The mouse olfactory sensory neuron (OSN) repertoire is composed of 10 million cells and each expresses one olfactory receptor (OR) gene from a pool of over 1000. Thus, the nose is sub-stratified into more than a thousand OSN subtypes. Here, we employ and validate an RNA-sequencing-based method to quantify the abundance of all OSN subtypes in parallel, and investigate the genetic and environmental factors that contribute to neuronal diversity. We find that the OSN subtype distribution is stereotyped in genetically identical mice, but varies extensively between different strains. Further, we identify cis-acting genetic variation as the greatest component influencing OSN composition and demonstrate independence from OR function. However, we show that olfactory stimulation with particular odorants results in modulation of dozens of OSN subtypes in a subtle but reproducible, specific and time-dependent manner. Together, these mechanisms generate a highly individualized olfactory sensory system by promoting neuronal diversity.https://doi.org/10.7554/eLife.21476.001
Smells are simply chemicals in the air that are recognized by nerves in our nose. Each nerve has a receptor that can identify a limited number of chemicals, and the nerve then relays this information to the brain. Animals have hundreds to thousands of different types of these nerves meaning that they can detect a wide array of smells.
Smell receptors are proteins, and the genes that encode these proteins can be very different in two unrelated people. This could partly explain, for example, why some people find certain odors intense and unpleasant while others do not. However, having different genes for smell receptors does not by itself completely explain why some people are more sensitive than others to particular smells. The amounts of each nerve type in the nose might also differ between people and have an effect, but to date it has not been possible to accurately count them all.
Ibarra-Soria et al. have now devised a new method to essentially count the number of each nerve type in the noses of mice from different breeds. The method makes use of a technique called RNA-sequencing, which can reveal which genes are active at any one time, and thus show how many nerves are producing each type of smell receptor. Ibarra-Soria et al. learned that different breeds of mice had remarkably different compositions of nerves in their noses. Further analysis revealed that this was due to changes to the DNA code near to the genes that encode the smell receptor.
Next, Ibarra-Soria et al. sought to find out how the amount of each nerve type is controlled by giving mice water with different smells for weeks and looking how this affected their noses. These experiments revealed that a small number of the nerve types became more or less common after exposure to a smell. The altered nerves were directly involved in recognizing the smells, proving that the very act of smelling can change the make-up of nerves in a mouse’s nose.
These results confirm that the diversity in the nose of each individual is not only dictated by the types of receptors found in there, but also by the number of each nerve type. The next challenge is to understand better how these differences change the way people perceive smells.https://doi.org/10.7554/eLife.21476.002
Mapping the neuronal diversity within a brain remains a fundamental challenge of neuroscience. Quantifying variance in a population of neurons within and between individuals first requires precise discrimination of cellular subtypes, followed by an accurate method of counting them. While this has been achieved in a simple invertebrate model containing hundreds of neurons (White et al., 1986), applying the same approach to mammalian brains that encompass many millions of neurons represents a significant challenge (Wichterle et al., 2013).
The main olfactory epithelium (MOE) is an essential component of the olfactory sensory system. It contains olfactory sensory neurons (OSNs) that express olfactory receptors (ORs), the proteins that bind odorants (Buck and Axel, 1991; Zhao et al., 1998). The mouse genome codes for over a thousand functional OR genes, but each mature OSN expresses only one abundantly, in a monoallelic fashion (Hanchate et al., 2015; Saraiva et al., 2016; Tan et al., 2015; Chess et al., 1994). This results in a highly heterogeneous repertoire of approximately 10 million OSNs (Kawagishi et al., 2014) within the nose of a mouse, stratified into more than a thousand functionally distinct subpopulations, each one characterized by the particular OR it expresses. The monogenic nature of OR expression serves as a molecular barcode for OSN subtype identity. Thus, the MOE offers a unique opportunity to generate a comprehensive neuronal map of a complex mammalian sensory organ, and investigate the mechanisms that influence its composition and maintenance.
To date only a few studies have quantified the number of OSNs that express a given OR (Bressel et al., 2016; Fuss et al., 2007; Khan et al., 2011; Rodriguez-Gil et al., 2010; Royal and Key, 1999; Young et al., 2003). For the scarce data available (<10% of the full repertoire) reproducible differences in abundance have been observed between OSNs expressing different ORs (Bressel et al., 2016; Fuss et al., 2007; Khan et al., 2011; Young et al., 2003). This suggests variance in the representation of OSN subtypes exists within an individual, but the extent of variation between individuals is unknown. Moreover, the mechanisms that dictate the abundance of OSN subtypes are poorly understood. Most promoters of OR genes contain binding sites for Olf1/Ebf1 (O/E) and homeodomain (HD) transcription factors (Young et al., 2011), and these are involved in determining the probability with which the OR genes are chosen for expression (Rothman et al., 2005; Vassalli et al., 2011). Enhancer elements also regulate the gene choice frequencies of nearby, but not distally located, ORs (Khan et al., 2011). To date, these studies have focused only on a handful of OSN subtypes.
In addition to OR gene choice regulation exerted by genetic elements, it is conceivable that the olfactory system adapts to the environment. The MOE is continually replacing its OSN pool and the birth of every neuron presents an opportunity to shape the proportion of different subpopulations. It is also possible that relative OSN abundances could be altered by regulating the lifespan of each OSN subtype. Indeed activation extends a sensory neuron’s life-span (Santoro and Dulac, 2012), suggesting that persistent exposure to particular odorants may, over time, increase the relative proportions of the OSNs responsive to them. Some OSN subtypes do reportedly increase in number in response to odor activation, but others do not (Cadiou et al., 2014; Cavallin et al., 2010; Watt et al., 2004). Whether this variation reflects differences in the biology of OSN subtypes or experimental procedures is unclear.
Here, we fully map OSN diversity in the MOE and characterize the influence of both genetic and environmental factors on its regulation. We show that RNA sequencing (RNAseq) is an accurate proxy for measuring the number of OSNs expressing a particular OR type, and use this approach to quantify, in parallel, the composition of 1115 OSN subtypes in the MOE. We report that, while the repertoire of OSN subtypes is stable across individuals from the same strain, it reproducibly and extensively differs between genetically divergent strains of laboratory mice. We show that under controlled environmental conditions, these stereotypic differences in OSN abundance are directed by genetic variation within regulatory elements of OR genes that predominantly act in cis and are independent of the function of the OR protein. However, we find that persistent, but not continuous, exposure to specific odorants can also subtly alter abundance of the OSN subtypes that are responsive to such stimuli. Taken together, these results show that the OSN repertoire is shaped by both genetic and environmental influences to generate a unique nose for each individual.
Previously, we characterized the transcriptional profile of the whole olfactory mucosa (WOM) in adult C57BL/6J animals (hereafter termed B6) to generate hundreds of new, extended OR gene annotations (Ibarra-Soria et al., 2014). As each OR gene is expressed in only a small fraction of cells within WOM, differences in their abundance are difficult to distinguish from sampling bias. We hypothesized that mapping RNAseq data to significantly extended OR transcripts should increase detection sensitivity. With these models, OR gene mRNA level estimates in adult WOM increase, on average, 2.3-fold, but some increase almost 20-fold (Figure 1—figure supplement 1A). Despite this improvement, most OR mRNAs still have relatively low-expression values (Figure 1A). Nevertheless, they show a dynamic range of abundance levels (Figure 1A, inset) that are consistent between biological replicates, as indicated by their very high correlation values (median rho = 0.89, p<2.2 × 10−16).
To assess whether these low OR mRNA expression values are biologically meaningful or if they represent low-level technical artifacts of RNAseq analysis, we sequenced RNA from WOM of a mouse strain that has a targeted homozygous deletion of the Olfr7 OR gene cluster on chromosome 9 (Xie et al., 2000; Khan et al., 2011), and compared their gene expression profile to control mice. From the 94 OR genes of the cluster that have been deleted, 83 (88.3%) have no counts in any of the three biological replicates. The 11 remaining genes have just one or two fragments mapped in only one of the replicates (Figure 1B), resulting in normalized counts of less than 0.4. In contrast, the control mice have from 14.2 to 498.1 normalized counts for the same genes. Together these experiments demonstrate that the use of extended gene models significantly increases the sensitivity to detect OR mRNA expression in WOM, and that the full dynamic range of abundances reflects true measures of OR gene expression.
The wide range of stereotypic OR gene expression can be explained by two scenarios, acting alone or in combination (Figure 1C): either (1) OR genes with high-expression values are monogenically expressed in more OSNs than those with low-expression values; and/or (2) OR genes are consistently expressed at different levels per OSN. To differentiate between these possibilities, we performed in situ hybridization (ISH) of probes specific to nine OR genes with expression values distributed across the dynamic range. We then counted the number of OSNs in which each OR is expressed (Figure 1D). We find very strong correlation between OSN number and RNAseq expression value (rho = 0.98, p=5 × 10−5). We additionally compared OR gene RNAseq expression levels with three independent measures of the number of OSNs expressing the same ORs (Bressel et al., 2016; Fuss et al., 2007; Khan et al., 2011). In all three cases, we find high correlations (Figure 1—figure supplement 1B–D). We next collected 63 single mature OSNs from WOM, and determined the OR gene most abundantly expressed in each using a single-cell RNAseq approach (Saraiva et al., 2016, unpublished data). If OR expression levels in WOM reflect the proportion of OSNs that express each receptor (Figure 1C), the probability of isolating each OSN type is not equal. Indeed, we find a strong selection bias towards OSNs that express OR genes with high RNAseq levels in WOM (hypergeometric test, p=6.44 × 10−9; Figure 1E), suggesting those OSN types are more numerous in the olfactory epithelium. Thus, consistent with a recent analysis in zebra fish (Saraiva et al., 2015), OR RNAseq values are an accurate measure of the number of each OSN subtype in the mouse WOM (scenario 1). But do consistent differences in OR mRNA levels per cell also contribute (scenario 2)? To test this, we quantified the mRNA levels of the most abundant OR gene in each of the 63 single, mature OSNs, normalized to three stably expressed OSN marker genes (Khan et al., 2013). We find OR mRNA levels vary within the single cells, but this does not correlate with expression levels across the WOM (rho = −0.04, p=0.7518) (Figure 1F). Analysis of ERCC spike-ins confirmed that the levels of OR mRNAs in single OSNs are reliable. Moreover, the single OSN transcript levels also positively correlate with transcript levels in pools of millions of OSNs (Saraiva et al., 2016). Together, these data demonstrate that OR mRNA levels obtained by RNAseq are an accurate proxy for quantifying the diversity of OSN subtypes that express each receptor.
The relative proportion of each OSN subtype is stable between genetically identical animals. We have previously reported the expression of OR genes in B6 male and female mice (Ibarra-Soria et al., 2014). By applying full gene models to these data, here we confirm their OSN distribution profiles are remarkably similar (Figure 2A); only 1.2% of the OR gene repertoire is significantly differentially expressed (Figure 2B). To investigate whether this OSN distribution is a stereotypic feature of the species, we next reconstructed the WOM transcriptome of a different laboratory strain, 129S5SvEv (hereafter termed 129). The 129 genome has 4.4 million single nucleotide polymorphisms (SNPs) and 0.81 million small indels compared to B6 (Keane et al., 2011), of which we find 13,484 SNPs and 1,936 indels within our extended OR gene transcripts. As OR genes are particularly variable in coding sequence between strains of mice (Logan, 2014), mapping RNAseq data from other strains to a B6 reference genome results in biases in OR gene expression estimates (Figure 2—figure supplement 1A). We therefore generated a pseudo-129 genome on which to map the RNAseq data, by editing the reference genome at all polymorphic sites. We confirmed that the RNAseq expression estimates correlate with the number of OSNs that express the corresponding receptor genes in 129 animals, as judged by in situ hybridization (rho = 1, p=5.5 × 10−6; Figure 2—figure supplement 1B). From the 1,249 OR genes, we find 462 are significantly differentially expressed (DE) compared to B6 (false discovery rate (FDR) < 5%), representing 37% of the whole repertoire (Figure 2C,D).
