Anti-salmonella properties of kefir yeast isolates: An in vitro screening for potential infection control

The rise of antibiotic resistance has increased the need for alternative ways of preventing and treating enteropathogenic bacterial infection. Various probiotic bacteria have been used in animal and human. However, Saccharomyces boulardii is the only yeast currently used in humans as probiotic. There is scarce research conducted on yeast species commonly found in kefir despite its claimed potential preventative and curative effects. This work focused on adhesion properties, and antibacterial metabolites produced by Kluyveromyces lactis and Saccharomyces unisporus isolated from traditional kefir grains compared to Saccharomyces boulardii strains. Adhesion and sedimentation assay, slide agglutination, microscopy and turbidimetry assay were used to analyze adhesion of Salmonella Arizonae and Salmonella Typhimurium onto yeast cells. Salmonella growth inhibition due to the antimicrobial metabolites produced by yeasts in killer toxin medium was analyzed by slab on the lawn, turbidimetry, tube dilution and solid agar plating assays. Alcohol and antimicrobial proteins production by yeasts in killer toxin medium were analyzed using gas chromatography and shotgun proteomics, respectively. Salmonella adhered onto viable and non-viable yeast isolates cell wall. Adhesion was visualized using scanning electron microscope. Yeasts-fermented killer toxin medium showed Salmonella growth inhibition. The highest alcohol concentration detected was 1.55%, and proteins with known antimicrobial properties including cathelicidin, xanthine dehydrogenase, mucin-1, lactadherin, lactoperoxidase, serum amyloid A protein and lactotransferrin were detected in yeasts fermented killer medium. These proteins are suggested to be responsible for the observed growth inhibition effect of yeasts-fermented killer toxin medium. Kluyveromyces lactis and Saccharomyces unisporus have anti-salmonella effect comparable to Saccharomyces boulardii strains, and therefore have potential to control Salmonella infection.     

Passive Diffusion of Ciprofloxacin and its Metalloantibiotic: A Computational and Experimental study

Fluoroquinolones (FQ) are antibiotics widely used in clinical practise, but the development of bacterial resistance to these drugs is currently a critical public health problem. In this context, ternary copper complexes of FQ (CuFQPhen) have been studied as a potential alternative. In this study, we compared the passive diffusion across the lipid bilayer of one of the most used FQ, ciprofloxacin (Cpx), and its ternary copper complex, CuCpxPhen, that has shown previous promising results regarding antibacterial activity and membrane partition. A combination of spectroscopic studies and molecular dynamics simulations were used and two different model membranes tested: one composed of anionic phospholipids, and the other composed of zwitterionic phospholipids. The obtained results showed a significantly higher membrane permeabilization activity, larger partition, and a more favourable free energy landscape for the permeation of CuCpxPhen across the membrane, when compared to Cpx. Furthermore, the computational results indicated a more favourable translocation of CuCpxPhen across the anionic membrane, when compared to the zwitterionic one, suggesting a higher specificity towards the former. These findings are important to decipher the influx mechanism of CuFQPhen in bacterial cells, which is crucial for the ultimate use of CuFQPhen complexes as an alternative to FQ to tackle multidrug-resistant bacteria.     

Telehealth for Endocrinology: Experience and Effect on Utilization in the Prepandemic Era

ObjectiveTelehealth (TH) use in endocrinology was limited before the COVID-19 pandemic but will remain a major modality of care postpandemic. Reimbursement policies have been limited historically due to concerns of overutilization of visits and testing. Additionally, there is limited literature on endocrinology care delivered via TH for conditions other than diabetes. We assess real-world TH use for endocrinology in a prepandemic environment with the hypothesis that TH would not increase the utilization of total visits or related ancillary testing services compared with conventional (CVL) face-to-face office visits.MethodsA single-institution retrospective cohort study assessing the prepandemic use of TH in endocrinology, consisting of 75 patients seen via TH and 225 patients seen in CVL visits. For most patients, TH was conducted via a clinic-to-clinic model. Outcomes measured were total endocrine visit frequency and frequency of related laboratory and radiology testing per patient, hemoglobin A1C, microalbumin, low-density lipoprotein, thyroid-stimulating hormone, thyroglobulin, and thyroid ultrasounds.ResultsFor all endocrine visits, TH patients had a median of 0.24 (interquartile range, 0.015-0.36) visits per month. CVL patients had a median of 0.20 visits per month (interquartile range, 0.11-0.37). Total visits per month did not vary significantly between groups (P = .051). Hemoglobin A1C outcomes were equivalent and there was no increase in ancillary laboratory testing for the TH group.ConclusionOur observations demonstrate that, in a prepandemic health care setting, TH visits can provide equivalent care for endocrinology patients, without increasing utilization of total visits or ancillary services.     

