Abstract
To capture the global gene network regulating the differentiation of
immature T cells in an unbiased manner, large-scale forward genetic
screens in zebrafish were conducted and combined with genetic
interaction analysis. After ENU mutagenesis, genetic lesions associated
with failure of T cell development were identified by meiotic
recombination mapping, positional cloning, and whole genome sequencing.
Recessive genetic variants in 33 genes were identified and confirmed as
causative by additional experiments. The mutations affected T cell
development but did not perturb the development of an unrelated cell
type, growth hormone-expressing somatotrophs, providing an important
measure of cell-type specificity of the genetic variants. The structure
of the genetic network encompassing the identified components was
established by a subsequent genetic interaction analysis, which
identified many instances of positive (alleviating) and negative
(synthetic) genetic interactions. Several examples of synthetic
lethality were subsequently phenocopied using combinations of small
molecule inhibitors. These drugs not only interfered with normal T cell
development, but also elicited remission in a model of T cell acute
lymphoblastic leukaemia. Our findings illustrate how genetic
interaction data obtained in the context of entire organisms can be
exploited for targeted interference with specific cell types and their
malignant derivatives.
Subject terms: Genetics, Immunology
__________________________________________________________________
O’Meara et al. utilize a panel of zebrafish mutants to perform a whole
organism genetic interaction screen, examining the network regulating T
cell differentiation. The authors use a T cell acute lymphoblastic
leukemia (T-ALL) model to integrate the effects of small molecule
inhibitors of the T cell differentiation pathway and establish a
combination therapy for T-ALL in juvenile zebrafish.
Introduction
Exhaustive pairwise combinatorial screens of genetic variants in
unicellular organisms, such as E. coli^[49]1, S. cerevisiae^[50]2, and
S. pombe^[51]3, and in cell lines of multicellular organisms, such as
D. melanogaster^[52]4, and H. sapiens^[53]5–[54]7 have illuminated the
fundamental structure of genetic networks regulating cell fitness.
These studies have also unravelled the molecular basis of general
molecular rules underlying genetic interaction and resolved previously
undefined gene functions in complex regulatory networks. Additional
multiparametric phenotyping studies^[55]4,[56]8, albeit focusing on
more manageable collections of genes, have added additional complexity,
revealing sub-networks regulating phenotype-specific interactions
(e.g., cell growth versus cell division). However, by design,
approaches using cell lines fall short of capturing the physiology of
an entire organism. In contrast, a genetic interaction screen focusing
on one or few cell types in the context of a living vertebrate organism
has the potential to reveal cell non-autonomous and tissue-specific
components of genetic networks, an approach that has yet to be fully
explored^[57]9. Here, we describe our efforts to establish the genetic
network governing the differentiation of T cells using the zebrafish as
a vertebrate model organism.
Genetic interaction is said to occur when the phenotype of a
double-mutant organism deviates from the expected neutral phenotype. It
can be positive (alleviating), when the phenotype is less severe than
expected, or negative (synthetic) when the combination of two
individually benign gene mutations into a single genetic background
results in a more severe phenotype, such as loss of cell
viability^[58]10. The latter outcome is particularly attractive from
the viewpoint of cancer therapy. In this context, synthetic lethality
screens seek to identify and perturb genes that are required for the
survival of a target cancer cell carrying a specific oncogenic
mutation, as exemplified by the success of small molecule PARP
inhibitors in patients with BRCA1-deficient tumours^[59]11.
Nonetheless, the known tissue-dependence of genetic interactions^[60]12
and unexpected collateral damage outside the target tissue suggest that
such synthetic lethality screens are ideally conducted in the context
of a whole organism, thereby also incorporating potentially important
non-cell-autonomous modulatory effects mediated by the tumour
microenvironment^[61]12.
T-cell acute lymphoblastic leukaemia (T-ALL) arises from an early T
cell progenitor in the thymus that somatically acquired a stemness
phenotype as a result of ectopic gene activation following chromosomal
translocations often involving T cell receptor genes^[62]13, and/or
genetic and epigenetic defects in key lineage determinants, such as
NOTCH1^[63]14. Interestingly, inhibiting the activities of genes highly
expressed during early T cell differentiation, such as BCL-2 or
JAK1/JAK2, significantly reduced leukaemic burden in mouse T-ALL
xenografts, regardless of their mutation status^[64]15–[65]17,
suggesting that interference with the function of genes expressed in a
tissue-specific or tissue-restricted fashion offers an opportunity for
targeted tumour therapy.
Here, we delineated the genetic network underlying the development of T
cells in zebrafish, established the nature of synthetic lethal
interactions, and exploited this information for combinatory inhibitor
treatments that proved effective in preventing tumour progression in an
in vivo model of T-ALL.
The present study encompassed four steps (Fig. [66]1). In step 1,
genetic variants perturbing T cell development were identified through
large-scale forward genetic screens in zebrafish larvae (Fig. [67]1a);
in step 2, the genetic network underlying the development of T cells
was delineated by pairwise interaction analyses (Fig. [68]1b); in step
3, some of the genetic interactions were phenocopied using small
molecule inhibitors (Fig. [69]1c); in step 4, the information obtained
on synthetic lethal interactions was exploited for tumour treatment in
an in vivo model of T-ALL (Fig. [70]1d).
Fig. 1. Outline of the four major components of the current study.
Fig. 1
[71]Open in a new tab
a Identification of genetic variants perturbing T cell development
through large-scale forward genetic screens in zebrafish larvae. b
Delineation of the genetic network underlying the development of T
cells by pairwise interaction analyses. c Phenocopy of a subset of
genetic interactions using small molecule inhibitors. d Tumour
treatment in an in vivo model of T-ALL based on the information
obtained on synthetic lethal interactions.
Our results illustrate how the structure of a genetic network,
established in the context of an entire organism rather than in a
particular cell line, guides the selection of drug combinations that
selectively interfere with the function and/or viability of a specific
cell type in vivo.
Results
T lymphocyte-focused forward genetic screens
To establish the key nodes in the genetic network regulating the
differentiation of immature T cells, we conducted two forward ENU
mutagenesis screens^[72]18,[73]19 to identify recessive genes
regulating developing T cells in the thymus of zebrafish in an unbiased
manner. Using whole-mount RNA in situ hybridization at 5 days post
fertilization (d.p.f.) as a read-out, we identified mutations that
impaired larval T cell development (defined as absence or reduction in
recombination activating gene [rag1] expression), but spared the
hypophysis (defined as unchanged growth hormone gene [gh] expression)
and lacked craniofacial defects as assessed by microscopic inspection.
Thus, by way of design, the initial selection focused on the
identification of genetic variants without overt pleiotropic effects.
In order to quantify the extent of thymopoiesis, the ratio of rag1/gh
hybridization signals was calculated from their two-dimensional
projections after whole mount RNA in situ hybridization^[74]18–[75]20,
and the distribution of values of rag1/gh ratios in mutant crosses was
compared to that of control embryos. In the gynogenetic screen (for
details, see ref. ^[76]18), 281 mutagenized genomes were analysed; in
25 instances, clutches were observed, in which about 50% of the embryos
exhibited abnormal rag1 signals with normal gh gene expression pattern
(that is, a statistically significant reduction of rag1/gh ratios when
compared to wild-type embryos) and normal craniofacial structures.
Ultimately, three mutant lines could be established, corresponding to
~1% of the number of genomes screened. In a subsequent F3 screen (for
details, see ref. ^[77]19), F[3] clutches of 4584 F[2] families,
representing 4253 mutagenized haploid genomes, were screened. A total
of 141 mutants with reduced rag1 signals but without severe
craniofacial defects and normal gh expression patterns were detected in
the primary analysis. Ultimately, 42 mutant lines could be established,
again corresponding to ~1% of the number of genomes screened. One
allele each of the top3a gene was identified in both the F3 and
gynogenetic screens^[78]21; two alleles of ikzf1 were identified in the
F3 screen^[79]19,[80]20 (Table [81]1). This observation indicates that
the two screens combined can be expected to cover a large fraction of
genes/pathways regulating T cell development in zebrafish larvae.
Table 1.
Genetic variants identified in ENU forward genetic screen.
Mutant Allele^a Affected gene ENSEMBL Gene ID^b Molecular defect^c
Reference
Haematopoietic development
IP109 t25127 myb ENSDARG00000053666 p.I181N ^[82]68
II032 t25880 ikzf1^d ENSDARG00000013539 p.R489X ^[83]20
HY022 t21380 il7r ENSDARG00000078970 p.L124FfsX5 ^[84]21
HX157 t22598 jak1 ENSDARG00000020625 p.R580X ^[85]21
IP045 t25078 jak3 ENSDARG00000010252 p.Q336X ^[86]21
JZ061 t26394 fli1a ENSDARG00000054632 p.Q246X This paper
HK017 t20463 zbtb17 ENSDARG00000074548 p.Q562K ^[87]89
DNA replication/repair
HG010 t20320 pole1 ENSDARG00000058533 p.I633K ^[88]20
IG335 t23336 mcm10 ENSDARG00000045815 p.L248R ^[89]20
HU319 t24593 atad5a ENSDARG00000070568 p.L430X This paper
WW20/12 fr17 top3a^e ENSDARG00000052827 p.I531S ^[90]90
IY071 t25501 dnmt1 ENSDARG00000030756 p.N1391K ^[91]20
Cell cycle regulation
JM087 t26113 anapc1 ENSDARG00000075687 p.Y86X This paper
IT429 t25333 nek7 ENSDARG00000056966 p.Q117X This paper
mRNA processing
KW059 t26426 snapc3 ENSDARG00000101474 p.C297X ^[92]20
WW18/10 fr100 lsm8 ENSDARG00000091656 p.E72X ^[93]20
KL069 t26393 gemin5 ENSDARG00000079257 p.Y437X ^[94]20
IU191 t25877 cstf3 ENSDARG00000018904 p.D313VfsX7 ^[95]20
HJ028 t20450 upf1 ENSDARG00000016302 p.Y163X ^[96]91
HA343 t22074 tnpo3 ENSDARG00000045680 p.R203X ^[97]20
Ribosome
IG438 t22881 spata5 ENSDARG00000104869 p.R679X This paper
HP327 t24596 nol9 ENSDARG00000077751 p.Q162X This paper
JI065 t26214 pnrc1 ENSDARG00000043904 p.R91H This paper
JM052 t26337 fcf1 ENSDARG00000102333 p.R44G This paper
Chaperone & protein transport/stability
HI020 t22231 tbcb ENSDARG00000068404 p.Y182X This paper
IL015 t23758 unc45a ENSDARG00000103643 IVS1-1G>A (1-1G>A) This paper
IM087 t24920 ube3d ENSDARG00000026178 p.L352P This paper
Phosphoinositol metabolism
HG002 t20082 pi4kaa ENSDARG00000076724 p.Y800X This paper
IG447 t23755 pip5k1ba ENSDARG00000044295 p.T139M This paper
Miscellaneous
HY062 t24600 mat2aa ENSDARG00000040334 p.Y101X This paper
JI073 t26215 naa50 ENSDARG00000027825 1922-4099del This paper
KH025 t26216 eif5 ENSDARG00000003681 p.Y52X This paper
JZ007 t25773 yeats2 ENSDARG00000078767 IVS25+1G>A (4247+1G>A) This
paper
[98]Open in a new tab
^aIsolated in the Freiburg gynogenetic screen (allele designation:
frx); all other lines originate from the Tübingen 2000 screen (allele
designation: t); see ref. ^[99]19 for details.
