Int J Mol Sci. 2024 Feb 27;25(5):2766. doi: 10.3390/ijms25052766.

ABSTRACT

Reperfusion stroke therapy is a modern treatment that involves thrombolysis and the mechanical removal of thrombus from the extracranial and/or cerebral arteries, thereby increasing penumbra reperfusion. After reperfusion therapy, 46% of patients are able to live independently 3 months after stroke onset. MicroRNAs (miRNAs) are essential regulators in the development of cerebral ischemia/reperfusion injury and the efficacy of the applied treatment. The first aim of this study was to examine the change in serum miRNA levels via next-generation sequencing (NGS) 10 days after the onset of acute stroke and reperfusion treatment. Next, the predictive values of the bioinformatics analysis of miRNA gene targets for the assessment of brain ischemic response to reperfusion treatment were explored. Human serum samples were collected from patients on days 1 and 10 after stroke onset and reperfusion treatment. The samples were subjected to NGS and then validated using qRT-PCR. Differentially expressed miRNAs (DEmiRNAs) were used for enrichment analysis. Hsa-miR-9-3p and hsa-miR-9-5p expression were downregulated on day 10 compared to reperfusion treatment on day 1 after stroke. The functional analysis of miRNA target genes revealed a strong association between the identified miRNA and stroke-related biological processes related to neuroregeneration signaling pathways. Hsa-miR-9-3p and hsa-miR-9-5p are potential candidates for the further exploration of reperfusion treatment efficacy in stroke patients.

PMID:38474013 | DOI:10.3390/ijms25052766

Int J Mol Sci. 2024 Feb 24;25(5):2637. doi: 10.3390/ijms25052637.

ABSTRACT

Metformin is a well-established drug for the treatment of type 2 diabetes; however, the mechanism of action has not been well described and many aspects of how it truly acts are still unknown. Moreover, regarding in vitro experiments, the glycaemic status when metformin is used is generally not considered, which, added to the suprapharmacological drug concentrations that are commonly employed in research, has resulted in gaps of its mechanism of action. The aim of this study was to determine how glucose and metformin concentrations influence cell culture. Considering that diabetic retinopathy is one of the most common complications of diabetes, a retinal pigment epithelial cell line was selected, and cell viability and proliferation rates were measured at different glucose and metformin concentrations. As expected, glucose concentration by itself positively influenced cell proliferation rates. When the metformin was considered, results were conditioned, as well, by metformin concentration. This conditioning resulted in cell death when high concentrations of metformin were used under physiological concentrations of glucose, while this did not happen when clinically relevant concentrations of metformin were used independently of glucose status. Our study shows the importance of in vitro cell growth conditions when drug effects such as metformin's are being analysed.

PMID:38473884 | DOI:10.3390/ijms25052637

Cancers (Basel). 2024 Feb 29;16(5):1011. doi: 10.3390/cancers16051011.

ABSTRACT

Multiple Myeloma (MM) is an incurable haematological malignancy caused by uncontrolled growth of plasma cells. MM pathogenesis is attributed to crosstalk between plasma cells and the bone marrow microenvironment, where extracellular vesicles (EVs) play a role. In this study, EVs secreted from a panel of MM cell lines were isolated from conditioned media by ultracentrifugation and fluorescently stained EVs were co-cultured with THP-1 monocyte cells. MM EVs from three cell lines displayed a differential yet dose-dependent uptake by THP-1 cells, with H929 EVs displaying the greatest EV uptake compared to MM.1s and U266 EVs suggesting that uptake efficiency is dependent on the cell line of origin. Furthermore, MM EVs increased the secretion of MMP-9 and IL-6 from monocytes, with H929 EVs inducing the greatest effect, consistent with the greatest uptake efficiency. Moreover, monocyte-conditioned media collected following H929 EV uptake significantly increased the migration and proliferation of MM cells. Finally, EV proteome analysis revealed differential cargo enrichment that correlates with disease progression including a significant enrichment of spliceosome-related proteins in H929 EVs compared to the U266 and MM.1s EVs. Overall, this study demonstrates that MM-derived EVs modulate monocyte function to promote tumour growth and metastasis and reveals possible molecular mechanisms involved.