Modeling Innate Antiviral Immunity in Physiological Context

An effective innate antiviral response is critical for the mitigation of severe disease and host survival following infection. In vivo, the innate antiviral response is triggered by cells that detect the invading pathogen and then communicate through autocrine and paracrine signaling to stimulate the expression of genes that inhibit viral replication, curtail cell proliferation, or modulate the immune response. In other words, the innate antiviral response is complex and dynamic. Notably, in the laboratory, culturing viruses and assaying viral life cycles frequently utilizes cells that are derived from tissues other than those that support viral replication during natural infection, while the study of viral pathogenesis often employs animal models. In recapitulating the human antiviral response, it is important to consider that variation in the expression and function of innate immune sensors and antiviral effectors exists across species, cell types, and cell differentiation states, as well as when cells are placed in different contexts. Thus, to gain novel insight into the dynamics of the host response and how specific sensors and effectors impact infection kinetics by a particular virus, the model system must be selected carefully. In this review, we briefly introduce key signaling pathways involved in the innate antiviral response and highlight how these differ between systems. We then review the application of tissue-engineered or 3D models for studying the antiviral response, and suggest how these in vitro culture systems could be further utilized to assay physiologically-relevant host responses and reveal novel insight into virus-host interactions.     

Probing the sORF-Encoded Peptides of Deinococcus radiodurans in Response to Extreme Stress

Organisms have developed different mechanisms to respond to stresses. However, the roles of small ORF–encoded peptides (SEPs) in these regulatory systems remain elusive, which is partially because of the lack of comprehensive knowledge regarding these biomolecules. We chose the extremophile Deinococcus radiodurans R1 as a model species and conducted large-scale profiling of the SEPs related to the stress response. The integrated workflow consisting of multiple omics approaches for SEP identification was streamlined, and an SEPome of D. radiodurans containing 109 novel and high-confidence SEPs was drafted. Forty-four percent of these SEPs were predicted to function as antimicrobial peptides. Quantitative peptidomics analysis indicated that the expression of SEP068184 was upregulated upon oxidative treatment and gamma irradiation of the bacteria. SEP068184 was conserved in Deinococcus and exhibited negative regulation of oxidative stress resistance in a comparative phenotypic assay of its mutants. Further quantitative and interactive proteomics analyses suggested that SEP068184 might function through metabolic pathways and interact with cytoplasmic proteins. Collectively, our findings demonstrate that SEPs are involved in the regulation of oxidative resistance, and the SEPome dataset provides a rich resource for research on the molecular mechanisms of the response to extreme stress in organisms.     

Discovery Proteomics Analysis Determines That Driver Oncogenes Suppress Antiviral Defense Pathways Through Reduction in Interferon-β Autocrine Stimulation

Since the discovery of oncogenes, there has been tremendous interest to understand their mechanistic basis and to develop broadly actionable therapeutics. Some of the most frequently activated oncogenes driving diverse cancers are c-MYC, EGFR, HER2, AKT, KRAS, BRAF, and MEK. Using a reductionist approach, we explored how cellular proteomes are remodeled in isogenic cell lines engineered with or without these driver oncogenes. The most striking discovery for all oncogenic models was the systematic downregulation of scores of antiviral proteins regulated by type 1 interferon. These findings extended to cancer cell lines and patient-derived xenograft models of highly refractory pancreatic cancer and osteosarcoma driven by KRAS and MYC oncogenes. The oncogenes reduced basal expression of and autocrine stimulation by type 1 interferon causing remarkable convergence on common phenotypic and functional profiles. In particular, there was dramatically lower expression of dsRNA sensors including DDX58 (RIG-I) and OAS proteins, which resulted in attenuated functional responses when the oncogenic cells were treated with the dsRNA mimetic, polyI:C, and increased susceptibility to infection with an RNA virus shown using SARS-CoV-2. Our reductionist approach provides molecular and functional insights connected to immune evasion hallmarks in cancers and suggests therapeutic opportunities.     