^bZv10.
^cNomenclature according to ref. ^[100]92
^dA second mutant allele of ikzf1 was identified (p.Q360X [t24980];
ref. ^[101]19).
^eA second mutant allele of top3a was identified (p.E331X [t22046];
ref. ^[102]90).
The genomic localizations of zebrafish mutations were determined by
meiotic recombination mapping using individual F[3] fish arising from
crosses of F[2]-mutant carriers and the genetically distant WIK
wild-type strain^[103]18–[104]20 and/or whole genome sequencing (see
the “Methods” section). After assignment of mutants to particular
linkage groups, complementation analysis was carried out for those
mutations that mapped to the same chromosomal region to determine
whether two mutations causing similar phenotypes reside in the same or
in two different genes. To this end, a heterozygous fish carrying one
mutation was crossed with a heterozygous fish carrying the other
mutation. In general, allelic mutations fail to complement each other
in trans-heterozygous embryos, which exhibit the mutant phenotype like
homozygotes of either allele. If, however, the mutations are in
different genes, the double heterozygous offspring are expected to
exhibit a wild-type phenotype, unless epistatic interactions modify the
phenotype. The critical intervals identified by high-resolution meiotic
recombination mapping often contained less than a dozen candidate
genes. Their coding exons (including flanking regions) were then
sequenced after PCR amplification from genomic DNA of phenotypically
wild-type (that is, a mixture of wild-types and heterozygous fish) and
mutant embryos, which were identified by prior RNA in situ
hybridization with the rag1 probe; alternatively, whole genome
sequencing was employed to establish the structure of the relevant
genomic region. Once the identities of the mutated genes (Table [105]1)
had been established, they were subsequently validated by at least one
of several techniques, including CRISPR/Cas9-mediated mutagenesis,
knock-down using antisense morpholino oligonucleotides, and
mRNA-mediated or BAC DNA-mediated phenotypic rescue (Supplementary
Fig. [106]1 and Supplementary Tables [107]1–[108]4). In total, 33
candidate genes were successfully validated. Whereas about one third of
the identified alleles exhibit deleterious missense mutations, the
majority of alleles are predicted to encode truncated proteins
(Table [109]1). Based on their gene ontologies and the information
obtained from literature surveys, the candidate genes were tentatively
grouped into several functional categories (Table [110]1), although
some of the genes function in more than one pathway. For example, the
dnmt1 gene is listed here in the DNA repair and replication group,
which clearly corresponds to its general functional properties;
however, the particular mutant allele described here specifically
impairs haematopoietic development by primarily affecting the
lymphocyte lineage^[111]22,[112]23. With these caveats in mind, we
derived the following categories. Haematopoietic regulators, c-myb,
ikzf1, il7r, jak1, jak3, fli1a, and zbtb17; DNA repair and replication
processes, pole1, mcm10, atad5a, dnmt1, and top3a; cell cycle
regulation, anapc1 and nek7; mRNA processing, snapc3, lsm8, gemin5,
cstf3, upf1, and tnpo3; ribosome biogenesis, spata5, nol9, pnrc1, and
fcf1; protein folding and stability, tbcb, unc45a, and ube3d;
phosphoinositol metabolism, pi4kaa and pip5k1ba. The four genes
belonging to miscellaneous pathways included, mat2aa
(S-adenosylmethionine synthesis), naa50 (N-terminal protein
acetylation), eif5 (protein translation), and yeats2 (histone H3K27ac
reader).
Transcriptional landscapes of mutants
To gain further insight into the functional consequences of the
different mutations, we compared the transcriptomes of whole mutant
fish larvae and their wild-type siblings at 5 d.p.f. To this end,
individual fish were genotyped and wild-type (+/+) and homozygous
mutant (m/m) individuals selected for RNA sequencing. We determined the
differentially expressed genes in the transcriptomes of 28 mutants from
the ENU screen; we also included fish with deleterious mutations in
rag1 (ref. ^[113]24) and foxn1 genes. This collection of mutants
(Fig. [114]2a) represents the core functional categories identified in
the genetic screens. For each mutant, we subjected their 750 most
significantly up- and down-regulated genes, respectively, to KEGG
pathway enrichment analysis (Fig. [115]2a). For 17 ENU mutants and
rag1-deficient fish, this analysis recovered at least one, but in most
cases, several significantly enriched pathways; the most frequently
flagged pathways were cytokine receptor (dre04060, dre64274),
spliceosome (dre03040), and cell cycle (dre04110) (Fig. [116]2a). The
results of additional analyses confirmed the congruency between KEGG
assignment and functional outcome for the mutants grouped into cell
cycle, spliceosome, endoplasmic reticulum, and ribosome biogenesis
categories (Supplementary Fig. [117]2a–d). For example, using animals
treated with nocodazole as a positive control, and the fli1a
haematopoietic mutant as a negative control, we found subtle changes of
the fractions of cells in G2/M phase in anapc1 and nek7 mutants; this
was accompanied by evidence for developmental retardation of
craniofacial cartilage, a rapidly proliferating tissue that is a
sensitive indicator of perturbations of cell proliferation in zebrafish
larvae (Supplementary Fig. [118]2a). Likewise, we confirmed that the
patterns of mRNA splicing was perturbed for mutants assigned to the
category of mRNA processing mutants (snapc3, lsm8, tnpo3, gemin5)
(Supplementary Fig. [119]2b); a notable exception to this general
phenomenon is the upf1 mutant, affecting the degradation of mRNAs by
the non-sense-mediated decay machinery (Table [120]1). By contrast,
mRNA splicing was found to be normal in representatives of other
categories (pole1, mcm10, zbtb17, and tbcb). Mutants assigned to the
category of protein folding and stability were found to cause ER
stress, as revealed by upregulation of several indicator proteins
(Supplementary Fig. [121]2c). Finally, mutations in genes associated
with ribosome biogenesis (spata5, and nol9) were found to exhibit
significantly altered ratios of 18S and 28S mature ribosomal RNAs,
indicative of abnormal processing of rRNA precursors; this phenotypic
aspect was normal in other mutants (Supplementary Fig. [122]2d).
Collectively, these data support the assignment of mutants to
particular functional categories.
Fig. 2. Phenotyping genetic variants identified from ENU screen, grouped by
biological function.
[123]Fig. 2
[124]Open in a new tab
a Transcriptome analysis and ClusterProfiler pathway enrichment of top
1500 differentially expressed genes (DEG) (FDR ≤ 0.05) from each
genetic variant. Genetic variants are shown in columns and enriched
KEGG pathways in rows, with brackets indicating the number of enriched
genes from each pathway; only 18 of 30 genetic variants exhibited
significant deregulation of KEGG pathways. Row side colours identify
pathways in similar biological functional categories. Point sizes
represent the percentages of genes enriched from each pathway for each
genetic variant; point colours specify Benjamini–Hochberg (BH)-adjusted
P values. b Top DEG in genetic variants. Genetic variants are shown in
columns and DEG in rows, grouped by biological functional categories.
The following KEGG identifiers were included. T cell development (T
cell receptor—mmu04660, primary immunodeficiency—mmu05340, Notch
signaling—mmu04330), DNA synthesis (DNA replication—mmu03030,
MMR—dre03430, BER—dre03410, HR—dre03440, p53 signaling—dre04115), cell
cycle (apoptosis—dre04210, cell cycle—dre04110, cellular
senescence—dre04218), mRNA processing (spliceosome—dre03040, mRNA
surveillance—dre03015, nonsense-mediated decay—dre03015), ribosome
function (ribosome biogenesis—dre03008, ribosome—dre03010) and
endoplasmic reticulum (ER) (protein processing in ER—dre04141,
proteasome—dre03015). Asterisks define the top six DEG (|log[2] fold
change| ≥ 0.5) per genetic variant. c Schematic of haematopoietic
differentiation pathways. Each cell type is marked with a signature
gene(s), whose expression levels are taken as indicative of their
presence. Note that the thymic epithelium represents a separate lineage
and originates from pharyngeal endoderm. At the time of analysis (5
d.p.f.), B cell development has not yet started, so that rag1
expression levels are indicative of developing T cells. d Expression
levels of signature genes (excluding rag1; see Fig. 2b, and text) as
determined by RNA-seq at 5 d.p.f. for all genetic variants. The log[2]
fold changes are indicated. Some of the gene expression changes
detected in the RNA-seq analysis were confirmed by RNA in situ
hybridization at different stages of development (see Supplementary
Fig. [125]3).
A detailed analysis of the transcriptional landscape of all
mutants—focussing on the expression levels of genes representing the
KEGG pathways (Fig. [126]2a) concordant with the known gene ontology of
each mutant—revealed uniformly reduced transcripts for rag1, albeit to
different extents (Fig. [127]2b). This finding is consistent with the
primary selection criterion of mutants, namely reduced signals in the
whole mount RNA in situ hybridization. Of note, the expression levels
of the growth hormone gene, gh, were not changed in any of the mutants,
in keeping with the primary selection criterion for the RNA in situ
hybridization screening. These findings indicate that the altered
rag1/gh ratios observed in the mutants are due to reduced rag1
expression levels rather than increased gh signals (cf., Supplementary
Figs. [128]1 and [129]3). Expression levels of several T
cell-associated genes, such as rag2, lck, and zap70, were also reduced,
reinforcing the notion of a severely impaired T cell development in all
mutants; in 25 of the 30 mutants analysed, rag1 belongs to the group of
their six most deregulated genes (asteriks in Fig. [130]2b), with the
lck gene (encoding a critical regulator of T cell development) being
included in this category in 20 mutants. Importantly, similar patterns
of transcriptional deregulation (Fig. [131]2b) were found among mutants
within individual gene ontology groups as defined in Table [132]1; the
largely overlapping downstream effects of the individual mutants
support their functional association. Collectively, these features
define a transcriptional landscape of mutant larval T cell development.
For instance, mutations in genes regulating pre-mRNA processing (lsm8,
snapc3, gemin5, tnpo3, and upf1) shared increased expression of
spliceosome components (lsm8, prpf3, and prpf31); likewise, unc45a and
tbcb mutations both induced higher levels of genes required in protein
homeostasis (dnajb1b and hsp90aa).
