In rodents and most other mammals, the accessory olfactory system controls conspecific chemical communication during social interactions (Brennan and Zufall, 2006; Dulac and Torello, 2003; Mohrhardt et al., 2018; Tirindelli et al., 2009). Ethologically meaningful chemostimuli that trigger stereotypic social and sexual behaviors are predominantly detected by the vomeronasal organ (VNO), a tubular sensory structure at the anterior base of the nasal septum. In a crescent-shaped medial neuroepithelium, the VNO harbors approximately 100,000–200,000 vomeronasal sensory neurons (VSNs; Wilson and Raisman, 1980), each extending a single unbranched apical dendrite that terminates in a paddle-shaped swelling (Mohrhardt et al., 2018). At the dendritic tips, microvilli are immersed in mucus that fills a central luminal canal, which extends via the vomeronasal duct into the nasal cavity. During social investigatory behavior, which in mice primarily involves periods of intense licking and sniffing of both facial and anogenital regions (Luo et al., 2003), semiochemicals are sucked into the VNO lumen. Upon binding to cognate vomeronasal receptors (VRs), the chemical signal is transduced into electrical VSN activity and, ultimately, neuronal discharge.

Behaviorally relevant natural chemostimuli are, typically, complex blends of compounds (Wyatt, 2017) in various bodily secretions (Albone, 1984). By far, the most widely studied secretion in animal chemosensory research is urine (see Mohrhardt et al., 2018, and references therein), which is a rich source of semiochemicals that serves a well-established function in social communication. While we still lack a comprehensive molecular description of this broadband vomeronasal stimulus, previous work has identified several putative semiochemicals in mouse urine and other bodily secretions, which activate VSNs and cover many structural groups and feature dimensions (Chamero et al., 2007; Doyle et al., 2016; Fu et al., 2015; Hurst et al., 2001; Kimoto et al., 2005; Leinders-Zufall et al., 2004; Leinders-Zufall et al., 2000; Nodari et al., 2008; Novotny, 2003; Overath et al., 2014; Rivière et al., 2009; Röck et al., 2006; Sturm et al., 2013; Wyatt, 2017). Prominent molecularly identified VSN stimuli include various sulfated steroids (Celsi et al., 2012; Fu et al., 2015; Haga-Yamanaka et al., 2015; Haga-Yamanaka et al., 2014; Isogai et al., 2011; Nodari et al., 2008; Turaga and Holy, 2012), which could reflect the dynamic endocrine state of an individual.

So far, virtually all research on vomeronasal physiology is based on urinary stimuli derived from inbred laboratory mice (but see Bansal et al., 2021). This facilitates standardization across studies but, while VSN sex and strain selectivity has previously been explored (Fu et al., 2015; He et al., 2008), it remains unclear whether secretions collected from inbred mice accurately represent the potentially more ethologically relevant stimuli found in the wild. In fact, while wild Mus musculus populations exhibit several-fold higher levels of genetic variation than those in human populations, commonly studied inbred strains of laboratory mice derive from a limited set of founders. Therefore, such strains contain only a small subset of the genetic variation that is present in nature (Phifer-Rixey and Nachman, 2015). Classical inbred strains of house mice are genetic mosaics of the three main wild subspecies, M. m. domesticus, musculus, and castaneus, which started to diverge ∼350,000–500,000 years ago. However, the genomes of prominent inbred strains, such as C57BL/6 or BALB/c, are predominantly derived from M. m. domesticus (Wade and Daly, 2005). The mitochondrial genomes of both laboratory strains are identical, implying a common descent along the maternal line. Given their overlapping geographical distribution, wild house mice subspecies have also undergone secondary contact and hybridization (Duvaux et al., 2011), which has diversified their genetic landscape.

Laboratory inbreeding, which has been ongoing for more than 100 years for C57BL/6 and BALB/c strains (Phifer-Rixey and Nachman, 2015), may have affected the chemical composition of bodily secretions and, consequently, their information content. Indeed, both within and between strains, laboratory mice lack variation in major urinary protein (MUP) patterns typically found among wild mice (Cheetham et al., 2009). This inbred homogeneity could have important implications for research investigating, for example, social recognition or mate choice: if there are marked qualitative and/or quantitative differences in chemical composition of secretions and, accordingly, in the neuronal representations of wild as compared to inbred derived stimuli, one might question the conclusions based on a large body of work using inbred secretions (Bansal et al., 2021).

On the recipients’ end, the level of chemosensory information available from con/heterospecific secretions is determined by individual VR expression profiles. Across strains, VR repertoires vary as a function of both genetics and experience (Duyck et al., 2017; Ibarra-Soria et al., 2017; Wynn et al., 2012; Xu et al., 2016). Therefore, it is unclear whether receptor arrays in laboratory strains, despite hundreds of generations of inbreeding and domestication (‘microevolution’) in a laboratory environment, have retained selectivity for more ethologically relevant wild-derived stimuli. Moreover, it remains uncertain whether VR tuning profiles enable VSNs to capture key ethological features from molecular concentration differences between sex- and strain-specific secretions (He et al., 2010; Leinders-Zufall et al., 2000). Together, four key biological questions arise from these lines of reasoning: (i) Do VSN response profiles reflect the global molecular content of urine (i.e. are these neurons sensitive to many/most compounds) or, by contrast, is the VNO a highly selective molecular detector (responding to just a few select molecules)? (ii) Are VSN response profiles strain-specific? (iii) Which semiochemicals provide information about sex and/or strain? (iv) Is there something unique about wild mouse secretions and/or the VSN response profiles they trigger?

Here, to address these unresolved issues, we combine a robust VSN activity assay with comparative molecular profiling of sex- and strain-specific mouse urine from two inbred laboratory strains and wild mice. Our study provides molecular portraits of these secretions. We report that large fractions of generic urine compounds are shared among both male and female mice of all genetic backgrounds. We further show that the urinary ‘secretome’ in wild mice does not differ dramatically from that found in laboratory strains. Surprisingly, while male urine contains much higher protein concentrations, females secrete a larger variety of proteins (including MUPs). For proteins common to all strains, concentrations are relatively low in C57BL/6, moderate in BALB/c, and high in wild animals. However, despite this concentration bias, there is no overrepresentation of wild-selective proteins. Notably, both the volatile organic compound (VOC) and protein profile of urine, each provides sufficient information to decode a given sex/strain combination (with protein content exhibiting stronger discriminative power). Moreover, we identify a rich lipocalin repertoire in urine, which alone could allow chemosensory discrimination of sex/strain combinations.

A key strength of this study is the use of the exact same stimuli as previously employed to investigate sensory representations in the accessory olfactory bulb (AOB) (Bansal et al., 2021), the first central processing stage along the accessory olfactory pathway. Our previous work demonstrated that AOB representations of ethologically relevant urine stimuli are similar for male mice from two different inbred strains (C57BL/6 and BALB/c), despite potential differences in VR repertoires. In addition, we found that wild mouse stimuli elicit responses that, although not identical, are nevertheless qualitatively similar to those from commonly used inbred strains (Bansal et al., 2021). VSN activity analysis now enables us to ask whether these features also manifest in the VNO and, thus, to assess (i) whether the information inherent in a sex- and strain-specific urinary secretome is accessible via vomeronasal sampling; and (ii) if, on the population level, VSN sensory representations differ between strains. Comparing responses from male C57BL/6 and BALB/c mice (as done previously on AOB level; Bansal et al., 2021), our data demonstrate that recipient strain identity is reflected by their VSN activity patterns. Moreover, when exposed to stimuli from different strains, large VSN fractions (often >50%) respond to only one stimulus, suggesting a substantial degree of selective sampling. Together, our study reveals selective and strain-dependent representations of urine chemical content, a surprising scarceness of selective responses to wild stimuli, as well as remarkably rich and sex/strain-specific molecular profiles that likely preserve most of the biologically relevant information.

In mice, the VNO is the predominant sensory structure mediating conspecific chemical communication. In this study, by comparing samples derived from two inbred strains (BALB/c and C57BL/6) as well as from wild mice, we pursue four main questions: (i) Which chemical components in mouse urine – a major source of semiochemicals – distinguish sex and/or strain? (ii) To what degree has inbreeding affected the chemical composition of urine (i.e. how unique are wild mouse secretions)? (iii) How much of the sex- and strain-specific chemical information in urine is accessible to a conspecific via vomeronasal sampling (i.e. how selective are VSN response profiles)? (iv) Upon stimulation with the same stimuli, do VSN response patterns differ between strains?

A ‘low noise’ assay to capture VSN population activity

To record VSN signal fingerprints in response to naturalistic stimuli, it is essential to establish a robust population activity assay that reliably captures the raw information content inherent in bodily secretions. To this end, we analyzed single-cell Ca²⁺ transients among large VSN populations in acute coronal VNO sections (Figure 1a and b). Our experimental design followed a standard pairwise comparison paradigm (Figure 1c). Pairs of pooled stimuli differed either in the donors’ sex (male versus female) or in their genetic background (BALB/c, C57BL/6, or wild). We repeated brief (10 s) alternating stimulus presentations twice at 180 s inter-stimulus intervals that ensured recovery from VSN adaptation (Wong et al., 2018). Experiments concluded with a brief exposure to elevated extracellular K⁺ (S₃) to depolarize neurons and test for integrity of each neuron’s spike generation machinery. Dependent on their individual urine response profiles, VSNs were categorized as either specialists (selective response to one stimulus) or generalists (responsive to both stimuli). Overall, we recorded >43,000 K⁺-sensitive neurons, of which a total of 16,715 VSNs (38.4%) responded to urine stimulation. Of these urine-sensitive neurons, 61.4% displayed generalist profiles, whereas 38.6% were categorized as specialists (Figure 1c and d). As a measure of intrinsic signal variability, we calculated reliability indices that quantify the similarity (or lack thereof) of two successive responses to the same stimulus (with small values reflecting high reliability; see Materials and methods). We analyzed both Ca²⁺ signal amplitudes (Figure 1d) and integrals (Figure 1—figure supplement 1a) as measures of response magnitude. For both generalist and specialist VSNs, response reliability indices are normally distributed around zero. While the distribution of reliability indices derived from response integral measurements is somewhat broad (Figure 1—figure supplement 1a), indices based on signal amplitudes proved relatively homogeneous (Figure 1d). Accordingly, we use average response amplitudes as indicators of signal strength throughout this study.

