Biology subjects

Bernardi, R. C.

Publications and source records attributed to Bernardi, R. C..

13 recordsLinked to original sources

bioRxiv2025

The urinary pathobiont Actinobaculum massiliense generates androgens via the dirAB pathway

While overlooked during the Human Microbiome Project, characterizing the urinary microbiota in health and disease is a new frontier in microbiome science. Recent studies have associated differential abundance of bacterial taxa including Propionimicrobium lymphophilum and Actinobaculum/Actinotignum spp. with prostate cancer. In this study, we collected urine from subjects prior to prostate biopsy and applied a novel Human Sterolbiome Discovery High-throughput (HSDH) assay to identify culturable urinary bacteria with the ability to generate androgens. Application of the HSDH assay to urine samples led to the isolation of eight P. lymphophilum strains positive for cortisol side-chain cleavage (steroid-17,20-desmolase), 17{beta}-HSDH activity, or both. In addition, we isolated three strains of Actinobaculum massiliense that encode DHEA isomerase reductase (dir) genes. The dirA gene encodes a novel 3{beta}/17{beta}-hydroxysteroid dehydrogenase/{Delta}4,5-isomerase and the dirB gene encodes a novel 17{beta}-hydroxysteroid dehydrogenase isoform. Structural prediction and molecular dynamics reveal probable catalytic mechanisms based on the shared catalytic triad but distinct binding pocket geometries of the DirA and DirB that describe their respective reactions. Phylogenetic analysis of DirA and DirB revealed homologs in urinary tract commensals as well as bacteria associated with steroid degradation found in aquatic and terrestrial environments. Taken together, the development of the HSDH assay and the identification of the dir pathway genes is a significant advance in microbial endocrinology, laying the methodological foundation and providing the molecular basis for understanding the role of urinary tract bacteria in host endocrine physiology.

microbiology↗

bioRxiv2024

Atomistic Insights into gp82 Binding: A Microsecond, Million-Atom Exploration of Trypanosoma cruzi Host-Cell Invasion

Chagas disease, caused by the protozoan Trypanosoma cruzi, affects millions globally, leading to severe cardiac and gastrointestinal complications in its chronic phase. The invasion of host cells by T. cruzi is mediated by the interaction between the parasites glycoprotein gp82 and the human receptor lysosome-associated membrane protein 2 (LAMP2). While experimental studies have identified a few residues involved in this interaction, a comprehensive molecular-level understanding has been lacking. In this study, we present a 1.44-million-atom computational model of the gp82 complex, including over 3,300 lipids, glycosylation sites, and full molecular representations of gp82 and LAMP2, making it the most complete model of a parasite-host interaction to date. Using microsecond-long molecular dynamics simulations and dynamic network analysis, we identified critical residue interactions, including novel regions of contact that were previously uncharacterized. Our findings also highlight the significance of the transmembrane domain of LAMP2 in stabilizing the complex. These insights extend beyond traditional hydrogen bond interactions, revealing a complex network of cooperative motions that facilitate T. cruzi invasion. This study not only confirms key experimental observations but also uncovers new molecular targets for therapeutic intervention, offering a potential pathway to disrupt T. cruzi infection and combat Chagas disease.

biochemistry↗

bioRxiv2024

An expanded metabolic pathway for androgen production by host-associated bacteria

A growing body of literature implicates host-associated microbiota in the modulation of circulating androgen levels in the host, which could have far-reaching implications for androgen-mediated diseases. However, the microbial genetic pathways involved in androgen production remain unknown. Here, we report the first host-associated microbial gene (desF) encoding an enzyme that catalyzes conversion of androstenedione to epitestosterone (epiT) in the gut bacterium, Clostridium scindens. Despite current dogma that epiT is a nuclear androgen-receptor (AR) antagonist, we demonstrate that epiT is a potent androgen, as assessed by its ability to promote prostate cancer cell growth and expression of prostate specific antigen (PSA). We then quantified the desF gene in fecal samples collected from individuals with advanced prostate cancer (rising blood PSA) undergoing androgen deprivation therapy combined with abiraterone acetate and prednisone (AA/P). Strikingly, fecal desF levels were elevated in a subset of individuals progressing on AA/P versus samples taken during AA/P response (stable). Importantly, we observed that AA does not inhibit the bacterial desmolase enzyme that is analogous to the human drug target of AA. We then determined that bacterial isolates from urine or prostatectomy tissue are capable of androgen production. From these isolates we detected 17{beta}-hydroxysteroid dehydrogenase (17{beta}-HSDH) activity, which has not been previously reported in urinary tract bacteria, and discovered the desG gene in urinary isolates encoding 17{beta}-HSDH that catalyzed conversion of androstenedione to testosterone. Applying advanced artificial intelligence and molecular dynamics, we predict the structures and ligand binding to DesF and DesG. Using a novel bioengineered microencapsulation technique, we demonstrate that urinary androgen-producing bacterial strains can also promote prostate cancer cell growth through steroid metabolism. Taken together, our results are a significant advance for steroid microbiology in humans and suggest that these microbial biotransformations should be further studied in the context of androgen-mediated physiological processes and diseases.