PMID:38473370 | DOI:10.3390/cancers16051011

Cancers (Basel). 2024 Feb 29;16(5):1001. doi: 10.3390/cancers16051001.

ABSTRACT

Drug resistance can evolve from a subpopulation of cancer cells that initially survive drug treatment and then gradually form a pool of drug-tolerant cells. Several studies have pinpointed the activation of a specific bypass pathway that appears to provide the critical therapeutic target for preventing drug tolerance. Here, we take a systems-biology approach, using proteomics and genomics to examine the development of drug tolerance to EGFR inhibitors in EGFR-mutant lung adenocarcinoma cells and BRAF inhibitors in BRAF-mutant melanoma cells. We found that there are numerous alternative mitogenic pathways that become activated in both cases, including YAP, STAT3, IGFR1, and phospholipase C (PLC)/protein kinase C (PKC) pathways. Our results suggest that an effective therapeutic strategy to prevent drug tolerance will need to take multiple alternative mitogenic pathways into account rather than focusing on one specific pathway.

PMID:38473364 | DOI:10.3390/cancers16051001

Cancers (Basel). 2024 Feb 22;16(5):880. doi: 10.3390/cancers16050880.

ABSTRACT

There is a rising trend in the consumption of dietary supplements, especially among adults, with the purpose of improving health. While marketing campaigns tout the potential health benefits of using dietary supplements, it is critical to evaluate the potential harmful effects associated with these supplements as well. The majority of the scarce research on the potential harmful effects of vitamins focuses on the acute or chronic toxicities associated with the use of dietary supplements. Quality research is still required to further investigate the risks of long-term use of dietary supplements, especially the risk of developing cancers. The present review concentrates on studies that have investigated the association between the risk of developing cancers and associated mortality with the risk of dietary supplements. Such an association has been reported for several vitamins, minerals, and other dietary supplements. Even though several of these studies come with their own shortcomings and critics, they must draw attention to further investigate long-term adverse effects of dietary supplements and advise consumers and healthcare providers to ponder the extensive use of dietary supplements.

PMID:38473246 | DOI:10.3390/cancers16050880

Cancers (Basel). 2024 Feb 20;16(5):845. doi: 10.3390/cancers16050845.

ABSTRACT

Estrogen receptor alpha (ER)-positive breast cancer is responsible for over 60% of breast cancer cases in the U.S. Among patients diagnosed with early-stage ER+ disease, 1/3 will experience recurrence despite treatment with adjuvant endocrine therapy. ER is a nuclear hormone receptor responsible for estrogen-driven tumor growth. ER transcriptional activity is modulated by interactions with coregulators. Dysregulation of the levels of these coregulators is involved in the development of endocrine resistance. To identify ER interactors that modulate transcriptional activity in breast cancer, we utilized biotin ligase proximity profiling of ER interactomes. Mass spectrometry analysis revealed tripartite motif containing 33 (TRIM33) as an estrogen-dependent interactor of ER. shRNA knockdown showed that TRIM33 promoted ER transcriptional activity and estrogen-induced cell growth. Despite its known role as an E3 ubiquitin ligase, TRIM33 increased the stability of endogenous ER in breast cancer cells. TRIM33 offers a novel target for inhibiting estrogen-induced cancer cell growth, particularly in cases of endocrine resistance driven by ER (ESR1) gene amplification or overexpression.

PMID:38473207 | DOI:10.3390/cancers16050845

Chempluschem. 2024 Mar 12:e202300774. doi: 10.1002/cplu.202300774. Online ahead of print.

