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Bardet Biedl Syndrome: Obesity Phenotype

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Published: Mon, 11 Jun 2018

In vivo

Obesity is one of the central features of Bardet-Biedl syndrome which in recapitulated in almost all Bbs mice models. The increased weight gain in BBS patients and in BBS mouse models has been attributed to increased food intake and potentially increased accumulation of adipose tissue [2].As numerous studies have shown, hyperphagic obesity results from deregulated food intake mechanisms where the hypothalamus plays a major role.

Our previous data showed Bbs5 mutants to be morbidly obese by 12 weeks whereas Bbs1 and Bbs4 mutants would have reached the same weight as Bbs5 mutants by approximately at 24 weeks (Fig 3.1). In order to investigate the molecular mechanisms underlying dis-regulation of the food intake in Bbs mice, we decided to take advantage of three available at ICH Bbs mice models and perform whole transcriptome sequencing (RNASeq) to assess differential expression of hypothalamic genes in three Bbs mice models. Two approaches have been used: i) to compare differentially expressed hypothalamic genes of Bbs1, Bbs4, and Bbs5 mice at the same age (12 weeks), ii) to compare differentially expressed hypothalamic genes of Bbs1, Bbs4, and Bbs5 mice with the same weight. In this study, we have already completed the analysis of 12 weeks old Bbs1, Bbs4, and Bbs5 mice. We collected hypothalamic tissues from Bbs wild type and mutant males at 12 weeks (3 animals per group)   

  • Bbs1: 3 controls and 3 nulls
  • Bbs4: 3 controls and 3 nulls
  • Bbs5: 3 controls and 3 nulls

Each mouse was resampled for verification of correct genotype (Fig.3.2, A-C) and weighted (Fig. 3.2 D). RNASeq analysis was performed as described in Methods.

Whole Transcriptome Sequencing (RNASeq) analysis

Firstly, to assess the quality of isolated RNA from hypothalamic tissues, principal component analysis (Figure 3.3) was performed. One Bbs5 control (B2) sample failed quality control and therefore was excluded from the analyses (figure 3.3.). DESeq2, analysis with a False Discovery Rate (FDR) of < 1 marked up 105 genes for Bbs1, 273 genes for Bbs4 and 2961 genes for Bbs5.

Genes showing altered expression with p < 0.05 and more than 1.5 fold changes (FC) were considered differentially expressed. The list of upregulated and downregulated genes is presented in table S1.

Compared with the genome background, 105 differentially expressed genes (DEGs) were identified in which 14 genes were downregulated and 99 genes were upregulated in Bbs1 model. 273 DEGs were identified in Bbs4 model in which 55 genes were downregulated and 218 upregulated. In Bbs5 model, 1583 DEGs were identified in which 634 genes were downregulated and 1026 genes were upregulated. Interestingly, the number of upregulated genes was significantly more than downregulated genes in all Bbs models. Differentially expressed up and down regulated genes were clustered and visualised in heat maps (Figure 3.4).

Gene and Protein ontology comparison analysis of Bbs1, Bbs4 and Bbs5 models

Gene Ontology (GO) Stat analysis is widely used to analyse high-throughput data. We performed Gene Ontology (GO) Stat analysis for all genes of Bbs1, Bbs4 and Bbs5 models greater than> log FC 1.5. All GO enrichment were observed by GO terms and P-values in ascending order. In each case, DEGs were grouped according to PANTHER (Protein ANalysis THrough Evolutionary Relationships) protein class, GO Molecular Function, GO Biological Process annotations and Pathway analysis. At this stage, grouping by protein class and pathway classification proved to be most informative than the other GO classifications.

Distribution of DEGs in Bbs1, Bbs4 and Bbs5 models according to PANTHER Protein Classification

A wide range of protein classes were represented in each model (figure 3.5). Table S3 summarises how DEGs in Bbs1, Bbs4 and Bbs5 models were classified. Interestingly,the number of DEGs in each model correlated with the degree of obesity. 1660 genes were dis-regulated in Bbs5 micewhich were morbidly obese at 12 weeks. 105 DEGs were found in Bbs1 and 273 DEGs in Bbs4, models without significant weight increase at 12 weeks. Notably, in spite of a difference in the number of DEGs, the pattern of DEGs distribution into protein class was similar in all Bbs models (figure 3.5).