Elephants in the Dark: Insights and Incongruities in Pentameric Ligand-gated Ion Channel Models

The superfamily of pentameric ligand-gated ion channels (pLGICs) comprises key players in electrochemical signal transduction across evolution, including historic model systems for receptor allostery and targets for drug development. Accordingly, structural studies of these channels have steadily increased, and now approach 250 depositions in the protein data bank. This review contextualizes currently available structures in the pLGIC family, focusing on morphology, ligand binding, and gating in three model subfamilies: the prokaryotic channel GLIC, the cation-selective nicotinic acetylcholine receptor, and the anion-selective glycine receptor. Common themes include the challenging process of capturing and annotating channels in distinct functional states; partially conserved gating mechanisms, including remodeling at the extracellular/transmembrane-domain interface; and diversity beyond the protein level, arising from posttranslational modifications, ligands, lipids, and signaling partners. Interpreting pLGIC structures can be compared to describing an elephant in the dark, relying on touch alone to comprehend the many parts of a monumental beast: each structure represents a snapshot in time under specific experimental conditions, which must be integrated with further structure, function, and simulations data to build a comprehensive model, and understand how one channel may fundamentally differ from another.     

Smash ridge tillage strongly influence soil functionality,physiology and rice yield

The practice of smash-ridging on dry land crop cultivation has shown much promise. However, the mechanism how does soil functionality and root traits can affect rice yield under smash ridge tillage with reduced nitrogen fertilization have not yet been explored. To fill this knowledge gap, we used three tillage methods—smash-ridging 40 cm (S40), smash-ridging 20 cm (S20), and traditional turn-over plowing 20 cm (T)—and two rice varieties (hybrid rice and conventional rice) and measured soil quality, root traits, rice yield and their correlation analysis at different growth stages. Soil physical and chemical properties were significantly improved by smash-ridging, including improvements in root morphological and physiological traits during three growth stages compared with T. S40 had the highest leaf area index (LAI), plant height (PH), and biomass accumulation (BA). Increment in biomass and panicle number (PN) resulted in higher grain yield (GY) of 6.9–9.4% compared with T. Correlation analysis revealed that root total absorption area (RTAA), root active absorption area (RAA), and root area ratio (RAR) were strongly correlated with soil quality. Root injury flow (RIF) and root biomass accumulation (RBA) were strongly correlated with LAI and above-ground plant biomass accumulation (AGBA). Conclusively, S40 is a promising option for improving soil quality, root traits, and consequently GY.     

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.     

Subclinical Hyperthyroidism: A Review of the Clinical Literature

Subclinical hyperthyroidism (SCHyper) is a biochemical diagnosis characterized by a decreased serum thyroid-stimulating hormone (TSH) and normal serum thyroxine (T4) and triiodothyronine (T3) concentrations. Because SCHyper can be resolved, it is recommended to repeat serum TSH, T3, and T4 concentrations in 3 to 6 months before confirming a diagnosis of SCHyper to consider treatment. Proposed grading systems distinguish between mild (TSH, 0.1-0.4 mIU/L) and severe SCHyper (TSH, <0.1 mIU/L) and are used alongside patients’ age and the presence of risk factors and symptoms to guide treatment. Appropriate evaluation includes an investigation of the underlying cause and assessment of an individual’s risk factors to determine the necessity and type of treatment that may be recommended. SCHyper may be associated with increased risks of cardiovascular-related adverse outcomes, bone loss, and in some studies, cognitive decline. Treatment may include observation without therapy, initiation of antithyroid medications, or pursuit of radioiodine therapy or thyroid surgery. Considerations for treatment include the SCHyper etiology, anticipated long-term natural history of the condition, potential benefits of correcting the thyroid dysfunction, and risks and benefits of each treatment option. The purpose of this overview is to provide a guide for clinicians in evaluating and managing SCHyper in the routine clinical practice.     