ABSTRACT

Pectin is a renewable, non-toxic and biodegradable polymer consisting of galacturonic acid. Due to its polar groups, it is suitable for complexing and supporting metallic nanoparticles (NPs). The aim of this work was to produce antibacterial nanocomposites using pectin under ultrasound (US). The metal NPs (Au or Ag) were dispersed by sonication (US, 21 kHz, 50 W) and compared with those obtained under stirring. The effects of the reducing agents (NaBH4, ascorbic acid) on the dispersion and morphology of the resulting NPs were also investigated. Characterization by diffuse reflectance spectroscopy and field emission scanning electron microscopy showed that the use of US improved the dispersion and reduced NPs size. In addition, for the Au NPs, the absence of external reducing agents resulted in smaller NPs and a more uniform size. The prepared NPs were functionalized with oxytetracycline in water and tested against Escherichia coli (gram-) and Staphylococcus epidermidis (gram+) via the Kirby-Bauer test. The results show better antibacterial activity of the functionalized nanoparticles compared to antibiotic-free NPs and pure oxytetracycline, indicating the potential of the nanoparticles as drug carriers. These results emphasise the importance of US-assisted synthesis and pave the way for new environmentally friendly antimicrobial materials.

PMID:38472117 | DOI:10.1002/cplu.202300774

Proc Biol Sci. 2024 Mar 13;291(2018):20232840. doi: 10.1098/rspb.2023.2840. Epub 2024 Mar 13.

ABSTRACT

Scientific knowledge is produced in multiple languages but is predominantly published in English. This practice creates a language barrier to generate and transfer scientific knowledge between communities with diverse linguistic backgrounds, hindering the ability of scholars and communities to address global challenges and achieve diversity and equity in science, technology, engineering and mathematics (STEM). To overcome those barriers, publishers and journals should provide a fair system that supports non-native English speakers and disseminates knowledge across the globe. We surveyed policies of 736 journals in biological sciences to assess their linguistic inclusivity, identify predictors of inclusivity, and propose actions to overcome language barriers in academic publishing. Our assessment revealed a grim landscape where most journals were making minimal efforts to overcome language barriers. The impact factor of journals was negatively associated with adopting a number of inclusive policies whereas ownership by a scientific society tended to have a positive association. Contrary to our expectations, the proportion of both open access articles and editors based in non-English speaking countries did not have a major positive association with the adoption of linguistically inclusive policies. We proposed a set of actions to overcome language barriers in academic publishing, including the renegotiation of power dynamics between publishers and editorial boards.

PMID:38471557 | DOI:10.1098/rspb.2023.2840

Comput Biol Chem. 2024 Mar 2;110:108048. doi: 10.1016/j.compbiolchem.2024.108048. Online ahead of print.

ABSTRACT

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

PMID:38471353 | DOI:10.1016/j.compbiolchem.2024.108048

Int J Radiat Biol. 2024 Mar 12:1-14. doi: 10.1080/09553002.2024.2327393. Online ahead of print.

ABSTRACT

PURPOSE: To identify sensitive genes for space radiation, we integrated the transcriptomic samples of spaceflight mice from GeneLab and predicted the radiation doses absorbed by individuals in space.

METHODS AND MATERIALS: A single-sample network (SSN) for each individual sample was constructed. Then, using machine learning and genetic algorithms, we built the regression models to predict the absorbed dose equivalent based on the topological structure of SSNs. Moreover, we analyzed the SSNs from each tissue and compared the similarities and differences among them.

RESULTS: Our model exhibited excellent performance with the following metrics: R2=0.980, MSE=6.74e-04, and the Pearson correlation coefficient of 0.990 (p value <.0001) between predicted and actual values. We identified 20 key genes, the majority of which had been proven to be associated with radiation. However, we uniquely established them as space radiation sensitive genes for the first time. Through further analysis of the SSNs, we discovered that the different tissues exhibited distinct mechanisms in response to space stressors.

CONCLUSIONS: The topology structures of SSNs effectively predicted radiation doses under spaceflight conditions, and the SSNs revealed the gene regulatory patterns within the organisms under space stressors.

PMID:38471034 | DOI:10.1080/09553002.2024.2327393

Anal Chem. 2024 Mar 12. doi: 10.1021/acs.analchem.3c05235. Online ahead of print.

