Gut. 2023 Oct 4:gutjnl-2023-330365. doi: 10.1136/gutjnl-2023-330365. Online ahead of print.

ABSTRACT

OBJECTIVE: Animal studies suggest that prebiotic, plant-derived nutrients could improve homoeostatic and hedonic brain functions through improvements in microbiome-gut-brain communication. However, little is known if these results are applicable to humans. Therefore, we tested the effects of high-dosed prebiotic fibre on reward-related food decision-making in a randomised controlled within-subject cross-over study and assayed potential microbial and metabolic markers.

DESIGN: 59 overweight young adults (19 females, 18-42 years, body mass index 25-30 kg/m2) underwent functional task MRI before and after 14 days of supplementary intake of 30 g/day of inulin (prebiotics) and equicaloric placebo, respectively. Short chain fatty acids (SCFA), gastrointestinal hormones, glucose/lipid and inflammatory markers were assayed in fasting blood. Gut microbiota and SCFA were measured in stool.

RESULTS: Compared with placebo, participants showed decreased brain activation towards high-caloric wanted food stimuli in the ventral tegmental area and right orbitofrontal cortex after prebiotics (preregistered, family wise error-corrected p <0.05). While fasting blood levels remained largely unchanged, 16S-rRNA sequencing showed significant shifts in the microbiome towards increased occurrence of, among others, SCFA-producing Bifidobacteriaceae, and changes in >60 predicted functional signalling pathways after prebiotic intake. Changes in brain activation correlated with changes in Actinobacteria microbial abundance and associated activity previously linked with SCFA production, such as ABC transporter metabolism.

CONCLUSIONS: In this proof-of-concept study, a prebiotic intervention attenuated reward-related brain activation during food decision-making, paralleled by shifts in gut microbiota.

TRIAL REGISTRATION NUMBER: NCT03829189.

PMID:37793780 | DOI:10.1136/gutjnl-2023-330365

Environ Pollut. 2023 Oct 2:122674. doi: 10.1016/j.envpol.2023.122674. Online ahead of print.

ABSTRACT

Environmental contamination constrains microbial communities impacting diversity and total metabolic activity. The former S-3 Ponds contamination site at Oak Ridge Reservation (ORR), TN, has elevated concentrations of nitric acid and multiple metals from decades of processing nuclear material. To determine the nature of the metal contamination in the sediment, a three-step sequential chemical extraction (BCR) was performed on sediment segments from a core located upgradient (EB271, non-contaminated) and one downgradient (EB106, contaminated) of the S-3 Ponds. The resulting exchangeable, reducing, and oxidizing fractions were analyzed for 18 different elements. Comparison of the two cores revealed changes in operational speciation for several elements caused by the contamination. Those present from the S-3 Ponds, including Al, U, Co, Cu, Ni, and Cd, were not only elevated in concentration in the EB106 core but were also operationally more available with increased mobility in the acidic environment. Other elements, including Mg, Ca, P, V, As, and Mo, were less operationally available in EB106 having decreased concentrations in the exchangeable fraction. The bioavailability of essential macro nutrients Mg, Ca, and P from the two types of sediment was determined using three metal-tolerant bacteria previously isolated from ORR. Mg and Ca were available from both sediments for all three strains; however, P was not bioavailable from either sediment for any strain. The decreased operational speciation of P in contaminated ORR sediment may increase the dependence of the microbial community on other pools of P or select for microorganisms with increased P scavenging capabilities. Hence, the microbial community at the former S-3 Ponds contamination site may be constrained not only by increased toxic metal concentrations but also by the availability of essential elements, including P.

PMID:37793542 | DOI:10.1016/j.envpol.2023.122674

Kidney Blood Press Res. 2023 Oct 4. doi: 10.1159/000534354. Online ahead of print.

ABSTRACT

BACKGROUND: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder.

METHODS: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed.

RESULTS: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas.

CONCLUSION: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies.

PMID:37793351 | DOI:10.1159/000534354

Placenta. 2023 Sep 19;143:12-15. doi: 10.1016/j.placenta.2023.09.007. Online ahead of print.

ABSTRACT

The placenta remains the key organ to pregnancy complications, such as preeclampsia, contrarily the pathophysiology underlying the placental dysfunctions remains elusive. Here, we present our Disease Map "NaviCenta", which is an online resource based on the interactions between tissues, cellular compartments, and molecules that mediate disease-related processes in the placenta. We built cellular and molecular interaction networks based upon manual curation and annotation of publicly available information in the scientific literature, pathways resources, and Omics data. NaviCenta (Navigate the plaCenta) serves as an open access, spatio-temporal, multi-scale knowledge base, and analytical tool for enhanced interpretation and hypothesis testing on various placental disease phenotypes.