Association of Angiotensin-Converting Enzyme gene polymorphism in Pakistani women with the atypical steroidogenesis in Polycystic ovarian syndrome: A case-control study

BackgroundPolymorphism in the angiotensin-converting enzyme gene (ACE) is responsible for elevated ACE concentrations in plasma. High ACE levels induce insulin resistance and hyperandrogenism, which are the main attributes of polycystic ovary syndrome (PCOS). Therefore, it was hypothesized that I/D polymorphism plays a role in the pathogenesis of PCOS.ObjectiveA case-control study was designed to investigate the association of I/D polymorphism of the ACE gene with PCOS in Pakistani women of reproductive age.MethodsACE I/D polymorphism was assessed in 252 women of age group 16–40 years. For genotypic analysis, PCR amplification of genomic DNA was carried out. Statistical analysis was performed to interpret the results using SPSS software.ResultsOur study showed that PCOS women were more likely to have a high body mass index and waist circumferences. Most PCOS patients had menstrual irregularities 99.3%, hirsutism 75.2% and cysts in ovaries 66.6%, along with other hyperandrogenic conditions (P-value = 0.001). The genotypic and allelic frequencies were significantly different between patients and controls. There was a significant association of three genotypes with the ratio of LH: FSH among PCOS patients (P = 0.05). Anthropometric characters, comorbidities, clinical symptoms, and PCOS conditions showed no statistical significance with ACE polymorphism.ConclusionsACE I/D polymorphism was not found associated with clinical conditions of PCOS in women of reproductive age. However, it was associated with atypical steroidogenesis. So, it indicates that ACE I/D polymorphism aggravates the pathogenesis of PCOS.     

Impact of High Deductible Health Plans on Diabetes Care Quality and Outcomes: Systematic Review

ObjectiveTo provide a review of the impact of high deductible health plans (HDHPs) on the utilizations of services required for optimal management of diabetes and subsequent health outcomes.MethodsSystematic literature review of studies published between January 1, 2000, and May 7, 2021, was conducted that examined the impact of HDHP on diabetes monitoring (eg, recommended laboratory and surveillance testing), routine care (eg, ambulatory appointments), medication management (eg, medication initiation, adherence), and acute health care utilization (eg, emergency department visits, hospitalizations, incident complications).ResultsOf the 303 reviewed articles, 8 were relevant. These studies demonstrated that HDHPs lower spending at the expense of reduced high-value diabetes monitoring, routine care, and medication adherence, potentially contributing to the observed increases in acute health care utilization. Additionally, patient out-of-pocket costs for recommended screenings doubled, and total health care expenditures increased by 49.4% for HDHP enrollees compared with enrollees in traditional health plans. Reductions in disease monitoring and routine care and increases in acute health care utilization were greatest in lower-income patients. None of the studies examined the impact of HDHPs on access to diabetes self-management education, technology use, or glycemic control.ConclusionAlthough HDHPs reduce some health care utilization and costs, they appear to do so at the expense of limiting high-value care and medication adherence. Policymakers, providers, and payers should be more cognizant of the potential for negative consequences of HDHPs on patients’ health.     

Mass spectrometry-based Cellular Thermal Shift Assay (CETSA®) for target deconvolution in phenotypic drug discovery

The recent renewed interest in phenotypic drug discovery has concomitantly put a focus on target deconvolution in order to achieve drug-target identification. Even though there are prescribed therapies whose mode of action is not fully understood, knowledge of the primary target will inevitably facilitate the discovery and translation of efficacy from bench to bedside. Elucidating targets and subsequent pathways engaged will also facilitate safety studies and overall development of novel drug candidates. Today, there are several techniques available for identifying the primary target, many of which rely on mass spectrometry (MS) to identify compound – target protein interactions. The Cellular Thermal Shift Assay (CETSA®) is well suited for identifying target engagement between ligands and their protein targets. Several studies have shown that CETSA combined with MS is a powerful technique that allows unlabeled target deconvolution in complex samples such as intact cells and tissues in addition to cell lysates and other protein suspensions. The applicability of CETSA MS for target deconvolution purposes will be discussed and exemplified in this mini review.     