Dysregulation of the p53 signalling pathway occurs in many mutants,
except for those of the haematopoietic group (Fig. [133]2a,b, and
Supplementary Fig. [134]4a). Interestingly, although the developing
nervous system in zebrafish embryos is known to be highly susceptible
to p53-mediated apoptosis^[135]25, elevated levels of neuronal
apoptosis, defined by double-strand breaks (DSB), were only found in
the mcm10 mutant (Supplementary Fig. [136]4b); previous findings
indicated that inactivation of mcm10 leads to increased generation of
DSB^[137]26. Hence, although the transcriptome data are indicative of
activation of the p53 pathway, this response—with the exception of that
in mcm10 mutants—must be restricted to fewer neuronal and/or
non-neuronal cell types in the other mutants. Developing T cells are a
prime candidate cell type contributing to the p53 activation signature
in transcriptomes of whole larvae; indeed, in most of the cases
examined, their demise can be rescued in the p53-deficient background
(Supplementary Fig. [138]4c); failing T cell development cannot be
reversed in dnmt1/p53 and mat2aa/p53 double mutants and thus most
likely is due to aberrations other than p53-mediated apoptosis
(Supplementary Fig. [139]4d).
Next, we examined the impact of the identified mutations on
haematopoietic development. To this end, we determined the expression
profiles of signature genes that characterize various intermediate
steps of haematopoietic differentiation (Fig. [140]2c). The expression
levels of rag1 were not considered in this analysis, because the
detrimental effects of mutations all converge on early T cell
differentiation (identified by low expression levels of rag1
[Fig. [141]2b]), and hence are not informative in this regard. This
analysis partitioned the mutations into three groups (Fig. [142]2d).
The largest group of genes appears to predominantly act in the T cell
differentiation pathway with little impact on haematopoietic precursor
stages (group A). A smaller group of mutations, such as those affecting
mat2aa, tbcb, ikzf1, unc45a, and nek7 impairs the differentiation of
both myeloid and lymphoid lineages (group B). The substructures within
groups A and B (which is apparent from the clustering shown to the left
of the panel) highlight unique aspects of each mutant, and provide the
starting point for future in-depth functional analyses. Finally, foxn1
expression is particularly diminished in pi4kaa mutants (C), indicating
that the malfunction of the thymic microenvironment underlies failing T
cell differentiation in this mutant.
Tissue-restricted effects of mutants
Our results indicate that T cells are particularly sensitive towards
the impaired activities of genes identified in the screen, as
haematopoietic cell types other than T cells (haematopoietic
progenitors, erythroid and myeloid cells) and the thymic epithelium
were largely unaffected in the majority of mutants (Fig. [143]2c, d).
In order to substantiate the conclusion of T-cell bias of genes
identified in the zebrafish screen, we analysed the tissue-specific
expression patterns of their mouse homologs, and compared them to the
expression patterns of genes assigned to KEGG pathways mmu04660 (T cell
receptor) and mmu04155 (p53 signalling) in the BioGPS datasets which
comprise more than 90 different tissues and cell types (see the
“Methods” section). A substantial number of genes identified in the ENU
screens exhibit high expression levels in the T cell subset of
immune-related cell types (upper and lower right quadrants in
Fig. [144]3a). This distinguishes them from the expression pattern of a
random selection of genes from the genome or those associated with the
p53 signalling pathway (Fig. [145]3b). Indeed, the patterns of
expression of the genes identified in the ENU screens is comparable to
the tissue-specific expression profile of genes involved in TCR
signalling, yet significantly different from either p53-related or
random collections of genes (Fig. [146]3c). Collectively, these
analyses not only indicate a strong expression bias of mutant genes
towards the T cell lineage, but also provide evidence for
evolutionarily conserved functions of these genes, with potential
relevance to the translation of our results to the mammalian T cell
system.
Fig. 3. Concordant tissue expression signatures between genes identified in
the ENU screens and genes regulating T cell receptor signalling.
[147]Fig. 3
[148]Open in a new tab
a Expression patterns of mouse homologs of genes identified in the ENU
forward genetic screen are compared to genes listed in KEGG pathways
designated T cell receptor (mmu04660; T cell), p53 signalling
(mmu04115; p53), and a random selection of genes from the mouse genome.
BioGPS microarray data were partitioned into four categories (see the
“Methods” section); the y-axis depicts expression bias according to
immune and non-immune categories, such that genes expressed at higher
levels in immune-related cells and tissues have higher values; genes
are also partitioned according to T cell and non-T immune cells
categories on the x-axis, such that genes expressed at higher levels in
T cells as compared to other immune-related cells receive higher
values. Relative expression values (log[2]) for each gene were
determined between mean expression values for immune/non-immune and T
cell/non-T immune cells partitions. Hence, genes in the upper right
quadrant (Q2) represent genes highly expressed by T cells. The
percentage of genes in Q2 are depicted; red, genes identified in the
forward genetic screen (ENU genes); black, T cell genes; blue, random
gene set (genes not depicted in diagram); yellow; p53 signalling genes
(genes not depicted in diagram). P values for accumulations was
determined by Fisher’s exact test. *P < 0.05; **P < 0.01. b Principal
component analysis (PCA) on expression data for the four groups of
genes analysed in (a). c Proportion of genes from four groups of genes
(T cell-related genes; ENU genes; p53 pathway-related genes; and a
random selection of genes from the genome) expressed in various tissue
and cells of the mouse. Genes were assigned to a specific origin, if
their expression levels were significantly greater than background
tissue expression (z score ≥ 1.96). Proportions of genes highly
expressed by each tissue were normalized to the numbers of genes. P
values for enrichments were determined by Fisher’s exact test. Note
that the proportion of ENU genes assigned to T cells in the BioGPS list
is indistinguishable from that of T cell genes. By comparison, the
proportion of p53-signalling pathway genes and random gene sets are
significantly underrepresented (***P < 0.001).
Cross-regulation of genes in different functional categories
Collectively, our results indicate that despite the diversity of
functional categories represented in the collection of ENU mutants, the
aberrations detectable in their transcriptomes converge on the T cell
lineage, as read out by reduced rag1 gene expression. We therefore
examined the potential overlap of functional changes among the
different variants as reflected in their transcriptomes. To this end,
we determined in more detail the degrees of overlap between the 1500
most deregulated genes in the transcriptomes of 25 mutants. The
resulting matrix (Fig. [149]4a) exhibits two key features. First, the
transcriptomes of fish carrying mutations in genes assigned to the same
functional category is readily apparent (see also Fig. [150]2b).
Second, substantial similarities are revealed also among functional
categories, suggesting that the genetically distinct pathways
regulating T cell development may be functionally interconnected. An
important prediction arising from this result is that the mutation in
one gene affects the expression of (at least some) other genes
identified in the screens. Our data provide evidence that this type of
cross-regulation exists (Fig. [151]4b). For example, positive
regulation of the spliceosome factor lsm8 in the tbcb mutant could be
the result of a feedback mechanism activated by an unfolded protein
response; likewise, failure of proper mRNA processing in the case of
mRNA processing mutants snapc3 and cstf3 could reduce expression of the
gene encoding the catalytic subunit (pole1) of DNA polymerase epsilon.
Collectively, the transcriptomes of individual mutants reveal the
presence of pervasive cross-regulation both within and across
functional categories, even when accounting for likely instances of
destabilization of mRNAs arising from the particular structures of
the mutant alleles (Fig. [152]4b).
Fig. 4. Transcriptional overlap between genetic variants and interconnected
gene co-regulation.
Fig. 4
[153]Open in a new tab
a Overlaps of top 1500 DEG (FDR ≤ 0.05) from each genetic variant
grouped by functional categories. Only Jaccard indices with significant
overlap (FDR ≤ 0.05), determined using the hypergeometric distribution,
are shown; cell notes indicate the percentages of overlap. b
Co-regulation of genes identified in the ENU screens. Genetic variants
are shown in columns and their expression levels are depicted in rows;
genes are grouped by functional categories. Cell notes identify genes
with |log[2] fold change | ≥ 0.5 and FDR ≤ 0.05.
Small molecule pathway mimics
With a view to future therapeutic interference with the genetic
pathways identified through the genetic screens, we aimed at mimicking
the effects of mutations in certain ontology groups of genes by
treatment of fish with known small molecule inhibitors (Supplementary
Table [154]5). To this end, wild-type fish were treated during a
48 h-period (from 72 to 120 h.p.f.) (Fig. [155]5a, b), a period when
the thymus is colonized by haematopoietic precursors and intrathymic T
cell development begins^[156]27,[157]28, to establish a dose–response
relationship with respect to the thymopoietic index (rag1/gh ratio)
(IC30 values for each small molecule inhibitor are listed in
Supplementary Table [158]5). To examine the extent with which specific
small molecule inhibitors recapitulate the mutant phenotypes, we
examined two drugs in more detail. Given the prominent representation
of genes important for mRNA processing, we chose pladienolide B (PB),
an inhibitor of the mRNA splice regulator SF3B1, which interferes with
proper recognition of intronic branch sites^[159]29; and NMD14, an
inhibitor of nonsense-mediated decay^[160]30. Reassuringly, the
incidence of aberrant alternative splicing events of PB-treated and
NMD14-treated zebrafish is similar to those seen in the most relevant
mRNA processing mutants (Fig. [161]5c). Moreover, the patterns of
transcriptional deregulation seen in individual mutants affecting mRNA
processing match the corresponding inhibitor profiles; indeed, NMD and
upf1 mutants cluster together, as do PB and the lsm8, gemin5 and snapc3
mutants (Fig. [162]5d). In a second confirmatory study, we found that
similarly to tbcb and unc45a mutants, tunicamycin, an inhibitor of
glycosylation in the ER^[163]31, and thapsigargin (THS)^[164]32,
induced ER stress and shared transcriptional profiles at the level of
pathway deregulation (Fig. [165]5e, f).
Fig. 5. Small molecule inhibitors recapitulate genetic variant phenotypes.