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In a first set of control experiments, we asked how VSNs from male C57BL/6 mice respond when challenged with urine samples from two groups of animals of the same sex and inbred strain (Figure 1—figure supplement 1b and c). Since the chemical composition of both stimuli should be similar, we expected that the vast majority of urine-sensitive VSNs will display generalist response profiles. That was indeed the case. Less than 2% of VSNs showed stimulus selectivity (Figure 1—figure supplement 1b and c). Moreover, response indices (reflecting a bias toward either of the paired stimuli) are normally (and narrowly) distributed around zero (Figure 1—figure supplement 1b and c). Both observations confirm a low level of biological and experimental noise in this setting. Thus, our assay is well suited to detect and compare VSN sensory responses upon pairwise stimulation.

Chemical characterization of urine content identifies distinctive molecular signatures of sex and strain

Which differences in chemical composition (i.e. which molecules) characterize sex/strain-specific secretions and may thus provide information about sex and strain to a recipient? Molecular profiling of urine content provides a unique opportunity to address this question and identify enriched compounds (i.e. present in ≥6 of 10 individual samples) with the potential capacity to signal an animal’s sex, strain, or both. Adopting this more conservative criterion, we detected 208 enriched VOCs and 264 enriched proteins. Almost half of all VOCs (47.6%) and about one-third of all proteins (31.4%) are found in all six sex/strain combinations tested and are, thus, considered generic mouse urine components (Figure 2a and b). Moreover, a total of 33 compounds (10 VOCs, 23 proteins) were identified across all strains, but in a sex-specific fashion. Similarly, 22 molecules (10 VOCs, 12 proteins) revealed strain selectivity, independent of sex. Notably, a large fraction of compounds, i.e., 34.1% of all VOCs (71/208) and 22.3% of proteins (59/264), were detected exclusively in one of the six sex/strain combinations. Only 18 of the 208 VOCs (8.7%) could not be categorized as either generic or specific (for either sex or strain, or a unique sex/strain combination). Among the 82 proteins that showed sex specificity, either across strains (23 proteins) or as part of a unique sex/strain combination (59 proteins), the vast majority (89.0%) was found in female samples. For VOCs, however, the opposite picture emerged. Here, 52 of 81 sex-specific compounds (64.2%) were selectively detected in male samples. Together, chemical profiling revealed (i) that large fractions of urine content are shared among both laboratory and wild mice; (ii) that roughly one-third of urinary VOCs and one-fourth of proteins are exclusively found in a given sex/strain combination; and (iii) that male and female mice might have adopted different chemical secretion strategies to signal their sex.

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If information coding along the accessory olfactory pathway would strictly follow a ‘labeled-line’ logic (Ishii et al., 2017), absence or presence of a given molecule could be adequate to signal sex or strain. Several recent lines of evidence, however, suggest a combinatorial coding strategy that also involves some level of circuit plasticity (Kaur et al., 2014; Mohrhardt et al., 2018; Xu et al., 2016). Therefore, we next asked if the total VOC or protein content of a given urine sample preserves sufficient predictive/discriminative information to classify samples according to sex, strain, or a specific sex/strain combination. We used sparse partial least-squares discriminant analysis (sPLS-DA) (Lê Cao et al., 2011), a chemometrics machine learning technique, to reduce data dimensionality and optimize sample separation (Gromski et al., 2015). When plotted on two-dimensional coordinates that represent the most discriminative variables (Figure 2c–h), both VOCs (Table 1) and proteins provide sufficient information to cluster stimuli according to sex (Figure 2c and f), strain (Figure 2d and g), or a combination of both variables (Figure 2e and h). We then calculated variable importance in projection scores as measures of a particular variable’s informative power, which is correlated to the variance explained by the model (Gromski et al., 2015; Lê Cao et al., 2011). Generally, we find that protein content exhibits stronger discriminative power (21–24% and 11–12% of explained variance) than VOC content (7–10% and 5–8% of explained variance).

Table 1

int ID	MS-DB-ID	bias/sex	comp	Chem-ID	alt. name	ChEBI / CID	ori
A140	Methane, nitro-	Male	1	CH3NO2	Nitrocarbol	77701	?
A336	Thiazole, 2-ethyl-4,5-dihydro-	Male	1	C5H9NS	2-Ethylthiazoline	86896	Rodents
A220	Butanenitrile, 2-methyl-	Male	1	C5H9N	2-Cyanobutane	51937	Insects
A305	2-sec-Butylthiazole	Male	1	C7H11NS	2-(1-Methylpropyl)thiazole	519539	Mouse
A209	2-Butenal, 2-methyl-	Male	1	C5H8O	Tiglic aldehyde	88419	ammals
A278	Furan, 2,3-dihydro-4-(1-methylethyl)-	Male	1	C7H12O	Furan	35559	Mammals
A102	2-Butanone	Female	1	C4H8O	Butan-2-one	28398	Mammals
A497	Benzyl alcohol	Female	1	C7H8O	Phenylmethanol	17987	Mammals
A28	Acetaldehyde	Female	1	C2H4O	Ethanal	15343	Mammals
A248	Butyric acid, 3-tridecyl ester	Female	1	C17H34O2	Tridecyl butyrate	169401	Rodents
A307	1-Octen-3-ol	Female	1	C8H16O	1-Vinylhexanol	34118	Ubiquitous
A765	1,2-Benzenedicarboxylic acid, butyl 2-methylpropyl ester	Female	1	C16H22O4	Butyl isobutyl phthalate	519539	Mammals
int ID	MS-DB-ID	bias/strain	comp	Chem-ID	alt. name	ChEBI/CID	ori
A51	Trichloromethane	C57BL/6	2	CHCl3	Chloroform	6212	Ubiquitous
A14	Cyclopropane, ethyl-	C57BL/6	2	C5H10	Ethylcyclopropane	70933	Ubiquitous
A74	Ethyl acetate	C57BL/6	2	C4H8O2	Acetoxyethane	8857	Yeast
A10	Ethylenimine	C57BL/6	2	C2H5N	Aziridine	9033	Ubiquitous
A123	n-Propyl acetate	Wild	2	CH3COOCH2CH2CH3	Propyl ethanoate	40116	Insects
A355	Pyrazine, 2-ethenyl-6-methyl-	Wild	2	C7H8N2	Pyrazine, 2-methyl-6-vinyl-	518838	Ubiquitous
A659	Dodecanoic acid, isooctyl ester	Wild	2	C20H40O2	Isooctyl laurate	2216918	Plants
A165	2-Pentanone	Wild	2	C5H10O	Ethyl acetone	7895	Ubiquitous
A372	Pyridine, 2,3,4,5-tetrahydro-	Wild	1	C5H9N	Piperideine	47858	Ubiquitous
A486	Benzenamine, 3-methyl-	Wild	1	C6H4CH3NH2	3-Toluidine	7934	?
A156	Pentanal	BALB/c	2	C5H10O	Valeraldehyde	8063	Insects
A181	Isopropylsulfonyl chloride	BALB/c	2	C3H7ClO2S	2-Propanesulfonyl chloride	82408	Bacteria
A456	Cyclohexane, isothiocyanato-	BALB/c	2	C7H11NS	Isothiocyanocyclohexane	14289	?
A45	Heptane, 2,4-dimethyl-	BALB/c	2	C9H20	2,4-Dimethylheptane	16656	Insects
A265	Benzene, 1,2,3-trimethyl-	BALB/c	1	C9H12	Hemellitol	1797279	Insects
A251	Benzene, 1,2,4-trimethyl-	BALB/c	1	C9H12	Pseudocumol	7247	Insects
A977	Propene	BALB/c	1	CH2CHCH3	Methylethylene	6378	?

We next aimed to identify the most relevant variables (i.e. molecules) for sex or strain classification. Training Random Forest classifiers (Breiman, 2001), we obtained feature significance scores, the ‘Gini importance’ (Breiman, 2001), that provide relative relevance rankings of the individual variables. Here, we list the 20 most informative VOCs (Table 2) and proteins that discriminate sex and strain, respectively (Figure 2—figure supplement 1a and b), along with the abundance of the corresponding top 5 molecules (VOCs or proteins) for each of the six sex/strain combinations (Figure 2—figure supplement 1c–f). Notably, major urinary protein 20 (Mup20/darcin) (Roberts et al., 2010), fatty acid-binding protein 5 (Fabp5) (Furuhashi and Hotamisligil, 2008), and N-acetylgalactosamine-6-sulfatase (Galns) (Hori et al., 1995) exhibit substantial power to discriminate sex across strains (Figure 2—figure supplement 1b and f). While Mup20 and Galns are considerably more abundant in male urine, Fabp5 appears to be specific for female samples. Strain discrimination, on the other hand, is optimal with protein-tyrosine kinase 2-beta (Ptk2b) (Lev et al., 1995), RNase T2 (Rnaset2) (Thorn et al., 2012), prosaposin (Psap) (O’Brien and Kishimoto, 1991), lymphocyte antigen 6A-2/6E-1 (Ly6a) (van de Rijn et al., 1989), and superoxide dismutase 1 (Sod1) (McCord and Fridovich, 1969; Figure 2—figure supplement 1b and d). Specifically, Ptk2b is detected exclusively in C57BL/6 mice. Ly6a is absent in BALB/c animals, whereas Rnaset2 is largely missing in both C57BL/6 and wild mice. With proteins exhibiting stronger discriminative power than VOCs (Figure 2c–h), future studies will have to focus on these proteins to identify potential functions as vomeronasal chemosignals.

