Till this end, our experiments provided a proof-of-concept that the BioID bait could firmly localize on the ribosomes, thus fulfilling an essential condition to capture authentic PPIs. Motivated by these observations, we performed another round of structural analysis of the ribosome to survey the regions that are acted upon by RAFs. Given the much better understanding of the 30S assembly landscape, we concentrated our efforts to dissect 30S late stage assembly events. These events include the formation of the 30S head region and are
accelerated by the concerted action of numerous RAFs such as RbfA, KsgA, Era, RimM and RimP (Shajani et al., 2011). A cleft between the head and the 30S platform region stabilizes the incoming mRNA and also binds to the N-terminal end of Initiation factor-3 (Hussain et al., 2016; Yusupova et al., 2001).
We decided to probe the events leading to the formation of this cleft using BioID.
Based on our earlier analysis, bS18 a candidate that matched all of our bait selection criteria (Fig. 2.6A) was chosen for BirA* fusion. We reasoned that since bS18 binds near the cleft region any assembly factors that transiently interact with the head-platform cleft should be biotinylated by the S18-BioID bait. Further, in order to bolster this hypothesis, we also decided to create a RAF-BioID bait that would in turn biotinylate any r-Proteins located in the vicinity of the cleft region. Interestingly, Era an essential GTPase is proposed to bind on the solvent exposed side of the 30S near the 3’ minor domain of the 16S rRNA on this cleft region (Sharma et al., 2005) (Fig. 2.6B). Era is also proposed to be involved in regulation of cell cycle events in bacteria (Britton et al., 2002), but its actual function is still elusive. Keeping these observations in mind, bS18 and Era seemed to be ideal candidates to probe important assembly related events as well as to decipher the function of Era. In order to minimize the artefacts that may arise due to overexpression of the BioID baits, we decided to introduce the birA* gene in-frame at native locus of era and rpsR by λ-red recombination (Fig. 2.6C &
2.6D). The two strains sR.BirA* & Era.BirA* would produce the BioID bait under the native promoters of era and rpsR. The sR.BirA* cells were also checked for integration of the bait onto ribosomes. Towards this, ribosomes were purified from sR.BirA* strain and analysed by SDS-PAGE. We found a distinct band indicating the presence of S18-BirA* bait in crude ribosomes purified from sR.BirA* cells (Fig. 2.6E)
Figure 2.6: Engineering of Era and S18 BioID baits
(A) A representation of the solvent exposed side of the 30S subunit. The 30S subunit is shown in grey ribbons with proteins uS11, uS7, uS2 & bS18 are shown in green, orange, red and pink, respectively. The region between the proteins forms the head- platform cleft and is also the proposed binding site of Era (PDB ID: 4WZO).
(B) A close up view of Era fitted into the head-platform cleft of 30S. Era is represented in blue ribbons (PDB ID: 1X18, 1X1L). The other r-Proteins surrounding the cleft are also marked.
(C) PCR verification for integration of birA*-cat at the 3’ end of rpsR. Locus flanking primers specific to rpsR were used to highlight the difference in the size of Wt locus (0.8 kB) and the engineered locus (2.6 kB).
(D) PCR verificaion for integration of birA*-cat at the 3’ end of era . Locus flanking primers specific to era were used to highlight the difference in the size of Wt locus (1.4 kB) and the engineered locus (3.5 kB).
(E) An SDS-PAGE analysis to check for the presence of S18-BirA* in ribosomes isolated from sR.BirA* cells. Crude ribosomes purified from sR.BirA* and Era.BirA* were loaded in the respective lanes. The protein marker is loaded in the lane ‘M’ and the sizes are indicated. A distinct band of roughly 42 kDa representing the S18-BirA* was observed only in sR.BirA* derived ribosomes (indicated by an arrow).