ABSTRACT

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which is a label-free imaging technique, determines the spatial distribution and relative abundance of versatile endogenous metabolites in tissues. Meanwhile, matrix selection is generally regarded as a pivotal step in MALDI tissue imaging. This study presents the first report of a novel MALDI matrix, 2-hydroxy-5-nitro-3-(trifluoromethyl)pyridine (HNTP), for the in situ detection and imaging of endogenous metabolites in rat liver and brain tissues by MALDI-MS in positive-ion mode. The HNTP matrix exhibits excellent characteristics, including strong ultraviolet absorption, μm-scale matrix crystals, high chemical stability, low background ion interference, and high metabolite ionization efficiency. Notably, the HNTP matrix also shows superior detection capabilities, successfully showing 185 detectable metabolites in rat liver tissue sections. This outperforms the commonly used matrices of 2,5-dihydroxybenzoic acid and 2-mercaptobenzothiazole, which detect 145 and 120 metabolites from the rat liver, respectively. Furthermore, a total of 152 metabolites are effectively detected and imaged in rat brain tissue using the HNTP matrix, and the spatial distribution of these compounds clearly shows the heterogeneity of the rat brain. The results demonstrate that HNTP is a new and powerful positive-ion mode matrix to enhance the analysis of metabolites in biological tissues by MALDI-MSI.

PMID:38470972 | DOI:10.1021/acs.analchem.3c05235

PLoS Comput Biol. 2024 Mar 12;20(3):e1011916. doi: 10.1371/journal.pcbi.1011916. eCollection 2024 Mar.

ABSTRACT

Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identification (gray-box) in the field of Systems Biology. The proposed framework-named AI-Aristotle-combines the eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) with symbolic regression (SR) techniques for parameter discovery and gray-box identification. We test the accuracy, speed, flexibility, and robustness of AI-Aristotle based on two benchmark problems in Systems Biology: a pharmacokinetics drug absorption model and an ultradian endocrine model for glucose-insulin interactions. We compare the two machine learning methods (X-TFC and PINNs), and moreover, we employ two different symbolic regression techniques to cross-verify our results. To test the performance of AI-Aristotle, we use sparse synthetic data perturbed by uniformly distributed noise. More broadly, our work provides insights into the accuracy, cost, scalability, and robustness of integrating neural networks with symbolic regressors, offering a comprehensive guide for researchers tackling gray-box identification challenges in complex dynamical systems in biomedicine and beyond.

PMID:38470870 | DOI:10.1371/journal.pcbi.1011916

Appl Environ Microbiol. 2024 Mar 12:e0211323. doi: 10.1128/aem.02113-23. Online ahead of print.

ABSTRACT

A major incident occurred at the Fukushima Daiichi Nuclear Power Station following the tsunami triggered by the Tohoku-Pacific Ocean Earthquake in March 2011, whereby seawater entered the torus room in the basement of the reactor building. Here, we identify and analyze the bacterial communities in the torus room water and several environmental samples. Samples of the torus room water (1 × 109 Bq137Cs/L) were collected by the Tokyo Electric Power Company Holdings from two sampling points between 30 cm and 1 m from the bottom of the room (TW1) and the bottom layer (TW2). A structural analysis of the bacterial communities based on 16S rRNA amplicon sequencing revealed that the predominant bacterial genera in TW1 and TW2 were similar. TW1 primarily contained the genus Limnobacter, a thiosulfate-oxidizing bacterium. γ-Irradiation tests on Limnobacter thiooxidans, the most closely related phylogenetically found in TW1, indicated that its radiation resistance was similar to ordinary bacteria. TW2 predominantly contained the genus Brevirhabdus, a manganese-oxidizing bacterium. Although bacterial diversity in the torus room water was lower than seawater near Fukushima, ~70% of identified genera were associated with metal corrosion. Latent environment allocation-an analytical technique that estimates habitat distributions and co-detection analyses-revealed that the microbial communities in the torus room water originated from a distinct blend of natural marine microbial and artificial bacterial communities typical of biofilms, sludge, and wastewater. Understanding the specific bacteria linked to metal corrosion in damaged plants is important for advancing decommissioning efforts.