According to the adapted PANTHER protein classification, ‘Enzymes and Enzyme modulator’ group had the most number of DEGs and ‘Receptor& Receptor regulatory/adaptor protein’ was in the second category in all three models. Also, extracellular proteins were noticeably dis-regulated in all models.

A number of publications suggested that aetiology of BBS lies in abnormal receptor trafficking, and therefore, in this study we decided to expand the analysis of “Receptor” group. For that reason, “Receptor” class was further categorised (Figure 3.6). 14 receptor proteins in Bbs1, 30 receptors in Bbs4, and 151 receptors in Bbs5 were categorised into ligand-gated channel, GPCR, cytokine receptors, nuclear hormone receptor and protein kinase receptors. The majority of the differentially expressed receptor proteins belonged to GPCR class of receptors. According to this classification Bbs1 GPCRs included three genes: P2rx2, Opn4 and Cysltr1. Bbs4 GPCRs included Gpr182, Fzd10, Mc4r, Gpr126, Drd2, Gpr34, S1pr2, Hcar1, Ccr5, Mrgprf and Celsr1. In Bbs5 model, 76 GPCRs were differentially expressed (figure 3.5 & 3.6)

Gene Set Pathway Analysis

To perform gene pathway analysis, we used PANTHER pathway analysis where DEGs were ranked by their correlation with known genes of a pathway and scored by their enrichment within the given pathway. Pathways enriched for each model is shown in table S3.

Pathway analysis resulted in identification of 14 signalling pathways for Bbs1, 38 for Bbs4 and more than 100 pathways in Bbs5. The pathway which contained the most differentially expressed genes in Bbs5 model was GPCR pathway. GPCR pathway which involves several heterotrimeric G-protein signalling pathway such as Gq alpha, Gi alpha, Gs alpha and Go alpha mediated pathway included 39 GPCRs in Bbs5 model. Given the importance of GPCRs in obesity and metabolism, even though only 1 GPCR was found in Bbs4 model and none in Bbs1 model, we decided to focus this study on GPCR signalling in Bbs (figure 3.7 and 3.8).

Differentially expressed G-protein coupled receptors (GPCRs) in Bbs1, Bbs4 and Bbs5 models

GPCRs are the largest group of membrane receptors comprising at least 800 different receptors in the human genome. To date, several GPCRs known to regulate energy homeostasis and in the last decade they have emerged as a very promising source of therapeutic targets for obesity [5]. Notably, GPCRs represent the most important targets in modern pharmacology, accounting for 40% of all available drugs.

To expand our analysis of differentially expressed GPCRs in Bbs mice models we compared DEGs dataset from all three models with published GPCRs classification (The IUPHAR Committee on Receptor Nomenclature and Drug Classification) (http://www.guidetopharmacology.org/). Notably, by using this classification we identified more differentially expressed GPCRs in all models than when we used PANTHER classification. We found 81 differentially expressed GPCRs in Bbs5 mice in comparison with 39 according to PANTHER, 14 differentially expressed GPCRs in Bbs4 model and 2 GPCRs in Bbs1 (Table 3.1)

Table 3.1. Differentially expressed GPCRs in Bbs1, Bbs4 and Bbs5 mutant models

Significant number of metabolism associated GPCR are differentially expressed in Bbs models

Next we compared differentially expressed GPCRs from our dataset with published metabolism/metabolism associated gene database. (figure 3.9). A list of > 10,000 metabolic and metabolic associated genes was downloaded from Genecard (http://www.genecards.org/). We found 2, 8 and 72 differentially expressed GPCRs in Bbs1, Bbs4 and Bbs5 model, respectively, which were associated with metabolism.