[166]Fig. 5
[167]Open in a new tab
a Representative images at 5 d.p.f. after whole-mount RNA in situ
hybridization with rag1- and gh-specific probes for wild-type fish
treated with small molecule inhibitors targeting the major pathways
impaired by mutant genes. Fish were exposed to drugs between 3 and 5
d.p.f.; DMSO-treated fish were used as negative controls. b rag1/gh
ratios of untreated fish (DMSO) and those treated with small molecule
inhibitors between 3 and 5 d.p.f. Significance was determined by
one-way ANOVA with Dunnett’s post-test. *P < 0.05; **P < 0.01;
***P < 0.001. Abbreviations: 5-fluorouracil (5FU [200 μM]), brefeldin A
(BFA [0.75 μM]), Chr-6494 (CHR [0.75 μM]), doxorubicin (DOX [0.75 μM]),
eeyarestatin (EEY [800 μM]), etoposide (ETO [1.72 μM]), isoginkgetin
(ISO [320 μM]), mitoxantrone dihydrochloride (MD [1.5 μM]), NMD14 (NMD
[100 μM]), nocodazole (NOC [0.4 μM]), NU7026 (NU7 [7.5 μM]),
pladienolide B (PB [0.2 μM]), thapsigargin (THS [0.5 μM]), and
tunicamycin (TUN [1.5 μM]). The concentrations used are indicated in
square brackets after the abbrevaitions. See source data file for b in
Supplementary Data [168]2. c Effect of small molecule spliceosome
inhibitor (PB) and an inhibitor of non-sense mediated decay (NMD14), on
pre-mRNA processing, compared to defects in mutants. Numbers of
significant skipped exon events are depicted atop each bar, as
determined by reads covering exon boundaries; FDR ≤ 0.05, |inclusion
level difference | ≥ 0.250) relative to wild-type siblings. *P < 0.05;
**P < 0.01; ***P < 0.001. d PB and NMD14 mimic the effects of mutants
acting in pre-mRNA processing pathways. Gene expression patterns of top
DEG known to regulate mRNA processing as defined by KEGG pathway IDs
(spliceosome—dre03040, mRNA surveillance—dre03015, nonsense-mediated
decay—dre03015) are depicted; genetic variants and treatment cohorts
are shown in columns and DEG in rows. e The small molecule ER
inhibitor, TUN, mimics the proteomic and transcriptional ER stress
response observed in mutants. A Western blot of protein lysates of 5
d.p.f. zebrafish mutants/treatment cohorts were resolved with
antibodies detecting the ER stress-related component GRP78 (BiP). Amido
black staining of total protein was used as a loading control.
Tunicamycin (TUN)-treated wild-type fish were included as positive
control of ER stress activation. Sizes of markers are indicated in kDa.
f Small molecule ER inhibitors, TUN and THS, mimic differential gene
expression patterns in mutants affecting protein processing pathways.
Gene expression patterns of top DEG known to regulate ER function as
defined by KEGG pathway IDs (protein processing in ER—dre04141,
proteasome—dre03015) are depicted; genetic variants and treatment
cohorts are shown in columns and DEG in rows.
Interconnected network of genes and pathways
To substantiate the conclusion of cross-regulation more fully, and to
establish the structure of the underlying genetic network, we performed
pairwise interaction analyses for selected mutants and small
inhibitors. We define fitness of a cell type as the strength of the RNA
in situ hybridization signal; for T cells, rag1 expression; for cells
in the hypophysis, gh expression. Wild-type values are assigned a
fitness value W of 1, whereas the values for the two experimental
(mutant or inhibitor-treated) conditions are defined as W[x] and W[y],
respectively. Under the multiplicative model (see the “Methods” section
for details), we calculated an expected fitness, E(W)[xy], for the
combination of the two conditions by calculating the product of the two
single fitness values, and compared this value to the observed fitness
W[xy] (Fig. [169]6a). The results are interpreted as follows.
Non-interactive interactions are defined as an observed double-mutant
fitness not significantly different from the expected fitness of the
double-mutant (range of black values in the schematic of Fig. [170]6a);
negative interactions are defined as an observed double-mutant fitness
significantly less than the expected fitness of the double-mutant
(range of red values in Fig. [171]6a); positive-coequal interaction is
an observed double-mutant fitness significantly greater than the
expected fitness of the double-mutant, but equivalent to the least fit
single mutant (range of light blue values in Fig. [172]6a);
positive-suppressive interaction is an observed double-mutant fitness
significantly greater than the expected fitness for the double mutant
and the least fit single mutant (range of dark blue values in
Fig. [173]6a). As expected, the diminished individual fitness values of
T cells of genetic variants and of inhibitor-treated animals are
reflected in significant median signal intensity reductions for rag1
expression (−80.9% and −19.8% [IC30 target], respectively), as opposed
to gh signals produced by somatotrophic epithelial cells, which remain
essentially unchanged (Fig. [174]6b).
Fig. 6. Analysis of interactions.
[175]Fig. 6
[176]Open in a new tab
a Schematic of the types of experiments underlying the interaction
network. The integrated interaction screen consists of mutant–mutant,
mutant–morpholino, morpholino–morpholino, mutant–inhibitor, and
inhibitor–inhibitor interactions, where the numbers of T cells as
determined by rag1 gene expression relative to growth hormone
(gh)-expressing somatotrophic epithelial cells is normalized to
wild-type levels (W[WT]; fitness). Single mutant (W[x], W[y]) and
double mutant fitness (W[xy]) values were determined by normalization
of their rag1/gh ratios to wild-type rag1/gh ratios. The expected
double mutant fitness E(W[xy]) is the product of single mutant fitness
values (W[x] × W[y]). A non-interactive line is assigned to an observed
double-mutant fitness value that is within the propagated error of
expected double-mutant fitness. Negative interaction is called when an
observed double-mutant fitness is significantly less than the expected
double-mutant fitness minus the propagated error. Positive–coequal
interaction is called when an observed double-mutant fitness is
significantly greater than the expected double-mutant fitness plus the
propagated error, but equivalent to the least fit single mutant.
Positive–suppressive interaction is called when an observed
double-mutant fitness is significantly greater than the expected
double-mutant fitness plus propagated error and greater than the least
fit single mutant. b Effect of gene mutations (mutants and morphants)
and inhibitor treatments on two different tissues, pituitary gland (as
determined by gh expression) and T cell (as determined by rag1
expression). The changes in expression levels in percent relative to
genetically wild-type (panel designated gene) or untreated controls
(panel designated inhibitor) are given for rag1 and gh hybridization
signals (cf., Figs. [177]4 and [178]5). For both types of analyses,
the differences between gh and rag1 expression levels are significant
at P < 0.001 (two-tailed Student´s t-test). c Effect of gene–gene
genetic interactions (mutant–mutant) and inhibitor genetic interactions
(inhibitor–inhibitor) on two different tissues, T cell (rag1) and
pituitary gland (gh). Relative log[2]-fold changes between observed
(W[xy]) and expected double mutant fitness E(W[xy]) values are given
for T cells and growth hormone-producing somatotropic cells. Colours
represent the interaction types (black, non-interactive; magenta,
negative; light-blue, positive-coequal; dark blue,
positive-suppressive). P values were determined by two-tailed Student´s
t-test. Statistical tests for homogeneity of variances were performed
using Bartlett’s test.
In order to ascertain that the cell-type specificity is maintained
under the conditions of gene–gene and inhibitor–inhibitor interactions,
we calculated the fold changes of gh and rag1 gene expression levels.
As expected, the great majority of interactions between genes and small
molecule inhibitors, respectively, had minimal effects on gh-expressing
cells in the hypophysis. By contrast, the rag1 expression levels varied
widely, as a result of strong positive and negative interactions
(Fig. [179]6c; Supplementary Data [180]1).
Identification of off-target effects in interaction studies
The separate analysis of gh and rag1 expression levels readily reveals
whether interactions between genes or inhibitors affect only T cells,
or both T cells and growth hormone-producing cells in the hypophysis,
the latter situation being indicative of potential off-target effects.
For example, combining NMD14 + brefeldin A (BFA) inhibitors is toxic to
not only T cells (Fig. [181]7a, left panel), but also to gh-producing
somatotrophs (Fig. [182]7a, right panel), an outcome which was not
expected from the individual effect of two inhibitors (Fig. [183]5a,
b). Indeed, such off-target effects can only be detected in the context
of the whole organism, illustrating the advantage of this approach over
cell-based screens. Many combinations of genetic variants and/or drugs
exhibited T cell-specific toxicity, such as seen with THS+ etoposide or
etoposide + doxorubicin (Fig. [184]7b, left panel), while neither of
these drug combinations exhibited off-target effects in gh-producing
cells (Fig. [185]7b, right panel). Collectively, the transcriptional
landscapes of mutants, the T cell expression signatures of mutant
genes, and the tissue-restricted effects of genetic interactions
provide further support for the notion that the development of T cells
is controlled by several interconnected pathways.
Fig. 7. Common and tissue-specific genetic interaction networks.
[186]Fig. 7
[187]Open in a new tab
a Shared genetic interactions between mutant genes and inhibitors
affecting fitness of both T cells (rag1) and growth hormone-producing
somatotropic cells (gh). Line font size reflects relative log[2]-fold
changes between observed and expected double mutant fitness values.
Nodes are grouped by primary biological pathways that are affected by
mutations or inhibitors. Solid or dashed circles around each node
denote either genes or inhibitors, respectively. For this presentation,
positive–suppressive and positive–coequal interactions are combined.
Colours represent interaction types (magenta, negative; blue, positive;
black, non-interactive). 5FU 5-fluorouracil), BFA brefeldin A, CHR
Chr-6494, DOX doxorubicin, EEY eeyarestatin, ETO etoposide, ISO
isoginkgetin, MD mitoxantrone dihydrochloride, NMD NMD14, NOC
nocodazole, NU7 NU7026, PB pladienolide B, THS thapsigargin, and TUN
tunicamycin. b, Cell type-specific genetic interactions between mutant
genes and inhibitors specifically affecting fitness of T cells.
Multiparametric genetic interaction network
In the next step, we expanded our analysis to all five types of
interactions; the contributions of some mutants were replaced by
knock-downs using anti-sense morpholino oligonucleotides to facilitate
the analyses. The results from mutant–mutant, mutant–morphant,
morphant–morphant, mutant–inhibitor, and inhibitor–inhibitor
interactions were incorporated into a single network built from 284
pairwise interactions (Supplementary Data [188]1). In 47% of all pairs
tested, no interaction could be detected; positive interactions were
seen in 23.1% of cases, which comprised positive–coequal (18.3%) and
positive–suppressive (4.8%) outcomes; negative interactions were found
in 29.9% of all tests (Fig. [189]8a; Supplementary Data [190]1).
Individual components of the DNA repair and replication ontology group
were enriched for positive interactions, when compared against the
overall average; for instance, for etoposide, 12 of 22 interactions
were scored as positive (P = 0.009) (Fig. [191]8b). Despite their
functional heterogeneity, an enrichment for positive interactions was
detectable at the level of some ontology groups; for instance, between
cell cycle and DNA repair/replication groups (27%, P = 0.29, n = 26
interactions) (Fig. [192]8b; Supplementary Fig. [193]5).
Mechanistically, we attribute these alleviating outcomes to the
improved error correction capacity when the cell cycle is slowed down,
thereby avoiding genomic catastrophe^[194]33,[195]34. Negative
interactions were apparent between ER and cell cycle (47%, P = 0.1,
n = 15) ontology groups (Fig. [196]8b, Supplementary Fig. [197]5), an
outcome reflected in clinical observations that proteasome inhibitors
sensitize cells to genotoxic agents^[198]35. In our present
experiments, we tested only a subset of all possible two-way
interactions; hence, when the interactions matrix is expanded in future
studies, we expect that more biologically relevant interactions will be
revealed between genes and drugs, both individually and at the level of
functional groups.
Fig. 8. Genetic landscape of developing T cells in zebrafish.