2.3.5 BioID probes the interaction between Era and bS18
Excited by these observations, we also wanted to further confirm if the baits retained their promiscuous biotinylation activity. For this, we purified cell lysates from Wt, sR.BirA*
& Era.BirA* cells and probed for presence of biotin modification on cellular proteins using anti-biotin antibody. The cell lysates from Wt cells failed to generate any signal upon immunoblotting (Fig. 2.7A) indicating the absence of any biotinylated proteins. However, both sR.BirA* & Era.BirA* showed significantly higher signals indicating the presence of many biotinylated proteins thus confirming the promiscuous biotinylaion activity of the respective baits. We were also curious to see if the localization of bS18 and Era on ribosomes would lead to biotinylation of r-Proteins as per our hypothesis. In order to address this, we probed for the presence of biotin modification in r-Proteins derived from crude ribosomes of sR.BirA* & Era.BirA*. The immunoblotting experiments clearly indicated presence of biotin modification on numerous r-Proteins in ribosomes from sR.BirA*. However, the number of modified r-Proteins was much lesser in case of Era.BirA* (Fig 2.7B).
Figure 2.7: Chromosomally integrated baits retain their biotinylation activity
(A) An immunoblot with cell lysates from Wt, sR.BirA* and Era.BirA* cells. The undeveloped blot is shown on the left with the respective lanes marked on the top. The blots were probed with an anti-Biotin antibody, which was visualized by HRP- generated chemiluminescence (right).
(B) An immunoblot for crude ribosomes from sR.BirA* and Era.BirA* cells. The undeveloped blot is shown on the left with the respective lanes marked on the top. The blots were probed with an anti-Biotin antibody, which was visualized by HRP- generated chemiluminescence (right).
This differential pattern of biotinylation is in line with our proposed hypothesis as bS18 bait is anticipated to localize on the ribosomes for much longer period of time in contrast to transient interactions between Era and ribosomes. These studies also bolster the credence of the approach, as it is evident from the differential biotinylation that the baits do not simply generate a population of biotinylated proteins but tag only the relevant proximal proteins.
Motivated by these observations, we decided to characterize these PPIs by mass spectrometry. We purified cells lysates from sR.BirA* & Era.BirA* cells grown in presence of 1 mM D-biotin. Biotinylated proteins were purified using streptavidin beads and identified by mass spectrometry. Mass spectrometry derived interactome of Era and bS18 contained 449 and 495 proteins, respectively (Fig. 2.8). Keeping in mind that enrichment by biotinylation may not follow a linear relationship with number of interaction events, any proteins having a Mass spectrometric score > 0 were considered as potential interaction partners. The interaction partners were classified into 7 broad functional classes, namely: (i) Cellular metabolism and
transport, (ii) Cell division and shape determination, (iii) Nucleic acid synthesis and regulation, (iv) Translation regulation, (v) Ribosomal proteins, (vi) Ribosome assembly factors, and (vii) Uncharacterized.
Figure 2.8: The BioID interactome of bS18 and Era
A distribution representing the interaction partners of bS18 and Era as captured by BioID.
The classifications were made on basis of known or predicted function of interacting proteins. The percentage of each representative class is indicated in each section of the pie- chart.
One of the most important highlights of the interactomes was that the mutual biotinylation event between bS18 and Era were captured with high confidence scores, clearly indicating the vicinity dependent biotinylation on the ribosome. The interactome of Era also comprised of r-Proteins uS1, uS2, uS3, uS5 and uS7 with high confidence scores indicating its localization within the cleft region. Additionally, Era was also seen to interact with other RAFs like Der, YchF, BipA, essential Initiation factor-2 and transcription termination factor Rho. These results indicate that the BioID bait does indeed capture molecular contacts with precision. The interactome also contained some uncharacterized proteins and the underlying molecular basis of these novel interactions requires further validation and can serve as starting point for identifying new RAFs.
Discussion
Here, we provide a proof-of–concept establishing the application of a high-throughput assay system to identify transient PPIs of the ribosome that can be extended to identify RAFs.
Relatively simple, the methodology makes use of the proximity dependent promiscuous biotinylaiton activity of BirA*. The only requirements that seem essential for application of BioID in context of ribosome assembly would be a careful choice of the bait candidate coupled with expression of the BioID bait preferably at true physiological concentrations of the candidate protein. Although widely applied in multicellular and unicellular eukaryotic organisms, this study, to our knowledge is the first application of BioID to a bacterial system.
The limited application of BioID to the prokaryotic systems may be attributed to the lack of cellular compartmentalization in bacteria, which further randomizes the localization of a single protein in the bacterial cell. This important lacuna must be considered while designing BioID experiments. In the context of ribosomes, we have tackled this problem by thoroughly verifying the localization of the respective baits on the ribosome.
