor. Mol. Cell Proteom. 8, 2119–2130 (2009).
Benz, C., Clucas, C., Mottram, J. C. & Hammarton, T. C. Cytokinesis in bloodstream stage Trypanosoma brucei requires a family of katanins and spastin. PLoS One 7, e30367 (2012).
Ralston, K. S. & Hill, K. L. Trypanin, a component of the flagellar dynein regulatory complex, is essential in bloodstream form African trypanosomes. PLoS Pathog. 2, e101 (2006).
May, S. F. et al. The Trypanosoma brucei AIR9-like protein is cytoskeleton-associated and is required for nucleus positioning and accurate cleavage furrow placement. Mol. Microbiol. 84, 77–92 (2012).
Portman, N. & Gull, K. Identification of paralogous life-cycle stage specific cytoskeletal proteins in the parasite Trypanosoma brucei. PLoS One 9, e106777 (2014).
Dostalova, A., Kaser, S., Cristodero, M. & Schimanski, B. The nuclear mRNA export receptor Mex67-Mtr2 of Trypanosoma brucei contains a unique and essential zinc finger motif. Mol. Microbiol. 88, 728–739 (2013).
Marques, C. A. et al. Diverged composition and regulation of the Trypanosoma brucei origin recognition complex that mediates DNA replication initiation. Nucleic Acids Res. 44, 4763–4784 (2016).
Gluenz, E., Sharma, R., Carrington, M. & Gull, K. Functional characterization of cohesin subunit SCC1 in Trypanosoma brucei and dissection of mutant phenotypes in two life cycle stages. Mol. Microbiol. 69, 666–680 (2008).
Ammerman, M. L. et al. Architecture of the trypanosome RNA editing accessory complex, MRB1. Nucleic Acids Res. 40, 5637–5650 (2012).
Zikova, A., Schnaufer, A., Dalley, R. A., Panigrahi, A. K. & Stuart, K. D. The F0F1-ATP synthase complex contains novel subunits and is essential for procyclic Trypanosoma brucei. PLoS Pathog. 5, e1000436 (2009).
Salazar-Roa, M. & Malumbres, M. Fueling the cell division cycle. Trends Cell Biol. 27, 69–81 (2017).
Tudzarova, S. et al. Two ubiquitin ligases, APC/C-Cdh1 and SKP1-CUL1-F (SCF)-beta-TrCP, sequentially regulate glycolysis during the cell cycle. Proc. Natl Acad. Sci. USA 108, 5278–5283 (2011).
Hu, J. W., Sun, P., Zhang, D. X., Xiong, W. J. & Mi, J. Hexokinase 2 regulates G1/S checkpoint through CDK2 in cancer-associated fibroblasts. Cell Signal 26, 2210–2216 (2014).
Morgan, G. A., Hamilton, E. A. & Black, S. J. The requirements for G1 checkpoint progression of Trypanosoma brucei S 427 clone 1. Mol. Biochem Parasitol. 78, 195–207 (1996).
Silvester, E., McWilliam, K. R. & Matthews, K. R. The cytological events and molecular control of life cycle development of Trypanosoma brucei in the mammalian bloodstream. Pathogens 6, 29 (2017).
Qiu, Y. et al. Glucose signaling is important for nutrient adaptation during differentiation of pleomorphic African trypanosomes. mSphere 3, e00366–18 (2018).
Naguleswaran, A., Doiron, N. & Roditi, I. RNA-Seq analysis validates the use of culture-derived Trypanosoma brucei and provides new markers for mammalian and insect life-cycle stages. BMC Genomics 19, 227 (2018).
Kalucka, J. et al. Metabolic control of the cell cycle. Cell Cycle 14, 3379–3388 (2015).
Burgers, P. M. J. & Kunkel, T. A. Eukaryotic DNA replication fork. Annu Rev. Biochem. 86, 417–438 (2017).
Mutomba, M. C., To, W. Y., Hyun, W. C. & Wang, C. C. Inhibition of proteasome activity blocks cell cycle progression at specific phase boundaries in African trypanosomes. Mol. Biochem. Parasitol. 90, 491–504 (1997).
Tinti, M., Guther, M. L. S., Crozier, T. W. M., Lamond, A. I. & Ferguson, M. A. J. Proteome turnover in the bloodstream and procyclic forms of Trypanosoma brucei measured by quantitative proteomics. Wellcome Open Res. 4, 152 (2019).