PMID:37793322 | DOI:10.1016/j.placenta.2023.09.007

Redox Biol. 2023 Sep 27;67:102908. doi: 10.1016/j.redox.2023.102908. Online ahead of print.

ABSTRACT

Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.

PMID:37793239 | DOI:10.1016/j.redox.2023.102908

Plant J. 2023 Oct 4. doi: 10.1111/tpj.16481. Online ahead of print.

ABSTRACT

Lateral roots are crucial for plant growth and development, making them an important target for research aiming to improve crop yields and food security. However, their endogenous ontogeny and, as it were, stochastic appearance challenge their study. Lateral Root Inducible Systems (LRIS) can be used to overcome these challenges by inducing lateral roots massively and synchronously. The combination of LRISs with transcriptomic approaches significantly advanced our insights in the molecular control of lateral root formation, in particular for Arabidopsis. Despite this success, LRISs have been underutilized for other plant species or for lateral root developmental stages later than the initiation. In this study, we developed and/or adapted LRISs in rice, Medicago, and Arabidopsis to perform RNA-sequencing during time courses that cover different developmental stages of lateral root formation and primordium development. As such, our study provides three extensive datasets of gene expression profiles during lateral root development in three different plant species. The three LRISs are highly effective but timing and spatial distribution of lateral root induction vary among the species. Detailed characterization of the stages in time and space in the respective species enabled an interspecies co-expression analysis to identify conserved players involved in lateral root development, as illustrated for the AUX/IAA and LBD gene families. Overall, our results provide a valuable resource to identify potentially conserved regulatory mechanisms in lateral root development, and as such will contribute to a better understanding of the complex regulatory network underlying lateral root development.

PMID:37793018 | DOI:10.1111/tpj.16481

Cell Rep. 2023 Oct 3;42(10):113212. doi: 10.1016/j.celrep.2023.113212. Online ahead of print.

ABSTRACT

Local immune activation at mucosal surfaces, mediated by mucosal lymphoid tissues, is vital for effective immune responses against pathogens. While pathogens like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread to multiple organs, patients with coronavirus disease 2019 (COVID-19) primarily experience inflammation and damage in their lungs. To investigate this apparent organ-specific immune response, we develop an analytical framework that recognizes the significance of mucosal lymphoid tissues. This framework combines histology, immunofluorescence, spatial transcript profiling, and mathematical modeling to identify cellular and gene expression differences between the lymphoid tissues of the lung and the gut and predict the determinants of those differences. Our findings indicate that mucosal lymphoid tissues are pivotal in organ-specific immune response to SARS-CoV-2, mediating local inflammation and tissue damage and contributing to immune dysfunction. The framework developed here has potential utility in the study of long COVID and may streamline biomarker discovery and treatment design for diseases with differential pathologies at the organ level.

PMID:37792533 | DOI:10.1016/j.celrep.2023.113212

Cell Rep. 2023 Oct 3;42(10):113191. doi: 10.1016/j.celrep.2023.113191. Online ahead of print.

ABSTRACT

In solid tumors, drug concentrations decrease with distance from blood vessels. However, cellular adaptations accompanying the gradated exposure of cancer cells to drugs are largely unknown. Here, we modeled the spatiotemporal changes promoting chemotherapy resistance in breast cancer. Using pairwise cell competition assays at each step during the acquisition of chemoresistance, we reveal an important priming phase that renders cancer cells previously exposed to sublethal drug concentrations refractory to dose escalation. Therapy-resistant cells throughout the concentration gradient display higher expression of the solute carriers SLC38A7 and SLC46A1 and elevated intracellular concentrations of their associated metabolites. Reduced levels of SLC38A7 and SLC46A1 diminish the proliferative potential of cancer cells, and elevated expression of these SLCs in breast tumors from patients correlates with reduced survival. Our work provides mechanistic evidence to support dose-intensive treatment modalities for patients with solid tumors and reveals two members of the SLC family as potential actionable targets.

PMID:37792528 | DOI:10.1016/j.celrep.2023.113191

Proc Natl Acad Sci U S A. 2023 Oct 10;120(41):e2303078120. doi: 10.1073/pnas.2303078120. Epub 2023 Oct 4.