IMPORTANCE: In the context of nuclear power station decommissioning, the proliferation of microorganisms within the reactor and piping systems constitutes a formidable challenge. Therefore, the identification of microbial communities in such environments is of paramount importance. In the aftermath of the Fukushima Daiichi Nuclear Power Station accident, microbial community analysis was conducted on environmental samples collected mainly outside the site. However, analyses using samples from on-site areas, including adjacent soil and seawater, were not performed. This study represents the first comprehensive analysis of microbial communities, utilizing meta 16S amplicon sequencing, with a focus on environmental samples collected from the radioactive element-containing water in the torus room, including the surrounding environments. Some of the identified microbial genera are shared with those previously identified in spent nuclear fuel pools in countries such as France and Brazil. Moreover, our discussion in this paper elucidates the correlation of many of these bacteria with metal corrosion.

PMID:38470121 | DOI:10.1128/aem.02113-23

mSphere. 2024 Mar 12:e0081623. doi: 10.1128/msphere.00816-23. Online ahead of print.

ABSTRACT

Anaerostipes hadrus (A. hadrus) is a dominant species in the human gut microbiota and considered a beneficial bacterium for producing probiotic butyrate. However, recent studies have suggested that A. hadrus may negatively affect the host through synthesizing fatty acid and metabolizing the anticancer drug 5-fluorouracil, indicating that the impact of A. hadrus is complex and unclear. Therefore, comprehensive genomic studies on A. hadrus need to be performed. We integrated 527 high-quality public A. hadrus genomes and five distinct metagenomic cohorts. We analyzed these data using the approaches of comparative genomics, metagenomics, and protein structure prediction. We also performed validations with culture-based in vitro assays. We constructed the first large-scale pan-genome of A. hadrus (n = 527) and identified 5-fluorouracil metabolism genes as ubiquitous in A. hadrus genomes as butyrate-producing genes. Metagenomic analysis revealed the wide and stable distribution of A. hadrus in healthy individuals, patients with inflammatory bowel disease, and patients with colorectal cancer, with healthy individuals carrying more A. hadrus. The predicted high-quality protein structure indicated that A. hadrus might metabolize 5-fluorouracil by producing bacterial dihydropyrimidine dehydrogenase (encoded by the preTA operon). Through in vitro assays, we validated the short-chain fatty acid production and 5-fluorouracil metabolism abilities of A. hadrus. We observed for the first time that A. hadrus can convert 5-fluorouracil to α-fluoro-β-ureidopropionic acid, which may result from the combined action of the preTA operon and adjacent hydA (encoding bacterial dihydropyrimidinase). Our results offer novel understandings of A. hadrus, exceptionally functional features, and potential applications.

IMPORTANCE: This work provides new insights into the evolutionary relationships, functional characteristics, prevalence, and potential applications of Anaerostipes hadrus.

PMID:38470044 | DOI:10.1128/msphere.00816-23

Environ Microbiome. 2024 Mar 11;19(1):15. doi: 10.1186/s40793-024-00556-7.

ABSTRACT

BACKGROUND: Poriferans (sponges) are highly adaptable organisms that can thrive in diverse marine and freshwater environments due, in part, to their close associations with internal microbial communities. This sponge microbiome can be acquired from the surrounding environment (horizontal acquisition) or obtained from the parents during the reproductive process through a variety of mechanisms (vertical transfer), typically resulting in the presence of symbiotic microbes throughout all stages of sponge development. How and to what extent the different components of the microbiome are transferred to the developmental stages remain poorly understood. Here, we investigated the microbiome composition of a common, low-microbial-abundance, Atlantic-Mediterranean sponge, Crambe crambe, throughout its ontogeny, including adult individuals, brooded larvae, lecithotrophic free-swimming larvae, newly settled juveniles still lacking osculum, and juveniles with a functional osculum for filter feeding.

RESULTS: Using 16S rRNA gene analysis, we detected distinct microbiome compositions in each ontogenetic stage, with variations in composition, relative abundance, and diversity of microbial species. However, a particular dominant symbiont, Candidatus Beroebacter blanensis, previously described as the main symbiont of C. crambe, consistently occurred throughout all stages, an omnipresence that suggests vertical transmission from parents to offspring. This symbiont fluctuated in relative abundance across developmental stages, with pronounced prevalence in lecithotrophic stages. A major shift in microbial composition occurred as new settlers completed osculum formation and acquired filter-feeding capacity. Candidatus Beroebacter blanensis decreased significatively at this point. Microbial diversity peaked in filter-feeding stages, contrasting with the lower diversity of lecithotrophic stages. Furthermore, individual specific transmission patterns were detected, with greater microbial similarity between larvae and their respective parents compared to non-parental conspecifics.