Hyperphagia induced obesity in Bbs

The increased weight gain in Bbs mouse models has been shown to result from increased food intake [2] . Therefore, we decided to include the analysis of obesity and hyperphagia associate genes into this study.

A recent review by Pigeyre and Meyre et al [6] presented a compiled list of obesity genes to date. We have used this data to highlight obesity genes within our RNASeq data. (Figure 3.10 and table 3.4).

For Bbs1 DEGs, none of the obesity genes were found to be in common with our list. However, for Bbs4 and Bbs5, Agpat2, Mc4r, Fezf1 and Drd2 were found to be significantly differentially expressed (table 3.2). Additionally, within Bbs5 dataset, Pomc was also significant.

Interestingly, we also found that in both Bbs5 and Bbs4 models, a number of orphan GPR receptors were differentially expressed pointing to the likelihood of having a role in the aetiology of BBS-associated obesity (Table 3.1). Although, the function of majority of GPRs remains elusive, some of them have a proven association with obesity (ref).

Additionally, pathway analysis of obesity genes, for both Bbs4 and Bbs5 highlighted the GPCRs Drd2, ADCY9, and KCNJ6 for Bbs5 and Drd2 for Bbs4.

Literature searches for hyperphagia and hyperphagia associated genes resulted in a gene list of >90.

Comparison of Bbs1, Bbs4, and Bbs5 DEG datasets with this list resulted in finding 21 hits for Bbs5, 2 for Bbs4 and 1 for Bbs1 (table 3.5). The extracted list in each case, was categorised through the pathway analysis of PANTHER. For Bbs5, Heterotrimeric G-protein signalling pathway-Gq alpha, Gi alpha, Gs alpha and Go alpha mediated pathway yielded DRD2 and OPRK1 and for Bbs4 it was only DRD2. Bbs1 had only I DEG in common that did not generate any pathways.

Table 3.3. Summary of Hyperphagia and hyperphagia associated genes against DEGs of Bbs1, Bbs4 and Bbs5  


Log FC Bbs1


Log FC Bbs4


Log FC Bbs5







































Initial study of differentially expressed obesity and hyperphagia genes in Bbs models suggested that there are significant differences but also insightful similarities in development of obesity in Bbs models. The number of obesity and hyperphagia DEGs found in Bbs depends on the severity of obesity making it important to perform DEG analysis at different stages of obesity development. Therefore, our next goal is to perform DEG analysis on Bbs5 pre-obesity mice (5 weeks old) and Bbs1 and Bbs4 mice at 24 weeks where the mice are obese.

Strategy of CRISPR-Cas9-based Bbs knockout in mouse embryonic hypothalamic cell line

The use of differential expression gene analysis as an ultimate tool to delinearise the molecular mechanism of certain conditions like obesity is problematic as it is influenced by a variety of confounding factors such as: cell type heterogeneity; compensatory mechanisms etc. Therefore, in this study, we decided to complement our in vivo data with in vitro data in order to eliminate the “unwanted variability” such as cell type heterogeneity.

Our goal is to characterise the hypothalamic cell populations and elucidate the effect of Bbs genes on obesity. Therefore, we started with one of the ‘First order neuron’ of hypothalamus, NPY (figure 3.12). Our overall design for this part of the project was the deletion of Bbs genes from the hypothalamic cell line (figure 3.11).


Selection of NPY-R and Leptin positive hypothalamic cell clones

In order to characterise the homogenous hypothalamic cell population, live cell staining and Fluorescence Activated Cell Sorting (FACS) enabled us to obtain clones positive for NPY-R and Leptin. (Figure 3.13 and 3.14). More than 20 clones were imaged, validated by further Immunohistochemistry. We chose clone 1 for our experiment.

Generation of the single guide (sg) RNA plasmid

We designed 2-3 sgRNA pairs to reduce the chances of off-target effects (figure 3.15). Guide RNA were devised to disrupt the gene at the start of methionine (ATG). According to the protocol described in section, sgRNA were designed, cloned into the pX330 expression vector.

pX330 was digested with Bbs1 enzyme and run on 1% gel. The band (8.5kb) was cut out and purified. The gRNAs in case all cases of Bbs 1, Bbs3, Bbs4 and Bbs5 were verified by sequencing (fig 3.16).