microbiology↗

bioRxiv2024

Engineering the Mechanical Stability of a Therapeutic Affibody/PD-L1 Complex by Anchor Point Selection

Protein-protein complexes can vary in mechanical stability depending on the direction from which force is applied. Here we investigated the anisotropic mechanical stability of a molecular complex between a therapeutic non-immunoglobulin scaffold called Affibody and the extracellular domain of the immune checkpoint protein PD-L1. We used a combination of single-molecule AFM force spectroscopy (AFM-SMFS) with bioorthogonal clickable peptide handles, shear stress bead adhesion assays, molecular modeling, and steered molecular dynamics (SMD) simulations to understand the pulling point dependency of mechanostability of the Affibody:(PD-L1) complex. We observed diverse mechanical responses depending on the anchor point. For example, pulling from residue #22 on Affibody generated an intermediate unfolding event attributed to partial unfolding of PD-L1, while pulling from Affibodys N-terminus generated force-activated catch bond behavior. We found that pulling from residue #22 or #47 on Affibody generated the highest rupture forces, with the complex breaking at up to [~] 190 pN under loading rates of [~]104-105 pN/sec, representing a [~]4-fold increase in mechanostability as compared with low force N-terminal pulling. SMD simulations provided consistent tendencies in rupture forces, and through visualization of force propagation networks provided mechanistic insights. These results demonstrate how mechanostability of therapeutic protein-protein interfaces can be controlled by informed selection of anchor points within molecules, with implications for optimal bioconjugation strategies in drug delivery vehicles.

biophysics↗

bioRxiv2024

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

biophysics↗

bioRxiv2023

Effects of Variants of Concern Mutations on the Force-Stability of the SARS-CoV-2:ACE2 Interface and Virus Transmissibility

Viruses mutate under a variety of selection pressures, allowing them to continuously adapt to their hosts. Mutations in SARS-CoV-2 have shown effective evasion of population immunity and increased affinity to host factors, in particular to the cellular receptor ACE2. However, in the dynamic environment of the respiratory tract forces act on the binding partners, which raises the question whether not only affinity, but also force-stability of the SARS-CoV-2:ACE2 bond, might be a selection factor for mutations. Here, we use magnetic tweezers (MT) to study the effect of amino acid substitutions in variants of concern (VOCs) on RBD:ACE2 bond kinetics with and without external load. We find higher affinity for all VOCs compared to wt, in good agreement with previous affinity measurements in bulk. In contrast, Alpha is the only VOC that shows significantly higher force stability compared to wt. Investigating the RBD:ACE2 interactions with molecular dynamics simulations, we are able to rationalize the mechanistic molecular origins of this increase in force-stability. Our study emphasizes the diversity of contributions to the assertiveness of variants and establishes force-stability as one of several factors for fitness. Understanding fitness-advantages opens the possibility for prediction of likely mutations allowing rapid adjustment of therapeutics, vaccination, and intervention measures.

biophysics↗

bioRxiv2022

May the force be with you: the role of hyper-mechanostability of the bone sialoprotein binding protein during early stages of Staphylococci infections