Impact on S. aureus and E. coli Membranes of Treatment with Chlorhexidine and Alcohol Solutions: Insights from Molecular Simulations and Nuclear Magnetic Resonance

Membranes form the first line of defence of bacteria against potentially harmful molecules in the surrounding environment. Understanding the protective properties of these membranes represents an important step towards development of targeted anti-bacterial agents such as sanitizers. Use of propanol, isopropanol and chlorhexidine can significantly decrease the threat imposed by bacteria in the face of growing anti-bacterial resistance via mechanisms that include membrane disruption. Here we have employed molecular dynamics simulations and nuclear magnetic resonance to explore the impact of chlorhexidine and alcohol on the S. aureus cell membrane, as well as the E. coli inner and outer membranes. We identify how sanitizer components partition into these bacterial membranes, and show that chlorhexidine is instrumental in this process.     

Modelling the binding mode of macrocycles: Docking and conformational sampling

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.     

First report of computational protein–ligand docking to evaluate susceptibility to HIV integrase inhibitors in HIV-infected Iranian patients

BackgroundIran has recently included integrase (INT) inhibitors (INTIs) in the first‐line treatment regimen in human immunodeficiency virus (HIV)-infected patients. However, there is no bioinformatics data to elaborate the impact of resistance-associated mutations (RAMs) and naturally occurring polymorphisms (NOPs) on INTIs treatment outcome in Iranian patients.MethodIn this cross-sectional survey, 850 HIV-1-infected patients enrolled; of them, 78 samples had successful sequencing results for INT gene. Several analyses were performed including docking screening, genotypic resistance, secondary/tertiary structures, post-translational modification (PTM), immune epitopes, etc.ResultThe average docking energy (E value) of different samples with elvitegravir (EVG) and raltegravir (RAL) was more than other INTIs. Phylogenetic tree analysis and Stanford HIV Subtyping program revealed HIV-1 CRF35-AD was the predominant subtype (94.9%) in our cases; in any event, online subtyping tools confirmed A1 as the most frequent subtype. For the first time, CRF-01B and BF were identified as new subtypes in Iran. Decreased CD4 count was associated with several factors: poor or unstable adherence, naïve treatment, and drug user status.ConclusionAs the first bioinformatic report on HIV-integrase from Iran, this study indicates that EVG and RAL are the optimal INTIs in first-line antiretroviral therapy (ART) in Iranian patients. Some conserved motifs and specific amino acids in INT-protein binding sites have characterized that mutation(s) in them may disrupt INT-drugs interaction and cause a significant loss in susceptibility to INTIs. Good adherence, treatment of naïve patients, and monitoring injection drug users are fundamental factors to control HIV infection in Iran effectively.     

Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction

Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a “beads-on-a-string” model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.     

Discovery and structure activity relationships of 7-benzyl triazolopyridines as stable,selective, and reversible inhibitors of myeloperoxidase

Myeloperoxidase (MPO) is a heme peroxidase found in neutrophils, monocytes and macrophages that efficiently catalyzes the oxidation of endogenous chloride into hypochlorous acid for antimicrobial activity. Chronic MPO activation can lead to indiscriminate protein modification causing tissue damage, and has been associated with chronic inflammatory diseases, atherosclerosis, and acute cardiovascular events. Triazolopyrimidine 5 is a reversible MPO inhibitor; however it suffers from poor stability in acid, and is an irreversible inhibitor of the DNA repair protein methyl guanine methyl transferase (MGMT). Structure-based drug design was employed to discover benzyl triazolopyridines with improved MPO potency, as well as acid stability, no reactivity with MGMT, and selectivity against thyroid peroxidase (TPO). Structure-activity relationships, a crystal structure of the MPO-inhibitor complex, and acute in vivo pharmacodynamic data are described herein.     

‘Design,synthesis, and strategic use of small chemical probes toward identification of novel targets for drug development’

Chemical probes are essential tools used to study and modulate biological systems. Here, we describe some of the recent scientific advancement in the field of chemical biology, as well as how the advent of new technologies is redefining the criteria of ‘good’ chemical probes and influencing the discovery of valuable drug leads. In this review, we report selected examples of the usage of linkered and linker-free chemical probes for target identification, biological discovery, and general mechanistic understanding. We also discuss the promises of chemogenomics libraries in phenotypic screens, as well as the limitation of their usage to identify the modulation of new targets and biology.