Hayashi, H. & Akiyoshi, B. Degradation of cyclin B is critical for nuclear division in Trypanosoma brucei. Biol. Open 7, bio031609 (2018).
Benz, C. & Clayton, C. E. The F-box protein CFB2 is required for cytokinesis of bloodstream-form Trypanosoma brucei. Mol. Biochem Parasitol. 156, 217–224 (2007).
Van Hellemond, J. J. & Mottram, J. C. The CYC3 gene of Trypanosoma brucei encodes a cyclin with a short half-life. Mol. Biochem Parasitol. 111, 275–282 (2000).
Ishii, M. & Akiyoshi, B. Characterization of unconventional kinetochore kinases KKT10 and KKT19 in Trypanosoma brucei. J. Cell Sci. 133, jcs240978 (2020).
Saldivia, M. et al. Targeting the trypanosome kinetochore with CLK1 protein kinase inhibitors. Nat. Microbiol. 5, 1207–1216 (2020).
Saldivia, M. et al. A CLK1-KKT2 signaling pathway regulating kinetochore assembly in Trypanosoma brucei. mBio. 12, e0068721 (2021).
Mugo, E. & Clayton, C. Expression of the RNA-binding protein RBP10 promotes the bloodstream-form differentiation state in Trypanosoma brucei. PLoS Pathog. 13, e1006560 (2017).
Minia, I. & Clayton, C. Regulating a post-transcriptional regulator: Protein phosphorylation, degradation and translational blockage in control of the trypanosome stress-response RNA-binding protein ZC3H11. PLoS Pathog. 12, e1005514 (2016).
Bishola Tshitenge, T. & Clayton, C. Interactions of the Trypanosoma brucei brucei zinc-finger-domain protein ZC3H28. Parasitology 149, 356–370 (2022).
Bravo Ruiz, G., Tinti, M., Ridgway, M. & Horn, D. Control of variant surface glycoprotein expression by CFB2 in Trypanosoma brucei and quantitative proteomic connections to translation and cytokinesis. mSphere 7, e0006922 (2022).
Melo do Nascimento, L. et al. Functional insights from a surface antigen mRNA-bound proteome. Elife 10, e68136 (2021).
Hammarton, T. C., Kramer, S., Tetley, L., Boshart, M. & Mottram, J. C. Trypanosoma brucei Polo-like kinase is essential for basal body duplication, kDNA segregation and cytokinesis. Mol. Microbiol. 65, 1229–1248 (2007).
Kumar, P. & Wang, C. C. Dissociation of cytokinesis initiation from mitotic control in a eukaryote. Eukaryot. Cell 5, 92–102 (2006).
McAllaster, M. R. et al. Proteomic identification of novel cytoskeletal proteins associated with TbPLK, an essential regulator of cell morphogenesis in Trypanosoma brucei. Mol. Biol. Cell 26, 3013–3029 (2015).
Tu, X. & Wang, C. C. The involvement of two cdc2-related kinases (CRKs) in Trypanosoma brucei cell cycle regulation and the distinctive stage-specific phenotypes caused by CRK3 depletion. J. Biol. Chem. 279, 20519–20528 (2004).
Menzies, S. K., Tulloch, L. B., Florence, G. J. & Smith, T. K. The trypanosome alternative oxidase: a potential drug target? Parasitology 145, 175–183 (2018).
Li, Z. & Wang, C. C. A PHO80-like cyclin and a B-type cyclin control the cell cycle of the procyclic form of Trypanosoma brucei. J. Biol. Chem. 278, 20652–20658 (2003).
Wei, Y., Hu, H., Lun, Z. R. & Li, Z. Centrin3 in trypanosomes maintains the stability of a flagellar inner-arm dynein for cell motility. Nat. Commun. 5, 4060 (2014).
Denninger, V. & Rudenko, G. FACT plays a major role in histone dynamics affecting VSG expression site control in Trypanosoma brucei. Mol. Microbiol. 94, 945–962 (2014).
Prendergast, L. et al. The CENP-T/-W complex is a binding partner of the histone chaperone FACT. Genes Dev. 30, 1313–1326 (2016).
Dean, S., Sunter, J. D. & Wheeler, R. J. TrypTag.org: A trypanosome genome-wide protein localisation resource. Trends Parasitol. 33, 80–82 (2017).
Halliday, C. et al. Cellular landmarks of Trypanosoma brucei and Leishmania mexicana. Mol. Biochem. Parasitol. 230, 24–36 (2019).