The bone sialoprotein-binding protein (Bbp) is a mechanoactive MSCRAMM protein expressed on the surface of Staphylococcus aureus that mediates adherence of the bacterium to fibrinogen- (Fg), a component of the bone and dentine extracellular matrix of the host cell. Mechanoactive proteins like Bbp have key roles in several physiological and pathological processes. Particularly, the Bbp:Fg interaction is important in the formation of biofilms, an important virulence factor of pathogenic bacteria. Here, we investigated the mechanostability of the Bbp:Fg complex using in silico single-molecule force spectroscopy (SMFS), in an approach that combines results from all-atom and coarse-grained steered molecular dynamics (SMD) simulations. Our results show that Bbp is the most mechanostable MSCRAMM investigated thus far, reaching rupture forces beyond the 2 nN range in typical experimental SMFS pulling rates. Our results show that high force-loads, which are common during initial stages of bacterial infection, stabilize the interconnection between the proteins amino acids, making the protein more "rigid". Our results offer new insights that are crucial on the development of novel anti-adhesion strategies.

biophysics↗

bioRxiv2022

Bridging the gab between in vitro and in silico single-molecule force spectroscopy

Staphylococci bacteria use an arsenal of virulence factors, mainly composed of proteins such as adhesins, to target and adhere to their host. Adhesins play critical roles during infection, mainly during the early steps of adhesion when cells are exposed to high mechanical stress. S. epidermidis SdrG:Fg{beta} force resilience has been investigated using AFM-based single molecule force spectroscopy experiments paired with steered molecular dynamics (SMD) simulations. However, there is still a gap between both kinds of experiments at high force-loading rates. Here, we leveraged the high-speed of coarse-grained (CG) SMD simulations to bridge the gap between the data obtained in vitro and in silico with all-atom SMD. We used the DHS theory to connect the two types of SMD simulations and the predictions are consistent with theory and experimentation. We believe that, when associated with all-atom SMD, course-grained SMD can be a powerful ally to help explain and complement the results of single-molecule force spectroscopy experiments.

biophysics↗

bioRxiv2022

Protein structure prediction in the era of AI: challenges and limitations when applying to in-silico force spectroscopy

Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how several mechanoactive proteins respond to mechanical forces. However, even SMFS has its limitations, including the lack of detailed structural information during force-loading experiments. That is where molecular dynamics (MD) methods shine, bringing atomistic details with femtosecond time-resolution. However, MD heavily relies on the availability of high-resolution structures, which is not available for most proteins. For instance, the Protein Data Bank currently has 192K structures deposited, against 231M protein sequences available on Uniprot. But many are betting that this gap might become much smaller soon. Over the past year, the AI-based AlphaFold created a buzz on the structural biology field by being able to, for the first time, predict near-native protein folds from their sequences. For some, AlphaFold is causing the merge of structural biology with bioinformatics. In this perspective, using an in silico SMFS approach, we investigate how reliable AlphaFold structure predictions are to investigate mechanical properties of staph bacteria adhesins proteins. Our results show that AlphaFold produce extremally reliable protein folds, but in many cases is unable to predict high-resolution protein complexes accurately. Nonetheless, the results show that AlphaFold can revolutionize the investigation of these proteins, particularly by allowing high-throughput scanning of protein structures. Meanwhile, we show that the AlphaFold results need to be validated and should not be employed blindly, with the risk of obtaining an erroneous protein mechanism.

bioinformatics↗

bioRxiv2021

A Tethered Ligand Assay to Probe SARS-CoV-2:ACE2 Interactions

SARS-CoV-2 infections are initiated by attachment of the receptor-binding domain (RBD) on the viral Spike protein to angiotensin-converting enzyme-2 (ACE2) on human host cells. This critical first step occurs in dynamic environments, where external forces act on the binding partners and multivalent interactions play critical roles, creating an urgent need for assays that can quantitate SARS-CoV-2 interactions with ACE2 under mechanical load and in defined geometries. Here, we introduce a tethered ligand assay that comprises the RBD and the ACE2 ectodomain joined by a flexible peptide linker. Using magnetic tweezers and atomic force spectroscopy as highly complementary single-molecule force spectroscopy techniques, we investigate the RBD:ACE2 interaction over the whole physiologically relevant force range. We combine the experimental results with steered molecular dynamics simulations and observe and assign fully consistent unbinding and unfolding events across the three techniques, enabling us to establish ACE2 unfolding as a molecular fingerprint. Measuring at forces of 2-5 pN, we quantify the force dependence and kinetics of the RBD:ACE2 bond in equilibrium. We show that the SARS-CoV-2 RBD:ACE2 interaction has higher mechanical stability, larger binding free energy, and a lower dissociation rate in comparison to SARS-CoV-1, which helps to rationalize the different infection patterns of the two viruses. By studying how free ACE2 outcompetes tethered ACE2, we show that our assay is sensitive to prevention of bond formation by external binders. We expect our results to provide a novel way to investigate the roles of mutations and blocking agents for targeted pharmaceutical intervention.