ABSTRACT

Living cells can leverage correlations in environmental fluctuations to predict the future environment and mount a response ahead of time. To this end, cells need to encode the past signal into the output of the intracellular network from which the future input is predicted. Yet, storing information is costly while not all features of the past signal are equally informative on the future input signal. Here, we show for two classes of input signals that cellular networks can reach the fundamental bound on the predictive information as set by the information extracted from the past signal: Push-pull networks can reach this information bound for Markovian signals, while networks that take a temporal derivative can reach the bound for predicting the future derivative of non-Markovian signals. However, the bits of past information that are most informative about the future signal are also prohibitively costly. As a result, the optimal system that maximizes the predictive information for a given resource cost is, in general, not at the information bound. Applying our theory to the chemotaxis network of Escherichia coli reveals that its adaptive kernel is optimal for predicting future concentration changes over a broad range of background concentrations, and that the system has been tailored to predicting these changes in shallow gradients.

PMID:37792515 | DOI:10.1073/pnas.2303078120

Mol Biol Evol. 2023 Oct 4;40(10):msad206. doi: 10.1093/molbev/msad206.

ABSTRACT

Amphibians are ideal for studying visual system evolution because their biphasic (aquatic and terrestrial) life history and ecological diversity expose them to a broad range of visual conditions. Here, we evaluate signatures of selection on visual opsin genes across Neotropical anurans and focus on three diurnal clades that are well-known for the concurrence of conspicuous colors and chemical defense (i.e., aposematism): poison frogs (Dendrobatidae), Harlequin toads (Bufonidae: Atelopus), and pumpkin toadlets (Brachycephalidae: Brachycephalus). We found evidence of positive selection on 44 amino acid sites in LWS, SWS1, SWS2, and RH1 opsin genes, of which one in LWS and two in RH1 have been previously identified as spectral tuning sites in other vertebrates. Given that anurans have mostly nocturnal habits, the patterns of selection revealed new sites that might be important in spectral tuning for frogs, potentially for adaptation to diurnal habits and for color-based intraspecific communication. Furthermore, we provide evidence that SWS2, normally expressed in rod cells in frogs and some salamanders, has likely been lost in the ancestor of Dendrobatidae, suggesting that under low-light levels, dendrobatids have inferior wavelength discrimination compared to other frogs. This loss might follow the origin of diurnal activity in dendrobatids and could have implications for their behavior. Our analyses show that assessments of opsin diversification in across taxa could expand our understanding of the role of sensory system evolution in ecological adaptation.

PMID:37791477 | DOI:10.1093/molbev/msad206

Iran J Med Sci. 2023 May;48(3):302-312. doi: 10.30476/IJMS.2022.94615.2598.

ABSTRACT

BACKGROUND: Reinfection with Coronavirus Diseases 2019 (COVID-19) has raised remarkable public health concerns globally. Therefore, the present retrospective cohort study intended to investigate COVID-19 reinfection in registered patients of Fars province in Iran from February 2020 to April 2021.

METHODS: The patients' data, including the COVID-19 infection, symptoms, comorbidities, and demographics, were collected using the Health Information Systems (HISs). The patients were divided into three groups in terms of the duration between the initial infection and reinfection, including 28-44, 45-89, and more than 90 days. Following the univariate analysis, logistic regression was used to investigate the factors effective on COVID-19 reinfection.

RESULTS: A total of 213768 patients had a positive Polymerase Chain Reaction (PCR) test. The reinfection rate was 0.97% (2079 patients). Of these re-infected individuals, 14.9%, 18.5%, and 66.6% had their second positive test 28-45, 45-89, and ≥90 days later, respectively. The mean duration between the initial infection and reinfection was 130.56 days (29-370 days). The chance of reinfection was significantly higher in the youths (Odds Ratio (OR)=2.055; P<0.001), men (OR=1.283; P<0.001), urban population (OR=1.313; P<0.001), and healthcare providers (OR=4.453; P<0.001). The patients with chronic pulmonary diseases, chronic kidney diseases, and malignancy were 1.421 (P=0.036), 2.239 (P<0.001), and 3.437 (P<0.001) times, respectively, more likely prone to reinfection.

CONCLUSION: The results of this study showed that there is a higher risk of reinfection in several vulnerable groups including healthcare providers, young individuals, residents of urban areas, men, and individuals with underlying diseases.

PMID:37791328 | PMC:PMC10542930 | DOI:10.30476/IJMS.2022.94615.2598

JACC Basic Transl Sci. 2023 May 31;8(9):1160-1176. doi: 10.1016/j.jacbts.2023.03.003. eCollection 2023 Sep.