[199]Fig. 8
[200]Open in a new tab
a Genetic interactions between genes and inhibitors affecting fitness
of T cells (rag1) normalized to fitness of growth hormone-producing
somatotropic cells (gh). Line font size reflects relative log[2]
fold-changes between observed and expected double-mutant fitness values
for rag1/gh ratios. Nodes are grouped by primary biological pathways
affected by mutation or inhibitor. Solid or dashed circles around each
node denote either genes or inhibitors, respectively. For this
presentation, positive–suppressive and positive–coequal interactions
are combined. Colours represent interaction types (magenta, negative;
blue, positive; black, non-interactive). b Proportions of interaction
types by genes and inhibitors, grouped by biological functional
categories. The number of interactions per node, including
non-interaction, is depicted atop each bar. P values for genetic
interaction type composition deviating from the average of all
interactions were determined by Fisher’s exact test. c rag1/gh ratios
(fitness) normalized to wild-type for genetic variants and inhibitor
treatments. The observed double mutant fitness values are compared to
the expected double mutant fitness values (E); negative genetic
interactions between ER–mRNA processing and ER–DNA are independent of
experiment types (mutant–inhibitor or inhibitor–inhibitor). P values
were determined by two-tailed Student’s t-test. d Genetic interaction
between inhibitors affecting fitness of T cells. Relative log[2]-fold
changes between observed and expected double mutant fitness values for
rag1/gh ratios. Inhibitor combinations denoted with blue arrow were
tested in adolescent fish and in the tumour model. e Discordant cell
type-specific genetic interactions between inhibitors affecting fitness
of T cells (rag1) and growth hormone-producing somatotropic cells (gh).
The identification of cell type-specific strongly negative interactions
informs choices for in vivo treatment of T-ALL. 5FU 5-fluorouracil, BFA
brefeldin A, CHR Chr-6494, DOX doxorubicin, EEY eeyarestatin, ETO
etoposide, ISO isoginkgetin, MD mitoxantrone dihydrochloride, NMD
NMD14, NOC nocodazole, NU7 NU7026, PB pladienolide B, THS thapsigargin,
and TUN tunicamycin.
Interference with larval T cell development
In our attempt to design new types of T cell cancer therapies, we
focused on the identification of inhibitor–inhibitor interactions that
mimicked the outcome of gene–gene interactions. In order to exclude
off-target effects on somatotrophs, we proceeded in a stepwise fashion,
starting with gene–inhibitor interactions. By way of example, we
treated the top3a DNA replication/repair mutant with the pre-mRNA
processing modulator PB^[201]36. Using the rag1/gh ratio as a measure
of fitness (i.e., thymopoietic capacity), this gene–inhibitor
combination revealed a negative interaction affecting only T cells;
note the reduced rag1 signal and the unchanged signal for gh in the
representative RNA in situ hybridization panels (Fig. [202]8c). We
likewise treated the pole1 DNA replication/repair mutant with the ER
stressor THS^[203]32, and we again observed a negative interaction
(Fig. [204]8c). We then replaced the contribution of each mutant in
these combinations with an inhibitor of DNA-dependent protein kinase,
NU7026 (NU7; ref. ^[205]37), a drug that targets the same ontology
pathway as top3a and pole1 (DNA replication/repair). Both combinations,
PB + NU7 and NU7 + THS, respectively, elicited strong synthetic
lethality (Fig. [206]8c) of developing T cells. When this type of
drug–drug combination screen was carried out in a systematic fashion, a
substantial number of strongly negative combinations were identified
(Fig. [207]8d). Analysis of the effects of the two drug combinations on
rag1 and gh expression individually indicated a strong negative (toxic)
effect on the former, whereas the latter was distinguished by a mild
positive effect (Fig. [208]8e).
Interference with T cell development in adolescent fish
Since we had integrated the effects and interactions of small molecule
inhibitors into the genetic landscape of larval T cell development, it
was important to verify the general properties of this network at later
stages of development. To this end, we again evaluated the NU7 and PB,
and NU7 and THS inhibitor combinations. NU7 and its derivatives have
shown activity in in vivo tumour models^[209]38, whereas PB^[210]39 and
THS (the latter in the form of its G-202 prodrug^[211]40) have already
been used individually in clinical trials. However, to the best of our
knowledge, the synthetic effects of these two drug combinations on T
cell development have not yet been described. To confirm the activities
of the inhibitors in adolescent fish, when the thymus has fully
matured, we used a lck:CFP transgenic fish line^[212]41 to mark T cells
with a fluorescent reporter throughout life (Fig. [213]9a). Although
the relative efficiencies of the two combinations differed from the
larval situation (Fig. [214]8c, d), a robust synthetic lethality was
observed for both NU7 + PB and NU7 + THS combinations when compared to
the appropriate single inhibitor controls (Fig. [215]9b).
Fig. 9. Exploiting synthetic lethality in T cells for T-ALL treatment.
Fig. 9
[216]Open in a new tab
a Treatment schedule of adolescent lck-CFP zebrafish (5 weeks of age)
with the indicated inhibitor combinations. The thymus size relative to
fish size was measured weekly. Representative dark field image of a
zebrafish with the lck-CFP fluorescent signal overlay (yellow arrow and
outline). The position of the eye is labelled for orientation (E). b
Thymus size during combination treatment of adolescent lck-CFP
zebrafish. The sizes of thymi are expressed as log[2]-fold changes
relative to the sizes at day 0 of treatment. The significance of
differences between day 0 and days following treatment was determined
by two-way ANOVA, for repeated measurements with Bonferroni post-test.
A z statistic was used to compare regression coefficients between
treated and untreated groups. See source data file for Fig. 9b in
Supplementary Data [217]2. c Treatment schedule of adolescent
rag2:Myc-GFP–injected zebrafish (5 weeks of age) with inhibitor
combinations, following the onset of T-ALL. Rag2:Myc-GFP and cmlc-GFP
containing plasmids were co-injected into wild-type fish at the
one-cell stage and sorted for heart expression of the cmlc-GFP reporter
at 2 d.p.f. as an indicator of successful transgenesis. Transgenic fish
were grown until the age of 5 weeks and then sorted for onset of T-ALL
using rag2:Myc-GFP. Tumor sizes relative to fish size were measured
weekly. Representative dark field image of a 5 week old zebrafish with
rag2:Myc-GFP fluorescent signal overlay (yellow arrow and outline). d
T-ALL tumor sizes following combination inhibitor treatment of
adolescent rag2:Myc-GFP. Tumor sizes are expressed as log[2]-fold
changes relative to day 0 of treatment. *P < 0.05; **P < 0.01. See
source data file for Fig. 9d in Supplementary Data [218]2.
Synthetic lethality for T-ALL therapy
Next, we set out to explore the efficacy of these inhibitor
combinations for the treatment of T-ALL, which arises from immature T
cells^[219]13,[220]14. To this end, we used an established zebrafish
model of T-ALL, where tumour development is driven by the expression of
the mouse c-Myc gene under the control of the zebrafish rag2
promoter^[221]42 (Fig. [222]9c); this model was chosen, because the
c-Myc gene is commonly upregulated in Notch1-dependent T-ALL^[223]14,
which represents one of the major groups of human T-ALLs^[224]43. For
this experiment, the concentrations of inhibitors were chosen such that
they would not inhibit the growth of the tumours when used alone.
However, when combinations were used, a significant degree of tumour
regression was induced; this synthetic lethality is most pronounced in
the NU7 + PB combination (Fig. [225]9d). Collectively, these
experiments indicate that the effects of mutations and drugs on the
progression of T-ALL can be predicted from their effects on normal T
cell development at larval and adolescent stages of development.
Discussion
Our study reports the results of large-scale genetic screens aimed at
identifying important nodes in the genetic network governing T cell
development in zebrafish larvae. This animal model was chosen for
several reasons. First, a large body of work indicates that the general
principles that underpin the haematopoietic and immune systems of fish
and mammals are very similar^[226]44. Second, because of their high
fecundity, and the low cost of maintenance, genetic screens in
zebrafish are an attractive alternative to conducting genetic screens
in mice, which require substantially larger infrastructures and
man-power^[227]45,[228]46. When a primary screen is subsequently
extended to the complex mating schemes required to establish the
structure of the genetic network(s) underlying a particular phenotypic
trait, the advantage of using the zebrafish model becomes particularly
relevant.
The present genetic screens had three main goals. The primary aim was
the identification of genes that affect the development of larval T
cells in a tissue-specific or a tissue-restricted fashion, and by way
of design, specifically excluding genes with pleiotropic modes of
action. Although we did not expect to identify the full complement of
the genes exhibiting the desired characteristics, the screen was
nonetheless of sufficient magnitude to uncover at least one of the
components of each of the major pathways underlying T cell development.
The fact that we recovered two alleles each for two of the 33 genes
identified here indicates that we approached our primary goal. Upon
further characterization of the 33 genes thus identified, 29 could be
assigned to 7 distinct developmental and/or cell biological pathways.
An unexpected outcome of our work was that mutations in several genes
that are known for fundamental and ubiquitous cellular functions, such
as DNA replication, give rise to tissue-restricted phenotypes. The
observation that these variants predominantly affected the development
of T progenitors, while largely sparing other cell types, might (at
least partially) be explained by the fact that such variants often
encoded hypomorphic rather null alleles, potentially affecting only a
subset of functionalities in these multi-domain proteins. The
sensitivity of lymphoid lineages and tissues to these genetic
aberrations may additionally arise from the tissue-specific patterns of
genes encoding co-factors, the functional redundancy arising from the
plasticity of relevant protein complexes, and/or the disruption of
dedicated signalling processes^[229]47–[230]50. Our findings thus
support the notion emerging from studies in mice and humans of
unexpected immunological roles for numerous genes involved in core
biological processes, many with no prior association with
lymphopoiesis^[231]51. Our results thus support the widely held view
that one of the main advantages of forward genetic screens lies in the
discovery of subtly modified proteins, whose tissue-specific functions
may be missed by complete or tissue-specific gene inactivation that are
at the heart of reverse genetic approaches. This favourable outcome is
illustrated by the identification of the dnmt1^t25501 allele, which
revealed a lymphoid lineage-specific function of maintenance
methylation in both zebrafish^[232]22 and mouse^[233]23, whereas the
null allele is embryonic lethal^[234]52.
A second goal of our screen was the identification of functional
interdependencies among the individual genetic variants. An early
indication that the identified variants may be part of a common network
structure was revealed by the analysis of their transcriptomes, which
identified a substantial overlap between differentially expressed
genes. Substructures in this network often mirrored the presumed
functional groups assigned to variants based on prior knowledge.
Remarkably, we found it possible to pharmacologically mimic the
inherent intolerance of T cells to defects in certain biological
pathways, a result that emerged after the expansion of the interaction
screen to include a select number of small molecule inhibitors. In this
way, alleviating (positive) and synthetic (negative) interactions could
be replicated using only a small number of well-established small
molecule inhibitors. This finding set the stage to exploit synthetic
lethal interdependencies in our network to specifically target
developing T cells, and their malignant counterparts, the third aspect
of our study goals.
