Computational inference of eIF4F complex function and structure in human cancers.

The canonical eukaryotic initiation factor 4F (eIF4F) complex, composed of eIF4G1, eIF4A1, and the cap-binding protein eIF4E, plays a crucial role in cap-dependent translation initiation in eukaryotic cells. An alternative cap-independent initiation can occur, involving only eIF4G1 and eIF4A1 through internal ribosome entry sites (IRESs). This mechanism is considered complementary to cap-dependent initiation, particularly in tumors under stress conditions. However, the selection and molecular mechanism of specific translation initiation remains poorly understood in human cancers. Thus, we analyzed gene copy number variations (CNVs) in TCGA tumor samples and found frequent amplification of genes involved in translation initiation. Copy number gains in EIF4G1 and EIF3E frequently co-occur across human cancers. Additionally, EIF4G1 expression strongly correlates with genes from cancer cell survival pathways including cell cycle and lipogenesis, in tumors with EIF4G1 amplification or duplication. Furthermore, we revealed that eIF4G1 and eIF4A1 protein levels strongly co-regulate with ribosomal subunits, eIF2, and eIF3 complexes, while eIF4E co-regulates with 4E-BP1, ubiquitination, and ESCRT proteins. Utilizing Alphafold predictions, we modeled the eIF4F structure with and without eIF4E binding. For cap-dependent initiation, our modeling reveals extensive interactions between the N-terminal eIF4E-binding domain of eIF4G1 and eIF4E. Furthermore, the eIF4G1 HEAT-2 domain positions eIF4E near the eIF4A1 N-terminal domain (NTD), resulting in the collaborative enclosure of the RNA binding cavity within eIF4A1. In contrast, during cap-independent initiation, the HEAT-2 domain directly binds the eIF4A1-NTD, leading to a stronger interaction between eIF4G1 and eIF4A1, thus closing the mRNA binding cavity without the involvement of eIF4E.

Integrating network pharmacology and in silico analysis deciphers Withaferin-A's anti-breast cancer potential via hedgehog pathway and target network interplay.

This study examines the remarkable effectiveness of Withaferin-A (WA), a withanolide obtained from Withania somnifera (Ashwagandha), in encountering the mortiferous breast malignancy, a global peril. The predominant objective is to investigate WA's intrinsic target proteins and hedgehog (Hh) pathway proteins in breast cancer targeting through the application of in silico computational techniques and network pharmacology predictions. The databases and webtools like Swiss target prediction, GeneCards, DisGeNet and Online Mendelian Inheritance in Man were exploited to identify the common target proteins. The culmination of the WA network and protein-protein interaction network were devised using Stitch and String web tools, through which the drug-target network of 30 common proteins was constructed employing Cytoscape-version 3.9. Enrichment analysis was performed by incorporating Gprofiler, Metascape and Cytoscape plugins. David compounded the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, and enrichment was computed through bioinformatics tools. The 20 pivotal proteins were docked harnessing Glide, Schrodinger Suite 2023-2. The investigation was governed by docking scores and affinity. The shared target proteins underscored the precise Hh and WA network roles with the affirmation enrichment P-value of <0.025. The implications for hedgehog and cancer pathways were profound with enrichment (P < 0.01). Further, the ADMET and drug-likeness assessments assisted the claim. Robust interactions were noticed with docking studies, authenticated through molecular dynamics, molecular mechanics generalized born surface area scores and bonds. The computational investigation emphasized WA's credible anti-breast activity, specifically with Hh proteins, implying stem-cell-level checkpoint restraints. Rigorous testament is imperative through in vitro and in vivo studies.

Integrated analysis of Wnt signalling system component gene expression.

Wnt signalling controls patterning and differentiation across many tissues and organs of the developing embryo through temporally and spatially restricted expression of multi-gene families encoding ligands, receptors, pathway modulators and intracellular components. Here, we report an integrated analysis of key genes in the 3D space of the mouse embryo across multiple stages of development. We applied a method for 3D/3D image transformation to map all gene expression patterns to a single reference embryo for each stage, providing both visual analysis and volumetric mapping allowing computational methods to interrogate the combined expression patterns. We identify territories where multiple Wnt and Fzd genes are co-expressed and cross-compare all patterns, including all seven Wnt paralogous gene pairs. The comprehensive analysis revealed regions in the embryo where no Wnt or Fzd gene expression is detected, and where single Wnt genes are uniquely expressed. This work provides insight into a previously unappreciated level of organisation of expression patterns, as well as presenting a resource that can be utilised further by the research community for whole-system analysis.