Table 2

nt ID	MS-DB-ID	bias/sex	Chem-ID	alt. name	ChEBI / CID	ori
A140	Methane, nitro-	Male	CH3NO2	Nitrocarbol	77701	?
A220	Butanenitrile, 2-methyl-	Male	C5H9N	2-Cyanobutane	51937	Insects
A235	3-Penten-2-one	Male	C5H8O	(E)-pent-3-en-2-one	637920	Insects
A336	Thiazole, 2-ethyl-4,5-dihydro-	Male	C5H9NS	2-Ethylthiazoline	86896	Rat
A13	Methanethiol	Male	CH3SH	MTMT	878	Mammals
A20	Methylamine, N,N-dimethyl-	Male	C3H9N	Trimethylamine	1146	Mouse
A765	1,2-Benzenedicarboxylic acid, butyl 2-methylpropyl ester	Female	C16H22O4	Butyl isobutyl phthalate	519539	Mammals
A240	2-Penten-1-ol, (Z)-	Male	C5H10O	trans-2-Pentenol	5364920	Insects
A209	2-Butenal, 2-methyl-	Male	C5H8O	Tiglic aldehyde	88419, CID:5321950	Mammals
A305	2-sec-Butylthiazole	Male	C7H11NS	2-(1-Methylpropyl)thiazole	519539	Mouse
A337	2-sec-Butylthiazole (2nd variant)	Male	C7H11NS	2-(1-Methylpropyl)thiazole	519539	Mouse
A102	2-Butanone	Female	C4H8O	Butan-2-one	28398	Mammals
A152	2,4-Dimethyl-1-hexene	Male	C8H16	2,4-Dimethylhex-1-ene	519301	?
A618	6-Methyl-2-pyridinecarbaldehyde	Male	C7H7NO	6-Methylpicolinaldehyde	70737	?
A84	4-Hexen-3-one, 5-methyl-	Male	C7H12O	5-Methylhex-4-en-3-one	256081	Insects
int ID	MS-DB-ID	bias/strain	Chem-ID	alt. name	ChEBI / CID	ori
A51	Trichloromethane	C57BL/6	CHCl3	Chloroform	6212	Ubiquitous
A265	Benzene, 1,2,3-trimethyl-	BALB/c	C9H12	Hemellitol	1797279	Insects
A63	Acetone	C57BL/6	C3H6O	Dimethyl ketone	180	Ubiquitous
A14	Cyclopropane, ethyl-	C57BL/6	C5H10	Ethylcyclopropane	70933	Ubiquitous
A172	(S)-(+)-2-Pentanol	Wild	C5H12O	2-Pentanol	22386	Mouse
A222	2-Hexanone	Wild	C6H12O	Hexanone	11583	Mouse
A165	2-Pentanone	Wild	C5H10O	Ethyl acetone	7895	Ubiquitous
A251	Benzene, 1,2,4-trimethyl-	BALB/c	C9H12	Pseudocumol	7247	Insects
A123	n-Propyl acetate	Wild	C5H10O2	Propyl acetate	7997	Insects
A355	Pyrazine, 2-ethenyl-6-methyl-	Wild	C7H8N2	Pyrazine, 2-methyl-6-vinyl-	518838	Mammals
A74	Ethyl acetate	C57BL/6	C4H8O2	Acetoxyethane	8857	Yeast
A156	Pentanal	BALB/c	C5H10O	Valeraldehyde	8063	Insects
A146	Cyclohexene,3-(1-methylpropyl)-	Wild	C10H18	3-Sec-butyl-1-cyclohexene	139917	Insects
A194	o-Xylene	Wild	C6H4(CH3)2	Ortho-Xylene	7237	?
A83	Benzene	Wild	C6H6	Benzole	241	Insects

Overall, we have detected similar amounts of proteins/VOCs across strains. However, individual variability in protein and VOC content was significantly higher in wild mice than in both laboratory strains (Figure 2—figure supplement 2a). We confirmed secretion of previously reported putative semiochemicals (Hurst et al., 2001; Kaur et al., 2014; Leinders-Zufall et al., 2000; Novotny, 2003; Roberts et al., 2012), including both VOCs (Figure 2—figure supplement 2b) and proteins (Figure 2—figure supplement 2c) in both wild and laboratory mice. Notably, however, all Mups (including Mup20) and most such VOCs were found in samples from either sex, albeit at male-biased concentrations for Mup3, Mup17, and Mup20 (Figure 2—figure supplement 2c). The only compounds showing male-specific secretion are 2-sec-butyl-4,5-dihydrothiazole and farnesenes (Figure 2—figure supplement 2b), which have previously been implicated as facilitators of female mouse puberty acceleration (Novotny, 2003).

Notably, we find a rich repertoire of 27 lipocalins in mouse urine. When based exclusively on lipocalin content, hierarchical clustering groups individual samples into a set of clusters that, with very few exceptions (5 out of 60), correspond to the six sex/strain combinations (Figure 2—figure supplement 3). This finding, therefore, demonstrates the power of urinary lipocalins for potential chemosensory discrimination of sex and/or strain.

VSNs are more selective for strain than for sex

Next, we challenged neurons with stimulus pairs from two same-sex/different-strain combinations and asked whether VSN response profiles reflect the molecular fingerprint of corresponding urine samples (e.g. male C57BL/6 versus male BALB/c; Figure 3a). We analyzed a total of 17,416 K⁺-sensitive VSNs (Figure 3a–f). Again, we distinguished between specialist VSNs that responded exclusively to one stimulus, and generalists, which responded to both stimuli. Along the same lines, we categorized either strain-specific or broadly detected VOCs and proteins. Several conclusions emerge from these classifications: (i) With one exception (i.e. upon stimulation with male C57BL/6 versus wild stimuli; Figure 3b), roughly half of all urine-sensitive VSNs are generalists – a result consistent with our finding that generally ~50% of urine molecules are shared among compared strains. Accordingly, the fraction of strain-selective (specialist) responses is considerably larger than observed for sex-specific responses (Figure 1e). (ii) In female urine, BALB/c-specific proteins are substantially underrepresented, a fact not reflected by VSN response profiles (Figure 3d and f). (iii) Surprisingly, the amount of strain-specific molecules in wild mouse urine does not vastly exceed that in inbred strains; and (iv) accordingly, selective VSN responses to wild stimuli are by no means more common (Figure 3b, c, e, and f).

Figure 3

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Pronounced strain-dependent concentration imbalances between common urinary compounds are not reflected by generalist VSNs

When comparing generalist VSN responses to male versus female C57BL/6 urine (Figure 1e and h), we noted that the narrow normally distributed stimulus response index histograms did not match the broader and heterogeneous distributions of concentration disparities between sexes (Figure 1h; Figure 1—figure supplement 1b-d). For sex-dependent cues, as mentioned above, this could indicate that semiochemical concentration differences carry only limited information. Next, we therefore asked whether strain-specific concentration differences between urine samples exist and, if so, whether such differences can convey information about strain. In total, we recorded strain-independent generalist responses from 3366 VSNs in male C57BL/6 mice (Figure 4). In all but one experimental condition (i.e. when comparing male stimuli from BALB/c versus wild mice), response preference indices were normally distributed and well fit by relatively narrow Gaussian curves that centered around zero. By contrast, chemical analysis revealed that many compounds identified in urine from both strains differ substantially in concentration. For proteins, in particular, strong concentration disparities exist in all strain combinations analyzed. In both male and female samples, concentrations of common proteins are relatively low in C57BL/6, moderate in BALB/c, and high in wild animals. As shown in Figures 2b and 3, this concentration bias toward wild proteins does not translate into any dramatic overrepresentation of proteins selectively found in wild male and/or female urine. In fact, the massively skewed distributions of protein concentration indices are not reflected by generalist VSN profiles. The latter better match VOC concentration distributions, which generally display broad, yet Gaussian shapes. We conclude that VSN population response strength might not be so strongly affected by strain-dependent concentration differences among common urinary proteins. In that case, it would appear somewhat unlikely that individual VSN activity provides fine-tuned information about distinct semiochemical concentrations. Alternatively, as some (or even many) of the identified proteins could not serve as vomeronasal ligands at all, generalist VSNs might sample information from only a subset of compounds which, in fact, are secreted at roughly similar concentrations.

Figure 4

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Vomeronasal representation of female semiochemicals differs between two inbred strains

So far, our approach was restricted to VSN signals recorded from male C57BL/6 mice. An important question, of course, is whether the response profiles we observed are themselves recipient strain-dependent. Thus, we next aimed to assess the extent to which our findings generalize to VSN populations from another laboratory animal strain. We therefore repeated all six pairwise same-sex/different-strain stimulation experiments, using acute VNO slices from male BALB/c mice (Figure 5 and Figure 5—figure supplement 1). Categorization as generalist or specialist VSNs revealed that proportions differed significantly between VSNs from BALB/c versus C57BL/6 mice, albeit at varying degrees. Similar to C57BL/6 neurons, selectivity of BALB/c neurons to wild-derived stimuli was rather rare. In fact, (i) compared to an average specialist rate of 11.2% ± 6.6% (mean ± SD) calculated over all 13 binary stimulus pairs (n=26 specialist types), we observed only few specialist responses upon stimulation with urine from wild females (2% and 3%, respectively), and (ii) we found comparatively few generalist signals when wild-derived female urine was among the paired stimuli (Figure 5e and f). This striking insensitivity is not observed in VSNs from C57BL/6 mice, suggesting substantial differences in VR expression between the two inbred laboratory strains. Notably, we can rule out that the observed differences result from differential sampling along the VNO’s anterior-to-posterior axis since, for each experiment, we routinely sample and average across slices cut along the organ’s entire anterior-to-posterior length (see Materials and methods).