The successful development of a synthetic lethal strategy applicable to
the interference with T cell development in vivo critically depends on
the exclusion of undesired off-target effects. Indeed, it is here,
where the advantage of an organismal level genetic interaction screen
becomes most relevant. Whereas screens based on cell lines allow
high-throughput screens for secondary and even tertiary interactions,
they invariably suffer from the problem that a particular cell line may
represent only one type of a developmental pathway or tissue, and, by
design, is agnostic to effects on other cell types. Synthetic lethality
has been considered as an effective strategy to specifically target
tumours carrying cancer-associated somatic mutations; however, the
existence of genetically and phenotypically distinct subpopulations
within each tumor undermines the effectiveness of these
approaches^[235]12. Genes sharing synthetic lethality are often derived
from a common genetic network of many cell lines^[236]5. Whilst this
improves target confidence and potentially expands treatment utility to
multiple cancer types, it could increase the prospect of unintended
side effects. Moreover, despite the considerable breadth of synthetic
lethality screens, few treatments targeting these interactions have
proven clinically useful^[237]11,[238]53; possible reasons for which
include cell type differences, an inability to replicate in vitro
findings in the context of an entire organism, and a failure to capture
non-cell autonomous effects^[239]54–[240]56. Although genetic
interaction screens in vertebrates are not amenable to high-throughput
procedures, they may yield useful information, when focused on a
specific cell type. As illustrated here, the structure of genetic
networks of normal cells established in the context of an entire
organism suggests synthetic lethal drug combinations, whose efficacy
against corresponding malignancies is independent of mutation status
and thus overcomes the confounding effects of pervasive tumour
heterogeneity.
In conclusion, the identification of key nodes in the genetic network
of zebrafish T cell development will provide the basis for further
studies both into the structure and function of sub-circuits regulating
particular aspects of T cell differentiation, as well as investigations
into the evolutionary conservation of network structure. Finally, it
will be worthwhile to examine in pre-clinical studies some of the drug
combinations that were found here to be useful in interfering with the
progression of T-ALL.
Methods
Animals
The zebrafish (Danio rerio) strains Ekkwill (EKK), Tüpfel long fin
(TL), wild-type-in-Kalkutta (WIK), AB, Assam (ASS), and Tubingen (TU)
were maintained in the animal facility of the Max Planck Institute of
Immunobiology and Epigenetics. The ikzf1-GFP transgenic
zebrafish^[241]27,[242]28, the lck:CFP transgenic zebrafish^[243]41 and
the rag1 mutant^[244]24 were described previously. The characterization
of the rag2:Myc-GPF transient leukaemia model was also described
previously^[245]42. All animal experiments were approved by the
institute’s review committee and conducted under licenses from the
local governments (Regierungspräsidium Freiburg [AZ 35-9185.81/G-19/69;
AZ 35-9185.81/G-14/41; AZ 35-9185.81/G-17/79; AZ 35-9185.81/G-13/70];
Regierungspräsidium Tübingen [AZ AP1/02]).
ENU mutagenesis
A detailed description of the forward genetic screen design, coverage,
complementation analysis, and mutant identification by positional
cloning and whole genome sequencing can be found in refs.
^[246]19,[247]20. The functional relevance of the ikzf1 mutation in the
II032 mutant was confirmed by complementation analysis with a
previously identified ikzf1 mutation (t24980; ref. ^[248]19).
Whole genome sequencing
For genomic libraries of mutant lines, 20–100 mutants at 5 d.p.f.
embryos (as judged by reduced rag1 signals after RNA in situ
hybridisation) from 4 different mating pairs were pooled; the exact
number of biological replicates per genotype is reported in the data
files associated with NCBI Sequence Read Archive (SRA) project
PRJNA622735. The combined analysis of at least 20 mutant embryos
ensures that the signals emanating from an occasional mis-sorted animal
does not affect the overall analysis for sequence polymorphisms. After
purification of genomic DNA, 6 μg were sheared to ~300 bp fragment size
using a Covaris S220 sonicator. Sonication was followed by a clean-up
step by adding an equal volume of Agencourt AMPure XP magnetic beads
(Beckman Coulter). Paired-end libraries were constructed with the
NEXTflex PCR-free DNA Sequencing Kit (Bioo Scientific, Cat#
NOVA-5142-02) according to the manufacturer’s instructions. Library
quality controls included assessment of size distribution using an
Agilent Bioanalyzer, and determination of DNA concentration using a
KAPA Library Quant Illumina Kit (Peqlab, Cat# KAPBKK4854). Sequencing
was carried out in the paired-end 100 bp run mode of an Illumina HiSeq
2500. Wild-type genomic libraries were made from the original adult
males used in the generation of the ENU-mutagenised lines, as well as
from five different in-house wild-type strains (TUE, WIK 7, WIK 11,
ASS, TLEK). The genomic DNAs for the ENU-mutant libraries were purified
from frozen somatic tissues of the original adult males. To generate
the genomic libraries of the in-house wild-type strains, 25–50 embryos
from each line were sequenced. Sequencing reads of each sample were
mapped to the Zv9.70 reference genome using the Bowtie 2
programme^[249]57. Mapping duplicates were removed using Picard Mark
Duplicates in Galaxy^[250]58–[251]61. In order to identify candidates
for the causal mutation, without previous knowledge of the linked
region classically obtained by generating outcrosses and subsequent
positional cloning after mapping of meiotic recombination events,
mutant lines were subjected to a sequential filtering of genetic
background and wild-type SNPs. Mutant SNPs were called using Mpileup
version 1.1.1 from SAM tools. The Mpileup file was subjected to two
sequential steps of SNP filtering using two bulk Mpileup files
generated by pooling: the six wild-type lines (ENU, Tue, Wik7, Wik11,
ASS, TLEK), and the respective mutant lines with unknown genetic
lesions. Filtering constraints for SNPsift included, (DP > 5) &
(DP < 100) & (QUAL > = 40) & (DP4[2] > 0) & (DP4[3] > 0)! (REF = ‘N’)!
(ALT = ‘N’). The filtering was performed using the SNP
Intersector/Substractor tool^[252]62,[253]63 from MegaMapper^[254]64.
The flagged nucleotide changes in the output file were evaluated for
their functional effect using SNPEff^[255]65. From that list, only
homozygous SNPs were kept, using Filter from Galaxy tools version
1.1.0. The resulting lists consisted of homozygous nonsense and
missense mutations throughout the genome, not present in any of our
wild-type lines or other mutant lines. The identified mutation was
visualised on IGV (Integrative Genomic Viewer)^[256]66,[257]67 using
the bam files of mutants and wild-type strains in parallel.
RNA in situ hybridization
Procedures for RNA in situ hybridisation probes and determination of
rag1/gh ratios were described previously^[258]19. Briefly, the areas of
rag1 and gh signals were measured using ImageJ from photographs taken
using the Leica MZFLIII stereomicroscope with the Leica DFC300 FX
camera. An average of rag1 signals from the two thymic lobes was
calculated and normalised to the gh signal to obtain the rag1/gh ratio
as a measure of thymopoietic capacity. RNA in situ hybridisations were
also performed on embryos from an in-cross of heterozygous carriers for
detection of defects in other haematopoietic lineages including,
thymocytes (5 d.p.f.—ikzf1) and thymic epithelial cell (5
d.p.f.—foxn1), haematopoietic stem cell (36 h.p.f.—c-myb, runx1),
lymphoid (24 h.p.f.—ikzf1), myeloid (24 h.p.f.—spi1b), erythrocyte
(24 h.p.f.—gata1), neutrophil (24 h.p.f.—mpx), macrophage
(24 h.p.f.—lcp1)^[259]24,[260]28,[261]68,[262]69. Images of whole
mounts were taken using the Zeiss Axioplan2 microscope with the AxioCam
MRc5 camera. Z section images were captured using Zeiss’ Zen Software
and focused stacked in Adobe Photoshop CS6.
Morphants
Morpholino antisense oligonucleotides (morpholinos) targeting the
sequences of either initiation codons (to block translation of both
maternal and zygotic mRNAs), or splice donor and/or acceptor sites (to
block processing of zygotic mRNAs; leaving processed maternal mRNAs
intact) of target gene mRNAs were designed by and sourced from
GeneTools, LLC (Supplementary Table [263]1). Lyophilised morpholinos
were resuspended in nuclease-free water at a concentration of 1 mM and
stored at 4 °C. Morpholinos diluted in 1× Danieau buffer (58 mM NaCl,
0.7 mM KCl, 0.4 mM MgSO[4], 0.6 mM CaCl[2], 5 mM HEPES, pH 7.6) were
titrated and injected in a volume of 1–2 nL into wild-type embryos at
the 1-cell stage as described previously^[264]20. The phenotypes of
morphants were determined by RNA in situ hybridisation, comparing the
rag1/gh ratio of injected versus un-injected fish at 5 d.p.f.
Phenotypic rescue
Recombinant clones containing full-length cDNAs or bacterial artificial
chromosomes (BAC) encompassing the genes of interest (Supplementary
Table [265]2) were obtained from Source BioScience. Clones were
cultured in antibiotic-containing Luria-Bertani (LB) broth specific to
each vector at 37 °C overnight. Plasmids were subsequently extracted
from bacteria using Plasmid Midi Kit (QIAGEN, Cat# 12143). Preparation
of mRNA from cDNA clones was performed by in vitro transcription using
mMESSAGE mMACHINE T7, T3 and SP6 Kits (ThermoFisher Scientific, Cat#
AM1344, AM1348, AM1340) using 1 μg of plasmid DNA, linearised using
vector-specific restriction enzymes at positions distal to the relevant
promoter sequence. Transcribed mRNA was precipitated using LiCl and
EtOH, resuspended in nuclease-free water and stored at −80 °C. BACs
were also grown from clones cultured in antibiotic-containing LB broth,
but plasmids were extracted using Large Construct Kits (QIAGEN, Cat#
12462). Purified mRNAs and BACs were titrated (50–400 ng/μL) and
injected in a volume of 1–2 nL into 1-cell stage embryos resulting from
an in-cross of heterozygous mutant carriers as described above. The
phenotypes of morphants were determined by performing RNA in situ
hybridisation, comparing the rag1/gh ratio of injected versus
uninjected mutant fish at 5 d.p.f.
CRISPR mutants
CRISPR guide RNAs were created by incubating overlapping primers
(Supplementary Table [266]3) (5 μg/primer, 100 mM MgCl[2], 0.1 M Tris
pH 7.5) at 95 °C for 5 min and cooling to RT. Annealed primers were
ligated into BsaI-digested pDR274 vector (50 ng annealed primers, 10 ng
BsaI-digested pDR274, 5 U T4 ligase, 1× T4 ligase Buffer) for 2 h at
22 °C, with the reaction inactivated at 65 °C for 10 min. The ligation
mixture was dialyzed and transformed into E.coli DH5α by
electroporation. Culture of transformants, plasmid extraction and in
vitro transcription of guide RNAs were carried out as described above.