To determine whether greater genetic diversity influences the variance in OSN repertoire, we repeated this experiment using a wild-derived strain from the Mus musculus castaneus subspecies (CAST/EiJ, henceforth CAST). This strain has more than 17.6 million SNPs and 2.7 million indels relative to B6 (Keane et al., 2011); of these, we counted that 45,688 SNPs and 6,303 indels are found within our extended OR transcripts. After mapping to a pseudo-CAST genome (Figure 2—figure supplement 1C), 634 OR genes are significantly differentially expressed (FDR < 5%) compared to B6, constituting 50.8% of the whole OR repertoire (Figure 2E,F). The changes in expression for some OR genes are dramatic: 132 genes have differences of at least eight fold. Taking all pairwise comparisons into account (including 129 vs CAST, Figure 2—figure supplement 1D,E), 821 OR genes (65.7%) are DE between at least two strains. One hundred and thirty-six of these are DE in all three pairwise comparisons (Figure 2G); for example, there are consistently different numbers of Olfr6-expressing OSNs in each strain (Figure 2H).
To determine if the DE OR genes between strains reflect differences in the proportions of OSN subtypes, we performed ISH with probes specific to OR genes with significantly different expression values between B6 and 129 (Figure 2I). We then counted the number of OSNs that express nine different OR mRNAs, in each strain, and compared this with their RNAseq expression values (Figure 2J). We find a high correlation between the difference in OSN number and the difference in RNAseq expression values between B6 and 129 (rho = 0.98, p=5 × 10−5; Figure 2K), demonstrating our RNAseq-based approach accurately measures the difference in OSN repertoires between strains.
OR gene clusters are enriched in copy number variants (CNVs) between individual human (Nozawa et al., 2007; Young et al., 2008) and mouse strain genomes (Graubert et al., 2007). Thus, it is possible that variance in OSN subtype representations are a consequence of different numbers of highly similar OR genes between strains. To assess this, we mined CAST genome sequence data (Keane et al., 2011) for heterozygous SNPs within annotated OR genes. We identified 51 ORs that contain 10 or more heterozygous SNPs, an indication of additional alleles. Using genome sequencing data from these genes, we identified 30 novel or misassembled OR genes. We remapped the CAST RNAseq data to a pseudo-CAST genome incorporating these new OR alleles and re-estimated the expression of the OR repertoire. The overall abundance profile remains unchanged except for 36 genes (Figure 2—figure supplement 1F). To assess whether this accounts for the observed differential expression between strains, we compared these estimates to B6. Only 12 of 634 OR genes lose their DE status, and 4 OR genes now become DE (Figure 2—figure supplement 1G). Thus, while differences in OR gene copy number minimally contribute to the diversity in OSN repertoire between three strains of mice, other mechanisms are responsible for most of the variation.
Genetically divergent mouse strains produce different chemical odortypes in their urine (Kwak et al., 2012; Yamaguchi et al., 1981) and amniotic fluid (Logan et al., 2012). Therefore, each strain of mouse, when housed in homogeneous groups, is exposed to a unique pre- and post-natal olfactory environment. As odor exposure alters the life-span of OSNs in an activity-dependent manner (François et al., 2013; Santoro and Dulac, 2012; Watt et al., 2004), genetic variation could regulate OSN population dynamics either directly or indirectly, via odortype. We, therefore, devised an experiment to test and differentiate the influence of the olfactory environment from the genetic background.
We transferred four to eight-cell stage B6 and 129 zygotes to F1 mothers to ensure they experienced an identical in utero environment. At birth, B6 litters were cross-fostered to B6 mothers and 129 litters to 129 mothers. In addition, B6 litters received a single 129 pup, and 129 litters a single B6 pup. Therefore, each litter experienced a characteristic olfactory environment, but one animal (the alien) had a different genetic background from the others (Figure 3A). At 10 weeks of age, we quantified the OSN repertoires of six alien animals and six cage-mates using RNAseq. We found that the OSN repertoires cluster in two groups, clearly defined by genetic background (Figure 3B). The correlation coefficient for any two B6 samples was on average 0.97, with no significant difference between the environments (t-test, p=0.09). In contrast, the correlation for any B6 with a 129 sample had a mean of 0.89, which is significantly lower (t-test, p=3.8 × 10−12). Five hundred and seven OR genes, among 5475 other genes are DE between these mice when grouped by strain (Figure 3—figure supplement 1A). In striking contrast, across the whole transcriptome we find only mRNA from two genes that show differences in expression according to odor environment, both of which encode ORs (Figure 3C, Figure 3—figure supplement 1B). These data demonstrate that the diversity in OSN repertoire we observe between strains is almost entirely dictated by direct genetic effects. In a controlled environment, the influence of odortype on the development and maintenance of the MOE is minimal, perhaps restricted to only a few OSN subtypes.
The indifference of the OSN repertoire to the olfactory environment suggests its development and maintenance is not influenced by the specific activity of OR proteins or, by inference, their protein coding sequence. To further test this, we analyzed the OSN repertoire of newborn pups. We identify the presence of 1,198 (95.9%) OSN subtypes across a dynamic range of abundance (Figure 4A). The differential proportions of OSNs expressing particular OR genes are therefore already present during the development of the MOE, suggesting that it is not dependent on the activity of the OSNs nor on differences in OSN life-span.
Next, we analyzed the expression of ORs that are pseudogenized and do not produce receptor proteins capable of odor-mediated activity, but can be co-expressed with functional ORs (Serizawa et al., 2003; Saraiva et al., 2016). These are represented in OSNs with a very similar distribution to functional OR genes (Figure 4B). Moreover, we analyzed the OR genes that encode identical protein-coding sequences between different strains. 36.3% of the OSN subtypes that express identical ORs are differentially represented between 129 and B6. 44.8% are differentially represented between CAST and B6. Together, these results suggest that the proportion of each OSN subtype is not dependent on its endogenous OR receptor activity.
To directly test whether the abundance of a particular OSN subtype is influenced by the identity of the receptor protein it expresses, we used CRISPR-Cas9 to replace only the coding sequence of Olfr1507 with that of Olfr2 (referred to as Olfr2 > Olfr1507), in a pure B6 genetic background (Figure 4C). OSNs expressing Olfr1507 are the most common subtype in B6, while Olfr2 expressing OSNs are ranked 334th by decreasing abundance. Homozygous Olfr2 > Olfr1507 animals have 47 fold more Olfr2-expressing OSNs compared to controls, and is the highest subtype in these animals (Figure 4D). DE analysis of OR genes supports the striking reciprocal differences in Olfr1507 and Olfr2-expressing OSNs in the Olfr2 > Olfr1507 animals, but we also find 118 other OSN subtypes with significant, albeit comparatively subtle, differences (over 90% have fold-changes < 2) (Figure 4E). Taken together, these data indicate that the extensive variance in OSN subtype composition we observe in mice is determined by the wider genetic architecture of the animal, and is independent of the function of the OR protein each subtype expresses.
To investigate how genetic background influences OSN subtype abundances, we mined 129 and CAST whole genome sequences (Keane et al., 2011) for SNPs and short indels in regulatory regions of OR genes. We find that differentially represented OSN subtypes express OR genes with significantly greater amounts of variation in their coding sequence, whole transcript and regions of 300 bp or 1 kb upstream of the transcription start site, for both the 129 and CAST genomes (Mann-Whitney one tail, p<0.02 for 129 and p<0.0002 for CAST; Figure 4—figure supplement 1A). Further, we scanned OR gene promoters for O/E and HD binding sites. In the CAST genome, 58 and 310 putative OR promoters have gains or losses of O/E and HD-binding sites respectively, compared to the B6 genome. In contrast, only 12 and 46 OR promoters show differences in the number of O/E and HD-binding sites, respectively, when comparing the 129 and B6 genomes.
We therefore hypothesized that OSN subtype repertoires are generated via sequence variance in OR gene promoter and/or local enhancer elements, which dictate the frequency of OR gene choice. For two OR gene clusters, it has been demonstrated that enhancer/promoter interactions act in cis and do not influence the expression of the homologous OR allele on the other chromosome (Fuss et al., 2007; Khan et al., 2011; Nishizumi et al., 2007). However, recent chromosome conformation capture experiments revealed interchromosomal interactions between OR enhancer elements (Markenscoff-Papadimitriou et al., 2014). Moreover, the differential representation of 118 other OSN subtypes in the Olfr2 > Olfr1507 line (Figure 4E), 108 of which express ORs that are located on a different chromosome from Olfr1507, is consistent with the possibility that genetic modification of one OR locus directly influences the probability of choice in other ORs, in trans.
To determine whether the genetic elements that instruct the whole OSN repertoire are cis- or trans-acting, we carried out an analysis at the OR allele level in B6 × CAST F1 hybrids. Following the logic of (Goncalves et al., 2012), if the genetic elements act in cis then we would expect the OSN subtypes that differ between B6 and CAST to be maintained between OSNs expressing the corresponding B6 and CAST alleles within an F1 hybrid. On the other hand, if the elements act in trans the number of OSNs that express the B6 derived allele in the F1 would not differ from those that express the CAST allele.
Within F1 mice, 840 OSN subtypes (67.2%) expressed OR mRNAs that could be distinguished at the allelic level. The ratios between B6 and CAST OSN subtype abundance (F0) strongly correlate with the ratios between alleles in the F1 hybrids at approximately 1:1 (concordance correlation coefficient (ccc) = 0.86; Figure 4F). In other words, taken across over 800 OSN subtypes, those expressing a B6 OR allele in F1 animals have the same repertoire as the B6 parent, while the subtypes expressing the CAST OR allele match the CAST parent (Figure 4G). To better understand the significance of any deviations from 1:1 concordance in specific OSN subtypes, we corrected for technical noise associated with distinguishing allelic expression at low counts (see Materials and methods; Figure 4—figure supplement 1B,C). The corrected data has a stronger correlation (ccc = 0.92) and the best fit of this data matches 1:1 concordance (Cb = 0.999). We therefore conclude that, collectively, the genetic elements dictating the abundance of over 800 OSN subtypes act in cis. However, we cannot exclude the possibility that a small contribution from trans-acting elements may account for subtle deviations from unity for some OSN subtypes.
Taken together, these data are consistent with a predominant model where genetic variation in local, non-coding regulatory elements determines the probability with which each OR gene is chosen early in OSN neurogenesis.
Previous studies have shown that OSNs activated by their cognate ligands have increased life-span (François et al., 2013; Santoro and Dulac, 2012; Watt et al., 2004). With time, longer survival rates should translate into enrichment in the neuronal population, compared to those OSN types that are mostly inactive (Santoro and Dulac, 2012). However, we found no evidence of different strain- or sex-derived odors influencing the OSN repertoire (Figures 2A and 3B). Because these odor exposures were temporally constant, we hypothesized that the absence of an observed environmental influence on OSN repertoire could be due to olfactory adaptation (a reduction of specific olfactory sensitivity due to prolonged odor exposure, reviewed in [Zufall and Leinders-Zufall, 2000]).
To test this, we exposed mice to a mix of four chemically distinct odorants (acetophenone, eugenol, heptanal and (R)-carvone). The odorant mixture was added to the drinking water supplied to the animals to avoid adaptation, such that they could smell the odor mixture when they approached the bottle to drink (Figure 5A). We collected the WOM from animals exposed to the odorants for 24 weeks from birth, along with water-exposed controls, and performed RNAseq. DE analysis reveals 36 OR genes with significantly different mRNA levels (FDR < 5%), with similar numbers more or less abundant in the exposed animals (Figure 5B, Figure 5—figure supplement 1). We selected seven OR genes with the biggest fold-changes in mRNA level for which specific TaqMan qPCR probes were available, and validated their expression levels in a larger cohort. The results indicate that all the tested genes have mRNA levels that are statistically significantly different from controls (t-test, FDR < 5%) and the direction of the expression changes are concordant with the RNAseq data (Figure 5C).