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Next, we examined whether generalist VSN response profiles differ between male BALB/c and C57BL/6 animals (Figure 5—figure supplement 1). In total, we recorded generalist responses from 1741 BALB/c neurons. When plotting response preference indices, we noticed less homogeneous distributions than previously observed in C57BL/6 mice. Five of the six histograms showed multiple peaks and thus could not be fitted by a single Gaussian, whereas the only histogram adhering to a normal distribution was comparatively broad (Figure 5—figure supplement 1a). As observed in C57BL/6 neurons, the skewed distributions of protein concentration indices were not reflected by BALB/c generalist VSN profiles. Comparison of generalist VSN response histograms between BALB/c and C57BL/6 mice (Figure 5—figure supplement 1c and d) revealed strong and consistent differences upon stimulation with female stimuli (Figure 5—figure supplement 1d). While single Gaussians around zero characterize C57BL/6 generalist distributions upon stimulation with female stimuli (Figure 5—figure supplement 1d), several prominent peaks emerged when fitting histograms derived from BALB/c VSNs. Notably, for some generalist BALB/c neurons, wild-derived female stimuli are less potent than their inbred strain counterparts. This finding either indicates reduced concentrations of the corresponding molecules in wild urine (rendering most proteins unlikely candidates; Figure 5—figure supplement 1a and b), or suggests some yet to be determined form of cooperativity upon receptor-ligand interaction. Together, for both generalist and specialist VSNs, vomeronasal representation of female semiochemicals differs considerably between the two inbred mouse strains.

In the present study, we have established a robust VSN activity assay that allows pairwise comparison of neural selectivity and response strength upon stimulation with chemically defined natural stimuli. In-depth chemical analysis of sex- and strain-specific individual urine samples revealed that (i) large fractions of urine content are shared among mice of all sex/strain combinations; (ii) the amount of molecules selectively found in wild mouse urine does not dramatically exceed the urinary secretome of inbred strains; (iii) across strains, female-specific proteins vastly outnumber the male-specific variety, while (iv) overall protein concentration is substantially enriched in male urine; (v) concentrations of common proteins are relatively low in C57BL/6, moderate in BALB/c, and high in wild animals; (vi) both secreted VOC and protein profiles provide sufficient information to distinguish sex, strain, or both; and (vii) the rich urinary lipocalin repertoire alone might allow chemosensory discrimination of sex and/or strain.

When asking how much of this chemical information is accessible to inbred male mice via vomeronasal sampling, we observe that (i) VSN population response profiles do not reflect the global molecular content of urine, suggesting that the VNO functions as a rather selective molecular detector; (ii) selective VSN responses to wild stimuli are by no means more common (in fact, selectivity to wild-derived stimuli is rather rare); (iii) VSN generalist signal strength is unlikely to encode semiochemical concentrations across the entire range of compounds; (iv) male BALB/c neurons display striking insensitivity when challenged with urine from wild females; and, thus, (v) vomeronasal representation of female semiochemicals differs considerably between inbred strains.

Urine is the primary source of social chemosignals among mice (and, in fact, many other mammals) and contains both ‘fixed’ (i.e. genomic) information about strain, sex, individual identity, genetic histocompatibility and background, as well as ‘variable’ (i.e. metabolic) information on current social, reproductive, and health status (Hurst, 2005). The ability to glean ethologically meaningful information from chemosensory sampling of urine (or any conspecific bodily secretion) depends on (i) the specific (semio)chemical composition of a urine sample, and (ii) the sensory apparatus used for sampling. For most mammals, the VNO is the key chemosensory structure involved in detecting conspecific chemical cues (Mohrhardt et al., 2018). The virtually universal use of inbred laboratory mice in research aimed at understanding VNO physiology – as both the donors of stimuli and the experimental subjects employed in these studies – could have resulted in misconceptions and biased notions about vomeronasal signaling and, thus, conspecific chemical communication. If that were the case, one might question the relevance and ethological validity of conclusions drawn from a large body of work using inbred secretions (Bansal et al., 2021). Here, we addressed this issue from both a chemical ecology and a physiological perspective. In-depth comparative molecular profiling of urine from two classical laboratory strains as well as wild mice reveals several shared features, but also qualitative and quantitative differences in composition. Furthermore, we observe substantial differences in vomeronasal representations of stimuli between C57BL/6 and BALB/c sensory neuron populations.

For analytical purposes, we separate the urinary ‘volatilome’ and proteome. In chemosensory research, this distinction has often been conceptualized as general (i.e. airborne) odors, which activate the main olfactory system, versus vomeronasal stimuli (Mucignat-Caretta et al., 2012). This notion, however, is misleading since organic compounds with low molecular weight and high vapor pressure (i.e. VOCs) in bodily secretions do not instantly evaporate, of course. Rather, they are readily accessible for vomeronasal sampling upon direct contact during investigatory behavior. Notably, our chemical profiling approach omits (sulfated) steroids, other non-volatile small organic molecules, which have previously been identified in mouse urine as VSN stimuli (Nodari et al., 2008). Caution should thus be exerted to not attempt to fully explain VSN response specificity based on VOC and protein content alone.

For both urinary VOCs and proteins, large fractions are shared among both male and female mice of either genetic background (Figure 2a and b). Such compounds could be considered generic (mouse) urine components and might not even serve any chemosensory signaling functions. Notably, both the urinary volatilome and proteome on their own, each entail sufficient information to discern an individual’s sex and strain, with protein content exhibiting stronger discriminative power. The protein that is most informative for discriminating between sexes is, perhaps not surprisingly, Mup20 (darcin) (Roberts et al., 2010). This well-described ‘maleness signal’ had previously been reported to elicit innate attraction and generate a conditioned place preference in females (Demir et al., 2020; Roberts et al., 2012), whereas, in males, Mup20 promotes aggression (Kaur et al., 2014). Chemosensory roles of the second and third best protein determinants of sex discrimination are basically unexplored. Fabp5 (Furuhashi and Hotamisligil, 2008) and Galns (Hori et al., 1995) are substantially enriched in female and male urine, respectively. Fatty acid-binding proteins, including Fabp5, are evolutionary conserved intracellular lipid chaperones that coordinate cellular lipid trafficking and signaling and are thus linked to metabolic and inflammatory pathways (Furuhashi and Hotamisligil, 2008). N-Acetylgalactosamine-6-sulfatase (Galns) on the other hand is a lysosomal hydrolase. Five urinary proteins – Ptk2b (Lev et al., 1995), Rnaset2 (Thorn et al., 2012), Psap (O’Brien and Kishimoto, 1991), Ly6a (van de Rijn et al., 1989), and Sod1 (McCord and Fridovich, 1969) – display pronounced strain-dependent differences in concentration. None of these have previously been attributed a chemosensory function. Challenging the mouse VNO with purified recombinant protein(s) will help elucidate whether such functions exist.

Proteomic profiling revealed three additional, rather unexpected findings: First, while male urine contains much higher protein concentrations, females of a given strain secrete a larger variety of proteins (including MUPs). In fact, we do not find a single protein that is exclusively detected in males across strains. By contrast, 23 urinary proteins, while present in all three strains, are found only in females. In line with these observations, only one additional Mup (i.e. Mup21) made the list of the 20 most informative proteins that discriminate sex. In general, our data provide little evidence for a sparse molecular code of (fe)maleness. Rather, the concept of ‘signature mixtures’ (Ben-Shaul, 2015; Kahan and Ben-Shaul, 2016; Stopková et al., 2023; Wyatt, 2017), which emphasizes a combinatorial ratio code instead of mere presence/absence phenomena, gains traction. Second, we identify a surprisingly rich lipocalin repertoire in urine, which alone could allow chemosensory discrimination of sex/strain combinations. Within a total of 27 lipocalins, individual patterns allow hierarchical clustering into sex/strain-specific groups (Figure 2—figure supplement 3). These data, thus, support the notion that the lipocalin ‘code’, if relevant, is combinatorial. Third, regarding its molecular spectrum, the urinary ‘secretome’ in wild mice does not differ dramatically from the repertoire found in laboratory strains. To paraphrase this generally reassuring conclusion: while inbreeding could have dramatically modified the nature of chemical secretions and, consequently, their perception by other mice, inbred chemostimuli are largely representative of (potentially) ethologically more relevant wild chemosignals. Notably, however, while mean VOC and protein concentrations show similar distributions across sex/strain combinations, individual variability is strongly increased in wild mouse urine (Figure 2—figure supplement 2a). Accordingly, this finding confirms previous reports of increased individual variation in wild mice (Beynon et al., 2002; Cheetham et al., 2009). Another inherent factor that could account for differences in urine secretions among each of the groups, and particularly for comparisons between inbred and wild stimuli, is the microbiome (Bansal et al., 2021; Moudra et al., 2021). Yet, we stress that all urine donors were housed in the same facility and fed with the same diet (Bansal et al., 2021).