Purified CRISPR guide RNAs were tested for specificity by in vitro
digestion of target DNA (80 ng PCR amplicon containing target sequence,
600 ng Cas9 protein from Streptococcus pyogenes [PNA Bio], 300 ng guide
RNA, 1× CutSmart buffer [New England Biolabs]) at 37 °C for 1 h. Guide
RNA was removed by adding 4 μg of RNAse A to the reaction for 15 min at
37 °C prior to visualization of cleavage products by agarose gel
electrophoresis. The extent of in vitro digestion of target DNA was
compared between reactions with and without addition of guide RNA. A
single guide RNA was designed and used for each target gene to create a
frameshift genetic lesion. CRISPRs were titrated and injected in a
volume of 1–2 nL into wild-type embryos at the 1-cell stage in a
solution containing (250 ng/μL guide RNA, 500 ng/μL Cas9 protein, 1%
phenol red, Danieau buffer). Carriers of genetic lesions were
outcrossed to wild-type fish, and stable carrier lines were created
from suitable frameshift mutations. RNA in situ hybridisation was
performed on an incross of heterozygous carriers for the genetic lesion
to compare rag1/gh ratio of homozygous mutant versus homozygous
wild-type fish at 5 d.p.f. The detailed characterization of the ikzf1
and foxn1 crispants will be described elsewhere.
RNA extraction and cDNA synthesis
Individual zebrafish embryos (5 d.p.f.) from an in-cross of
heterozygous carriers were homogenised in 100 μL of Tri Reagent (Sigma,
Cat#93289) and transferred to 2 mL deep 96-well plates containing an
additional 400 μL of Tri Reagent. The RNA-containing aqueous phase was
stored at −80 °C until genotyping was completed. DNA was extracted from
the interphase and organic phase according to the manufacturer’s
instructions and genotyping was performed using the primers listed in
Supplementary Table [267]4. Following genotyping, RNA was extracted
from homozygous mutants and homozygous wildtype siblings. DNA was
removed from RNA extraction using TURBO DNA-free kit (Invitrogen,
Cat#AM1907). RNA was quantified using the Qubit RNA HS Assay Kit
(ThermoFisherScientific, Cat#[268]Q32852) and the Qubit 4 Fluorometer
(ThermoFisherScientific, [269]Q33226). RNA quality was checked by
determining the 18S/28S rRNA ratio using the Fragment Analyzer RNA Kit
(ThermoScientific, Cat#DNF-471-0500) and the 5200 Fragment Analyzer
System (ThermoScientific, Cat#M5310AA). cDNA libraries were prepared
from 1 μg of mRNA following poly-A selection using TruSeq stranded mRNA
Library Prep (Illumina, Cat#20020595) according to manufacturer’s
instructions.
RNA sequencing and computational analysis of RNA-seq data
RNA-Seq was performed using mutant and wild-type siblings from each
zebrafish line (n = 2–6; the exact number of biological replicates is
reported in the files associated with NCBI Gene Expression Omnibus
(GEO) project [270]GSE147555). The libraries were sequenced in
paired-end 75 bp mode at a depth of 25 million reads on an Illumina
HiSeq 2500/3000 instrument. Reads were aligned to the reference genome
with STAR 2.5.2b-1 (ref. ^[271]70) and the reference annotation from
Ensembl (Zv10.85,
[272]http://www.ensembl.org/info/data/ftp/index.html). The resulting
alignments were quantified at the gene level with featureCounts version
1.6.0.1 (ref. ^[273]71) and differential expression performed using
DESeq2 version 2.11.40.1 (ref. ^[274]72). The analysis was orchestrated
on the in-house version of the Galaxy server based on the Galaxy
platform^[275]58. All tools were used with default parameters unless
otherwise stated.
Pathway enrichment analysis
Pathway enrichment analysis of differentially expressed genes was
performed using ClusterProfiler^[276]73. A detailed description of data
processing can be found at
[277]https://github.com/connoromeara/OMeara_et_al_2021. Briefly, KEGG
enrichment analysis was performed on the top 1500 differential
expressed genes (DEG) (FDR ≤ 0.05) from each genetic variant. Genes
regulating T cell development and differentiation (T cell
receptor—mmu04660, primary immunodeficiency—mmu05340, notch
signalling—mmu04330), DNA synthesis (DNA replication—dre03030,
MMR—dre03430, BER—dre03410, HR—dre03440, p53 signalling—dre04115), cell
cycle (apoptosis—dre04210, cell cycle—dre04110, cellular
senescence—dre04218), mRNA processing (spliceosome—dre03040, mRNA
surveillance—dre03015, nonsense-mediated decay—dre03015), ribosome
function (ribosome biogenesis—dre03008, ribosome—dre03010) and ER
function (protein processing in ER—dre04141, proteasome—dre03015) were
defined by KEGG pathway IDs extracted from enriched pathways using
KEGGREST^[278]74. All pathway enrichment analyses and heat-maps were
clustered by Ward’s method using Euclidean distance.
Functional characterization of mutant fish
Mutant lines were out-crossed to ikzf1-GFP transgenic fish, sorted for
GFP expression at 24 h.p.f., and for carrier detection by genotyping
fin clips at 3 weeks of age. Embryos from an in-cross of heterozygous
carriers for the mutation and ikzf1-GFP transgene were sorted at 5
d.p.f. into fish with (+/+; +/−) and without (−/−) GFP expression in
the thymus. These embryos were used in alcian blue staining, cell cycle
analysis, TUNEL staining, and protein lysate preparation for Western
blots.
Alcian blue staining
Embryos were fixed with 4% w/v PFA and stored in 100% MeOH at −20 °C
until use. Embryos were rehydrated in a decreasing gradient of MeOH to
PBST and incubated in a solution containing H[2]O[2] (10% v/v) and KOH
(0.5% w/v) for 3-4 h at RT until bubbles disappeared. Embryos were
washed with PBST and incubated in Alcian blue staining solution (0.01%
w/v Alcian blue 8 GX [Sigma, Cat#A5268], 1% v/v HCl and 70% v/v EtOH)
at RT overnight. Background staining was removed by incubation in a
clearing solution (5% v/v HCl and 70% v/v EtOH) at RT overnight.
Embryos were dehydrated in an increasing EtOH gradient and imaged in
80% v/v Glycerol.
Cell cycle analysis
The yolk sac was removed from embryos by gentle aspiration in 0.5×
Ginzburg Fish Ringer solution (55 mM NaCl, 1.8 mM KCl, 1.25 mM
NaHCO[3], without Ca^2+). Embryos were digested in CO[2]-independent
media (Life Technologies, Cat# 18045088) containing 3 mg/mL of
collagenase II (Santa Cruz, Cat#sc-506177) at 37 °C under gentle
rotation. The cell suspension was passed through a 70 μm cell strainer
(BD, Cat#352350), washed with PBS and pelleted at 2800×g. Cells were
stained for viability using ZombieRed Fixable Viability Kit (Biolegend,
Cat# 423110) for 20 min at RT, protected from light. Cells were washed
with FACS buffer (PBST, 1% BSA w/v), fixed and permeabilized with the
FoxP3/TF Staining Buffer Set (eBioscience, Cat#00-5523-00) according to
the manufacturer’s instructions. Cells were stained with 1 μg/mL
Hoechst 33258 (Thermo Fisher Scientific, Cat# 33258) at 4 °C for 15 min
and analysed using the BD Fortessa II and FACSDiva Software on a linear
scale. Wild-type fish treated with 0.1 μg/mL nocodazole or 3 μM
etoposide were used as G2/M and S phase inhibitor controls,
respectively.
Western blotting
Embryos were mechanically disrupted in 10 μL/embryo of lysis buffer (1×
RIPA lysis buffer [Millipore, Cat#20-188], 0.1% SDS, 1 tablet/10 mL
complete mini protease inhibitor cocktail [Roche, Cat#11836170001]).
Lysates were centrifuged at 18,000 × g for 5 min and the resulting
supernatants transferred to a fresh tube prior to snap freezing.
Protein concentrations were determined using the BCA Protein Assay Kit
(ThermoFisherScientific, Cat#23225). Protein lysates (10 μg) were
incubated with 2× loading buffer (5× = 10% SDS w/w, 10 mM DTT, 20%
Glycine, 0.05% bromophenol blue w/w, 0.2 M Tris, pH 6.8) at 95 °C for
10 min. Heat-denatured protein samples were separated in precast
Mini-Protean TGX Gels (Biorad, Cat#456-8093) by SDS–PAGE and
transferred to Immuno-Blot PVDF membranes (Biorad, Cat#1620177) in
transfer buffer (25 mM Tris, 192 mM Glycine, 20% MeOH v/v) for 40 min
at 100 V. Membranes were stained with amido black (Sigma,
Cat#A8181-1EA) according to the manufacturer’s instructions to quantify
protein loading. Membranes were blocked using 5% BSA and blotted for
rabbit α-GRP78/HSP5 (1:2000, Cat#[279]PA524963, Life Technologies),
rabbit α-GADD153/CHOP (1:2000, Cat#G6916, Sigma-Aldrich), rabbit
α-eIF2a (1:1000, Cat# PA5-41916, Thermo Fisher Scientific), and mouse
α-vertebrate-β-actin (1:2000, Cat# A2066, Sigma-Aldrich) overnight at
4 °C. Membranes were probed with goat α-rabbit-HRP secondary (1:2000,
Cat# P0448, Sigma-Aldrich), goat α-mouse-HRP secondary (1:2000, Cat#
P0447, Sigma-Aldrich) with ECL Prime Western Blotting Detection Reagent
(Amersham, Cat# RPN2232) and CX-BL + X-ray film (AGFA Healthcare) were
used for detection. Contrast and brightness were globally adjusted
using Adobe Photoshop CS6.
TUNEL assay
For TUNEL staining, mutant embryos sorted from an in-cross of
heterozygous carriers were dechorionated and fixed at 32 h.p.f. with 4%
PFA w/v. Embryos were treated with 10 μg/mL of proteinase K for 40 min
at RT and post-fixed with 4% PFA w/v at RT for 20 min. Embryos were
permeabilized for 1 h with PBS containing Triton X-100 (0.1% v/v).
TUNEL staining was performed using the DeadEnd Fluorometric TUNEL
System (Promega, Cat# G3250) according to the manufacturer’s
instructions. Stained embryos were imaged in 80% v/v Glycerol.
Alternative splicing analysis
Detection of differential splicing events was performed using rMATS
v.4.0.2 (ref. ^[280]75) on bam files downloaded from the Galaxy
history. The following parameters were specified: read length (100
nucleotides), read type (paired), cutoff splicing difference (0.0001),
analysis type (unpaired), library type (fr-firststrand). Mutant bam
files were submitted as group 1 (-b1), and wild type as group 2 (-b2).
