Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors.

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds' intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane.

The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer. MuMMI captures long-timescale events while preserving detailed atomic descriptions, providing testable models for experimental validation. Both in vitro and computational studies reveal that RBDCRD binding alters KRAS lateral diffusion on the lipid bilayer, increasing cluster size and decreasing diffusion. RAS and membrane binding cause hydrophobic residues in the CRD region to penetrate the bilayer, stabilizing complexes through ß-strand elongation. These cooperative interactions among lipids, KRAS4b, and RAF1 are proposed as essential for forming nanoclusters, potentially a critical step in MAP kinase signal activation.

Transition from laparoscopic to robotic approach in rectal cancer: a single-center short-term analysis based on the learning curve.

As a novel procedure becomes more and more used, knowledge about its learning curve and its impact on outcomes is useful for future implementations. Our aim is (i) to identify the phases of the robotic rectal surgery learning process and assess the safety and oncological outcomes during that period, (ii) to compare the robotic rectal surgery learning phases outcomes with laparoscopic rectal resections performed before the implementation of the robotic surgery program. We performed a retrospective study, based on a prospectively maintained database, with methodological quality assessment by STROBE checklist. All the procedures were performed by the same two surgeons. A total of 157 robotic rectal resections from June 2018 to January 2022 and 97 laparoscopic rectal resections from January 2018 to July 2019 were included. The learning phase was completed at case 26 for surgeon A, 36 for surgeon B, and 60 for the center (both A & B). There were no differences in histopathological results or postoperative complications between phases, achieving the same ratio of mesorectal quality, circumferential and distal resection margins as the laparoscopic approach. A transitory increase of major complications and anastomotic leakage could occur once overcoming the learning phase, secondary to the progressive complexity of cases. Robotic rectal cancer surgery learning curve phases in experienced laparoscopic surgeons was completed after 25-35 cases. Implementation of a robotic rectal surgery program is safe in oncologic terms, morbidity, mortality and length of stay.

Direct Modulators of K-Ras-Membrane Interactions.

Protein-membrane interactions (PMIs) are ubiquitous in cellular signaling. Initial steps of signal transduction cascades often rely on transient and dynamic interactions with the inner plasma membrane leaflet to populate and regulate signaling hotspots. Methods to target and modulate these interactions could yield attractive tool compounds and drug candidates. Here, we demonstrate that the conjugation of a medium-chain lipid tail to the covalent K-Ras(G12C) binder MRTX849 at a solvent-exposed site enables such direct modulation of PMIs. The conjugated lipid tail interacts with the tethered membrane and changes the relative membrane orientation and conformation of K-Ras(G12C), as shown by molecular dynamics (MD) simulation-supported NMR studies. In cells, this PMI modulation restricts the lateral mobility of K-Ras(G12C) and disrupts nanoclusters. The described strategy could be broadly applicable to selectively modulate transient PMIs.

Type 2 Diabetes Mellitus and Nonvalvular Atrial Fibrillation in Mexico: National Registries Raise a Red Flag.

Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases worldwide and is highly prevalent in Mexico, as 10.2% of the adult population harbors this condition. T2DM is usually associated with cardiovascular comorbidities, including arrhythmias. Metabolic impairment is one of the mechanisms that contribute to tissue remodeling that affects atrial structure, and concomitant, the cardiac conduction system, both could result in atrial fibrillation (AF). AF is estimated to affect more than a half million Mexicans, and its incidence is expected to keep rising. According to national registries, T2DM is present in 28.4% of Mexican patients with AF and the coexistence of both diseases is associated with a higher risk of stroke. In clinical practice, the CHA2DS2-VASc risk score is useful for stroke risk stratification in patients with AF to facilitate the adequate use of anticoagulation therapy. T2DM is among the items of the CHA2DS2-VASc score because it correlates with an intrinsic prothrombotic state. In this narrative review, we present information that highlights the need for optimal glucose control and adequate anticoagulation in subjects with T2DM and AF.