The BioID bait once installed on the ribosomes would record the PPIs of the respective r-Protein or RAF, thus giving a glimpse of its lifetime inside the cells. Given the ability of the BioID bait to capture real-time and transient interactions on a high-throughput basis, it provides a significant advancement over a reductionist approach towards identifying indivisual interaction events during ribosome assembly. However, it must be noted that all interactions recorded by the BioID bait may not be meaningful as they may originate due to cellular crowding and the omnipresence of ribosomes in the bacterial cytoplasm. It is also possible that some interactions recorded by bait may represent its lifecycle before getting installed on the ribosome and thus may lead to false positives. The problem of false positives due to molecular crowding can be overcome by increasing the number of biological replicates used to generate the interactome. It is also possible to cross verify some of the captured PPIs using the existing large scale PPI screens before ursuing any further validation (Ooi, 2010;
Rajagopala et al., 2014; von Mering et al., 2005; Wuchty and Uetz, 2014). Further, coupling of BioID with a quantitative mass spectrometric methodology like SILAC (Chen et al., 2015) or iTRAQ (Rauniyar and Yates, 2014) can provide better quantitative metrics of PPIs. Recent advancements in the field have led to the development of new smaller, engineered versions of
BirA* (Kim et al., 2016) that can decrease the molecular radius of biotinylation, further ascertaining better localization of the BioID bait. Finally, it should also be deliberated that BioID is essentially a discovery tool and does not always describe the nature of interactions in a biological setting. Careful consideration and cross-validation by other techniques are essential to correctly interpret a novel PPI event.
Summary
This chapter describes the development of a technology directed towards identifying RAF candidates by capturing the protein-protein interactions during ribosome assembly. The PPIs were captured using proximity dependent biotinylation (BioID) that employs a variant of the E. coli BirA protein. Using a combination of in vitro and in vivo experiments, we demonstrate the first ever application of BioID to the bacterial ribosomes and captured the molecular events leading up to the formation of the cleft between the head and the 30S platform region. This entailed determination of the BioID interactome of the cleft localizing r-Protein bS18 and the essential RAF Era. Interestingly, BioID established the prevalence of stable interactions between Era, bS18 and other components of the cleft region. Additionally, novel contacts of both Era and bS18 were also identified. This proof-of-concept established the first stage of our systematic efforts directed towards identifying and characterizing RAFs in bacteria.
Chapter III
Bimolecular fluorescence complementation assisted characterization of putative
ribosome assembly factors
The work embodied in this chapter is published.
Himanshu Sharma, B. Anand (2016). Fluorescence Bimolecular Complementation Enables Facile Detection of Ribosome Assembly Defects in Escherichia coli. RNA Biology, 13, 872- 882.
3
Chapter 3
Introduction
Assembly factors are known to transiently associate with premature ribosomal subunits to facilitate and organize the seemingly interrelated events ranging from RNA processing, folding and coordinated binding of r-Proteins. It is, therefore, the loss of assembly factors which ensues premature subunits that are inept to associate to form functional ribosomes competent for protein synthesis and this state is captured as an altered ribosome profile in the density gradient fractionation. Cold-sensitive phenotype coupled with an altered ribosome profile in density gradient fractionation has been considered as a symptom to identify the involvement of candidate assembly factors in ribosome maturation (Stokes et al., 2014a). The profiles are obtained by employing laborious and time-consuming sucrose density gradient ultracentrifugation followed by fractionation of ribosomal subunits (Mehta et al., 2012). Therefore, this approach though robust doesn’t lend itself for scalability to identify potential assembly factors. This has been a long-standing deterrent in characterizing factors affecting assembly and thus poses a potential setback of overlooking bona fide candidate RAFs. Our pursuit of systematic characterization of RAFs calls for a technique that can complement BioID to validate the candidates in a scalable manner. Here, we have adopted the concept of Bimolecular Fluorescence Complementation (BiFC) and designed and deployed it as a facile tool to capture assembly defect in E. coli. The potential utility of this tool is validated by capturing the assembly defects induced by the loss of known assembly factors, viz., RsgA and SrmB in E. coli.
Materials and Methods