To characterize the temporal dynamics of these OR mRNAs, we tested their expression after different periods of exposure (1, 4 and 10 weeks) in independent samples. After 1 week of treatment, none showed significant differences from controls, which is expected since young pups do not drink from the odorized water bottle. After 4 weeks, three of the OR genes are DE from controls, and at 10 weeks, five out of the seven receptors are DE (t-test, FDR < 5%; Figure 5C). To assess the plasticity of these changes, we stimulated a group of animals for four weeks, and then removed the odor stimuli for an additional 6 weeks. In these mice, none of the OR genes are DE from controls (Figure 5D). Thus, the abundance of specific OR types in WOM is increasingly altered, over a period of weeks to months, upon frequent environmental exposure to defined olfactory cues. These differences are reversible and require persistent stimulation to be maintained.
To investigate whether olfactory adaptation blocks this effect, we presented the same odor mixture on a cotton ball inside a tea strainer (Figure 5E), such that the stimuli are present in a sustained manner. None of the same seven OR genes are DE after 24 weeks, nor are any consistently dysregulated during the course of the exposure experiment (t-test, FDR < 5%; Figure 5E). Therefore, when odorants are present in the environment in a constant manner (similar to those differentially produced by gender or strains of mice), the OR mRNA abundance levels most responsive to acute exposure remain unchanged.
If temporal differences in OR mRNA abundance are a consequence of odorant-specific activity, exposure to different odorants should lead to the differential expression of discrete subsets of OR genes. To test this, we odorized the drinking water with (R)-carvone alone, heptanal alone, or with the combination of both (Figure 6A). After 10 weeks of exposure, we tested the expression of the seven DE OR mRNAs that were responsive to the four odor mix (acetophenone, eugenol, heptanal and (R)-carvone), by TaqMan qRT-PCR. None of the genes are significantly DE in the animals exposed to (R)-carvone alone. However, four of the seven OR genes have mRNA levels significantly different in the animals exposed to heptanal, or to the combination of both odorants (t-test, FDR < 5%; Figure 6—figure supplement 1A). We next carried out a transcriptome-wide analysis by RNAseq, finding 43 OR genes significantly DE in at least one of the conditions (FDR < 5%) of which 32 (74.4%) are upregulated in the odor-stimulated animals (Figure 6B). Exposure to (R)-carvone or heptanal resulted in a change in mRNA expression of 15 and 20 OR genes, respectively. These sets of receptors are almost completely independent, with only one OR mRNA significantly upregulated in both groups (Olfr538; Figure 6C–D). The animals that were exposed to both odorants simultaneously showed significant changes in mRNA levels for 24 OR genes, 15 of which are shared with the individually exposed groups (hypergeometric test, p=1.87 × 10−19). Almost 40% of the ORs that show significant changes when exposed to all four odorants (Figure 5B) are also significantly altered in one or more of the groups exposed to (R)-carvone, heptanal or their combination. Thus, together these data demonstrate that acute environmental exposure to the odorants alters the global expression of around 1.2–1.6% of OR genes in the WOM. These changes are odor-specific and reproducible in isolation and in increasingly complex mixtures.
To investigate whether DE OR genes are directly activated by the environmental odorants, we expressed a subset (Olfr538, Olfr902, Olfr916, Olfr1182, Olfr347 and Olfr524) in a heterologous system (Zhuang and Matsunami, 2008) and challenged them with increasing concentrations of (R)-carvone and heptanal. Half of the DE ORs we tested were responsive in vitro (Figure 6E–F, Figure 6—figure supplement 1B): for example, Olfr538 displayed a dose-dependent response to (R)-carvone (Figure 6E) and Olfr524 was responsive to heptanal (Figure 6F).
Some odorants, including heptanal, are known to be decomposed by enzymes present in the nasal mucus (Nagashima and Touhara, 2010) such that in vivo exposure to an odorant may result in stimulation of the OSNs with chemically distinct byproducts. We therefore employed a recently published deorphanization system to identify the ORs that respond to heptanal stimulation in vivo (Jiang et al., 2015). This strategy exploits the phosphorylation of the S6 ribosomal subunit when an OSN is activated. Thus, by coupling pS6-immunoprecipitation (ps6-IP) and RNAseq, the OR mRNAs expressed in the activated OSNs can be identified. We exposed mice to two concentrations of heptanal for an hour, and sequenced the mRNAs from OSNs after pS6-IP. Twelve and 210 OR mRNAs were significantly enriched (FDR < 5%) upon exposure to 1% and 100% heptanal, respectively, compared to controls. Over half of the DE ORs after 10-week acute exposure to heptanal (Figure 6B) are also DE in the pS6+ cells (11 out of 20; Figure 6G), which is significantly more than expected by chance (hypergeometric test, p=0.0001). Thus, using both in vitro and in vivo methods, we conclude that long-term odor-mediated changes in OR gene expression occurs via direct activation of OSNs expressing those receptors.
We have exploited the power of RNA sequencing and the monogenic and monoallelic nature of OR gene expression, to comprehensively characterize the full neuronal diversity of a mammalian nose. We have discovered that the representation of OSN subtypes within an individual is highly unequal but stereotyped between animals of the same genetic background. We show that non-coding genetic variation results in high divergence of the relative proportions of different OSN subtypes, with most being susceptible to altered abundance. OSN diversity is predominantly controlled by genetic elements that act in cis and the abundance of a given OSN subtype is not affected by the sequence or function of the receptor it expresses, in a sustained olfactory environment. However, the persistent but interleaved presentation of olfactory stimuli can alter the representation of specific ORs in an activity-dependent manner, thus subtly shaping the genetically-encoded neuronal repertoire to the olfactory environment.
The process of OR gene choice, stabilization and exclusion during OSN maturation is poorly understood. Occasionally, it is referred to as a random process (McClintock, 2010; Rodriguez, 2013), suggesting there is no pattern or predictability to the outcome. However, our data indicates that, at the OSN population level, the result of this process is deterministic. A particular genetic background in controlled environmental conditions reproducibly generates an OSN population with fixed, unequal proportions of the different OSN subtypes. Thus, the process that generates this profile is more accurately described as stochastic. Despite divergence in the profiles generated by different genomes, all show a similarly shaped distribution: a small proportion of OSN subtypes are present at high levels with a rapid decay in abundance thereafter. In fact, 3.6% or less of the OSN subtypes contribute to 25% of the overall neuronal content of the WOM. We find that unequal OSN distributions are already present at birth (Figure 4A), suggesting the genetic influence is on the process of OR gene choice/stabilization rather than modulating neuronal survival.
Proximity to the H element, a cluster-specific enhancer, increases the frequency in which an OR is represented within the OSN population (Khan et al., 2011). Therefore, the most highly represented OSN types may express OR genes located close to other strong enhancers. However, we propose that genetic variation in enhancers is not sufficient to account for the full diversity of differences in OSN subtypes between strains, as different ORs located adjacent to one another within a cluster are frequently represented very differently. Recently, it has been proposed that higher levels of OR transcription per cell may result in more OSNs expressing that receptor due to increased success in a post-selection refinement process (Abdus-Saboor et al., 2016). Our measurements of OR mRNA expression levels in 63 single OSNs (Figure 1F) do not support this hypothesis. Instead, our data are consistent with a model where non-uniform probabilities of OR choice are instructed by genetic variation in both specific OR promoters and enhancers. Supporting this model, we identified many putative promoters for differentially represented ORs where genetic variation has altered the number of Olf1/Ebf1 (O/E) and homeodomain (HD) transcription factors binding sites between the mouse strains, both sequences known to influence the probability of OR choice (D'Hulst et al., 2016; Vassalli et al., 2011). Moreover, through analysis of F1 hybrids, we confirmed the finding that the probability of choice of OR genes linked to the H element are regulated in cis (Fuss et al., 2007), and extended this to over 800 additional OR genes distributed throughout the genome that show little to no evidence in support of trans-acting regulation. Instead, the haploid CAST- and B6-derived OR alleles within an F1 are each regulated almost identically as they are in a diploid state within their original genetic backgrounds (Figure 4F,G). Our data are inconsistent with trans-interactions of multiple enhancers acting additively to regulate the probability of OR choice (Markenscoff-Papadimitriou et al., 2014). These trans interactions may, however, stabilize or maintain OR singularity after choice has been instructed in cis, and thus could also be necessary for the stereotypic representation of OSNs in a fixed genetic background.
Many existing studies into OR gene choice, especially those utilizing transgenic mouse lines, use animals with a mixed 129/B6 genetic background. The remarkable diversity in the OSN repertoire between these strains (Figure 2) suggests caution should be exercised in their interpretation. Here, we created a mouse line that carries the coding sequence of Olfr2 in the locus of Olfr1507, the most frequently selected OR gene, in a pure B6 genetic background. Olfr2-expressing OSNs, which rank 334th across the repertoire in the original B6 strain, are the most abundant OSN subtype in this modified line, demonstrating the critical importance of the genetic context in the regulation of the probability of OR gene choice. Curiously, we also observed that ~10% of other OSN types show comparatively subtle but reproducible differences in abundance. The mechanism underlying these differences is unclear. One plausible hypothesis is that the transposition of Olfr2 OSNs to a different olfactory zone alters the dynamics and spatial organization of the glomeruli in the olfactory bulb. This in turn may impact on the proliferation and survival of proximal OSN subtypes. Alternatively, stabilizing trans interactions could be affected by the OR gene swap, resulting in slightly altered repertoires for some OSN subtypes. In this case, the sequence of an OR receptor gene would influence the relative abundance of other OSN subtypes, but our Olfr2 > Olfr1507 swap experiment suggests the sequence of an OR receptor does not dictate the abundance of its own OSN subtype.
The main olfactory epithelium regenerates throughout the life of an animal. It has been suggested that activity-mediated mechanisms may shape the olfactory system by increasing OSN survival (François et al., 2013; Santoro and Dulac, 2012; Watt et al., 2004; Zhao and Reed, 2001), although other studies have found that the number of specific OSN subtypes decrease or are unaffected by odor-exposure (Cadiou et al., 2014; Cavallin et al., 2010). Each of these studies focused on one or two OSN subtypes and the odor exposure procedures varied significantly in frequency, persistence and length. Here, we took a comprehensive approach, measuring the response of over 1,000 ORs to four odorants, after different types of exposure from 1 week to 6 months. We find that mice living in stable chemical environments maintain the olfactory transcriptomes of their genetically dictated OSN repertoire. However, when frequently recurring odor stimulation is introduced, the abundances of responsive ORs are modified. We propose that this difference is a result of olfactory adaptation in the presence of continuous stimulation. However, other factors may also contribute, for example the continuous exposure odors were likely detected orthonasally, while the intermittently exposure odors were retronasally detected.
The lengthy timeframe for odor-mediated differences to emerge is consistent with modulation of OSN lifespan. It is mechanistically unlikely that odor-activity could influence the probability of OR gene choice, but it could promote OR expression stabilization or singularity. Compared to the dramatic influence of genetic variation on OSN repertoire, odor-evoked changes are subtle, typically less than a two fold change after 6 months of exposure. The limited effect magnitude and long-time scales precluded a more detailed analysis to confirm a correlative alteration in OSN number.
Interestingly, we identified ORs that became more abundant after exposure to specific odorants and, within the same animals, others that became less abundant. Both types were marked by phosphorylation of the S6 ribosomal subunit, a feature of activated OSNs (Jiang et al., 2015), indicating that the differential expression is mediated by OSN subtype-specific olfactory stimulation. This may explain why very different conclusions were drawn from previous exposure studies on a small number of ORs (Cadiou et al., 2014; Cavallin et al., 2010; François et al., 2013; Watt et al., 2004). Short-term odor exposures (30 min to 24 hr) result in a temporary down-regulation of activated OR mRNA (von der Weid et al., 2015), presumably as part of the olfactory adaptation process. It is possible that our analyses are capturing this dynamic short-term response in addition to changes in OSN numbers resulting from long-term exposures. We could not identify any phylogenetic or chromosomal predictor of the ORs that responded with contrasting directional effects, and at this time, the logic underpinning the difference in the direction of expression changes remains unexplained.