Some limitations of our study need to be acknowledged. Regarding stimuli, the six secretion sets we used do not cover the entire coding capacity of the accessory olfactory system (Isogai et al., 2011). Moreover, for VSN response profiling, stimulus samples were pooled across 10 individuals in each of the 6 sex/strain categories. While pooling stimuli reduces individual variability across samples (e.g. regarding fluctuating physiological states), relevant stimulus aspects could be masked. Thus, given the increased chemical variability we observed among individual wild urine samples, pooling might obscure distinctive molecular features of wild mouse secretions. The same holds true for estrus cycle-dependent female stimuli. Because we did not monitor the estrus stage of female urine donors, mixes likely contain samples across the entire cycle. This is relevant as VSN responses may be affected by a donor’s cycle stage (Bansal et al., 2021; Cichy et al., 2015; Uchida et al., 2014). Another limitation stems from the use of male inbred mice as experimental subjects. The rationale behind this experimental strategy is to allow for comparisons with our previous study on AOB response profiles (Bansal et al., 2021), which used the exact same settings (see below). Nonetheless, future efforts will have to reveal (i) whether our main findings apply to female recipients; and (ii) if our observations generalize to other inbred, outbred, or wild mouse strains. As we recently declared (Bansal et al., 2021), the latter considerations underscore the importance of recording from wild recipients. While this endeavor presents significant practical challenges, it remains an important goal for future studies.

A surprising, but overall reassuring observation is that responses to wild and inbred stimuli are qualitatively similar. Strikingly, and somewhat counterintuitively, selective VSN responses to wild stimuli are rather rare. This does not result from a generally reduced compound content in wild urine as molecular profiling revealed comparably rich chemical portfolios in wild and inbred samples alike. Rather, we speculate that inbreeding over hundreds of generations in laboratory settings (Phifer-Rixey and Nachman, 2015) has resulted in ‘microevolutionary’ pressure to maintain sensitivity to signals from same- or similar-strain individuals. Indeed, compared to the C57BL/6 reference genome, genetic variability within VR repertoires is massively increased among wild-derived mice (particularly of the M. musculus subspecies) (Wynn et al., 2012). Among the approximately 200 orthologous receptor genes compared, BALB/c genes display 184 non-synonymous and just one private (i.e. unique to a given strain) single-nucleotide polymorphism (SNP). By contrast, VR genes of the wild-derived M. musculus PWK/PhJ strain show 789 non-synonymous SNPs and 508 private SNPs (Wynn et al., 2012).

Initially, we asked whether VSN response profiles reflect the global molecular content of urine or, by contrast, if the VNO serves as a rather selective semiochemical detector. Our findings support a high level of selectivity. When challenged with same-sex/different-strain stimuli, large VSN fractions selectively respond to just one stimulus. Intriguingly, this fraction of strain-selective specialists is considerably larger than observed for sex-specific responses. In line with the notion of highly selective vomeronasal sampling is our observation that the concentration differences between compounds shared among strains, which are often substantial, are not reflected by similarly pronounced differences in response strength among generalist VSNs. There are several, not necessarily mutually exclusive explanations for this finding: First, concentration could simply not be a read-out parameter for VSNs, which would support previous ideas of concentration-invariant VSN activity (Leinders-Zufall et al., 2000). Second, the concentrations in freshly released urine could just exceed the dynamic tuning range of VSNs since, particularly for VOCs, natural signals (e.g. in scent marks) must be accessible to a recipient for a prolonged amount of time (sometimes days). A similar rationale could explain the increased protein concentrations in male urine, since male mice use scent marking to establish and maintain their territories and urinary lipocalins serve as long-lasting reservoirs of VOCs (Hurst et al., 1998). Third, generalist VSNs might sample information only from a select subset of urinary compounds, which, given their role as biologically relevant chemosignals, might be released at tightly controlled (and thus similar) concentrations. In fact, in the most extreme scenario, several compounds that do display substantial strain- and/or sex-specific differences in concentration might not act as chemosignals at all. Forth, to some extent, different response profiles could be attributed to non-volatile small organic molecules such as steroids (Nodari et al., 2008), which were beyond the focus of our chemical analysis.

While, compared to wild-derived mice, the genetic differences in VR repertoires between C57BL/6 and BALB/c animals appear rather modest (Wynn et al., 2012) (see above), vomeronasal representation of female semiochemicals differs considerably between both inbred strains. We conclude that, even in closely related inbred mice, strain-to-strain VR variation must be prominent, an idea supported by various reports of differences in genetic VR makeup across strains (Lilue et al., 2018; Miller et al., 2020; Park et al., 2011). With monoallelic VR expression and 184 described non-synonymous SNPs between C57BL/6 and BALB/c receptor genes, it is likely that even individuals of the same strain express functionally different arrays of VSNs. Adding another layer of complexity, state- and experience-dependent changes in VSN sensitivity have recently been described (Dey et al., 2015; Xu et al., 2016).

By adopting the same experimental design (i.e. using the same sets of stimuli in male C57BL/6 versus BALB/c mice) in both this study on VSN response profiles and our previous analysis of sensory representations in the AOB (Bansal et al., 2021), we provide a unique comparative perspective on signal transformation along the initial processing nodes of the accessory olfactory pathway. We observe several differences in representations of ethologically relevant urine stimuli between the VNO and AOB. Notably, while stimulus representations across the two inbred recipient strains were very similar in AOB recordings (Bansal et al., 2021), we here observe clear differences in VSN activity, particular upon stimulation with female stimuli. Moreover, a substantial fraction of AOB neurons were selective to wild rather than inbred stimuli, whereas relatively few VSNs showed such selectivity. Consistent with the elaborate wiring patterns within the AOB, these observations imply the presence of non-trivial transformations between VSN and AOB representations. Understanding the exact nature, purpose, and neuronal substrates of these transformations remains an important topic for future studies.

All data is available in the main text or the supplementary materials. nLC-MS/MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2022) partner repository with the dataset identifier PXD042324. Metabolomics data have been deposited to the EMBL-EBI MetaboLights database with the identifier MTBLS7439.

1. 1. Stopka P

   (2024) MetaboLights

   ID MTBLS7439. Volatile urine metabolomes of the house mouse (Mus musculus).

   https://www.ebi.ac.uk/metabolights/MTBLS7439
2. 1. Stopka P

   (2024) PRIDE

   ID PXD042324. Urine proteomes of the house mouse.

   https://www.ebi.ac.uk/pride/archive/projects/PXD042324/

1. Book

   1. Albone ES

   (1984)

   Mammalian Semiochemistry: The Investigation of Chemical Signals between Mammals

   John Wiley & Sons, Inc.

   • Google Scholar
2. 1. Bansal R
   2. Nagel M
   3. Stopkova R
   4. Sofer Y
   5. Kimchi T
   6. Stopka P
   7. Spehr M
   8. Ben-Shaul Y

   (2021) Do all mice smell the same? Chemosensory cues from inbred and wild mouse strains elicit stereotypic sensory representations in the accessory olfactory bulb

   BMC Biology 19:133.

   https://doi.org/10.1186/s12915-021-01064-7

   • PubMed
   • Google Scholar
3. 1. Ben-Shaul Y

   (2015) Extracting social information from chemosensory cues: consideration of several scenarios and their functional implications

   Frontiers in Neuroscience 9:439.

   https://doi.org/10.3389/fnins.2015.00439

   • PubMed
   • Google Scholar
4. 1. Beynon RJ
   2. Veggerby C
   3. Payne CE
   4. Robertson DHL
   5. Gaskell SJ
   6. Humphries RE
   7. Hurst JL

   (2002) Polymorphism in major urinary proteins: molecular heterogeneity in a wild mouse population

   Journal of Chemical Ecology 28:1429–1446.

   https://doi.org/10.1023/a:1016252703836

   • PubMed
   • Google Scholar
5. 1. Breiman L

   (2001) Random Forests

   Machine Learning 45:5–32.

   https://doi.org/10.1023/A:1010933404324

   • Google Scholar
6. 1. Brennan PA
   2. Zufall F

   (2006) Pheromonal communication in vertebrates

   Nature 444:308–315.

   https://doi.org/10.1038/nature05404

   • PubMed
   • Google Scholar
7. 1. Celsi F
   2. D’Errico A
   3. Menini A

   (2012) Responses to sulfated steroids of female mouse vomeronasal sensory neurons

   Chemical Senses 37:849–858.

   https://doi.org/10.1093/chemse/bjs068

   • PubMed
   • Google Scholar
8. 1. Černá M
   2. Kuntová B
   3. Talacko P
   4. Stopková R
   5. Stopka P

   (2017) Differential regulation of vaginal lipocalins (OBP, MUP) during the estrous cycle of the house mouse

   Scientific Reports 7:11674.

   https://doi.org/10.1038/s41598-017-12021-2

   • PubMed
   • Google Scholar
9. 1. Chamero P
   2. Marton TF
   3. Logan DW
   4. Flanagan K
   5. Cruz JR
   6. Saghatelian A
   7. Cravatt BF
   8. Stowers L

   (2007) Identification of protein pheromones that promote aggressive behaviour

   Nature 450:899–902.

   https://doi.org/10.1038/nature05997

   • Google Scholar
10. 1. Cheetham SA
    2. Smith AL
    3. Armstrong SD
    4. Beynon RJ
    5. Hurst JL

    (2009) Limited variation in the major urinary proteins of laboratory mice

    Physiology & Behavior 96:253–261.

    https://doi.org/10.1016/j.physbeh.2008.10.005

    • PubMed
    • Google Scholar
11. 1. Cichy A
    2. Ackels T
    3. Tsitoura C
    4. Kahan A
    5. Gronloh N
    6. Söchtig M
    7. Engelhardt CH
    8. Ben-Shaul Y
    9. Müller F
    10. Spehr J
    11. Spehr M

    (2015) Extracellular pH regulates excitability of vomeronasal sensory neurons

    The Journal of Neuroscience 35:4025–4039.