Origin of iodine preferential attack at sulfur in phosphorothioate and subsequent P-O or P-S bond dissociation.

Iodine-induced cleavage at phosphorothioate DNA (PT-DNA) is characterized by extremely high sensitivity (∼1 phosphorothioate link per 106 nucleotides), which has been used for detecting and sequencing PT-DNA in bacteria. Despite its foreseeable potential for wide applications, the cleavage mechanism at the PT-modified site has not been well established, and it remains unknown as to whether or not cleavage of the bridging P-O occurs at every PT-modified site. In this work, we conducted accurate ωB97X-D calculations and high-performance liquid chromatography-mass spectrometry to investigate the process of PT-DNA cleavage at the atomic and molecular levels. We have found that iodine chemoselectively binds to the sulfur atom of the phosphorothioate link via a strong halogen-chalcogen interaction (a type of halogen bond, with binding affinity as high as 14.9 kcal/mol) and thus triggers P-O bond cleavage via phosphotriester-like hydrolysis. Additionally, aside from cleavage of the bridging P-O bond, the downstream hydrolyses lead to unwanted P-S/P-O conversions and a loss of the phosphorothioate handle. The mechanism we outline helps to explain specific selectivity at the PT-modified site but also predicts the dynamic stoichiometry of P-S and P-O bond breaking. For instance, Tris is involved in the cascade derivation of S-iodo-phosphorothioate to S-amino-phosphorothioate, suppressing the S-iodo-phosphorothioate hydrolysis to a phosphate diester. However, hydrolysis of one-third of the Tris-O-grafting phosphotriester results in unwanted P-S/P-O conversions. Our study suggests that bacterial DNA phosphorothioation may more frequently occur than previous bioinformatic estimations have predicted from iodine-induced deep sequencing data.

VIPPID: a gene-specific single nucleotide variant pathogenicity prediction tool for primary immunodeficiency diseases.

Distinguishing pathogenic variants from non-pathogenic ones remains a major challenge in clinical genetic testing of primary immunodeficiency (PID) patients. Most of the existing mutation pathogenicity prediction tools treat all mutations as homogeneous entities, ignoring the differences in characteristics of different genes, and use the same model for genes in different diseases. In this study, we developed a single nucleotide variant (SNV) pathogenicity prediction tool, Variant Impact Predictor for PIDs (VIPPID; https://mylab.shinyapps.io/VIPPID/), which was tailored for PIDs genes and used a specific model for each of the most prevalent PID known genes. It employed a Conditional Inference Forest model and utilized information of 85 features of SNVs and scores from 20 existing prediction tools. Evaluation of VIPPID showed that it had superior performance (area under the curve = 0.91) over non-specific conventional tools. In addition, we also showed that the gene-specific model outperformed the non-gene-specific models. Our study demonstrated that disease-specific and gene-specific models can improve SNV pathogenicity prediction performance. This observation supports the notion that each feature of mutations in the model can be potentially used, in a new algorithm, to investigate the characteristics and function of the encoded proteins.

Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics.

BACKGROUND: This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes. METHODS: Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging. RESULTS: Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99). CONCLUSION: While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.

Enterocin DD14 can inhibit the infection of eukaryotic cells with enveloped viruses.

Bacteriocins are ribosomally synthesized bacterial peptides endowed with antibacterial, antiprotozoal, anticancer and antiviral activities. In the present study, we evaluated the antiviral activities of two bacteriocins, enterocin DD14 (EntDD14) and lacticaseicin 30, against herpes simplex virus type 1 (HSV-1), human coronavirus 229E (HCoV-229E) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Vero, Huh7 and Vero E6 cells, respectively. In addition, the interactions of these bacteriocins with the envelope glycoprotein D of HSV-1 and the receptor binding domains of HCoV-229E and SARS-CoV-2 have been computationally evaluated using protein-protein docking and molecular dynamics simulations. HSV-1 replication in Vero cells was inhibited by EntDD14 and, to a lesser extent, by lacticaseicin 30 added to cells after virus inoculation. EntDD14 and lacticaseicin 30 had no apparent antiviral activity against HCoV-229E; however, EntDD14 was able to inhibit SARS-CoV-2 in Vero E6 cells. Further studies are needed to elucidate the antiviral mechanism of these bacteriocins.