Genetic variation has great impact on individual phenotypic traits. Humans differ in up to a third of their OR alleles by functional variation (Mainland et al., 2014), which contributes to an individually unique sense of smell (Secundo et al., 2015). Segregating OR alleles have been functionally linked to perceptual differences of their odor ligands, by altering intensity, valence or detection threshold (Jaeger et al., 2013; Keller et al., 2007; Mainland et al., 2014; McRae et al., 2013; Menashe et al., 2007). However, in most cases, these OR coding genetic variants explain only a small proportion of the observed phenotypic variance (reviewed in Logan ), suggesting that other factors contribute to individual differences in perception. Recently, it has been demonstrated that increasing the number of a particular OSN subtype in a mouse nose increases olfactory sensitivity to its ligand (D'Hulst et al., 2016). Therefore, the very different OSN repertoires present between strains of mice are likely to result in significant phenotypic variation in olfactory thresholds, and thus contribute to the individualization of olfaction.
Although it remains to be determined whether human OSN repertoires are as variable as the mice reported here, an array-based study of OR expression in 26 humans found unequal expression of ORs within and between individual noses (Verbeurgt et al., 2014). Moreover, a recent systematic survey of olfactory perception in humans found high levels of individual variability in reporting the intensity of some odors (for example, benzenethiol and 3-pentanone) but not others (Keller and Vosshall, 2016). Further, a non-coding variant within an OR cluster associated with insensitivity to 2-heptanone has been shown to be dominant to the sensitive allele (McRae et al., 2013). As OR genes are regulated monoallelicaly, this implies that a 50% reduction in the sensitive OR allele dosage is, in some cases, sufficient to influence perception. On the other hand, because many odorants activate multiple OSN subtypes (Malnic et al., 1999), a differential representation of one subtype may have a limited influence on the overall perception of its odor.
Further investigation into the functional consequence of diverse OSN repertoires will be necessary to determine the full extent to which they individualize the sense of smell.
Animal experiments were carried out under the authority of a UK Home Office license (80/2472), after review by the Wellcome Trust Sanger Institute Animal Welfare and Ethical Review Board. All mice were housed in single sex groups within individually ventilated cages, with access to food and water ad libitum. All WOM samples were obtained from a single animal, except the pup WOM samples, which were the pool of three or four individuals. Details of the strain, age and sex of each animal sequenced can be found in Supplementary file 1. MOEs were dissected and immediately homogenized in lysis RLT buffer (Qiagen, Germantown, Maryland). Total RNA was extracted using the RNeasy mini kit (Qiagen) with on-column DNAse digestion, following the manufacturer’s protocol. mRNA was prepared for sequencing using the TruSeq RNA sample preparation kit (Illumina, San Diego, California). All RNA sequencing was paired-end and produced 100-nucleotide-long reads.
Sequencing data were aligned with STAR 2.3 (Dobin et al., 2013) to the GRCm38 mouse reference genome (B6) or to pseudo-genomes created for the different strains using Seqnature (Munger et al., 2014) to impute the high-quality variants reported by the Mouse Genomes Project, release v3 (http://www.sanger.ac.uk/science/data/mouse-genomes-project). The parameters used for mapping were as follows: --outFilterMultimapNmax 1000 --outFilterMismatchNmax 4 --outFilterMatchNmin 100 --alignIntronMax 50000 --alignMatesGapMax 50500 --outSAMstrandField intronMotif --outFilterType BySJout. The annotation used was from the Ensembl mouse genome database version 72 (http://jun2013.archive.ensembl.org/info/data/ftp/index.html), modified to include all reconstructed gene models for OR genes as reported in (Ibarra-Soria et al., 2014).
The numbers of fragments uniquely aligned to each gene were obtained using the HTSeq 0.6.1 package (RRID:SCR_005514), with the script htseq-count, mode intersection-nonempty (Anders et al., 2015). Raw counts were normalized to account for sequencing depth between samples, using the procedure implemented in the DESeq2 package (Love et al., 2014). Data analysis, statistical testing and plotting were carried out in R (http://www.R-project.org).
To compare OR expression levels between datasets, normalization to account for the number of OSNs present in the WOM samples was carried out subsequent to depth normalization. For this, we used a method proposed by Khan et al. (2013) that uses marker genes known to be stably expressed in mature OSNs only. This allows estimating the proportion of WOM RNA contributed by the OSNs. Five different marker genes were considered: Omp, Adcy3, Ano2, Cnga2 and Gnal (except for the single-cell data, where Adcy3 and Ano2 were excluded because they are not expressed in many cells). To normalize for OSN number, we first computed the correlation coefficient between the expression of each marker gene and the total counts in OR genes; those marker genes with strong correlation values were used for normalization. Then, we calculated the geometric mean of all marker genes for each sample. The average of all geometric means was obtained, and divided by each individual mean; this results in the generation of size factors. Finally, the OR normalized counts were multiplied by the corresponding size factor. Normalized OR expression estimates for the three strains, the Olfr2 > Olfr1507 and the odor-exposed animals are provided in Figure 2—source data 1, Figure 4—source data 1, Figure 5—source data 1 and Figure 6—source data 1.
Differential expression analysis was performed with DESeq2 1.8.1 (Love et al., 2014) with standard parameters. To test for differential expression (DE) on the OR repertoire the double normalized counts (accounting for OSN number per sample) were provided directly, and the normalizationFactors function was used with size factors of 1 to turn off further normalization. Genes were considered significantly DE if they had an adjusted p-value of 0.05 or less. For the cross-fostering dataset, a likelihood ratio test (nbinomLRT function in DESeq2) was used to compare the full model genetics+environment+genetics:environment to a reduced one accounting only for the genetics. Detailed results are provided in Figure 2—source data 1, Figure 4—source data 1, Figure 5—source data 1 and Figure 6—source data 1.
Probes were designed against nine OR genes chosen among genes covering the receptor expression dynamic range and DE between 129 and B6 strains (Olfr24, Olfr31, Olfr78, Olfr124, Olfr323, Olfr374, Olfr543, Olfr736 and Olfr1512). The following gene-specific oligonucleotides were used to amplify by PCR an amplicon of ~1000 bp from each transcript:
The reverse primer in each case includes the T7 RNA polymerase promoter sequence, and both oligos were designed to amplify a fragment with <75% sequence similarity to other OR genes in the mouse genome. Amplicons were purified from the PCR reactions using the Wizard PCR cleanup kit (Promega, Fitchburg, Wisconsin) and used in riboprobe in vitro transcription with T7 RNA polymerase (ThermoFisher Scientific, Waltham, Massachusetts) and DIG-labeled UTP.
WOM from 10-week-old 129 or B6 mice were collected by dissection, fixed for 48 hr in 4% paraformaldehyde in 1x PBS and demineralized for 10 days in 0.45M EDTA pH 8.0/1x PBS. Samples were then cryoprotected in 0.45M EDTA pH 8.0/1x PBS/20% sucrose for 24 hr, before embedding in OCT medium and sectioning on a Leica CM1850 cryostat to produce slides containing 16 μm coronal MOE sections. Slides were air-dried for 10 min, followed by fixation with 4% paraformaldehyde for 20 min, and treated with 0.1M HCl for 10 min. Tissue acetylation proceeded in 250 mL of 0.1M triethanolamine (pH 8.0) with 1 mL of acetic anhydride for 10 min, with gentle stirring. Two washes in 1× PBS were performed between incubations.
Riboprobe hybridization was done with DIG-labeled probes (1000 ng/mL) at 60°C in hybridization buffer (50% formamide, 10% dextran sulfate, 600 mM NaCl, 200 μg/mL yeast tRNA, 0.25% SDS, 10 mM Tris-HCl pH 8.0, 1× Denhardt’s solution, 1 mM EDTA pH 8.0) overnight. Post-hybridization washes included one wash in 2× SSC, one wash in 0.2× SSC and one wash in 0.1× SSC at 55°C, for 30, 20 and 20 min, respectively. Tissue permeabilization was performed in 1× PBS, 0.1% Tween-20 for 10 min, followed by two washes in TN buffer (100 mM Tris-HCl pH 7.5, 150 mM NaCl) for 5 min at room temperature, followed by blocking in TNB buffer (100 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.05% blocking reagent [Perkin Elmer, Waltham, Massachusetts]). Slides were then incubated overnight at 4°C with sheep anti-DIG-AP (Roche, Germany) diluted in TNB (1:800), washed in TNT (100 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.5% Tween 20) six times, 5 min each, transferred to alkaline phosphatase buffer (100 mM Tris-HCl pH 9.8, 100 mM NaCl, 50 mM MgCl2, 0.1% Tween 20) twice for 5 min each.
Signal development was performed in the same buffer containing 5% poly-vinyl alcohol (Mowiol MW 31,000, Sigma-Aldrich, St. Louis, Missouri), 50 μg/mL BCIP and 100 μg/mL NBT, until the purple precipitate was clearly visible with minimum background. Due to the large size of each MOE section, we collected serially scanned images with the 'Scan Large Image' function on NIS Elements software (3.22 version, Nikon Instruments), on a motorized upright Nikon Eclipse 90i microscope equipped with a planar PlanFluor 10x/0.30 DIC L/N1 objective (Nikon, Japan). Background correction was applied on individual images with the NIS elements software and stitched together using Image Composite Editor (Microsoft, Redmond, Washington) with no projection. Linear image adjustments were performed on Photoshop, using the 'Brightness and Contrast' and 'Levels' functions, to equalize the background tone across images. OR-expressing cells were counted by visual inspection. For each gene, three to four animals were analyzed; two to four sections were counted for each animal, and the cell counts were collected independently for each MOE side (hemi-section). The mean number of OR-positive cells per section was calculated (from the two hemi-section counts), followed by calculation of the mean number of OR-positive cells per animal (from the two to four counted sections). Mean and s.e.m. descriptive statistics were then calculated across the three to four animals analyzed.
Venn diagrams with areas proportional to the number of elements represented were created using the eulerAPE version 3 software (Micallef and Rodgers, 2014).
We mined the Mouse Genomes Project data (Keane et al., 2011), release v5 (http://www.sanger.ac.uk/science/data/mouse-genomes-project; RRID:IMSR_JAX:000928). Regions with high numbers of het SNPs indicate multiple alleles being mapped to a single locus in the reference genome. The sequences in the C57BL/6J (RRID:IMSR_JAX:000664) genome (GRCm38) for OR genes with het SNPs in CAST were used to construct a neighbor-joining phylogenetic tree using MEGA6 (Tamura et al., 2013). From the tree we selected 33 clades that contained the OR genes with highest number of het SNPs. Then we extracted all of the CAST/EiJ whole-genome Illumina sequencing reads produced by the Mouse Genomes Project that were mapped to these loci (http://www.sanger.ac.uk/science/data/mouse-genomes-project) (Keane et al., 2011), realigned these to the members of the respective clade, and then extracted the read pairs poorly aligned to known Olfr members (>2% mismatch). We created a de novo assembly of these reads to produce a set of contigs with Geneious R7 (Kearse et al., 2012). The contigs were scaffolded and gap-filled using the llumina reads. From the resulting scaffold sequences, we identified putative new alleles in CAST/EiJ (Supplementary file 1). The new allele's sequences are reported in Figure 2—source data 2.