ABSTRACT

Chronic kidney disease is a global health problem affecting 10% to 12% of the population. Uremic cardiomyopathy is often characterized by left ventricular hypertrophy, fibrosis, and diastolic dysfunction. Dysregulation of neuregulin-1β signaling in the heart is a known contributor to heart failure. The systemically administered recombinant human neuregulin-1β for 10 days in our 5/6 nephrectomy-induced model of chronic kidney disease alleviated the progression of uremic cardiomyopathy and kidney dysfunction in type 4 cardiorenal syndrome. The currently presented positive preclinical data warrant clinical studies to confirm the beneficial effects of recombinant human neuregulin-1β in patients with chronic kidney disease.

PMID:37791301 | PMC:PMC10543921 | DOI:10.1016/j.jacbts.2023.03.003

Nat Mach Intell. 2022;4(8):710-719. doi: 10.1038/s42256-022-00519-y. Epub 2022 Aug 30.

ABSTRACT

Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the lack of kinetic data, traditional kinetic modelling often yields only a few or no kinetic models with desirable dynamical properties, making the analysis unreliable and computationally inefficient. We present REKINDLE (Reconstruction of Kinetic Models using Deep Learning), a deep-learning-based framework for efficiently generating kinetic models with dynamic properties matching the ones observed in cells. We showcase REKINDLE's capabilities to navigate through the physiological states of metabolism using small numbers of data with significantly lower computational requirements. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in biotechnology and health.

PMID:37790987 | PMC:PMC10543203 | DOI:10.1038/s42256-022-00519-y

Front Mol Biosci. 2023 Sep 18;10:1238509. doi: 10.3389/fmolb.2023.1238509. eCollection 2023.

ABSTRACT

Introduction: Staphylococcus aureus is a dangerous pathogen which causes a vast selection of infections. Antimicrobial peptides have been demonstrated as a new hope for developing antibiotic agents against multi-drug-resistant bacteria such as S. aureus. Yet, most studies on developing classification tools for antimicrobial peptide activities do not focus on any specific species, and therefore, their applications are limited. Methods: Here, by using an up-to-date dataset, we have developed a hierarchical machine learning model for classifying peptides with antimicrobial activity against S. aureus. The first-level model classifies peptides into AMPs and non-AMPs. The second-level model classifies AMPs into those active against S. aureus and those not active against this species. Results: Results from both classifiers demonstrate the effectiveness of the hierarchical approach. A comprehensive set of physicochemical and linguistic-based features has been used, and after feature selection steps, only some physicochemical properties were selected. The final model showed the F1-score of 0.80, recall of 0.86, balanced accuracy of 0.80, and specificity of 0.73 on the test set. Discussion: The susceptibility to a single AMP is highly varied among different target species. Therefore, it cannot be concluded that AMP candidates suggested by AMP/non-AMP classifiers are able to show suitable activity against a specific species. Here, we addressed this issue by creating a hierarchical machine learning model which can be used in practical applications for extracting potential antimicrobial peptides against S. aureus from peptide libraries.

PMID:37790874 | PMC:PMC10544327 | DOI:10.3389/fmolb.2023.1238509

Front Plant Sci. 2023 Sep 18;14:1260393. doi: 10.3389/fpls.2023.1260393. eCollection 2023.

ABSTRACT

Soybean is an important global source of plant-based protein. A persistent trend has been observed over the past two decades that soybeans grown in western Canada have lower seed protein content than soybeans grown in eastern Canada. In this study, 10 soybean genotypes ranging in average seed protein content were grown in an eastern location (control) and three western locations (experimental) in Canada. Seed protein and oil contents were measured for all lines in each location. RNA-sequencing and differential gene expression analysis were used to identify differentially expressed genes that may account for relatively low protein content in western-grown soybeans. Differentially expressed genes were enriched for ontologies and pathways that included amino acid biosynthesis, circadian rhythm, starch metabolism, and lipid biosynthesis. Gene ontology, pathway mapping, and quantitative trait locus (QTL) mapping collectively provide a close inspection of mechanisms influencing nitrogen assimilation and amino acid biosynthesis between soybeans grown in the East and West. It was found that western-grown soybeans had persistent upregulation of asparaginase (an asparagine hydrolase) and persistent downregulation of asparagine synthetase across 30 individual differential expression datasets. This specific difference in asparagine metabolism between growing environments is almost certainly related to the observed differences in seed protein content because of the positive correlation between seed protein content at maturity and free asparagine in the developing seed. These results provided pointed information on seed protein-related genes influenced by environment. This information is valuable for breeding programs and genetic engineering of geographically optimized soybeans.

PMID:37790790 | PMC:PMC10544915 | DOI:10.3389/fpls.2023.1260393

Soft Matter. 2023 Oct 4. doi: 10.1039/d3sm00874f. Online ahead of print.