biophysics↗

bioRxiv2020

Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories

Molecular interactions are essential for regulation of cellular processes, from the formation of multiprotein complexes, to the allosteric activation of enzymes. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions, or optimization of a chemical process using bioengineered molecules. In order to identify important residues and information pathways within molecular complexes, the Dynamical Network Analysis method was developed and has since been broadly applied in the literature. However, in the dawn of exascale computing, this method is generally limited to relatively small biomolecular systems. In this work we provide an evolution of the method, application and interface. All data processing and analysis is conducted through Jupyter notebooks, providing automatic detection of important solvent and ion residues, an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, and subsequent community clustering, calculation of betweenness of contacts, and determination optimal paths. Using the popular visualization program VMD, high-quality renderings of the networks over the biomolecular structures can be produced. Our new implementation was employed to investigate three different systems, with up to 2.5 M atoms, namely the OMP-decarboxylase, the Leucyl-tRNA synthetase complexed with its cognate tRNA and adenylate, and the respiratory complex I in a membrane environment. Our enhanced and updated protocol provides the community with an intuitive and interactive interface, which can be easily applied to large macromolecular complexes.

biophysics↗

bioRxiv2020

Bacteria on steroids: the enzymatic mechanism of an NADH-dependent dehydrogenase that regulates the conversion of cortisol to androgen in the gut microbiome

Microorganisms extensively modify host steroids, but whether these reactions merely eliminate hormones or create signals with new biological identities is largely unknown. Gut bacteria have been known for more than four decades to reduce cortisol to 20-dihydrocortisol, yet the physiological consequence of this transformation remained unresolved. Here we show that microbial cortisol reduction creates a distinct host signalling molecule. A 2.0-[A] structure of the bacterial enzyme DesC, together with molecular dynamics, biochemical perturbation and hybrid quantum mechanics/molecular mechanics simulations, defines substrate recognition and an ordered hydride-transfer and proton-relay mechanism. In gnotobiotic mice, isogenic bacteria expressing active DesC--but not a catalytically inactive S47A variant produced 20-dihydrocortisol in the intestine and circulation and reprogrammed colonic transcription. In primary intestinal epithelial cells, 20-dihydrocortisol, but not cortisol, activated ERK-dependent inflammatory and growth-associated programmes. These responses required nuclear receptor subfamily 4 group A member 3 (NR4A3), whose purified ligand-binding domain bound 20-dihydrocortisol but showed no detectable binding to cortisol. A Chicago colonoscopy cohort linked chronic cortisol exposure, faecal desC, the microbial metabolite and colorectal phenotypes. Thus, bacterial metabolism can change receptor selectivity and biological activity rather than simply terminate host hormone action, expanding the endocrine chemistry of the host.

microbiology↗

bioRxiv2020

High Force Catch Bond Mechanism of Bacterial Adhesion in the Human Gut

Bacterial colonization of the human intestine requires firm adhesion of bacteria to insoluble targets under hydrodynamic flow. Here we report the molecular mechanism behind an mechanostable protein complex responsible for resisting high shear forces and adhering bacteria to cellulose fibers in the human gut. Using single-molecule force spectroscopy (SMFS), single-molecule FRET (smFRET), and molecular dynamics (MD) simulations, we resolved two binding modes and three unbinding reaction pathways of a mechanically ultrastable R. champanellensis (Rc) Dockerin-Cohesin (Doc-Coh) complex. The complex assembles in two discrete binding modes with significantly different mechanical properties, with one breaking at ~500 pN and the other at ~200 pN at loading rates from 1-100 nN/sec. A neighboring X-module domain allosterically regulates the binding interaction and inhibits one of the low-force pathways at high loading rates, giving rise to a new mechanism of catch bonding that manifests under force ramp protocols. Multi-state Monte Carlo simulations show strong agreement with experimental results, validating the proposed kinetic scheme. These results explain mechanistically how gut microbes regulate cell adhesion strength at high shear stress through intricate molecular mechanisms including dual-binding modes, mechanical allostery and catch bonds.

biophysics↗