    https://doi.org/10.1523/JNEUROSCI.2593-14.2015

    • PubMed
    • Google Scholar
12. 1. Cox J
    2. Hein MY
    3. Luber CA
    4. Paron I
    5. Nagaraj N
    6. Mann M

    (2014) Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ

    Molecular & Cellular Proteomics 13:2513–2526.

    https://doi.org/10.1074/mcp.M113.031591

    • PubMed
    • Google Scholar
13. 1. Demir E
    2. Li K
    3. Bobrowski-Khoury N
    4. Sanders JI
    5. Beynon RJ
    6. Hurst JL
    7. Kepecs A
    8. Axel R

    (2020) The pheromone darcin drives a circuit for innate and reinforced behaviours

    Nature 578:137–141.

    https://doi.org/10.1038/s41586-020-1967-8

    • PubMed
    • Google Scholar
14. 1. Dey S
    2. Chamero P
    3. Pru JK
    4. Chien MS
    5. Ibarra-Soria X
    6. Spencer KR
    7. Logan DW
    8. Matsunami H
    9. Peluso JJ
    10. Stowers L

    (2015) Cyclic regulation of sensory perception by a female hormone alters behavior

    Cell 161:1334–1344.

    https://doi.org/10.1016/j.cell.2015.04.052

    • PubMed
    • Google Scholar
15. 1. Doyle WI
    2. Dinser JA
    3. Cansler HL
    4. Zhang X
    5. Dinh DD
    6. Browder NS
    7. Riddington IM
    8. Meeks JP

    (2016) Faecal bile acids are natural ligands of the mouse accessory olfactory system

    Nature Communications 7:11936.

    https://doi.org/10.1038/ncomms11936

    • PubMed
    • Google Scholar
16. 1. Dulac C
    2. Torello AT

    (2003) Molecular detection of pheromone signals in mammals: from genes to behaviour

    Nature Reviews. Neuroscience 4:551–562.

    https://doi.org/10.1038/nrn1140

    • PubMed
    • Google Scholar
17. 1. Duvaux L
    2. Belkhir K
    3. Boulesteix M
    4. Boursot P

    (2011) Isolation and gene flow: inferring the speciation history of European house mice

    Molecular Ecology 20:5248–5264.

    https://doi.org/10.1111/j.1365-294X.2011.05343.x

    • PubMed
    • Google Scholar
18. 1. Duyck K
    2. DuTell V
    3. Ma L
    4. Paulson A
    5. Yu CR

    (2017) Pronounced strain-specific chemosensory receptor gene expression in the mouse vomeronasal organ

    BMC Genomics 18:965.

    https://doi.org/10.1186/s12864-017-4364-4

    • PubMed
    • Google Scholar
19. 1. Fluegge D
    2. Moeller LM
    3. Cichy A
    4. Gorin M
    5. Weth A
    6. Veitinger S
    7. Cainarca S
    8. Lohmer S
    9. Corazza S
    10. Neuhaus EM
    11. Baumgartner W
    12. Spehr J
    13. Spehr M

    (2012) Mitochondrial Ca(2+) mobilization is a key element in olfactory signaling

    Nature Neuroscience 15:754–762.

    https://doi.org/10.1038/nn.3074

    • PubMed
    • Google Scholar
20. 1. Fu X
    2. Yan Y
    3. Xu PS
    4. Geerlof-Vidavsky I
    5. Chong W
    6. Gross ML
    7. Holy TE

    (2015) A molecular code for identity in the vomeronasal system

    Cell 163:313–323.

    https://doi.org/10.1016/j.cell.2015.09.012

    • PubMed
    • Google Scholar
21. 1. Furuhashi M
    2. Hotamisligil GS

    (2008) Fatty acid-binding proteins: role in metabolic diseases and potential as drug targets

    Nature Reviews. Drug Discovery 7:489–503.

    https://doi.org/10.1038/nrd2589

    • PubMed
    • Google Scholar
22. 1. Gromski PS
    2. Muhamadali H
    3. Ellis DI
    4. Xu Y
    5. Correa E
    6. Turner ML
    7. Goodacre R

    (2015) A tutorial review: Metabolomics and partial least squares-discriminant analysis--A marriage of convenience or A shotgun wedding

    Analytica Chimica Acta 879:10–23.

    https://doi.org/10.1016/j.aca.2015.02.012

    • PubMed
    • Google Scholar
23. 1. Haga-Yamanaka S
    2. Ma L
    3. He J
    4. Qiu Q
    5. Lavis LD
    6. Looger LL
    7. Yu CR

    (2014) Integrated action of pheromone signals in promoting courtship behavior in male mice

    eLife 3:e03025.

    https://doi.org/10.7554/eLife.03025

    • PubMed
    • Google Scholar
24. 1. Haga-Yamanaka S
    2. Ma L
    3. Yu CR

    (2015) Tuning properties and dynamic range of type 1 vomeronasal receptors

    Frontiers in Neuroscience 9:244.

    https://doi.org/10.3389/fnins.2015.00244

    • PubMed
    • Google Scholar
25. 1. Hagendorf S
    2. Fluegge D
    3. Engelhardt CH
    4. Spehr M

    (2009) Homeostatic control of sensory output in basal vomeronasal neurons: activity-dependent expression of ether-à-go-go-related gene potassium channels

    The Journal of Neuroscience 29:206–221.

    https://doi.org/10.1523/JNEUROSCI.3656-08.2009

    • PubMed
    • Google Scholar
26. 1. Hamacher C
    2. Degen R
    3. Franke M
    4. Switacz VK
    5. Fleck D
    6. Katreddi RR
    7. Hernandez-Clavijo A
    8. Strauch M
    9. Horio N
    10. Hachgenei E
    11. Spehr J
    12. Liberles SD
    13. Merhof D
    14. Forni PE
    15. Zimmer-Bensch G
    16. Ben-Shaul Y
    17. Spehr M

    (2024) A revised conceptual framework for mouse vomeronasal pumping and stimulus sampling

    Current Biology 34:1206–1221.

    https://doi.org/10.1016/j.cub.2024.01.036

    • PubMed
    • Google Scholar
27. 1. He J
    2. Ma L
    3. Kim S
    4. Nakai J
    5. Yu CR

    (2008) Encoding gender and individual information in the mouse vomeronasal organ

    Science 320:535–538.

    https://doi.org/10.1126/science.1154476

    • PubMed
    • Google Scholar
28. 1. He J
    2. Ma L
    3. Kim S
    4. Schwartz J
    5. Santilli M
    6. Wood C
    7. Durnin MH
    8. Yu CR

    (2010) Distinct signals conveyed by pheromone concentrations to the mouse vomeronasal organ

    The Journal of Neuroscience 30:7473–7483.

    https://doi.org/10.1523/JNEUROSCI.0825-10.2010

    • PubMed
    • Google Scholar
29. 1. Hori T
    2. Tomatsu S
    3. Nakashima Y
    4. Uchiyama A
    5. Fukuda S
    6. Sukegawa K
    7. Shimozawa N
    8. Suzuki Y
    9. Kondo N
    10. Horiuchi T
    11. Ogura S
    12. Orii T

    (1995) Mucopolysaccharidosis type IVA: common double deletion in the N-acetylgalactosamine-6-sulfatase gene (GALNS)

    Genomics 26:535–542.

    https://doi.org/10.1016/0888-7543(95)80172-i

    • PubMed
    • Google Scholar
30. 1. Hurst JL
    2. Robertson DHL
    3. Tolladay U
    4. Beynon RJ

    (1998) Proteins in urine scent marks of male house mice extend the longevity of olfactory signals

    Animal Behaviour 55:1289–1297.

    https://doi.org/10.1006/anbe.1997.0650

    • PubMed
    • Google Scholar
31. 1. Hurst JL
    2. Payne CE
    3. Nevison CM
    4. Marie AD
    5. Humphries RE
    6. Robertson DH
    7. Cavaggioni A
    8. Beynon RJ

    (2001) Individual recognition in mice mediated by major urinary proteins

    Nature 414:631–634.

    https://doi.org/10.1038/414631a

    • PubMed
    • Google Scholar
32. Book

    1. Hurst JL

    (2005) Scent Marking and Social communicationAnimal Communication Networks

    Cambridge University Press.

    https://doi.org/10.1017/CBO9780511610363.014

    • Google Scholar
33. 1. Ibarra-Soria X
    2. Nakahara TS
    3. Lilue J
    4. Jiang Y
    5. Trimmer C
    6. Souza MAA
    7. Netto PHM
    8. Ikegami K
    9. Murphy NR
    10. Kusma M
    11. Kirton A
    12. Saraiva LR
    13. Keane TM
    14. Matsunami H
    15. Mainland J
    16. Papes F
    17. Logan DW

    (2017) Variation in olfactory neuron repertoires is genetically controlled and environmentally modulated

    eLife 6:21476.

    https://doi.org/10.7554/eLife.21476

    • PubMed
    • Google Scholar
34. 1. Ishii KK
    2. Osakada T
    3. Mori H
    4. Miyasaka N
    5. Yoshihara Y
    6. Miyamichi K
    7. Touhara K

    (2017) A labeled-line neural circuit for pheromone-mediated sexual behaviors in mice

    Neuron 95:123–137.

    https://doi.org/10.1016/j.neuron.2017.05.038

    • PubMed
    • Google Scholar
35. 1. Isogai Y
    2. Si S
    3. Pont-Lezica L
    4. Tan T
    5. Kapoor V
    6. Murthy VN
    7. Dulac C

    (2011) Molecular organization of vomeronasal chemoreception

    Nature 478:241–245.