Type 2 Diabetes Mellitus and Nonvalvular Atrial Fibrillation in Mexico: National Registries Raise a Red Flag

ABSTRACT Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases worldwide and is highly prevalent in Mexico, as 10.2% of the adult population harbors this condition. T2DM is usually associated with cardiovascular comorbidities, including arrhythmias. Metabolic impairment is one of the mechanisms that contribute to tissue remodeling that affects atrial structure, and concomitant, the cardiac conduction system, both could result in atrial fibrillation (AF). AF is estimated to affect more than a half million Mexicans, and its incidence is expected to keep rising. According to national registries, T2DM is present in 28.4% of Mexican patients with AF and the coexistence of both diseases is associated with a higher risk of stroke. In clinical practice, the CHA2DS2-VASc risk score is useful for stroke risk stratification in patients with AF to facilitate the adequate use of anticoagulation therapy. T2DM is among the items of the CHA2DS2-VASc score because it correlates with an intrinsic prothrombotic state. In this narrative review, we present information that highlights the need for optimal glucose control and adequate anticoagulation in subjects with T2DM and AF.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 µm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

Comparative genomics and interactomics of polyadenylation factors for the prediction of new parasite targets: Entamoeba histolytica as a working model.

Protein-protein interactions (PPI) play a key role in predicting the function of a target protein and drug ability to affect an entire biological system. Prediction of PPI networks greatly contributes to determine a target protein and signal pathways related to its function. Polyadenylation of mRNA 3'-end is essential for gene expression regulation and several polyadenylation factors have been shown as valuable targets for controlling protozoan parasites that affect human health. Here, by using a computational strategy based on sequence-based prediction approaches, phylogenetic analyses, and computational prediction of PPI networks, we compared interactomes of polyadenylation factors in relevant protozoan parasites and the human host, to identify key proteins and define potential targets for pathogen control. Then, we used Entamoeba histolytica as a working model to validate our computational results. RT-qPCR assays confirmed the coordinated modulation of connected proteins in the PPI network and evidenced that silencing of the bottleneck protein EhCFIm25 affects the expression of interacting proteins. In addition, molecular dynamics simulations and docking approaches allowed to characterize the relationships between EhCFIm25 and Ehnopp34, two connected bottleneck proteins. Interestingly, the experimental identification of EhCFIm25 interactome confirmed the close relationships among proteins involved in gene expression regulation and evidenced new links with moonlight proteins in E. histolytica, suggesting a connection between RNA biology and metabolism as described in other organisms. Altogether, our results strengthened the relevance of comparative genomics and interactomics of polyadenylation factors for the prediction of new targets for the control of these human pathogens.

New understanding of multidrug efflux and permeation in antibiotic resistance, persistence, and heteroresistance.

Antibiotics effective against Gram-negative ESKAPE pathogens are a critical area of unmet need. Infections caused by these pathogens are not only difficult to treat but finding new therapies to overcome Gram-negative resistance is also a challenge. There are not enough antibiotics in development that target the most dangerous pathogens and there are not enough novel drugs in the pipeline. The major obstacle in the antibiotic discovery pipeline is the lack of understanding of how to breach antibiotic permeability barriers of Gram-negative pathogens. These barriers are created by active efflux pumps acting across both the inner and the outer membranes. Overproduction of efflux pumps alone or together with either modification of the outer membrane or antibiotic-inactivating enzymes and target mutations contribute to clinical levels of antibiotics resistance. Recent efforts have generated significant advances in the rationalization of compound efflux and permeation across the cell envelopes of Gram-negative pathogens. Combined with earlier studies and novel mathematical models, these efforts have led to a multilevel understanding of how antibiotics permeate these barriers and how multidrug efflux and permeation contribute to the development of antibiotic resistance and heteroresistance. Here, we discuss the new developments in this area.

Molecular docking analysis of a dermatan sulfate tetra-saccharide to human alpha-L-iduronidase.

