Uncovering the In Vivo Proxisome Using Proximity‐Tagging Methods

Distinct dynamics and proximity networks of hub proteins at the prey-invading cell pole in a predatory bacterium

In bacteria, cell poles function as subcellular compartments where proteins localize during specific lifecycle stages, orchestrated by polar "hub" proteins. Whereas most described bacteria inherit an "old" pole from the mother cell and a "new" pole from cell division, generating cell asymmetry at birth, non-binary division poses challenges for establishing cell polarity, particularly for daughter cells inheriting only new poles. We investigated polarity dynamics in the obligate predatory bacterium Bdellovibrio bacteriovorus, proliferating through filamentous growth followed by non-binary division within prey bacteria. Monitoring the subcellular localization of two proteins known as polar hubs in other species, RomR and DivIVA, revealed RomR as an early polarity marker in B. bacteriovorus. RomR already marks the future anterior poles of the progeny during the predator's growth phase, during a precise period closely following the onset of divisome assembly and the end of chromosome segregation. In contrast to RomR's stable unipolar localization in the progeny, DivIVA exhibits a dynamic pole-to-pole localization. This behavior changes shortly before the division of the elongated predator cell, where DivIVA accumulates at all septa and both poles. In vivo protein interaction networks for DivIVA and RomR, mapped through endogenous miniTurbo-based proximity labeling, further underscore their distinct roles in cell polarization and reinforce the importance of the anterior "invasive" cell pole in prey-predator interactions. Our work also emphasizes the precise spatiotemporal order of cellular processes underlying B. bacteriovorus proliferation, offering insights into the subcellular organization of bacteria with filamentous growth and non-binary division.IMPORTANCEIn bacteria, cell poles are crucial areas where "hub" proteins orchestrate lifecycle events through interactions with multiple partners at specific times. While most bacteria exhibit one "old" and one "new" pole, inherited from the previous division event, setting polar identity poses challenges in bacteria with non-binary division. This study explores polar proteins in the predatory bacterium Bdellovibrio bacteriovorus, which undergoes filamentous growth followed by non-binary division inside another bacterium. Our research reveals distinct localization dynamics of the polar proteins RomR and DivIVA, highlighting RomR as an early "hub" marking polar identity in the filamentous mother cell. Using miniTurbo-based proximity labeling, we uncovered their unique protein networks. Overall, our work provides new insights into the cell polarity in non-binary dividing bacteria.

Identification of Protein Partners by APEX2 Proximity Labeling

Proximity labeling methods enable the identification of proteins in the vicinity of a protein of interest in living cells. Among them, APEX2 proximity is a powerful method to spatiotemporally define in vivo "proxisomes" in dynamic bacterial protein systems. Here we describe a standardized APEX2 proximity labeling protocol and possible adaptations to capture protein partners in native conditions.

Proximity-dependent biotin identification links cholesterol catabolism with branched-chain amino acid degradation in Mycobacterium smegmatis

Cholesterol is a crucial component in Mycobacterium tuberculosis virulence as it is required for phagocytosis of mycobacteria by macrophages. In addition, the tubercle bacilli can grow using cholesterol as the sole carbon source. Thus, cholesterol catabolism represents a valuable target for the development of new antitubercular drugs. However, the molecular partners of cholesterol catabolism remain elusive in mycobacteria. Here, we focused on HsaC and HsaD, enzymes involved in two consecutive steps of cholesterol ring degradation and identified putative partners, using a BirA-based proximity-dependent biotin identification (BioID) approach in Mycobacterium smegmatis. In rich medium, the fusion protein BirA-HsaD was able to fish the endogenous cognate HsaC, thus validating this approach to study protein-protein interactions and to infer metabolic channeling of cholesterol ring degradation. In chemically defined medium, both HsaC and HsaD interacted with four proteins, BkdA, BkdB, BkdC, and MSMEG_1634. BkdA, BkdB, and BkdC are enzymes that participate in the degradation of branched-chain amino acids. As cholesterol and branched-chain amino acid catabolism both generate propionyl-CoA, which is a toxic metabolite for mycobacteria, this interconnection suggests a compartmentalization to avoid dissemination of propionyl-CoA into the mycobacterial cytosol. Moreover, the BioID approach allowed us to decipher the interactome of MSMEG_1634 and MSMEG_6518, two proteins of unknown function, which are proximal to the enzymes involved in cholesterol and branched-chain amino acid catabolism. In conclusion, BioID is a powerful tool to characterize protein-protein interactions and to decipher the interconnections between different metabolic pathways, thereby facilitating the identification of new mycobacterial targets.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Proximity-dependent biotinylation approaches to study apicomplexan biology