Europium-based ß-hydroxyketone complexes: synthesis, optoelectronic, thermal and computational analyses.

Six red-light-emitting Eu(III) complexes having a ß-hydroxyketone as ligand and heterocyclic ring containing compounds as ancillary ligands were synthesized to explore their use in displays and optoelectronics. The coordinating behavior of complexes was determined by various techniques such as FTIR (Fourier transform infrared), 1H-NMR (Nuclear magnetic resonance), and 13C-NMR that establishes a bonding of ligand and ancillary ligand with the Eu(III) ion. Morphology and purity were investigated through XRD (X-ray diffraction), SEM (scanning electron microscopy) and EDS (energy-dispersive X-ray spectroscopy) analyses that suggest semicrystalline and pure complex formation. Thermal analysis of complexes by TGA/DTG (thermogravimetric/derivative thermogravimetric) indicates that complexes are stable upto 200 ºC temperature making them suitable for use in display devices. Analysis of the photophysical properties was carried out in both solid and solution states using PL (photoluminescence) studies, color parameters, J-O (Judd-Ofelt) analysis and bandgap. Most emissive transition (5D0 → 7F2) is responsible for the red emission in the complexes. The CIE (Commission International de I'Eclairage) coordinates of complexes also indicate the red emission on UV excitation. The bandgap which was obtained in the range of 2.54-3.02 eV reveals the semiconducting behavior of complexes. Values of J-O parameters and Ω2 in the complexes reflect asymmetric chemical environment around Eu (III) and less covalence and the Ω4 indicates that complexes are less rigid. Bandgap calculated through DFT (density function theory) for complexes is in range of 2.37-2.77 eV, and intensity parameters (J-O), energy transfer rates, and spherical coordinates were determined by LUMPAC software. The computational data are in good harmony with the experimental data. Further biological aspects of complexes were studied using antioxidant and antimicrobial studies.

Tackling antibiotic contaminations in wastewater with novel Modified-MOF nanostructures: A study of molecular simulations and DFT calculations.

The contamination of wastewater with antibiotics has emerged as a critical global challenge, with profound implications for environmental integrity and human well-being. Adsorption techniques have been meticulously investigated and developed to mitigate and alleviate their effects. In this study, we have investigated the adsorption behaviour of Erythromycin (ERY), Gentamicin (GEN), Levofloxacin (LEVO), and Metronidazole (MET) antibiotics as pharmaceutical contaminants (PHCs) on amide-functionalized (RC (=O)NH2)/MIL-53 (Al) (AMD/ML53A), using molecular simulations and density functional theory (DFT) calculations. Based on our DFT calculations, it becomes apparent that the adsorption tendencies of antibiotics are predominantly governed by the presence of AMD functional groups on the adsorbent surface. Specifically, hydrogen bonding (HB) and van der Waals (vdW) interactions between antibiotics and AMD groups serve as the primary mechanisms facilitating adsorption. Furthermore, we have observed that the adsorption behaviors of these antibiotics are influenced by their respective functional groups, molecular shapes, and sizes. Our molecular simulations delved into how the AMD/ML53A surfaces interact with antibiotics as PHCs. Moreover, various chemical quantum descriptors based on Frontier Molecular Orbitals (FMO) were explored to elucidate the extent of AMD/ML53A adsorption and to assess potential alterations in their electronic properties throughout the adsorption process. Monte Carlo simulation showed that ERY molecules adsorb stronger to the adsorbent in acidic and basic conditions than other contaminants, with high energies: -404.47 kcal/mol in acidic and -6375.26 kcal/mol in basic environments. Molecular dynamics (MD) simulations revealed parallel orientation for the ERY molecule's adsorption on AMD/ML53A with 80% rejection rate. In conclusion, our study highlighted the importance of modeling in developing practical solutions for removing antibiotics as PHCs from wastewater. The insights gained from our calculations can facilitate the design of more effective adsorption materials, ultimately leading to a more hygienic and sustainable ecosystem.

Recombinant expression, downstream optimization, and therapeutic evaluation of recombinant human interleukin-37 for cancer therapy.