Human alpha-L-iduronidase (IDUA) is a 653 amino acid protein involved in the sequential degradation of glycos-amino-glycans (GAG), heparan sulfate (HS), and dermatan sulfate (DS). Some variants in the IDUA gene produce a deficient enzyme that causes un-degraded DS and HS to accumulate in multiple tissues, leading to an organ dysfunction known as muco-poly-saccharidosis type I (MPS I). Molecular and catalytic activity assays of new or rare variants of IDUA do not predict the phenotype that a patient will develop. Therefore, it is of interest to describe the molecular docking analysis, to locate binding regions of DS to IDUA to better understand the effect of a variant on MPS I development. The results presented herein demonstrate the presence of a polar/acidic catalytic site and a basic region in the putative binding site of DS to IDUA. Further, synthetic substrate docking with the enzyme could help in the predictions of the MPS I phenotype.

A Zika virus-specific IgM elicited in pregnancy exhibits ultrapotent neutralization.

Congenital Zika virus (ZIKV) infection results in neurodevelopmental deficits in up to 14% of infants born to ZIKV-infected mothers. Neutralizing antibodies are a critical component of protective immunity. Here, we demonstrate that plasma IgM contributes to ZIKV immunity in pregnancy, mediating neutralization up to 3 months post-symptoms. From a ZIKV-infected pregnant woman, we isolated a pentameric ZIKV-specific IgM (DH1017.IgM) that exhibited ultrapotent ZIKV neutralization dependent on the IgM isotype. DH1017.IgM targets an envelope dimer epitope within domain II. The epitope arrangement on the virion is compatible with concurrent engagement of all ten antigen-binding sites of DH1017.IgM, a solution not available to IgG. DH1017.IgM protected mice against viremia upon lethal ZIKV challenge more efficiently than when expressed as an IgG. Our findings identify a role for antibodies of the IgM isotype in protection against ZIKV and posit DH1017.IgM as a safe and effective candidate immunotherapeutic, particularly during pregnancy.

Acoustic force spectroscopy reveals subtle differences in cellulose unbinding behavior of carbohydrate-binding modules.

Protein adsorption to solid carbohydrate interfaces is critical to many biological processes, particularly in biomass deconstruction. To engineer more-efficient enzymes for biomass deconstruction into sugars, it is necessary to characterize the complex protein-carbohydrate interfacial interactions. A carbohydrate-binding module (CBM) is often associated with microbial surface-tethered cellulosomes or secreted cellulase enzymes to enhance substrate accessibility. However, it is not well known how CBMs recognize, bind, and dissociate from polysaccharides to facilitate efficient cellulolytic activity, due to the lack of mechanistic understanding and a suitable toolkit to study CBM-substrate interactions. Our work outlines a general approach to study the unbinding behavior of CBMs from polysaccharide surfaces using a highly multiplexed single-molecule force spectroscopy assay. Here, we apply acoustic force spectroscopy (AFS) to probe a Clostridium thermocellum cellulosomal scaffoldin protein (CBM3a) and measure its dissociation from nanocellulose surfaces at physiologically relevant, low force loading rates. An automated microfluidic setup and method for uniform deposition of insoluble polysaccharides on the AFS chip surfaces are demonstrated. The rupture forces of wild-type CBM3a, and its Y67A mutant, unbinding from nanocellulose surfaces suggests distinct multimodal CBM binding conformations, with structural mechanisms further explored using molecular dynamics simulations. Applying classical dynamic force spectroscopy theory, the single-molecule unbinding rate at zero force is extrapolated and found to agree with bulk equilibrium unbinding rates estimated independently using quartz crystal microbalance with dissipation monitoring. However, our results also highlight critical limitations of applying classical theory to explain the highly multivalent binding interactions for cellulose-CBM bond rupture forces exceeding 15 pN.

Zika Virus Replicates in the Vagina of Mice with Intact Interferon Signaling.