To dissect the influence of the genetic background from the olfactory environment, C57BL/6N and 129S5 four- to eight-cell stage embryos were transferred into F1 (C57BL/6J×CBA) pseudo-pregnant females. One day after birth, the C57BL/6N and 129S5 litters were cross-fostered to C57BL/6N and 129S5 wild-type mothers, respectively. Then, a single pup from the other strain was transferred to the cross-fostered litter (the alien). At weaning, animals from the same sex as the alien animal were kept, always in a 4:1 ratio between strains. If not enough animals of the correct sex were available in the litter, surplus animals from other litters were used. At 10 weeks of age, the WOM was collected form the alien and a randomly selected cage-mate, and RNA was extracted and sequenced as described.
CRISPR-Cas9 technology was used to generate double strand breaks on either side of the Olfr1507 coding sequence and facilitate homologous recombination. Two guideRNAs -with sequences AAACTAGATACTTGGCTCATAGG and CATATTCTAGACATTGTCATAGG- were produced with the Ambion T7 MEGAshortscript kit and the Cas9 RNA with the Ambion mMessage mMachine T7 Ultra kit (ThermoFisher Scientific) as specified by the manufacturer’s protocols. All RNA were purified with Ambion MegaClear columns, eluting with pre-heated (95°C) elution solution. The eluate was then precipitated with ammonium acetate, and resuspended in ultrapure water (Sigma-Aldrich).
For homologous recombination, we produced a DNA vector containing the coding sequence of Olfr2 and homology arms for the Olfr1507 locus of ~1 kb. This was cloned into a modified pUC19 backbone via Gibson Assembly. The sequence-verified plasmid was purified with the NucleoBond Xtra Midi Plus EF Kit (ThermoFisher Scientific) following the manufacturer’s protocol. The plasmid was digested to remove the backbone and gel-purified with the QIAquick Gel Extraction Kit (Qiagen) following the kit’s protocol. The DNA was precipitated with sodium acetate and resuspended in ultrapure water (Sigma-Aldrich). Finally, the DNA was spin through an Ultrafree-MC centrifugal filter (Merck, Germany).
All components were microinjected into the cytoplasm of 112 C57BL/6N zygotes at the following concentrations: 25 ng/µl for each gRNA, 100 ng/µl of Cas9 RNA and 200 ng/µl of vector DNA. Thirty-eight pups were born and four were positive for the homologous recombination event. One of these was the correct substitution, while the others contained several copies of the DNA vector.
To map the RNAseq data from the Olfr2 > Olfr1507 homozygous mice, we modified the reference B6 mouse genome (GRCm38) to substitute the Olfr1507 CDS with that of Olfr2. Additionally, the Olfr2 CDS in the endogenous locus was removed to avoid multimapping. All the counts from both the endogenous Olfr2 UTRs and the modified Olfr1507 locus were added together and reported as the Olfr2 counts; Olfr1507 was set to zero. The WT controls were mapped to the unmodified reference genome. Data processing and DE analysis were performed as previously described.
We used the RegionMiner tool from the Genomatix software suite (https://www.genomatix.de/solutions/genomatix-software-suite.html) to identify overrepresented transcription factor binding sites (TFBSs) in the regions 1 kb upstream of the transcription start site of OR genes as annotated in (Ibarra-Soria et al., 2014), for all B6, 129 and CAST sequences. We extracted the match details for the matrix families NOLF and HOMF (Matrix Family Library version 9.3), which correspond to Olf1/Ebf1 and homeodomain TFs, respectively. Ad hoc perl scripts were used to parse out the core sequence coordinates of each motif match (defined as the central 10 nucleotides of the reported match), and then to compare the results for each promoter across the strains. We identified those OR genes that had differing number of predicted sites. The number and coordinates of the predicted TFBS are provided in Figure 4—source data 2.
The RNAseq data from the WOM of B6 x CAST F1 hybrids were obtained from a pre-publication release by the Wellcome Trust Sanger Institute (ERP004533). Data was processed as described above. Total expression estimates were obtained by mapping the RNAseq data to the B6 or pseudo-CAST genomes, with standard parameters. The expression estimates obtained with each genome were very highly correlated (rho = 0.99, p<2.2 × 10−16). Therefore, the data mapped to the B6 reference was used in downstream analyses.
To obtain allele-specific expression estimates, the RNAseq data was mapped to both the B6 and the pseudo-CAST genomes, without mismatches. Therefore, reads that span SNPs could only map to the genome corresponding to the allele they come from. Subsequent analyses were performed on the OR repertoire only. All reads mapped across each SNP were retrieved with SAMtools (RRID:SCR_002105) (Li et al., 2009); ad hoc perl scripts were used to exclude reads that splice across the SNP or that were not uniquely mapped. Finally, the number of different reads mapping across all SNPs of each gene was obtained.
To normalize for depth of sequencing, the total expression raw data was combined with the estimates from the parental strains, and normalized all together. The OR data was then further normalized to account for the number of OSNs, as described above. The same size factors were used to normalize the expression estimates from SNP positions.
To deconvolve the total expression into allele-specific expression, a ratio of the expression of each allele was obtained from the counts in SNP positions by dividing the counts in B6 over the total counts in B6 and CAST. Then, the total expression normalized counts were multiplied by the ratio to obtain the B6 expression, and to the inverse of the ratio for the CAST-specific expression. Only those genes with normalized counts in SNP positions above the lowest quartile were used (840 OR genes).
To estimate the amount of technical noise inherent to allelic expression estimation in genes with low counts such as the ORs, we created an in silico F1 dataset. To do this, we used SAMtools (Li et al., 2009) to downsample the F0 B6 and CAST samples to 50% of the median depth of the F1 samples; we used the three male B6 samples only. We combined each B6 sample with a CAST sample to create an F1 where the contribution from each allele was 50%. We processed the resulting F1 data as described above. The ratio of the two alleles in the F1 data should match the corresponding ratios in the parental calculations, and we can attribute any deviations from unity to technical noise. Thus, we computed the deviation form the x = y line for each gene in the in silico F1 and used it to correct the fold-change estimate between the allelic expression values in the real F1 data. We used the concordance correlation coefficient as a measurement of the correlation of the fold-change estimates between the parental and allelic expression values (Lin, 1989). Unlike a regular correlation coefficient, it corrects for the agreement on the x = y line, where the data should fall if regulation occurs in cis. Furthermore, it provides the parameter Cb which quantifies the deviation of the best fit to the data from the x = y line; Cb = 1 means no deviation. For both the in silico F1 and the real F1 data, the Cb was 0.99, indicating that the OR repertoire as a whole lies on the diagonal.
B6 mice were exposed to a mix of heptanal, (R)-carvone, eugenol and acetophenone at 1 mM concentration each, diluted in mineral oil (all odorants from Sigma-Aldrich except acetophenone, from Alfa Aesar). For the acute exposure experiments, the odor mix was added to the water bottles of the animals; mineral oil alone was used for controls. Water bottles were replaced twice a week with freshly prepared ones. The exposure started from at least embryonic day (E)14.5 and the WOM was collected from age-matched exposed and control groups at different time-points after the start of the treatment. For the chronic exposure experiments, the odor mixture or mineral oil only, were applied to a cotton ball with a plastic pasteur pipette; these were put into metal tea strainers that were then introduced into the cage of the animals. The cotton ball was replaced fresh daily. The exposure started from birth and the WOM was collected from age-matched exposed and control groups at different time-points after the start of the treatment.
For the follow-up experiments, animals were acutely exposed only to (R)-carvone, or heptanal, or to the combination of both. The final concentration of each odorant was 1 mM. The odorants were directly added to the water bottles, without dilution in mineral oil. Therefore, the controls were kept with pure water. The water bottles were changed twice a week. The exposure started from at least E16.5 and the WOM was collected at 10 weeks of age.
For qRT-PCR experiments, RNA from WOM was extracted as previously described. 1 μg of RNA was reversed-transcribed into cDNA using the High-Capacity RNA-to-cDNA kit (Applied Biosystems, Waltham, Massachusetts) with the manufacturer’s protocol. Predesigned TaqMan gene expression assays were used on a 7900HT Fast Real-Time PCR System (ThermoFisher Scientific) following the manufacturer’s instructions. Mean cycle threshold (Ct) values were obtained from two technical replicates, each normalized to Actb using the ΔCt method. Relative quantity (RQ) values were calculated using the formula RQ = 2ΔCt. Differential expression between groups was tested in R, by a two-tailed t-test, with multiple-testing correction by the Benjamini and Hochberg (FDR) method.
For OR response in vitro, a Dual-Glo Luciferase Assay System (Promega) was employed using the previously described method (Zhuang and Matsunami, 2008). Modified HEK293T cells, Hana3A cells, were obtained directly from the Matsunami Laboratory. Cell line identity and negative mycoplasma status was confirmed by PCR after the completion of all experiments. Cells were plated on 96-well PDL plates (ThermoFisher Scientific) for transfection with 5 ng/well of RTP1S-pCI (Saito et al., 2004; Zhuang and Matsunami, 2007), 5 ng/well of pSV40-RL, 10 ng/well pCRE-luc, 2.5 ng/well of M3-R-pCI (Li and Matsunami, 2011), and 5 ng/well of plasmids encompassing the six olfactory receptors of interest. (R)-carvone and heptanal (Sigma-Aldrich) were diluted to a 1 mM solution in CD293 (ThermoFisher Scientific) from 1M stocks in DMSO. 24 hr following transfection, we applied 10-fold serial dilutions of each odorant from 1 mM to 1 nM in triplicate. Luminescence was measured after a 4-hr odor stimulation period using a Synergy two plate reader (BioTek, Winooski, Vermont). Transfection efficiency was controlled for by normalizing all luminescence values by the Renilla luciferase activity. The data were fit to a sigmoidal curve and every OR-odorant pair was compared to a vector-only control using an extra sums-of-squares F test (significantly different from empty vector if p<0.05, the s.d. of the fitted log(EC50) was less than one log unit, and the 95% confidence intervals of the top and bottom parameters did not overlap). Data were analyzed with GraphPad Prism 7.00 and R.
Three- to four-week old C57BL/6 mice were placed individually into sealed containers (volume ≈ 2.7L) inside a fume hood and allowed to rest for 1 hr in an odorless environment. For odor stimulus, 10 µl odor solution or 10 µl distilled water (control) was applied to 1cm × 1cm filter paper held in a cassette. The cassette was placed into a new mouse container into which the mouse was also transferred, and the mouse was exposed to the odor solution or control for 1 hr. Experiments were performed in triplicates or quadruplicates, and within each replication the experimental and control mice were littermates of the same sex.
Following odor stimulation, the mouse was sacrificed and the OE was dissected in 25 ml of dissection buffer (1 × HBSS ( with Ca2+ and Mg2+), 2.5 mM HEPES (pH 7.4 adjusted with KOH), 35 mM glucose, 100 µg/ml cycloheximide, 5 mM sodium fluoride, 1 mM sodium orthovanadate, 1 mM sodium pyrophosphate, 1 mM beta–glycerophosphate) on ice. The dissected OE was transferred to 1.35 ml homogenization buffer (150 mM KCl, 5 mM MgCl2, 10 mM HEPES (pH 7.4 adjusted with KOH), 100 nM Calyculin A, 2 mM DTT, 100 U/ml RNasin (Promega), 100 µg/ml cycloheximide, 5 mM sodium fluoride, 1 mM sodium orthovanadate, 1 mM sodium pyrophosphate, 1 mM beta–glycerophosphate, protease inhibitor (Roche, one tablet/10 ml)) and homogenized three times at 250 rpm and nine times at 750 rpm. The homogenate was transferred to a 1.5 ml lobind tube (Eppendorf, Germany), and centrifuged at 4600 rpm for 10 min at 4°C. The supernatant was then transferred to a new 1.5 ml lobind tube, to which 90 µl 10% NP–40 and 90 µl 300 mM DHPC (Avanti Polar Lipids, Alabaster, Alabama) was added. The mixture was centrifuged at 13,000 rpm for 10 min at 4°C. The supernatant was transferred to a new 1.5 ml lobind tube, and mixed with 20 µl pS6 antibody (Cell Signaling Technology, Danvers, Massachusetts). Antibody binding was allowed by incubating the mixture for 1.5 hr at 4°C with rotation. During antibody binding, Protein A Dynabeads (ThermoFisher Scientific, 100 µl/sample) was washed three times with 900 µl beads wash buffer 1 (150mM KCl, 5 mM MgCl2, 10 mM HEPES (pH 7.4 adjusted with KOH), 0.05% BSA, 1% NP–40). After antibody binding, the mixture was added to the washed beads and gently mixed, followed by incubation for 1 hr at 4°C with rotation. After incubation, the RNA-bound beads were washed 4 times with 700 µl beads wash buffer 2 (RNase free water containing 350 mM KCl, 5 mM MgCl2, 10 mM HEPES (pH 7.4 adjusted with KOH), 1% NP–40, 2 mM DTT, 100 U/ml recombinant RNasin (Promega), 100 µg/ml cycloheximide, 5 mM sodium fluoride, 1 mM sodium orthovanadate, 1 mM sodium pyrophosphate, 1 mM beta–glycerophosphate). During the final wash, beads were placed onto the magnet and moved to room temperature. After removing supernatant, RNA was eluted by mixing the beads with 350 µl RLT (Qiagen). The eluted RNA was purified using RNeasy Micro kit (Qiagen). Chemicals were purchased from Sigma if not specified otherwise.