ABSTRACT

Using a mean field approach and simulations, we study the non-linear mechanical response of the vertex model (VM) of biological tissue to compression and dilation. The VM is known to exhibit a transition between solid and fluid-like, or floppy, states driven by geometric incompatibility. Target perimeter and area set a target shape which may not be geometrically achievable, thereby engendering frustration. Previously, an asymmetry in the linear elastic response was identified at the rigidity transition between compression and dilation. Here we show that the asymmetry extends away from the transition point for finite strains. Under finite compression, an initially solid VM can completely relax perimeter tension, resulting in a drop discontinuity in the mechanical response. Conversely, an initially floppy VM under dilation can rigidify and have a higher response. These observations imply that re-scaling of cell area shifts the transition between rigid and floppy states. Based on this insight, we calculate the re-scaling of cell area engendered by intrinsic curvature and write a prediction for the rigidity transition in the presence of curvature. The shift of the rigidity transition in the presence of curvature for the VM provides a new metric for predicting tissue rigidity from image data of curved tissues in a manner analogous to the flat case.

PMID:37789810 | DOI:10.1039/d3sm00874f

Mol Ecol. 2023 Oct 3. doi: 10.1111/mec.17151. Online ahead of print.

ABSTRACT

Insect pollination is fundamental for natural ecosystems and agricultural crops. The bumblebee species Bombus terrestris has become a popular choice for commercial crop pollination worldwide due to its effectiveness and ease of mass rearing. Bumblebee colonies are mass produced for the pollination of more than 20 crops and imported into over 50 countries including countries outside their native ranges, and the risk of invasion by commercial non-native bumblebees is considered an emerging issue for global conservation and biological diversity. Here, we use genome-wide data from seven wild populations close to and far from farms using commercial colonies, as well as commercial populations, to investigate the implications of utilizing commercial bumblebee subspecies in the UK. We find evidence for generally low levels of introgression between commercial and wild bees, with higher admixture proportions in the bees occurring close to farms. We identify genomic regions putatively involved in local and global adaptation, and genes in locally adaptive regions were found to be enriched for functions related to taste receptor activity, oxidoreductase activity, fatty acid and lipid biosynthetic processes. Despite more than 30 years of bumblebee colony importation into the UK, we observe low impact on the genetic integrity of local B. terrestris populations, but we highlight that even limited introgression might negatively affect locally adapted populations.

PMID:37789741 | DOI:10.1111/mec.17151

Mol Hortic. 2022 Jul 23;2(1):17. doi: 10.1186/s43897-022-00038-9.

ABSTRACT

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

PMID:37789425 | DOI:10.1186/s43897-022-00038-9

BMC Bioinformatics. 2023 Oct 3;24(1):374. doi: 10.1186/s12859-023-05479-7.

ABSTRACT

BACKGROUND: Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.

RESULTS: This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%.

CONCLUSION: We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .

PMID:37789314 | DOI:10.1186/s12859-023-05479-7

J Cancer Res Clin Oncol. 2023 Oct 3. doi: 10.1007/s00432-023-05435-1. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer, and comprehending its molecular mechanisms is pivotal for advancing treatment efficacy. This study aims to explore the prognostic and functional significance of base excision repair (BER)-related long non-coding RNAs (BERLncs) in LUAD.

METHODS: A risk score model for BERLncs was developed using the least absolute shrinkage and selection operator regression and Cox regression analysis. Model validation and prognostic evaluation were performed using Kaplan-Meier and receiver-operating characteristic curve analyses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential biological functions of BERLncs. Comparative analyses were carried out to investigate disparities in tumor mutation burden (TMB), immune infiltration, tumor immune dysfunction and exclusion (TIDE) score, chemosensitivity, and immune checkpoint gene expression between the two risk groups.

RESULTS: A predictive risk score model comprising 19 BERLncs was successfully developed. Patients were divided into high-risk and low-risk groups according to the median risk score. The high-risk subgroup exhibited significantly inferior overall survival. Functional enrichment analysis revealed pathways associated with lung cancer development, notably the neuroactive ligand-receptor interaction pathway. High-risk patients demonstrated elevated TMB, diminished TIDE scores, and an immunosuppressive tumor microenvironment, while low-risk patients displayed potential benefits from immunotherapy. Additionally, the risk model identified potential anticancer agents.

CONCLUSION: The risk score model based on BERLncs shows promise as a prognostic biomarker for LUAD patients, providing valuable insights for clinical decision-making, therapeutic strategies, and understanding of underlying biological mechanisms.

PMID:37789154 | DOI:10.1007/s00432-023-05435-1