    https://doi.org/10.1038/nature10437

    • PubMed
    • Google Scholar
36. 1. Kahan A
    2. Ben-Shaul Y

    (2016) Extracting behaviorally relevant traits from natural stimuli: benefits of combinatorial representations at the accessory olfactory bulb

    PLOS Computational Biology 12:e1004798.

    https://doi.org/10.1371/journal.pcbi.1004798

    • PubMed
    • Google Scholar
37. 1. Kaur AW
    2. Ackels T
    3. Kuo TH
    4. Cichy A
    5. Dey S
    6. Hays C
    7. Kateri M
    8. Logan DW
    9. Marton TF
    10. Spehr M
    11. Stowers L

    (2014) Murine pheromone proteins constitute a context-dependent combinatorial code governing multiple social behaviors

    Cell 157:676–688.

    https://doi.org/10.1016/j.cell.2014.02.025

    • PubMed
    • Google Scholar
38. 1. Kimoto H
    2. Haga S
    3. Sato K
    4. Touhara K

    (2005) Sex-specific peptides from exocrine glands stimulate mouse vomeronasal sensory neurons

    Nature 437:898–901.

    https://doi.org/10.1038/nature04033

    • PubMed
    • Google Scholar
39. 1. Lê Cao KA
    2. Boitard S
    3. Besse P

    (2011) Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems

    BMC Bioinformatics 12:0–16.

    https://doi.org/10.1186/1471-2105-12-253

    • PubMed
    • Google Scholar
40. 1. Leinders-Zufall T
    2. Lane AP
    3. Puche AC
    4. Ma W
    5. Novotny MV
    6. Shipley MT
    7. Zufall F

    (2000) Ultrasensitive pheromone detection by mammalian vomeronasal neurons

    Nature 405:792–796.

    https://doi.org/10.1038/35015572

    • PubMed
    • Google Scholar
41. 1. Leinders-Zufall T
    2. Brennan P
    3. Widmayer P
    4. Maul-Pavicic A
    5. Jäger M
    6. Li XH
    7. Breer H
    8. Zufall F
    9. Boehm T

    (2004) MHC class I peptides as chemosensory signals in the vomeronasal organ

    Science 306:1033–1037.

    https://doi.org/10.1126/science.1102818

    • PubMed
    • Google Scholar
42. 1. Lev S
    2. Moreno H
    3. Martinez R
    4. Canoll P
    5. Peles E
    6. Musacchio JM
    7. Plowman GD
    8. Rudy B
    9. Schlessinger J

    (1995) Protein tyrosine kinase PYK2 involved in Ca(2+)-induced regulation of ion channel and MAP kinase functions

    Nature 376:737–745.

    https://doi.org/10.1038/376737a0

    • PubMed
    • Google Scholar
43. 1. Lilue J
    2. Doran AG
    3. Fiddes IT
    4. Abrudan M
    5. Armstrong J
    6. Bennett R
    7. Chow W
    8. Collins J
    9. Collins S
    10. Czechanski A
    11. Danecek P
    12. Diekhans M
    13. Dolle DD
    14. Dunn M
    15. Durbin R
    16. Earl D
    17. Ferguson-Smith A
    18. Flicek P
    19. Flint J
    20. Frankish A
    21. Fu B
    22. Gerstein M
    23. Gilbert J
    24. Goodstadt L
    25. Harrow J
    26. Howe K
    27. Ibarra-Soria X
    28. Kolmogorov M
    29. Lelliott CJ
    30. Logan DW
    31. Loveland J
    32. Mathews CE
    33. Mott R
    34. Muir P
    35. Nachtweide S
    36. Navarro FCP
    37. Odom DT
    38. Park N
    39. Pelan S
    40. Pham SK
    41. Quail M
    42. Reinholdt L
    43. Romoth L
    44. Shirley L
    45. Sisu C
    46. Sjoberg-Herrera M
    47. Stanke M
    48. Steward C
    49. Thomas M
    50. Threadgold G
    51. Thybert D
    52. Torrance J
    53. Wong K
    54. Wood J
    55. Yalcin B
    56. Yang F
    57. Adams DJ
    58. Paten B
    59. Keane TM

    (2018) Sixteen diverse laboratory mouse reference genomes define strain-specific haplotypes and novel functional loci

    Nature Genetics 50:1574–1583.

    https://doi.org/10.1038/s41588-018-0223-8

    • PubMed
    • Google Scholar
44. 1. Luo M
    2. Fee MS
    3. Katz LC

    (2003) Encoding pheromonal signals in the accessory olfactory bulb of behaving mice

    Science 299:1196–1201.

    https://doi.org/10.1126/science.1082133

    • PubMed
    • Google Scholar
45. 1. McCord JM
    2. Fridovich I

    (1969) Superoxide Dismutase

    Journal of Biological Chemistry 244:6049–6055.

    https://doi.org/10.1016/S0021-9258(18)63504-5

    • Google Scholar
46. 1. Miller CH
    2. Campbell P
    3. Sheehan MJ

    (2020) Distinct evolutionary trajectories of V1R clades across mouse species

    BMC Evolutionary Biology 20:99.

    https://doi.org/10.1186/s12862-020-01662-z

    • PubMed
    • Google Scholar
47. 1. Mohrhardt J
    2. Nagel M
    3. Fleck D
    4. Ben-Shaul Y
    5. Spehr M

    (2018) Signal detection and coding in the accessory olfactory system

    Chemical Senses 43:667–695.

    https://doi.org/10.1093/chemse/bjy061

    • PubMed
    • Google Scholar
48. 1. Moudra A
    2. Niederlova V
    3. Novotny J
    4. Schmiedova L
    5. Kubovciak J
    6. Matejkova T
    7. Drobek A
    8. Pribikova M
    9. Stopkova R
    10. Cizkova D
    11. Neuwirth A
    12. Michalik J
    13. Krizova K
    14. Hudcovic T
    15. Kolar M
    16. Kozakova H
    17. Kreisinger J
    18. Stopka P
    19. Stepanek O

    (2021) Phenotypic and clonal stability of antigen-inexperienced memory-like T cells across the genetic background, hygienic status, and aging

    Journal of Immunology 206:2109–2121.

    https://doi.org/10.4049/jimmunol.2001028

    • PubMed
    • Google Scholar
49. 1. Mucignat-Caretta C
    2. Redaelli M
    3. Caretta A

    (2012) One nose, one brain: contribution of the main and accessory olfactory system to chemosensation

    Frontiers in Neuroanatomy 6:46.

    https://doi.org/10.3389/fnana.2012.00046

    • PubMed
    • Google Scholar
50. 1. Nodari F
    2. Hsu FF
    3. Fu X
    4. Holekamp TF
    5. Kao LF
    6. Turk J
    7. Holy TE

    (2008) Sulfated steroids as natural ligands of mouse pheromone-sensing neurons

    The Journal of Neuroscience 28:6407–6418.

    https://doi.org/10.1523/JNEUROSCI.1425-08.2008

    • PubMed
    • Google Scholar
51. 1. Novotny MV

    (2003) Pheromones, binding proteins and receptor responses in rodents

    Biochemical Society Transactions 31:117–122.

    https://doi.org/10.1042/bst0310117

    • PubMed
    • Google Scholar
52. 1. O’Brien JS
    2. Kishimoto Y

    (1991) Saposin proteins: structure, function, and role in human lysosomal storage disorders

    FASEB Journal 5:301–308.

    https://doi.org/10.1096/fasebj.5.3.2001789

    • PubMed
    • Google Scholar
53. 1. Overath P
    2. Sturm T
    3. Rammensee HG

    (2014) Of volatiles and peptides: in search for MHC-dependent olfactory signals in social communication

    Cellular and Molecular Life Sciences 71:2429–2442.

    https://doi.org/10.1007/s00018-014-1559-6

    • PubMed
    • Google Scholar
54. 1. Park SH
    2. Podlaha O
    3. Grus WE
    4. Zhang J

    (2011) The microevolution of V1r vomeronasal receptor genes in mice

    Genome Biology and Evolution 3:401–412.

    https://doi.org/10.1093/gbe/evr039

    • PubMed
    • Google Scholar
55. 1. Pavelka N
    2. Pelizzola M
    3. Vizzardelli C
    4. Capozzoli M
    5. Splendiani A
    6. Granucci F
    7. Ricciardi-Castagnoli P

    (2004) A power law global error model for the identification of differentially expressed genes in microarray data

    BMC Bioinformatics 5:203.

    https://doi.org/10.1186/1471-2105-5-203

    • PubMed
    • Google Scholar
56. 1. Perez-Riverol Y
    2. Bai J
    3. Bandla C
    4. García-Seisdedos D
    5. Hewapathirana S
    6. Kamatchinathan S
    7. Kundu DJ
    8. Prakash A
    9. Frericks-Zipper A
    10. Eisenacher M
    11. Walzer M
    12. Wang S
    13. Brazma A
    14. Vizcaíno JA

    (2022) The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

    Nucleic Acids Research 50:D543–D552.

    https://doi.org/10.1093/nar/gkab1038

    • PubMed
    • Google Scholar
57. 1. Phifer-Rixey M
    2. Nachman MW

    (2015) Insights into mammalian biology from the wild house mouse Mus musculus

    eLife 4:05959.

    https://doi.org/10.7554/eLife.05959

    • PubMed
    • Google Scholar
58. 1. Rivière S
    2. Challet L
    3. Fluegge D
    4. Spehr M
    5. Rodriguez I

    (2009) Formyl peptide receptor-like proteins are a novel family of vomeronasal chemosensors

    Nature 459:574–577.

    https://doi.org/10.1038/nature08029

    • PubMed
    • Google Scholar
59. 1. Roberts SA
    2. Simpson DM
    3. Armstrong SD
    4. Davidson AJ
    5. Robertson DH
    6. McLean L
    7. Beynon RJ
    8. Hurst JL

    (2010) Darcin: a male pheromone that stimulates female memory and sexual attraction to an individual male’s odour

    BMC Biology 8:75.

    https://doi.org/10.1186/1741-7007-8-75

    • PubMed
    • Google Scholar
60. 1. Roberts SA
    2. Davidson AJ
    3. McLean L
    4. Beynon RJ
    5. Hurst JL

    (2012) Pheromonal induction of spatial learning in mice

    Science 338:1462–1465.

    https://doi.org/10.1126/science.1225638

    • PubMed
    • Google Scholar
61. 1. Röck F
    2. Mueller S
    3. Weimar U
    4. Rammensee HG
    5. Overath P

    (2006) Comparative analysis of volatile constituents from mice and their urine

    Journal of Chemical Ecology 32:1333–1346.

    https://doi.org/10.1007/s10886-006-9091-2

    • PubMed
    • Google Scholar
62. 1. Rodriguez J
    2. Gupta N
    3. Smith RD
    4. Pevzner PA

    (2008) Does trypsin cut before proline?