1.5 µl purified RNA was mixed with 5 µl reaction mix (1× PCR buffer (Roche), 1.5 mM MgCl2, 50 µM dNTPs, 2 ng/µl poly–T primer (TATAGAATTCGCGGCCGCTCGCGATTTTTTTTTTTTTTTTTTTTTTTT), 0.04 U/µl RNase inhibitor (Qiagen), 0.4 U/µl recombinant RNasin (Promega)). This mixture was heated at 65°C for 1 min and cooled to 4°C. 0.3 µl RT mix (170 U/µl Superscript II (ThermoFisher Scientific), 0.4 U/µl RNase inhibitor (Qiagen), 4 U/µl recombinant RNasin (Promega), 3 µg/µl T4 gene 32 protein (Roche)) was added to each tube and incubated at 37°C for 10 min then at 65°C for 10 min. 1 µl ExoI mix (2 U/µl ExoI (NEB), 1× PCR buffer (Roche), 1.5 mM MgCl2) was added to each tube and incubated at 37°C for 15 min then 80°C for 15 min. 5 µl TdT mix (1.25 U/µl TdT (Roche), 0.1 U/µl RNase H (ThermoFisher Scientific), 1× PCR buffer (Roche), 3 mM dATP, 1.5 mM MgCl2) was added to each tube and incubated at 37°C for 20 min then 65°C for 10 min. 3.5 µl of the product was added to 27.5 µl PCR mix (1× LA Taq reaction buffer (TaKaRa, Japan), 0.25 mM dNTPs, 20 ng/µl poly–T primer, 0.05 U/µl LA Taq (TaKaRa)) and incubated at 95°C for 2 min, 37°C for 5 min, 72°C for 20 min, then 16 cycles of 95°C for 30 s, 67°C for 1 min, 72°C for 3 min with 6 s extension for each cycle, and then 72°C for 10 min. The PCR product was purified by gel purification, and 50 ng of the purified product was used for library preparation with Nextera DNA Sample Prep kits (Illumina). Libraries were sequenced on a HiSeq 2000/2500 (12 libraries pooled per lane) to produce 50 base pair single-end reads. The sequencing data have been deposited in the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) under accession GSE87695.
Short reads were aligned to the mouse reference genome mm10 using Bowtie (Langmead et al., 2009). The reads mapped to annotated genes were then counted using BEDTools (Quinlan and Hall, 2010); the gene models for OR genes were replaced by those reported in Ibarra-Soria et al. (2014). A rescuing scheme was used as implemented in Jiang et al. (2015) (code available at https://github.com/Yue-Jiang/RNASeqQuant (Jiang, 2017) with a copy archived at https://github.com/elifesciences-publications/RNASeqQuant). The read count tables were then analyzed using EdgeR (Robinson et al., 2010) to identify differentially expressed ORs.
Transcriptome of the MOE in odour exposed micePublicly available at the EMBL-EBI European Nucleotide Archive (accession no: PRJEB5984).
Transcriptome of the MOE in different mouse strainsPublicly available at the EMBL-EBI European Nucleotide Archive (accession no: PRJEB5013).
Transcriptome of the olfactory system of newborn micePublicly available at the EMBL-EBI European Nucleotide Archive (accession no: PRJEB1607).
RNAseq of an Olfr2>Olfr1507 mouse MOEPublicly available at the EMBL-EBI European Nucleotide Archive (accession no: PRJEB13659).
Transcriptome of the CAST MOEPublicly available at the EMBL-EBI European Nucleotide Archive (accession no: PRJEB6462).
Single cell transcriptomics in olfactory sensory neuronsPublicly available at the EMBL-EBI European Nucleotide Archive (accession no: PRJEB4461).
Postnatal odorant exposure induces peripheral olfactory plasticity at the cellular levelJournal of Neuroscience 34:4857–4870.https://doi.org/10.1523/JNEUROSCI.0688-13.2014
Early survival factor deprivation in the olfactory epithelium enhances activity-driven survivalFrontiers in Cellular Neuroscience 7:271.https://doi.org/10.3389/fncel.2013.00271
Molecular profiling of activated olfactory neurons identifies odorant receptors for odors in vivoNature Neuroscience 18:1446–1454.https://doi.org/10.1038/nn.4104
Temporal patterns of odorant receptor gene expression in adult and aged miceMolecular and Cellular Neuroscience 57:120–129.https://doi.org/10.1016/j.mcn.2013.08.001
Do you smell what I smell? genetic variation in olfactory perceptionBiochemical Society Transactions 42:861–865.https://doi.org/10.1042/BST20140052
The missense of smell: functional variability in the human odorant receptor repertoireNature Neuroscience 17:114–120.https://doi.org/10.1038/nn.3598
Achieving singularity in mammalian odorant receptor gene choiceChemical Senses 35:447–457.https://doi.org/10.1093/chemse/bjq041
Chromosomal location-dependent nonstochastic onset of odor receptor expressionJournal of Neuroscience 30:10067–10075.https://doi.org/10.1523/JNEUROSCI.1776-10.2010
The promoter of the mouse odorant receptor gene M71Molecular and Cellular Neuroscience 28:535–546.https://doi.org/10.1016/j.mcn.2004.11.006
MEGA6: molecular evolutionary genetics analysis version 6.0Molecular Biology and Evolution 30:2725–2729.https://doi.org/10.1093/molbev/mst197
Homeodomain binding motifs modulate the probability of odorant receptor gene choice in transgenic miceMolecular and Cellular Neuroscience 46:381–396.https://doi.org/10.1016/j.mcn.2010.11.001
The structure of the nervous system of the nematode caenorhabditis elegansPhilosophical Transactions of the Royal Society B: Biological Sciences 314:1–340.https://doi.org/10.1098/rstb.1986.0056
Extensive copy-number variation of the human olfactory receptor gene familyThe American Journal of Human Genetics 83:228–242.https://doi.org/10.1016/j.ajhg.2008.07.005
Synergism of accessory factors in functional expression of mammalian odorant receptorsJournal of Biological Chemistry 282:15284–15293.https://doi.org/10.1074/jbc.M700386200
The cellular and molecular basis of odor adaptationChemical Senses 25:473–481.https://doi.org/10.1093/chemse/25.4.473
Kristin ScottReviewing Editor; University of California, Berkeley, Berkeley, United States
In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.
Thank you for submitting your article "Variation in olfactory neuron repertoires is genetically controlled and environmentally modulated" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Patricia Wittkopp as the Senior Editor. The reviewers have opted to remain anonymous.
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
The authors performed a comprehensive analysis for expression levels of over 100 odorant receptors (ORs) in different mouse strains. They use the large data sets to explore individual- and strain-specific variation in OR expression, the putative role of cis-regulatory elements in choice probability, and the influence of long-term environmental exposure to odors on the OR repertoire. The comprehensiveness is the appealing point of this paper, and the study is thorough and well done. Thus, the major advancement in this paper is to expand data that had been available only from a limited number of ORs in previous studies.
1) The authors show that OR gene counts may be used as a proxy for OSN number within a single genetic background and infer that this holds for other strains. To make the case that cell number changes (rather than expression levels per cell) vary between strains, the authors need to validate more OR genes by in situ hybridizations in the different strains. Alternatively, single cell RNAseq could be used to demonstrate the variability in expression for neurons expressing the same OR within and across strains. The conclusions of the manuscript are significantly weakened without a more convincing validation that across-species DE can be used to infer species-specific OSN numbers.
2) A major claim of this paper derives from the observation that each allele of F1 hybrids expresses ORs at levels similar to that expressed by the parental strains. This leads to the claim that "choice" requires regulation in cis but not in trans. There are three reasons why this claim is not supported by the evidence. First, although Figure 4F seems more or less linear, there is substantial diversity (in log space) about the equality line, and no r-squared or equivalent metric is calculated. In addition to remediating the statistical deficits in this key figure, the deviation from unity is important and not commented upon; indeed it is unclear how the authors might interpret this deviation conceptually (i.e., how much deviation would "disprove" their hypothesis that regulation is in cis). Second, the broad (and nearly wholly unexplained) influence of an OR sequence on the expression of 10% of the OR repertoire means that there is no way to disambiguate OR sequence effects from enhancer/promoter effects (particularly since many points on 4F fall off the unity line). Third, even accepting that the data show a clear influence of cis elements on choice frequency (which is likely but not definitively shown given the data), these experiments do not afford any insight regarding the potential role for trans-interactions in choice per se; they only show that choice frequency is determined in cis (and leave open the possibility that trans-interactions are permissive for choice and therefore are critical for the singularity of gene expression), which was the prevailing model even before these data were generated. Indeed the authors repeatedly invoke trans-interactions in the paper as a possible explanation for various aspects of the data, despite strong claims that trans-interactions are not important for "choice". Constraining the interpretation of these data (largely through clarifying the language throughout to make clear that by "choice" what is meant is "choice probability") will more fairly contextualize this result. Note, also, that the definitive experiment here is one that was not performed: swapping the cis-elements between species (rather than two OR cDNAs within a species) to show that polymorphisms in cis-elements determine choice frequency.
3) The relevance of odor exposure experiments in Figure 5 and 6 is unclear, as we really don't know how the odor exposures differed from the perspective of the animal nor do we understand the perceptual consequences of these gene expression changes. Furthermore, the interpretation of these experiments is also incomplete – the receptors seem to be bidirectionally modulated (in equal measure), and only a fraction of these receptors are associated with pS6 signals.
4) In Figure 4E, the coding sequence swap of Olfr1507 with that of Olfr2 significantly changed the expression of ~10% of OR genes. This broad change in the expression of the OR repertoire due to swapping single OR gene apparently contradicts to the main claim of this paper, cis-elements determine the OR choice frequency. Is it possible that the odd gene expression effects of the CRISPR experiment reflect off-target indel formation in the OR repertoire? Was any targeted genomic sequencing done to address this concern? Is genomic architecture altered in these animals, consistent with a fundamental rearrangement of trans-interactions (which would be very interesting)?; this could be assessed by chromosome conformation capture experiments (4C against Olfr1507 promoter or enhancer or Hi-C), but performing these experiments would be only important if the authors decided to make this the focus of a revised manuscript.
[Editors' note: further revisions were requested prior to acceptance, as described below.]
Thank you for resubmitting your work entitled "Variation in olfactory neuron repertoires is genetically controlled and environmentally modulated" for further consideration at eLife. Your revised article has been favorably evaluated by Patricia Wittkopp (Senior editor), a Reviewing editor and one reviewer.