In the last 10 years, proximity-dependent biotinylation (PDB) techniques greatly expanded the ability to study protein environments in the living cell that range from specific protein complexes to entire compartments. This is achieved by using enzymes such as BirA* and APEX that are fused to proteins of interest and biotinylate proteins in their proximity. PDB techniques are now also increasingly used in apicomplexan parasites. In this review, we first give an overview of the main PDB approaches and how they compare with other techniques that address similar questions. PDB is particularly valuable to detect weak or transient protein associations under physiological conditions and to study cellular structures that are difficult to purify or have a poorly understood protein composition. We also highlight new developments such as novel smaller or faster-acting enzyme variants and conditional PDB approaches, providing improvements in both temporal and spatial resolution which may offer broader application possibilities useful in apicomplexan research. In the second part, we review work using PDB techniques in apicomplexan parasites and how this expanded our knowledge about these medically important parasites.

Towards a systems-level understanding of mitochondrial biology

Human mitochondria are complex and highly dynamic biological systems, comprised of over a thousand parts and evolved to fully integrate into the specialized intracellular signaling networks and metabolic requirements of each cell and organ. Over the last two decades, several complementary, top-down computational and experimental approaches have been developed to identify, characterize and modulate the human mitochondrial system, demonstrating the power of integrating classical reductionist and discovery-driven analyses in order to de-orphanize hitherto unknown molecular components of mitochondrial machineries and pathways. To this goal, systematic, multiomics-based surveys of proteome composition, protein networks, and phenotype-to-pathway associations at the tissue, cell and organellar level have been largely exploited to predict the full complement of mitochondrial proteins and their functional interactions, therefore catalyzing data-driven hypotheses. Collectively, these multidisciplinary and integrative research approaches hold the potential to propel our understanding of mitochondrial biology and provide a systems-level framework to unraveling mitochondria-mediated and disease-spanning pathomechanisms.

Proximity Labeling Techniques to Study Chromatin

Mammals contain over 200 different cell types, yet nearly all have the same genomic DNA sequence. It is a key question in biology how the genetic instructions in DNA are selectively interpreted by cells to specify various transcriptional programs and therefore cellular identity. The structural and functional organization of chromatin governs the transcriptional state of individual genes. To understand how genomic loci adopt different levels of gene expression, it is critical to characterize all local chromatin factors as well as long-range interactions in the 3D nuclear compartment. Much of our current knowledge regarding protein interactions in a chromatin context is based on affinity purification of chromatin components coupled to mass spectrometry (AP-MS). AP-MS has been invaluable to map strong protein-protein interactions in the nucleus. However, the interaction is detected after cell lysis and biochemical enrichment, allowing for loss or gain of false positive or negative interaction partners. Recently, proximity-dependent labeling methods have emerged as powerful tools for studying chromatin in its native context. These methods take advantage of engineered enzymes that are fused to a chromatin factor of interest and can directly label all factors in proximity. Subsequent pull-down assays followed by mass spectrometry or sequencing approaches provide a comprehensive snapshot of the proximal chromatin interactome. By combining this method with dCas9, this approach can also be extended to study chromatin at specific genomic loci. Here, we review and compare current proximity-labeling approaches available for studying chromatin, with a particular focus on new emerging technologies that can provide important insights into the transcriptional and chromatin interaction networks essential for cellular identity.