Zika virus (ZIKV) is unusual among flaviviruses in its ability to spread between humans through sexual contact, as well as by mosquitoes. Sexual transmission has the potential to change the epidemiology and geographic range of ZIKV compared to mosquito-borne transmission and potentially could produce distinct clinical manifestations, so it is important to understand the host mechanisms that control susceptibility to sexually transmitted ZIKV. ZIKV replicates poorly in wild-type mice following subcutaneous inoculation, so most ZIKV pathogenesis studies use mice lacking type I interferon (IFN-αß) signaling (e.g., Ifnar1-/-). We found that wild-type mice support ZIKV replication following intravaginal infection, consistent with prior studies, although the infection remained localized to the lower female reproductive tract. Vaginal ZIKV infection required a high-progesterone state (pregnancy or pretreatment with depot medroxyprogesterone acetate [DMPA]) even in Ifnar1-/- mice that otherwise are highly susceptible to ZIKV infection. Progesterone-mediated susceptibility did not appear to result from a compromised epithelial barrier, blunted antiviral gene induction, or changes in vaginal leukocyte populations, leaving open the mechanism by which progesterone confers susceptibility to vaginal ZIKV infection. DMPA treatment is a key component of mouse vaginal infection models for herpes simplex virus and Chlamydia, but the mechanisms by which DMPA increases susceptibility to those pathogens also remain poorly defined. Understanding how progesterone mediates susceptibility to ZIKV vaginal infection may provide insights into host mechanisms influencing susceptibility to diverse sexually transmitted pathogens. IMPORTANCE Zika virus (ZIKV) is transmitted by mosquitoes, similar to other flaviviruses. However, ZIKV is unusual among flaviviruses in its ability also to spread through sexual transmission. We found that ZIKV was able to replicate in the vaginas of wild-type mice, even though these mice do not support ZIKV replication by other routes, suggesting that the vagina is particularly susceptible to ZIKV infection. Vaginal susceptibility was dependent on a high-progesterone state, which is a common feature of mouse vaginal infection models for other pathogens, through mechanisms that have remained poorly defined. Understanding how progesterone mediates susceptibility to ZIKV vaginal infection may provide insights into host mechanisms that influence susceptibility to diverse sexually transmitted pathogens.

Exploring CRD mobility during RAS/RAF engagement at the membrane.

During the activation of mitogen-activated protein kinase (MAPK) signaling, the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF bind to active RAS at the plasma membrane. The orientation of RAS at the membrane may be critical for formation of the RAS-RBDCRD complex and subsequent signaling. To explore how RAS membrane orientation relates to the protein dynamics within the RAS-RBDCRD complex, we perform multiscale coarse-grained and all-atom molecular dynamics (MD) simulations of KRAS4b bound to the RBD and CRD domains of RAF-1, both in solution and anchored to a model plasma membrane. Solution MD simulations describe dynamic KRAS4b-CRD conformations, suggesting that the CRD has sufficient flexibility in this environment to substantially change its binding interface with KRAS4b. In contrast, when the ternary complex is anchored to the membrane, the mobility of the CRD relative to KRAS4b is restricted, resulting in fewer distinct KRAS4b-CRD conformations. These simulations implicate membrane orientations of the ternary complex that are consistent with NMR measurements. While a crystal structure-like conformation is observed in both solution and membrane simulations, a particular intermolecular rearrangement of the ternary complex is observed only when it is anchored to the membrane. This configuration emerges when the CRD hydrophobic loops are inserted into the membrane and helices α3-5 of KRAS4b are solvent exposed. This membrane-specific configuration is stabilized by KRAS4b-CRD contacts that are not observed in the crystal structure. These results suggest modulatory interplay between the CRD and plasma membrane that correlate with RAS/RAF complex structure and dynamics, and potentially influence subsequent steps in the activation of MAPK signaling.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework.