Haanstra, J. R. et al. Targeting pathogen metabolism without collateral damage to the host. Sci. Rep. 7, 40406 (2017).
Khare, S. et al. Proteasome inhibition for treatment of leishmaniasis, Chagas disease and sleeping sickness. Nature 537, 229–233 (2016).
Torrie, L. S. et al. Identification of inhibitors of an unconventional Trypanosoma brucei kinetochore kinase. PLoS One 14, e0217828 (2019).
Urbaniak, M. D. et al. Chemical proteomic analysis reveals the drugability of the kinome of Trypanosoma brucei. ACS Chem. Biol. 7, 1858–1865 (2012).
El-Sayed, N. M. et al. Comparative genomics of trypanosomatid parasitic protozoa. Science 309, 404–409 (2005).
Mensa-Wilmot, K., Hoffman, B., Wiedeman, J., Sullenberger, C. & Sharma, A. Kinetoplast division factors in a trypanosome. Trends Parasitol. 35, 119–128 (2019).
Klebanov-Akopyan, O., Mishra, A., Glousker, G., Tzfati, Y. & Shlomai, J. Trypanosoma brucei UMSBP2 is a single-stranded telomeric DNA binding protein essential for chromosome end protection. Nucleic Acids Res. 46, 7757–7771 (2018).
Milman, N., Motyka, S. A., Englund, P. T., Robinson, D. & Shlomai, J. Mitochondrial origin-binding protein UMSBP mediates DNA replication and segregation in trypanosomes. Proc. Natl Acad. Sci. USA 104, 19250–19255 (2007).
Sela, D. & Shlomai, J. Regulation of UMSBP activities through redox-sensitive protein domains. Nucleic Acids Res. 37, 279–288 (2009).
Chiu, J. & Dawes, I. W. Redox control of cell proliferation. Trends Cell Biol. 22, 592–601 (2012).
Currier, R. B. et al. An essential thioredoxin-type protein of Trypanosoma brucei acts as redox-regulated mitochondrial chaperone. PLoS Pathog. 15, e1008065 (2019).
Groitl, B. & Jakob, U. Thiol-based redox switches. Biochim Biophys. Acta 1844, 1335–1343 (2014).
Alsford, S. & Horn, D. Single-locus targeting constructs for reliable regulated RNAi and transgene expression in Trypanosoma brucei. Mol. Biochem. Parasitol. 161, 76–79 (2008).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Okonechnikov, K., Conesa, A. & Garcia-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292–294 (2016).
Ewels, P., Magnusson, M., Lundin, S. & Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
Egorov, A. A. et al. svist4get: a simple visualization tool for genomic tracks from sequencing experiments. BMC Bioinforma. 20, 113 (2019).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Marques, C.A., Ridgway, M., Tinti, M., Cassidy, A. & Horn, D. Genome-scale RNA interference profiling of Trypanosoma brucei cell cycle progression defects. GitHub. https://doi.org/10.5281/zenodo.7002689 (2022).
Marques, C.A., Ridgway, M., Tinti, M., Cassidy, A. & Horn, D. Genome-scale RNA interference profiling of Trypanosoma brucei cell cycle progression defects. GitHub. https://doi.org/10.5281/zenodo.7002686 (2022).
Acknowledgements
We thank R. Clark, A. Rennie and M. Lee of the Flow Cytometry and Cell Sorting Facility, which is supported by the Wellcome Trust (097418/Z/11/Z). We also thank L. Glover for advice on RNAi library manipulation, S. Hutchinson for advice on RIT-seq data analysis and J. Faria and G. Bravo Ruiz for fruitful discussions. The work was funded by Wellcome Trust Investigator Awards [100320/Z/12/Z and 217105/Z/19/Z to D.H.]. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Author information
Authors and Affiliations
Contributions
C.A.M., M.R., A.C. and D.H. designed the experiments. C.A.M. and M.R. performed the experiments. C.A.M., M.R., M.T. and D.H. analysed experimental data. M.T. performed data curation and visualisation. D.H. supervised the project and acquired funding. C.A.M., M.R., M.T. and D.H. wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Marques, C.A., Ridgway, M., Tinti, M. et al. Genome-scale RNA interference profiling of Trypanosoma brucei cell cycle progression defects. Nat Commun 13, 5326 (2022). https://doi.org/10.1038/s41467-022-33109-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-022-33109-y
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