Interleukin-37 is a cytokine with potent immunosuppressive properties that has been shown to have potential to treat autoimmune and chronic inflammatory diseases, as well as certain types of cancer. IL-37 is a 19 kDa protein which interacts with proteins in receptor-dependent and receptor-independent pathways. The expression of the IL-37 protein cloned into the pET-28a vector was optimized in Rosetta 2(DE3) after comparing its expression with Rosetta-gami 2(DE3) and Rosetta 2(DE3) pLysS, which was then used for the large-scale production of IL-37. IMAC purification of IL-37 yielded > 97% pure 0.9 mg/mL protein from auto-induced fermentation. The IC50 value of IL-37 was < 1 µM, which was similar to that of doxorubicin, and proliferation of > 80% of all cancer cells was inhibited by 100 µg/mL of IL-37 protein. IL-37 may be a promising theragnostic target for cancer due to its comparable IC50 value with that of doxorubicin.

Anti-Inflammatory and Immunomodulatory Properties of a Crude Polysaccharide Derived from Green Seaweed Halimeda tuna: Computational and Experimental Evidences.

In this study, we investigated for the first time the anti-inflammatory and immunomodulatory properties of crude polysaccharide (PSHT) extracted from green marine algae Halimeda tuna. PSHT exhibited anti-oxidant activity in vitro through scavenging 1, 1-diphenyl-2-picryl hydroxyl free radical, reducing Fe3+/ferricyanide complex, and inhibiting nitric oxide. PSHT maintained the erythrocyte membrane integrity and prevented hemolysis. Our results also showed that PSHT exerted a significant anti-edematic effect in vivo by decreasing advanced oxidation protein products and malondialdehyde levels and increasing the superoxide dismutase and glutathione peroxidase activities in rat's paw model and erythrocytes. Interestingly, PSHT increased the viability of murine RAW264.7 macrophages and exerted an anti-inflammatory effect on lipopolysaccharide-stimulated cells by decreasing pro-inflammatory molecule levels, including nitric oxide, granulocyte-macrophage colony-stimulating factor (GM-CSF) and tumor necrosis factor-alpha (TNF-α). Our findings indicate that PSHT could be used as a potential immunomodulatory, anti-inflammatory, anti-hemolytic, and anti-oxidant agent. These results could be explained by the computational findings showing that polysaccharide building blocks bound both cyclooxygenase-2 (COX-2) and TNF-α with acceptable affinities.

Synthesis and investigation of new indole-containing vinyl sulfide derivatives: In silico and in†vitro studies for potential therapeutic applications.

Indoles featuring organosulfur compounds serve as privileged structural scaffolds in various biologically active compounds. This study investigates the biological properties of five synthetic sulphenyl vinyl indoles (3 a-e) using both in silico and in vitro methods. Computational analyses employing Swiss ADME and Molinspiration software reveal the remarkable inhibitory activity of compound 3 d against proteases and kinases (scores of 0.18 and 0.06, respectively). Furthermore, it demonstrates the ability to modulate ionic and G protein-coupled receptors (scores: -0.06 and 0.31, respectively) and serves as a ligand for nuclear receptors (score 0.15). In vitro investigations highlight the compounds' efficacy in countering ABTS+ radical attacks and reducing lipid peroxidation levels. Particularly noteworthy is the superior efficacy of compounds 3 a, 3 b, and 3 e in DPPH (EC50 3 a: 268.5 µM) and TEAC assays (EC50 3 a: 49.9 µM; EC50 3 b: 133.4 µM, and EC50 3 e: 84.9 µM), as well as TBARS levels. Compound 3 c significantly reduces acetylcholinesterase activity, positioning itself as a noteworthy enzyme inhibitor. This study emphasizes the versatile biological potential of synthetic indole derivatives, suggesting their applicability for therapeutic purposes.

Thiazepine-based Hybrids as Promising Anti-colon Cancer Agents: Design, Synthesis, Computational and In Vitro Screening.