    Journal of Proteome Research 7:300–305.

    https://doi.org/10.1021/pr0705035

    • PubMed
    • Google Scholar
63. 1. Rohart F
    2. Gautier B
    3. Singh A
    4. Lê Cao KA

    (2017) mixOmics: An R package for ’omics feature selection and multiple data integration

    PLOS Computational Biology 13:e1005752.

    https://doi.org/10.1371/journal.pcbi.1005752

    • PubMed
    • Google Scholar
64. 1. Stopkova R
    2. Klempt P
    3. Kuntova B
    4. Stopka P

    (2017) On the tear proteome of the house mouse (Mus musculus musculus) in relation to chemical signalling

    PeerJ 5:e3541.

    https://doi.org/10.7717/peerj.3541

    • PubMed
    • Google Scholar
65. 1. Stopková R
    2. Otčenášková T
    3. Matějková T
    4. Kuntová B
    5. Stopka P

    (2021) Biological roles of lipocalins in chemical communication, reproduction, and regulation of microbiota

    Frontiers in Physiology 12:740006.

    https://doi.org/10.3389/fphys.2021.740006

    • PubMed
    • Google Scholar
66. 1. Stopková R
    2. Matějková T
    3. Dodoková A
    4. Talacko P
    5. Zacek P
    6. Sedlacek R
    7. Piálek J
    8. Stopka P

    (2023) Variation in mouse chemical signals is genetically controlled and environmentally modulated

    Scientific Reports 13:8573.

    https://doi.org/10.1038/s41598-023-35450-8

    • PubMed
    • Google Scholar
67. 1. Sturm T
    2. Leinders-Zufall T
    3. Maček B
    4. Walzer M
    5. Jung S
    6. Pömmerl B
    7. Stevanović S
    8. Zufall F
    9. Overath P
    10. Rammensee HG

    (2013) Mouse urinary peptides provide a molecular basis for genotype discrimination by nasal sensory neurons

    Nature Communications 4:1616.

    https://doi.org/10.1038/ncomms2610

    • PubMed
    • Google Scholar
68. 1. Thévenaz P
    2. Ruttimann UE
    3. Unser M

    (1998) A pyramid approach to subpixel registration based on intensity

    IEEE Transactions on Image Processing 7:27–41.

    https://doi.org/10.1109/83.650848

    • PubMed
    • Google Scholar
69. 1. Thorn A
    2. Steinfeld R
    3. Ziegenbein M
    4. Grapp M
    5. Hsiao HH
    6. Urlaub H
    7. Sheldrick GM
    8. Gärtner J
    9. Krätzner R

    (2012) Structure and activity of the only human RNase T2

    Nucleic Acids Research 40:8733–8742.

    https://doi.org/10.1093/nar/gks614

    • PubMed
    • Google Scholar
70. 1. Tirindelli R
    2. Dibattista M
    3. Pifferi S
    4. Menini A

    (2009) From pheromones to behavior

    Physiological Reviews 89:921–956.

    https://doi.org/10.1152/physrev.00037.2008

    • PubMed
    • Google Scholar
71. 1. Turaga D
    2. Holy TE

    (2012) Organization of vomeronasal sensory coding revealed by fast volumetric calcium imaging

    The Journal of Neuroscience 32:1612–1621.

    https://doi.org/10.1523/JNEUROSCI.5339-11.2012

    • PubMed
    • Google Scholar
72. 1. Uchida N
    2. Poo C
    3. Haddad R

    (2014) Coding and transformations in the olfactory system

    Annual Review of Neuroscience 37:363–385.

    https://doi.org/10.1146/annurev-neuro-071013-013941

    • Google Scholar
73. 1. van de Rijn M
    2. Heimfeld S
    3. Spangrude GJ
    4. Weissman IL

    (1989) Mouse hematopoietic stem-cell antigen Sca-1 is a member of the Ly-6 antigen family

    PNAS 86:4634–4638.

    https://doi.org/10.1073/pnas.86.12.4634

    • PubMed
    • Google Scholar
74. 1. Veitinger S
    2. Veitinger T
    3. Cainarca S
    4. Fluegge D
    5. Engelhardt CH
    6. Lohmer S
    7. Hatt H
    8. Corazza S
    9. Spehr J
    10. Neuhaus EM
    11. Spehr M

    (2011) Ca 2+ in mouse Sertoli cells

    The Journal of Physiology 589:5033–5055.

    https://doi.org/10.1113/jphysiol.2011.216309

    • PubMed
    • Google Scholar
75. 1. Voukali E
    2. Veetil NK
    3. Němec P
    4. Stopka P
    5. Vinkler M

    (2021) Comparison of plasma and cerebrospinal fluid proteomes identifies gene products guiding adult neurogenesis and neural differentiation in birds

    Scientific Reports 11:5312.

    https://doi.org/10.1038/s41598-021-84274-x

    • PubMed
    • Google Scholar
76. 1. Wade CM
    2. Daly MJ

    (2005) Genetic variation in laboratory mice

    Nature Genetics 37:1175–1180.

    https://doi.org/10.1038/ng1666

    • PubMed
    • Google Scholar
77. 1. Wilson KCP
    2. Raisman G

    (1980) Age-related changes in the neurosensory epithelium of the mouse vomeronasal organ: Extended period of post-natal growth in size and evidence for rapid cell turnover in the adult

    Brain Research 185:103–113.

    https://doi.org/10.1016/0006-8993(80)90675-7

    • Google Scholar
78. 1. Wong WM
    2. Nagel M
    3. Hernandez-Clavijo A
    4. Pifferi S
    5. Menini A
    6. Spehr M
    7. Meeks JP

    (2018) Sensory adaptation to chemical cues by vomeronasal sensory neurons

    eNeuro 5:ENEURO.0223-18.2018.

    https://doi.org/10.1523/ENEURO.0223-18.2018

    • PubMed
    • Google Scholar
79. 1. Wyatt TD

    (2017) Pheromones

    Current Biology 27:R739–R743.

    https://doi.org/10.1016/j.cub.2017.06.039

    • PubMed
    • Google Scholar
80. 1. Wynn EH
    2. Sánchez-Andrade G
    3. Carss KJ
    4. Logan DW

    (2012) Genomic variation in the vomeronasal receptor gene repertoires of inbred mice

    BMC Genomics 13:415.

    https://doi.org/10.1186/1471-2164-13-415

    • PubMed
    • Google Scholar
81. 1. Xu PS
    2. Lee D
    3. Holy TE

    (2016) Experience-dependent plasticity drives individual differences in pheromone-sensing neurons

    Neuron 91:878–892.

    https://doi.org/10.1016/j.neuron.2016.07.034

    • PubMed
    • Google Scholar

Author details

1. Maximilian Nagel

   Department of Chemosensation, Institute for Biology II, RWTH Aachen University, Aachen, Germany

   Present address

   Sensory Cells and Circuits Section, National Center for Complementary and Integrative Health, Bethesda, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Marco Niestroj

   Department of Chemosensation, Institute for Biology II, RWTH Aachen University, Aachen, Germany

   Contribution

   Investigation

   Competing interests

   No competing interests declared

3. Rohini Bansal

   Department of Medical Neurobiology, Institute for Medical Research Israel Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. David Fleck

   Department of Chemosensation, Institute for Biology II, RWTH Aachen University, Aachen, Germany

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6692-2388

5. Angelika Lampert

   1. Institute of Neurophysiology, Uniklinik RWTH Aachen University, Aachen, Germany
   2. Research Training Group 2416 MultiSenses – MultiScales, RWTH Aachen University, Aachen, Germany

   Contribution

   Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6319-6272

6. Romana Stopkova

   BIOCEV group, Department of Zoology, Faculty of Science, Charles University, Prague, Czech Republic

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

7. Pavel Stopka

   BIOCEV group, Department of Zoology, Faculty of Science, Charles University, Prague, Czech Republic

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Visualization, Methodology, Project administration, Writing – review and editing

   For correspondence

   pavel.stopka@natur.cuni.cz

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1104-1655

8. Yoram Ben-Shaul

   Department of Medical Neurobiology, Institute for Medical Research Israel Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   For correspondence

   yoramb@ekmd.huji.ac.il

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0407-4221

9. Marc Spehr

   1. Department of Chemosensation, Institute for Biology II, RWTH Aachen University, Aachen, Germany
   2. Research Training Group 2416 MultiSenses – MultiScales, RWTH Aachen University, Aachen, Germany

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   m.spehr@sensorik.rwth-aachen.de

   Competing interests

   No competing interests declared

   Additional information

   Lead contact

   "This ORCID iD identifies the author of this article:" 0000-0001-6616-4196

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