CONCLUSIONS: These findings suggest a putative vertical transmission of the dominant symbiont, which could provide some metabolic advantage to non-filtering developmental stages of C. crambe. The increase in microbiome diversity with the onset of filter-feeding stages likely reflects enhanced interaction with environmental microbes, facilitating horizontal transmission. Conversely, lower microbiome diversity in lecithotrophic stages, prior to filter feeding, suggests incomplete symbiont transfer or potential symbiont digestion. This research provides novel information on the dynamics of the microbiome through sponge ontogeny, on the strategies for symbiont acquisition at each ontogenetic stage, and on the potential importance of symbionts during larval development.

PMID:38468324 | DOI:10.1186/s40793-024-00556-7

Methods Mol Biol. 2024;2760:309-318. doi: 10.1007/978-1-0716-3658-9_18.

ABSTRACT

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

PMID:38468096 | DOI:10.1007/978-1-0716-3658-9_18

Methods Mol Biol. 2024;2760:35-56. doi: 10.1007/978-1-0716-3658-9_3.

ABSTRACT

Establishing a mapping between (from and to) the functionality of interest and the underlying network structure (design principles) remains a crucial step toward understanding and design of bio-systems. Perfect adaptation is one such crucial functionality that enables every living organism to regulate its essential activities in the presence of external disturbances. Previous approaches to deducing the design principles for adaptation have either relied on computationally burdensome brute-force methods or rule-based design strategies detecting only a subset of all possible adaptive network structures. This chapter outlines a scalable and generalizable method inspired by systems theory that unravels an exhaustive set of adaptation-capable structures. We first use the well-known performance parameters to characterize perfect adaptation. These performance parameters are then mapped back to a few parameters (poles, zeros, gain) characteristic of the underlying dynamical system constituted by the rate equations. Therefore, the performance parameters evaluated for the scenario of perfect adaptation can be expressed as a set of precise mathematical conditions involving the system parameters. Finally, we use algebraic graph theory to translate these abstract mathematical conditions to certain structural requirements for adaptation. The proposed algorithm does not assume any particular dynamics and is applicable to networks of any size. Moreover, the results offer a significant advancement in the realm of understanding and designing complex biochemical networks.

PMID:38468081 | DOI:10.1007/978-1-0716-3658-9_3

Adv Neurobiol. 2024;36:313-328. doi: 10.1007/978-3-031-47606-8_16.

ABSTRACT

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

PMID:38468040 | DOI:10.1007/978-3-031-47606-8_16

Mol Syst Biol. 2024 Mar 11. doi: 10.1038/s44320-024-00019-8. Online ahead of print.

ABSTRACT

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

PMID:38467836 | DOI:10.1038/s44320-024-00019-8

Nat Commun. 2024 Mar 11;15(1):2188. doi: 10.1038/s41467-024-46070-9.

ABSTRACT

Hormones mediate long-range cell communication and play vital roles in physiology, metabolism, and health. Traditionally, endocrinologists have focused on one hormone or organ system at a time. Yet, hormone signaling by its very nature connects cells of different organs and involves crosstalk of different hormones. Here, we leverage the organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), to systematically map source and target cells for 84 classes of hormones. This work uncovers previously-uncharacterized sites of hormone regulation, and shows that the hormonal signaling network is densely connected, decentralized, and rich in feedback loops. Evolutionary comparisons of hormonal genes and their expression patterns show that mouse lemur better models human hormonal signaling than mouse, at both the genomic and transcriptomic levels, and reveal primate-specific rewiring of hormone-producing/target cells. This work complements the scale and resolution of classical endocrine studies and sheds light on primate hormone regulation.

PMID:38467625 | DOI:10.1038/s41467-024-46070-9