Novel thiazepine-based hybrids (9a-d) were designed and synthesized to create lead molecules with exceptional anti-colon cancer efficacy. Analytical methods, including IR, NMR, and HR-MS, characterized the synthesized compounds. The in vitro colorectal study was carried out to compare the biological activity of newly developed compounds with the computational data. The tested compounds induced cytotoxicity in HT-29 cells for both 24h and 48h in a dose-dependent manner. However, compound 9a induced cytotoxicity at much higher concentrations compared to the rest of the compounds. 9b and 9c caused 50% cell death (compared to the untreated cells) at a dose of ~ 50µM and 40 µM in case of 24-hour exposure, respectively. On the contrary, for 48h exposure, both 9b and 9c induced 50% cell death concerning untreated cells at a dose of around ~20 µM, whereas 9d exhibited 50% cell death at 5 µM in the case of 48 h exposure. In silico ADMET was also carried out to understand the pharmacokinetics and safety profiles of the drug candidates. We found some of the critical targets of these compounds, which eventually will be integral to exploring the mechanistic actions of these compounds in colon cancer.

RNAlysis: analyze your RNA sequencing data without writing a single line of code.

BACKGROUND: Among the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code since most available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results. RESULTS: We have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files (adapter trimming, alignment, and feature counting), through exploratory data analysis and data visualization, clustering analysis, and gene set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA sequencing data from different studies using C. elegans nematodes. We note that the software applies equally to data obtained from any organism with an existing reference genome. CONCLUSIONS: RNAlysis is suitable for investigating various biological questions, allowing researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses.

High delta angle after reverse total shoulder arthroplasty increases stresses of the acromion: biomechanical study of different implant positions.

BACKGROUND: Acromial fractures after reverse total shoulder arthroplasty (RTSA) are a common complication. Nevertheless, only a few studies have identified risk factors for acromial fractures after RTSA. High delta angle (combination of inferiorization and medialization of the center of rotation [COR]) after RTSA was identified as a risk factor in recent studies. The aim of this study was the biomechanical exploration of different delta angles and implant configurations with regard to the acromial stress. METHODS: In a rigid body model of the upper extremity muscle, forces of the deltoid muscle were calculated before and after implanting RTSA in different arm and implant positions. The deltoid muscle was divided into an anterior, middle, and posterior part. Implant positions of the glenoid components were changed in the medialization, lateralization, and inferiorization of the COR as well as lateralization of the humeral component. Further, in a finite element model of the upper extremity, the stresses of the acromion in the same implant design configurations were measured. RESULTS: Differences in acromial stress between different delta angle model configurations were observed. Lateralization (5 and 10 mm) of the glenosphere reduced the maximal acromial stress by 21% (1.5 MPa) and 31% (1.3 MPa), respectively. Inferiorization (5 and 10 mm) of the glenosphere increased the maximal acromial stress by 5% (2.0 MPa) and 15% (2.2 MPa), respectively. Changes in positioning the humeral component was found to have the highest impact in this model configuration. A 10-mm-lateralized humeral component reduced acromial stress by 37% (1.2 MPa), whereas in the 6-mm-medialized configuration, an increase in acromial stress by 83% (3.48 MPa) was observed. There was a high correlation between the delta angle and acromial stress (R2 = 0.967). CONCLUSION: Implant design configuration has an impact on the acromial stress. High delta angles correlate with an increase in acromial stress. Both lateralization of the COR and the humerus decreased the acromial stress in our study. The lateralization of the humerus has the highest impact in influencing acromial stress. Because of contrary results in the current literature, further studies with focus on the acromial stress influenced by different anatomic variants of the shoulder and the acromion are needed before a clinical recommendation can be made.

Clinical and Computational Evaluation of an Anatomic Patellar Component.

BACKGROUND: Anatomic patellar components for total knee arthroplasty (TKA) have demonstrated favorable in vivo kinematics. A novel failure mechanism in TKA patients with an anatomic patellar component was observed prompting an investigation to identify patient- and implant-related factors associated with suboptimal performance. METHODS: A retrospective evaluation was performed comparing 100 TKA patients with an anatomic patellar component to 100 gender-, age-, and body mass index-matched patients with a medialized dome component. All surgeries were performed with the same posterior-stabilized TKA system with minimum of 1-year follow-up. Several radiographic parameters were assessed. A separate computational evaluation was performed using finite-element analysis, comparing bone strain energy density through the patella bone remnant. RESULTS: Patients with an anatomic patellar component had significantly higher rates of anterior knee pain (18 versus 2%, P < .001), chronic effusions (18 versus 2%, P < .001), and superior patellar pole fragmentation (36 versus 13%, P < .001) compared to those with a dome component. Radiographically, the anatomic group demonstrated more lateral patellar subluxation (2.3 versus 1.1 mm, P < .001) and lateral tilt (5.4 versus 4.0 mm, P = .013). Furthermore, there were more revisions in the anatomic group (7 versus 3, P = .331). On computational evaluation, all simulations demonstrated increased bone strain energy density at the superior patellar pole with the anatomic patella. Resection thickness <13 mm resulted in over 2-fold higher strain energy density, and negative resection angle of 7° resulted in 6-fold higher superior pole strain energy. CONCLUSIONS: Patients with an anatomic patellar component showed higher rates of anterior knee pain, chronic effusion, and superior pole fragmentation compared to patients with a dome patella, with higher superior patellar pole strain energy confirmed on computational evaluation. Avoiding higher resection angles and excessive patellar resection may improve the performance and survivorship of the anatomic patella.