The manuscript has been greatly improved but there are minor textual changes requested to discuss the potential role of coding and non-coding sequences (see below). These text revisions should be straightforward to complete and I do not anticipate that they will cause a significant time delay.
The paper entitled "Variation in olfactory neuron repertoires is genetically controlled and environmentally modulated" is a resubmission from Logan et al. that uses RNASeq and genetics to probe the diversity of OR expression in the olfactory epithelium. This manuscript is substantially improved from the previous submission, and in particular now appropriately characterizes what is learned from the paper. The improved statistical analyses are also very helpful. However, there are a couple small points that are worth revisiting through text revisions before publication of this very nice paper.
The authors seem at pains to argue that the primary sequence of each OR does not contribute to the pattern of expression of that OR, but I'm still having trouble seeing the argument clearly. Just to be transparent about my point of view here, I think the authors have done a very nice job of showing that sequences that are linked to the OR are instructive for choice frequency. I just don't see any evidence that rules out OR primary sequences in this process, and indeed see some evidence that it may play a role, for the following reasons:
a) the fact that similar protein sequences are differentially regulated across strains (the main argument leveled here) doesn't rule out the converse – that distinct protein sequences might contribute to differential regulation.b) The attempt to call the differences in OR expression in the receptor swap experiment "subtle" and therefore not important ("the extensive variance…is independent of the coding sequence […] of the OR […]") is really a qualitative judgment rather than a quantitative argument, especially given that in the same manuscript similar effect sizes are argued to be relevant in the context of odor exposures, and given the argument in the discussion that fold changes in OR expression have perceptual meaning.c) Most importantly, unless I really am missing something, the F1 analysis doesn't distinguish coding from non-coding effects, as these are linked in the intercross.
I think some additional circumspection is merited here. The authors have the benefit that the model they favor – that cis elements are instructive and that protein sequences don't matter that much if at all – is likely to be right. It is just that given the data assembled here this argument seems less definitive than the authors seek to make it.https://doi.org/10.7554/eLife.21476.034
1) The authors show that OR gene counts may be used as a proxy for OSN number within a single genetic background and infer that this holds for other strains. To make the case that cell number changes (rather than expression levels per cell) vary between strains, the authors need to validate more OR genes by in situ hybridizations in the different strains.
Although it might appear there are a multitude of ORs to select from, identifying appropriate receptors to compare by in situ hybridization is surprisingly difficult from a technical perspective, because it is imperative that:
1) They must be selected from across the dynamic range of expression (yet the vast majority of receptors are expressed at very low levels, see Figure 1A)
2) The probes must not cross-hybridize to any other receptors within each strain (yet most ORs have high sequence identity to at least one other OR)
3) The probes must equally hybridize to the same receptor transcript between strains (yet there are over 15,000 distinct sequence variations in ORs between the strains we tested)
4) The ORs must be differentially expressed between strains by RNAseq (yet two thirds of the repertoire are not, see Figure 2C-D).
Through a process of elimination we identified 6 receptor genes that best met these criteria across two strains (B6 and 129). When we analysed these data we found almost perfect correlations between OSN counts and RNAseq expression data within a strain for both B6 (Figure 1D) and 129. To make more evident that this holds across strains, we have explicitly stated this in the text and now present in Figure 2—figure supplement 2B the same plot shown in Figure 1D but with the data from 129 animals. We further showed very strong correlations between the fold change in the expression and OSN counts across strains (Figure 2I-K). We believe these data strongly support the conclusion that OSN counts differ between strains and that RNAseq approximates these differences.
To further supplement this analysis we have now identified and carried out in situ hybridization on three further receptor genes, including Olfr78 which is not significantly different in the RNAseq data and Olfr124 and Olfr1512 that have smaller differences between the strains. All data points show a strong linear relationship and the fold-change values estimated with either technique are strongly correlated (Spearman’s rho = 0.98, p = 0.00005) These additional data confirm our correlations, and now show, in Figure 2K, that for 9 different ORs the differences in RNAseq expression between strains are a proxy for the differences in OSN numbers.
Alternatively, single cell RNAseq could be used to demonstrate the variability in expression for neurons expressing the same OR within and across strains. The conclusions of the manuscript are significantly weakened without a more convincing validation that across-species DE can be used to infer species-specific OSN numbers.
We agree that single cell RNAseq would be an elegant and powerful tool to further address this question. It is something we have considered, however our previous experience in single cell RNAseq of OSNs and VSNs (Saraiva, Ibarra-Soria et al. 2015, Untiet et al. 2016) taught us that it would be a prohibitively lengthy and expensive undertaking. All the existing genetic tools that fluorescently label specific OSN types were generated on mixed genetic backgrounds, making them unsuitable for these experiments. Thus taking a targeted approach would require the de novo generation of many new mouse lines. An approach involving random selection of neurons would require many thousands of OSNs to be hand-picked and sequenced to ensure the same OSN types were represented enough times from each strain. We concluded that this approach is not feasible with the technology currently available.
2) A major claim of this paper derives from the observation that each allele of F1 hybrids expresses ORs at levels similar to that expressed by the parental strains. This leads to the claim that "choice" requires regulation in cis but not in trans. There are three reasons why this claim is not supported by the evidence. First, although Figure 4F seems more or less linear, there is substantial diversity (in log space) about the equality line, and no r-squared or equivalent metric is calculated.
We plotted the axes in Figure 4F in such a way that if OR choice is only regulated in cis, the data would be distributed diagonally along a 45 degree line. If choice is only regulated in trans, the data would be distributed vertically along a 90 degree line. As the reviewers note, collectively the data is distributed diagonally along the equality (45 degree) line. We have now indicated this as a metric in Figure 4F, as requested, by calculating the concordance correlation coefficient (ccc=0.86) which, in contrast to r-squared, adjusts for agreement on the x=y line (a value of 1 equals perfect concordance (Lin, 1989)). These data supports our conclusion that, taken as a whole, the OR repertoire is regulated in cis. The novelty of this analysis is that it captures over 800 ORs, whereas previous studies have focused on single or a handful of ORs. This scale is important because, as the reviewers also note, there is individual OR diversity from the equality line; thus drawing conclusions from any single or small number of ORs could be misleading.
In addition to remediating the statistical deficits in this key figure, the deviation from unity is important and not commented upon; indeed it is unclear how the authors might interpret this deviation conceptually (i.e., how much deviation would "disprove" their hypothesis that regulation is in cis).
To ascertain whether the observed deviation from the equality line is biologically meaningful or technical noise from the complexity of distinguishing alleles from each other, we carried out an in silico pseudo F1-cross. In essence we combined RNAseq read data from B6 and CAST F0 mice then down-sampled by 50% to approximate expression from one allele. We then carried out the same analysis as in Figure 4F. We now show in Figure 4—figure supplement 1B that in this in silico F1 the data falls largely along the 45 degree line but, as with the real F1 data, there is still some deviation (ccc=0.95). This variance can be attributed to technical noise, since we know the data contains a 50:50 proportion of the parental data. We computed the deviation from the equality line for each gene and used this measurement to correct the real F1 data. The resulting plot is now shown in Figure 4—figure supplement 1C. We have used the concordance correlation coefficient (ccc) to measure the correlation of the fold-change estimates, while accounting for agreement on the x=y line; this is further quantified by the Cb parameter, which indicates how far the best fit to the data deviates from the 45 degree line. The corrected real data has a ccc = 0.92 and a Cb = 0.999 which indicates that the data, as a whole, is best fit by the x=y line and thus shows a pattern entirely consistent with regulation in cis. Our interpretation of this additional analysis is that the observed deviation is technical noise but we cannot exclude a very small contribution from trans-acting elements. We have now indicated this in subsection “OSN Diversity Profiles are Independent of OR Function and are Controlled in cis”.
Second, the broad (and nearly wholly unexplained) influence of an OR sequence on the expression of 10% of the OR repertoire means that there is no way to disambiguate OR sequence effects from enhancer/promoter effects (particularly since many points on 4F fall off the unity line).
We acknowledge that the influence of a single OR sequence on differential representation of 10% of the OR repertoire is consistent with trans regulation (subsection “OSN Diversity Profiles are Independent of OR Function and are Controlled in cis”). However, the size of the enhancer/promoter effect (a 47 fold difference) compared to the OR sequence effects (<2 fold) does disambiguate them, suggesting that any trans influence is subtle (Figure 4E). Our hypothesis to explain this unexpected observation, which we have now further elaborated on in subsection “The MOE is a genetically-determined mosaic of OSN subtypes”, is that the artificial transposition of an OSN type to a different olfactory zone (the consequence of the OR swap) will undoubtedly influence the dynamics and spatial orientation of its projections to form glomeruli in the olfactory bulb. The impact of this transposition on projection routes, and the size, shape and location of neighbouring glomeruli could conceivably influence the proliferation and survival of other OSNs during development, and thus explain the subtle differences in OR expression we observe. This process would occur post-“choice” and thus be independent of cis/trans regulation of OR expression. Nevertheless, we accept that extensive, albeit subtle, trans effects is an alternative hypothesis. This could also potentially explain the deviations from the line of equality in Figure 4F. We have altered our conclusions to address this possibility.
Third, even accepting that the data show a clear influence of cis elements on choice frequency (which is likely but not definitively shown given the data), these experiments do not afford any insight regarding the potential role for trans-interactions in choice per se; they only show that choice frequency is determined in cis (and leave open the possibility that trans-interactions are permissive for choice and therefore are critical for the singularity of gene expression), which was the prevailing model even before these data were generated. Indeed the authors repeatedly invoke trans-interactions in the paper as a possible explanation for various aspects of the data, despite strong claims that trans-interactions are not important for "choice". Constraining the interpretation of these data (largely through clarifying the language throughout to make clear that by "choice" what is meant is "choice probability") will more fairly contextualize this result.
We disagree that our data does not show a definitive influence of cis elements on choice frequency. We are unaware of another credible hypothesis to explain the collective distribution of the data in Figure 4F, other than through cis regulation of OR choice. Indeed, the same approach has been used to distinguish cis from trans regulation in a number of other studies (e.g. Goncalves et al. 2012; Emmerson et al. 2010). We acknowledge that trans-interactions may indeed stabilize or maintain OR singularity, and are also clear where our data is consistent with, and inconsistent with, previously proposed models (subsection “The MOE is a genetically-determined mosaic of OSN subtypes”). As indicated above, we have now added additional text to temper our conclusions and discuss the possibility of trans-regulation explaining the observations from Figure 4E. We have also clarified our language around the use of the word “choice” throughout the manuscript, as suggested. Together we believe these edits more fairly contextualise our results.
Note, also, that the definitive experiment here is one that was not performed: swapping the cis-elements between species (rather than two OR cDNAs within a species) to show that polymorphisms in cis-elements determine choice frequency.
We agree that, conceptually, this would be an ideal experiment. However unlike the cDNA sequence of an OR, which has a clear functional beginning and end point, the promoters and enhancers that control OR choice are ill-defined. We therefore felt we could not clearly interpret the results of a promoter-swap experiment as the possibility would always remain that only part of the promoter was transposed. We investigated swapping one of the few known, better defined OR enhancer elements between strains. However bioinformatic analysis revealed that, between the strains, there was substantial differences in the distance between the enhancer elements and their neighbouring OR cluster. Considering strains we have expression data for, there appears to be an association between distance of an element from the cluster and the abundances of the OR expression within that cluster. Given these data, we felt it is likely that both cis-element sequence and its distance-from-OR were potential factors in controlling cis regulation. We did not consider it technically feasible to genetically engineer the multiple mouse lines necessary to control for these multiple variables.