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Ethnoracial Disparities in SARS-CoV-2 Seroprevalence in a Large Cohort of Individuals in Central North Carolina from April to December 2020.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of deaths around the world within the past 2 years. Transmission within the United States has been heterogeneously distributed by geography and social factors with little data from North Carolina. Here, we describe results from a weekly cross-sectional study of 12,471 unique hospital remnant samples from 19 April to 26 December 2020 collected by four clinical sites within the University of North Carolina Health system, with a majority of samples from urban, outpatient populations in central North Carolina. We employed a Bayesian inference model to calculate SARS-CoV-2 spike protein immunoglobulin prevalence estimates and conditional odds ratios for seropositivity. Furthermore, we analyzed a subset of these seropositive samples for neutralizing antibodies. We observed an increase in seroprevalence from 2.9 (95% confidence interval [CI], 1.8 to 4.5) to 12.8 (95% CI, 10.6 to 15.2) over the course of the study. Latinx individuals had the highest odds ratio of SARS-CoV-2 exposure at 6.56 (95% CI, 4.66 to 9.44). Our findings aid in quantifying the degree of asymmetric SARS-CoV-2 exposure by ethnoracial grouping. We also find that 49% of a subset of seropositive individuals had detectable neutralizing antibodies, which was skewed toward those with recent respiratory infection symptoms. IMPORTANCE PCR-confirmed SARS-CoV-2 cases underestimate true prevalence. Few robust community-level SARS-CoV-2 ethnoracial and overall prevalence estimates have been published for North Carolina in 2020. Mortality has been concentrated among ethnoracial minorities and may result from a high likelihood of SARS-CoV-2 exposure, which we observe was particularly high among Latinx individuals in North Carolina. Additionally, neutralizing antibody titers are a known correlate of protection. Our observation that development of SARS-CoV-2 neutralizing antibodies may be inconsistent and dependent on severity of symptoms makes vaccination a high priority despite prior exposure.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

Differentially expressed proteins in platelets derived from patients with hypertension.

Hypertension (HTN) causes end-organ damage and is a major cause of morbidity and mortality globally. Recent studies suggested blood cells participate in the maintenance of HTN. Platelets-anucleated cell fragments derived from megakaryocytes-exert diverse functions, including their well-characterized role in the formation of hemostatic clots. However, platelets from patients with HTN exhibit altered membrane lipid and protein compositions that impact platelet function and lead to formation of aggregates and vascular obstructions. Here, for the first time, we have identified, by proteomic analyses, the most relevant 11 proteins that show the greatest difference in their expression in platelets derived from patients with HTN, in comparison with those from normotensive individuals. These proteins are involved in cytoskeletal organization and the coagulation cascade that contributes to platelet activation, release of granule contents, and aggregation, which culminate in thrombus formation. These results have important implications in our understanding of the molecular mechanisms associated with the development of HTN, and in consequence, the development of new strategies to counteract the cardiovascular disorders associated with constitutive activation of platelets in HTN.

The novel EhHSTF7 transcription factor displays an oligomer state and recognizes a heat shock element in the Entamoeba histolytica parasite.

The heat shock response is a conserved mechanism that allows cells to respond and survive stress damage and is transcriptionally regulated by the heat shock factors and heat shock elements. The P-glycoprotein confer the multidrug resistance phenotype; Entamoeba histolytica has the largest multidrug resistance gene family described so far; one of these genes, the EhPgp5 gene, has an emetine-inducible expression. A functional heat shock element was localized in the EhPgp5 gene promoter, indicating transcriptional regulation by heat shock factors. In this work, we determined the oligomer state of EhHSTF7 and the recognition of the heat shock element of the EhPgp5 gene. The EhHSTF7 recombinant protein was obtained as monomer and oligomer. In silico molecular docking predicts protein-DNA binding between EhHSTF7 and 5'-GAA-3' complementary bases. The rEhHSTF7 protein specifically binds to the heat shock element of the EhPgp5 gene in gel shift assays. The competition assays with heat shock element mutants indicate that 5'-GAA-3' complementary bases are necessary for the rEhHSTF7 binding. Finally, the siRNA-mediated knockdown of Ehhstf7 expression causes downregulation of EhPgp5 expression, suggesting that EhHSTF7 is likely to play a key role in the E. histolytica multidrug resistance. This is the first report of a transcription factor that recognizes a heat shock element from a gene involved in drug resistance in parasites. However, further analysis needs to demonstrate the biological relevance of the EhHSTF7 and the rest of the heat shock factors of E. histolytica, to understand the underlying regulation of transcriptional control in response to stress.