Computational Screening to Predict MicroRNA Targets in the Flavivirus 3' UTR Genome: An Approach for Antiviral Development.

MicroRNAs (miRNAs) are molecules that influence messenger RNA (mRNA) expression levels by binding to the 3' untranslated region (3' UTR) of target genes. Host miRNAs can influence flavivirus replication, either by inducing changes in the host transcriptome or by directly binding to viral genomes. The 3' UTR of the flavivirus genome is a conserved region crucial for viral replication. Cells might exploit this well-preserved region by generating miRNAs that interact with it, ultimately impacting viral replication. Despite significant efforts to identify miRNAs capable of arresting viral replication, the potential of all these miRNAs to interact with the flavivirus 3' UTR is still poorly characterised. In this context, bioinformatic tools have been proposed as a fundamental part of accelerating the discovery of interactions between miRNAs and the 3' UTR of viral genomes. In this study, we performed a computational analysis to reveal potential miRNAs from human and mosquito species that bind to the 3' UTR of flaviviruses. In humans, miR-6842 and miR-661 were found, while in mosquitoes, miR-9-C, miR-2945-5p, miR-11924, miR-282-5p, and miR-79 were identified. These findings open new avenues for studying these miRNAs as antivirals against flavivirus infections.

A Novel Bisquaternary Ammonium Compound as an Anion Sensor-ESI-MS and Fluorescence Study.

Electrospray ionization mass spectrometry (ESI-MS) analysis is frequently associated with noncovalent adduct formation, both in positive and negative modes. Anion binding and sensing by mass spectrometry, notably more challenging compared to cation binding, will have major research potential with the development of appropriate sensors. Here, we demonstrated identification of stable bisquaternary dication adducts with trifluoroacetate (TFA-), Cl- and HSO4- in positive-mode ESI-MS analysis. The observed adducts were stable in MS/MS mode, leading to the formation of characteristic fragment ions containing a covalently bound anion, which requires bond reorganization. This phenomenon was confirmed by computational methods. Furthermore, given that anion detection and anion sensor chemistry have gained significant prominence in chemistry, we conducted an analysis of the fluorescent properties of bisquaternary ammonium compound as a potential anion sensor.

Enabling cryo-EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures.

In the cellular context, proteins participate in communities to perform their function. The detection and identification of these communities as well as in-community interactions has long been the subject of investigation, mainly through proteomics analysis with mass spectrometry. With the advent of cryogenic electron microscopy and the "resolution revolution," their visualization has recently been made possible, even in complex, native samples. The advances in both fields have resulted in the generation of large amounts of data, whose analysis requires advanced computation, often employing machine learning approaches to reach the desired outcome. In this work, we first performed a robust proteomics analysis of mass spectrometry (MS) data derived from a yeast native cell extract and used this information to identify protein communities and inter-protein interactions. Cryo-EM analysis of the cell extract provided a reconstruction of a biomolecule at medium resolution (∼8 Å (FSC = 0.143)). Utilizing MS-derived proteomics data and systematic fitting of AlphaFold-predicted atomic models, this density was assigned to the 2.6 MDa complex of yeast fatty acid synthase. Our proposed workflow identifies protein complexes in native cell extracts from Saccharomyces cerevisiae by combining proteomics, cryo-EM, and AI-guided protein structure prediction.

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.

BACKGROUND: Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. METHODS: We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles. RESULTS: The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. CONCLUSION: In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.