Generation of HAs and GFP

To extract the desired length of backbone pUC18 and GFP and the homology arms of all 4 gene primer (table) specific PCR was done (figure 3.13).

All PCR products were verified by sequencing.

Gibson DNA Assembly

An exact matching overlaps of the corresponding homologous arms at the ends of the linearised vector and linear insert, GFP in this case, was required for Gibson assembly [7]. The first fragment 5’HA of Bbs (1, 3, 4 or 5) overlapped with the backbone pUC18 and GFP and the 3’HA of Bbs (1,3 ,4 or 5) overlapped with GFP and 3′ of pUC18 (fig) forming a seamless single vector expressing GFP, henceforth known as the repairing construct.

Bbs1,3,4 or 5 repairing constructs were verified by sequencing. Primers were designed across each junction for example in case of Bbs1, forward primers were designed across the junction of pUC18 and 5’HA, 5’HA and GFP, GFP and 3’HA and 3’HA and 3’pUC18 backbone (figure3.17).

Transfection of guide RNA and Repairing construct

The concentration of gRNAs and repairing constructs were determined by nanodrop. The first gene edition we attempted was Bbs3. Three guide RNAs were separately transfected with Bbs3 repairing construct. The transfected cells were subjected to FACS and strict gating was applied to collect the GFP positive single cells.

Screening CRISPR/Cas9 Clones for Deletions and Clone Selection

Following FACs and clonal expansion, genomic DNA was extracted for genotyping. More than 50 clones were genotyped and 15 were found to be heterozygous (figure 3.18). For further verification gel extracted band of the heterozygous were sequenced. Our analysis showed one band aligned nicely with the wild type while the larger band had a GFP insert.

However, we were unsuccessful in creating a homozygous clone that would have served as a stable Bbs3 null cell line for further study.

Validation of clones

In addition to cloning we performed reverse transcriptase PCR for further validation of clones.

Our results showed there no insertion of GFP at the initiation site of exon (figure 3.19).


[1] H. Heberle, G.V. Meirelles, F.R. da Silva, G.P. Telles, R. Minghim, InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams, BMC Bioinformatics, 16 (2015) 169.

[2] R.E. Davis, R.E. Swiderski, K. Rahmouni, D.Y. Nishimura, R.F. Mullins, K. Agassandian, A.R. Philp, C.C. Searby, M.P. Andrews, S. Thompson, C.J. Berry, D.R. Thedens, B. Yang, R.M. Weiss, M.D. Cassell, E.M. Stone, V.C. Sheffield, A knockin mouse model of the Bardet-Biedl syndrome 1 M390R mutation has cilia defects, ventriculomegaly, retinopathy, and obesity, Proceedings of the National Academy of Sciences of the United States of America, 104 (2007) 19422-19427.

[3] K.W. Williams, J.K. Elmquist, Lighting up the hypothalamus: coordinated control of feeding behavior, Nat Neurosci, 14 (2011) 277-278.

[4] A. Agrotis, R. Ketteler, A new age in functional genomics using CRISPR/Cas9 in arrayed library screening, Frontiers in Genetics, 6 (2015).

[5] C. Bjenning, H. Al-Shamma, W. Thomsen, J. Leonard, D. Behan, G protein-coupled receptors as therapeutic targets for obesity and type 2 diabetes, Curr Opin Investig Drugs, 5 (2004) 1051-1062.

[6] M. Pigeyre, F.T. Yazdi, Y. Kaur, D. Meyre, Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity, Clin Sci (Lond), 130 (2016) 943-986.

[7] D.G. Gibson, L. Young, R.Y. Chuang, J.C. Venter, C.A. Hutchison, 3rd, H.O. Smith, Enzymatic assembly of DNA molecules up to several hundred kilobases, Nat Methods, 6 (2009) 343-345.

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