Alternative splicing events supported by reads spanning splice
junctions were reported as counts of significant events of alternative
splicing (skipped and retained intron) relative to wild-type siblings
(read covering exon boundary) (FDR ≤ 0.05, |inclusion level
difference| ≥ 0.250).
Ribosome riogenesis
Defects in ribosome biogenesis were determined by comparison of mutant
18S/28S rRNA ratios^[281]76 using BioAnalyzer. Log[2] fold-changes of
ribosomal RNA 18S/28S ratios were calculated relative to the values of
corresponding wild-type siblings.
Chemical inhibition
Inhibitors^[282]32,[283]36,[284]77–[285]88 (Supplementary Table [286]5)
were resuspended in DMSO or water at the manufacturers’ specified
concentration and stored at −20 °C. Inhibitors were titrated and
depicted at concentrations that showed no or minimal gross
morphological defects (Fig. [287]3). IC30 values were determined using
a three parameter log-logistic function. IC30 (equivalent to a fitness
value of 0.7) were chosen for the analysis of genetic interactions,
since the expected fitness of the inhibitor combinations corresponds to
IC50 (0.7 × 0.7 fitness by the multiplicative method). A concentration
eliciting a 50% reduction in rag1/gh ratio relative to untreated fish
affords the best sensitivity for detecting positive and negative
effects on T cell development. Zebrafish were grown in 35 mm dishes
from 3 to 5 d.p.f. in 2.5 mL of E3 medium with or without inhibitor.
RNA in situ hybridisation and rag1/gh ratios were compared between
inhibitor-treated and control groups of an equivalent DMSO
concentration at 5 d.p.f.; in all cases, DMSO concentrations were kept
below 0.5% (v/v).
Genetic interaction determination and nomenclature
T cell fitness values for single mutants (
[MATH: Wx
:MATH]
and
[MATH: Wy
:MATH]
) and double mutants (
[MATH:
Wxy
:MATH]
) were calculated by normalizing mutant rag1/gh values to the
corresponding wild-type rag1/gh values. Raw data for the genetic
interaction network are listed in Supplementary Data [288]1. The
multiplicative model was used to calculate expected fitness (
[MATH:
E(W
xy) :MATH]
) as this model was the most accurate in predicting observed fitness as
determined by the residual mean squared error^[289]10. Expected fitness
values were also determined using other methods (Additive, Log and
Minimum) for comparative purposes, without material differences in
outcome.
Multiplicative
[MATH:
E(W
xy)=<
mrow>Wx×Wy :MATH]
1
Additive
[MATH:
E(W
xy)=<
mrow>Wx+(1−Wy) :MATH]
2
Log
[MATH:
E(W
xy)=log
2(2Wx−1)×(2Wy−1)+1 :MATH]
3
and minimum
[MATH:
E(W
xy)=min
(Wx,Wy<
/mrow>) :MATH]
4
Additive propagated error for the expected fitness (
[MATH: ϵEWxy<
/mrow> :MATH]
) was determined using standard deviations (
[MATH:
δW<
mi>x :MATH]
and
[MATH:
δW<
mi>y :MATH]
) and fitness values (
[MATH: Wx
:MATH]
and
[MATH: Wy
:MATH]
) of the single mutants in the following equation:
Propagated error
[MATH: ϵEWxy<
/mrow>=δWxWx2+δWyWy2 :MATH]
5
The degree of genetic interaction was determined as the log[2]
fold-change between observed
[MATH:
Wxy
:MATH]
and expected
[MATH:
E(W
xy) :MATH]
fitness values.
Genetic interactions were categorised into four types, non-interactive,
negative, positive-co-equal and positive-suppressive. Non-interactive
interactions were defined as an observed double-mutant fitness not
significantly (H[0]) different from the expected fitness of the
double-mutant.
Non-interactive
[MATH:
H0:Wxy
=E(Wxy) :MATH]
6
Negative interactions were defined as an observed double-mutant fitness
significantly (H[A]) less than the expected fitness of the
double-mutant.
Negative interaction
[MATH: HA:Wxy<E(Wxy)
:MATH]
7
Positive–coequal interaction is an observed double-mutant fitness
significantly greater than the expected fitness of the double-mutant,
but equivalent to the least fit single mutant.
Positive–coequal interaction
[MATH:
min(Wx,Wy)+δmin(Wx,Wy)>HA:Wx
y>E(Wxy)
:MATH]
8
Positive–suppressive interaction is said to occur when an observed
double-mutant fitness is significantly greater than the expected
fitness for the double mutant and the least fit single mutant.
Positive–suppressive interaction
[MATH:
HA:Wxy>min(<
mi>Wx,Wy)+δmin(<
mrow>Wx,Wy) :MATH]
9
Genetic interactions were considered valid and retained in the network
if, (i) the fitness values of both single mutants were less than that
of the wildtype (
[MATH: Wx
:MATH]
and
[MATH: Wy
:MATH]
< 1), and (ii) the type of genetic interaction as determined by the
multiplicative method was concordant with the majority vote of the
three other methods (additive, log and minimum).
P value estimates for between-pathway interactions were obtained by
bootstrapping. Genetic interaction types (non-interactive, negative,
and positive) were resampled from the proportion of genetic interaction
types in the network (P[non-interactive] = 0.47, P[negative] = 0.299,
P[positive] = 0.231 [P[positive–coequal] = 0.183[,]
P[positive-suppressive] = 0.048]). The observed between-pathway
proportion was then compared to the sampling distribution to determine
a P value estimate.
Transient leukaemia model
Wild-type zebrafish were injected with SceI-flanked rag2:Myc-GFP and
cmlc:GFP vectors at the 1-cell stage^[290]24. The injection solution
consisted of 1 μg/μL of combined plasmids, 0.5× CutSmart Buffer (New
England BioLabs—NEB, Cat#B7204S), 0.3 U/μL I-SceI (NEB, Cat#R0694S) in
Danieau buffer. Fish were sorted at 2 d.p.i. for GFP expression in the
heart (cmlc), as this indicates successful incorporation of both
transgenes in the majority of positive fish. Transgenic fish were
sorted again at 5 weeks post-injection to remove those without Myc-GFP
expression in the thymus. As the injected fish develop leukaemia at
different time points (5–30 weeks of age), fish were partitioned
appropriately to ensure an even split of leukaemia progression in each
treatment group. Combinations of inhibitors (Supplementary
Table [291]6) were initially titrated in 5-week old adolescent lck-CFP
fish^[292]41. Combination treatments were compared to single inhibitor
treatments carried out at the same concentrations to examine the
presence of synthetic lethality. All drug solutions were kept to a
consistent volume (50 μL) and compared to an equivalent quantity of
DMSO solution (untreated). Fish were kept in tanks with 600 mL of water
containing inhibitors, which was changed every two days over 3 weeks.
Images were taken on days 0, 7, 14, and 21, using Myc-GFP expression to
monitor tumour remission/progression. Leukaemia progression was
quantified by measuring the GFP-positive pixel area relative to size of
the fish using ImageJ software; the results were normalised to
fluorescence observed at day 0. A z statistic was used for comparing
regression slope coefficients (b) of tumour progression.
z statistic
[MATH:
z=b1−b2
S
Eb12+SEb22 :MATH]
10
Tissue-specific expression of genes
Expression of genes identified from the ENU forward genetic screen with
their mouse orthologs was determined for each tissue from the genome
wide BioGPS microarray datasets (GNF1M and MOE430,
[293]http://biogps.org/dataset/). Consisting of 93 tissue types and
cell lines of mouse origin, the dataset was partitioned into several
categories. The category T cell refers to CD4+ T cells, CD8+ T cells,
FoxP3+ T cells, CD4+CD8+ DP thymocytes, CD4+ SP thymocytes, CD8+ SP
thymocytes and thymus. The category non-T immune cell refers to B
cells, follicular B cells, marginal zone B cells NK cells, granulocyte,
dendritic cells, plasmacytoid DCs, macrophage, mast cells, GMP, CMP,
MEP, HSC. The category Immune combines the categories T cell and non-T
immune cell. The category Non-immune refers to adipose tissue, adrenal
gland, brain, bladder, bone, eye, heart, liver, lung, mammary gland,
ovary, pancreas, placenta, prostate, muscle, stomach, testis and
uterus. Lymphoid organs containing a mixture of immune cells (spleen,
bone marrow and lymph nodes), tissues with ancient immune function
(kidney), T cell enriched tissues (intestines), and all cell lines were
excluded from the analysis. Relative expression levels (log[2]) for
each gene were determined between the mean expression levels for
relevant categories. The tissue expression levels of genes identified
in the ENU screens (ENU genes) were compared to those of known T cell
genes (T cell receptor–KEGG pathway mmu04660), p53 genes (p53
signalling–KEGG pathway dre04115) and a random selection of genes from
the genome (1000 replicates of the same numbers of genes that were
identified in the ENU screens). Genes were defined as tissue-specific,
if their expression was significantly greater than background tissue
expression (z score ≥ 1.96). The proportions of tissue-specific genes
were determined for genes identified in the ENU screen (ENU genes), T
cell-related genes, p53-pathway components, and a random selection of
genes from the genome after a normalization step considering the
numbers of genes in different categories.
Statistics and reproducibility
All data analysis and plotting was performed using R version 3.3.3 and
R studio version 1.1.153 or GraphPad Prism 5. R scripts used for
analysis are included in the GitHub repository
([294]https://github.com/connoromeara/OMeara_et_al_2021). The numbers
of biological replicates are indicated in the files associated with the
NCBI Gene Expression Omnibus (GEO) ([295]GSE147555) and NCBI Sequence
Read Archive (SRA) (PRJNA622735) depositions, in Supplementary
Data [296]1 and Supplementary Data [297]2 (comprising source files),
and in the figure legends, where appropriate, as are the statistical
tests used for the evaluation of experimental outcomes.
Data repositories and image processing
Paired-end raw RNA-Seq reads from mutant lines, normalised counts and
differential gene expression output from DESeq2 can be found at NCBI
Gene Expression Omnibus (GEO) ([298]GSE147555). Paired-end raw WGS-seq
reads from mutant lines can be found in NCBI Sequence Read Archive
(SRA) (PRJNA622735). Contrast, brightness and colour balance were
globally adjusted for in situ, fluorescent and Western blot images
using Adobe Photoshop CS6. Uncropped Western blots are provided as
Supplementary Fig. [299]6.
Reporting summary
Further information on research design is available in the [300]Nature
Research Reporting Summary linked to this article.
Supplementary information
[301]Supplementary Information^ (26.3MB, pdf)
[302]42003_2021_2694_MOESM2_ESM.pdf^ (376.2KB, pdf)
Description of Additional Supplementary Files
[303]Supplementary Data 1^ (95KB, zip)
[304]Supplementary Data 2^ (721.3KB, zip)
[305]Reporting Summary^ (2MB, pdf)
Acknowledgements