The purpose of the odor exposure experiments to our study were two-fold. Firstly, we were aware that different strains of mice have different odortypes, and that this could influence OSN subtype repertoire. Hence we first carried out the experiments in Figure 3 to test this directly. We clearly showed that long term exposure to different strain odor-types did not influence the OSN repertoire (Figure 3B). This result appeared to directly conflict the findings by Santoro and Dulac (eLife, 2012), who reported a mechanism through which long term odor exposure should theoretically alter OSN subtype representation in an OR-specific manner. To reconcile these apparent differences we carried out the experiments in Figure 5, to establish that the exposure paradigm critically determines the effect on OR abundance, and Figure 6, to demonstrate this effect is ligand and OR-specific.
Secondly, other previous studies had reported that long-term odor exposure had variously increased, decreased or had no effect on the number of OSN subtypes. However each of these studies used different exposure methods and measured only a single, or a handful of ORs. Our approach permits, for the first time, analysis of the entire repertoire in parallel. As the reviewers note, we demonstrate reproducible bidirectional changes in an OR-specific manner (Figure 5A-C, 6A-B). We also show that changing the exposure paradigm alters whether these changes occur or are maintained (Figure 5D-E). Thus we have reconciled the conflicting literature by exploiting the power afforded by large-scale analysis. We agree that we do not know how the odor exposures differ in terms of perception, and acknowledge this in the text. However, we do not consider this to be critical for the purposes of the experiment.
Nor do we understand the perceptual consequences of these gene expression changes.
In this manuscript, we do not functionally investigate the perceptual consequences of either the genetically-instructed variance in OSN repertoire nor the influence of environmental exposure. Because individual odors activate multiple ORs, it is near impossible to devise controlled experiments to investigate how differences in individual OSN subtype representation alter odor perception using wild-type strains. We anticipated that the Olfr2>Olfr1507 line could be uniquely suited for this purpose, but the unexpected changes in the representation of over 100 other OSN subtypes meant its usefulness for phenotypic studies was compromised. However, recent work from D’Hulst et al. 2016 has demonstrated that odor sensitivity increases when the number of responsive OSNs in the nose is greater. Thus we speculate on how the differences we catalogue in this study may alter the perception of smell (subsection “An individually unique olfactory nose”).
Furthermore, the interpretation of these experiments is also incomplete – the receptors seem to be bidirectionally modulated (in equal measure).
We acknowledge that the bidirectional modulation is mechanistically puzzling (although not inconsistent with the existing literature). Despite our best efforts we have been unable to find any pattern or logic that may distinguish the receptors that are up-regulated from those that are down-regulated, thus we do not have a credible hypothesis to test. Moreover, the time-frames involved in each long-term exposure experiment prohibit using this technique as a fishing expedition. For example, the completion of the experiments in Figures 5 and 6 took a number of years.
And only a fraction of these receptors are associated with pS6 signals.
Indeed, 55% of them were associated with pS6 signals, a highly statistically significant enrichment (p=0.0001). A number of other ORs identified in each of the experiments were just above the p-value threshold in the other (examples of this can be seen just below the dotted line in Figure 6G), thus marginally relaxing the somewhat arbitrary p-value cut-offs would increase the proportion of associated ORs. Moreover we do not consider the lack of a complete association between receptors identified using the two techniques to be unexpected, because the odor exposure and RNA processing methods used for each experiment differed. We purposely applied the exposure method previously reported for pS6 immunoprecipitation (Jian et al. 2015) as it had been optimised for receptor deorphanisation, and the goal of this experiment was to prove that the DE ORs in the long term experiments were linked to direct odor activation. The highly significant statistical association between the ORs identified in the two methods supports this conclusion.
4) In Figure 4E, the coding sequence swap of Olfr1507 with that of Olfr2 significantly changed the expression of ~10% of OR genes. This broad change in the expression of the OR repertoire due to swapping single OR gene apparently contradicts to the main claim of this paper, cis-elements determine the OR choice frequency.
As detailed in our response to point 2.3 above, we believe there are other possible explanations for this observation, especially when considered in the context of the other data in our manuscript. Nevertheless, we agree that a relatively subtle effect on trans regulation is an alternative hypothesis and have altered our conclusions to address this possibility.
Is it possible that the odd gene expression effects of the CRISPR experiment reflect off-target indel formation in the OR repertoire? Was any targeted genomic sequencing done to address this concern?
Although we did not carry out additional targeted sequencing, our analysis suggests this possibility is unlikely. When designing our CRISPR guide RNAs (gRNAs) we identified the coordinates of potential off-target binding sites; the gRNAs we chose showed very few off-targets in exonic regions and none in other OR genes. We have now further applied two more tools to predict potential off-targets –CCTop (Stemmer et al. PLoS One 2015) and Breaking-Cas (Olvieros et al. NAR 2016) – and obtained 84 and 193 for the upstream gRNA (u-gRNA) and 132 and 206 for the downstream gRNA (d-gRNA), respectively:
|in OR exons||0||0||0||0|
|within 10kb of OR genes||0||1||1||2|
Only three OR genes have a predicted off-target site within 10kb and of these, only one (Olfr1258) is significantly differentially expressed. In this case, the predicted off-target cleavage site lies within the 3’ UTR; we have inspected the sequencing reads spanning the predicted site in all the Olfr2>Olfr1057 animals and we did not identify any evidence of edits. In addition, we inspected the sequence reads spanning the three other off-target sites within exons and again found no edits were present. Thus, we consider very unlikely that off-target effects could explain the observed expression changes in OR genes.
Is genomic architecture altered in these animals, consistent with a fundamental rearrangement of trans-interactions (which would be very interesting)?; this could be assessed by chromosome conformation capture experiments (4C against Olfr1507 promoter or enhancer or Hi-C), but performing these experiments would be only important if the authors decided to make this the focus of a revised manuscript.
We cannot rule out alterations to the genomic architecture have occurred upon the swap of the coding sequences of these OR genes. However, the proposed experiments are technically challenging because it would be necessary to isolate the OSN population from the other cell types in the WOM. The Olfr2>Olfr1507 line does not carry an OSN-subtype specific or a pan-OSN marker to facilitate this, and we are not aware of an appropriate marker line in a pure B6 genetic background that we could inter-cross with.
[Editors' note: further revisions were requested prior to acceptance, as described below.]
The paper entitled "Variation in olfactory neuron repertoires is genetically controlled and environmentally modulated" is a resumbission from Logan et al. that uses RNASeq and genetics to probe the diversity of OR expression in the olfactory epithelium. This manuscript is substantially improved from the previous submission, and in particular now appropriately characterizes what is learned from the paper. The improved statistical analyses are also very helpful. However, there are a couple small points that are worth revisiting through text revisions before publication of this very nice paper.
The authors seem at pains to argue that the primary sequence of each OR does not contribute to the pattern of expression of that OR, but I'm still having trouble seeing the argument clearly. Just to be transparent about my point of view here, I think the authors have done a very nice job of showing that sequences that are linked to the OR are instructive for choice frequency. I just don't see any evidence that rules out OR primary sequences in this process, and indeed see some evidence that it may play a role, for the following reasons:
a) the fact that similar protein sequences are differentially regulated across strains (the main argument leveled here) doesn't rule out the converse – that distinct protein sequences might contribute to differential regulation.b) The attempt to call the differences in OR expression in the receptor swap experiment "subtle" and therefore not important ("the extensive variance […] is independent of the coding sequence […] of the OR […]") is really a qualitative judgment rather than a quantitative argument, especially given that in the same manuscript similar effect sizes are argued to be relevant in the context of odor exposures, and given the argument in the discussion that fold changes in OR expression have perceptual meaning.
We recognise the point the reviewer makes from the way we phrased our interpretation. We believe our data, particularly the analysis of the Olfr2>Olfr1507 animals in Figure 4D, E, strongly argues that the primary sequence/function of the OR expressed in a particular subtype does not instruct the abundance of that particular subtype. In other words, when Olfr2 is expressed in (what would otherwise be) Olfr1507-expressing neurons, their abundance does not change to the levels of Olfr2-expressing neurons. The fact that identical (not similar) protein sequences are differentially regulated across strains is consistent with this conclusion.
However, we agree that the differences in other ORs expressed in the Olfr2>Olfr1507 animals is consistent with the Olfr2 primary sequence influencing the choice of other ORs, but importantly not Olfr2 itself. We realise that we did not make clear this distinction in the text, and have now done so at multiple points.:
In subsection “OSN Diversity Profiles are Independent of OR Function and are Controlled in cis.”: “Together these results suggest that the proportion of each OSN subtype is not dependent on its endogenous OR receptor activity.”
In the same subsection: “the extensive variance in OSN subtype composition we observe <strike>within and between</strike> in mice is determined by the wider genetic architecture of the animal, and is independent of the <strike>coding sequence and</strike> function of the OR protein each subtype expresses.
Discussion, first paragraph: “OSN diversity is predominantly controlled by genetic elements that act in cis, and the abundance of a given OSN subtype is not affected by <strike>receptor</strike> the sequence or function of the receptor it expresses, in a sustained olfactory environment.
Subsection “The MOE is a genetically-determined mosaic of OSN subtypes.”: “In this case the sequence of an OR receptor gene would influence the relative abundance of other OSN subtypes, but our Olfr2>Olfr1507 swap experiment suggests the sequence of an OR receptor does not dictate the abundance of its own OSN subtype.”
We used the term “subtle” only to compare it to the scale of the effect observed in the swapped receptor, not to diminish the potential functional consequences. Indeed, we also use “subtle” to describe the environmental effect in our Abstract. To make this clearer, we now use “comparatively subtle” instead.
c) Most importantly, unless I really am missing something, the F1 analysis doesn't distinguish coding from non-coding effects, as these are linked in the intercross.
Reviewer #2 is correct, the F1 analysis does not provide insight into coding from non-coding effects.https://doi.org/10.7554/eLife.21476.035
- Ximena Ibarra-Soria
- Jingtao Lilue
- Mairi Kusma
- Andrea Kirton
- Luis R Saraiva
- Thomas M Keane
- Darren W Logan
- Fabio Papes
- Fabio Papes
- Darren W Logan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
We thank M Khan and P Mombaerts for providing RNA from the Olfr7 cluster deletion mouse line that was used in Figure 1B; G Gurria and SS Gerety for experimental support and insightful discussions; and the Sanger Institute Research Support Facility, microinjection and sequencing pipeline staff for invaluable technical support.
Animal experimentation: The breeding of mice and experimental procedures were carried out under the authority of a UK Home Office license (80/2472), after review and approval by the Animal Welfare and Ethical Review Body of the Wellcome Trust Sanger Institute.
- Kristin Scott, University of California, Berkeley, Berkeley, United States
- Received: September 13, 2016
- Accepted: March 21, 2017
- Version of Record published: April 25, 2017 (version 1)
© 2017, Ibarra-Soria et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Previously, we described a large collection of Drosophila strains that each carry an artificial exon containing a T2AGAL4 cassette inserted in an intron of a target gene based on CRISPR-mediated homologous recombination. These alleles permit numerous applications and have proven to be very useful. Initially, the homologous recombination-based donor constructs had long homology arms (>500 bps) to promote precise integration of large constructs (>5 kb). Recently, we showed that in vivo linearization of the donor constructs enables insertion of large artificial exons in introns using short homology arms (100–200 bps). Shorter homology arms make it feasible to commercially synthesize homology donors and minimize the cloning steps for donor construct generation. Unfortunately, about 58% of Drosophila genes lack a suitable coding intron for integration of artificial exons in all of the annotated isoforms. Here, we report the development of new set of constructs that allow the replacement of the coding region of genes that lack suitable introns with a KozakGAL4 cassette, generating a knock-out/knock-in allele that expresses GAL4 similarly as the targeted gene. We also developed custom vector backbones to further facilitate and improve transgenesis. Synthesis of homology donor constructs in custom plasmid backbones that contain the target gene sgRNA obviates the need to inject a separate sgRNA plasmid and significantly increases the transgenesis efficiency. These upgrades will enable the targeting of nearly every fly gene, regardless of exon–intron structure, with a 70–80% success rate.
Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the Bact complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.