J Biol Chem. 2024 Mar 20:107206. doi: 10.1016/j.jbc.2024.107206. Online ahead of print.

ABSTRACT

Melanoma is a type of skin cancer that originates in melanin-producing melanocytes. It is considered a multifactorial disease caused by both genetic and environmental factors, such as UV radiation. Dual-specificity tyrosine-phosphorylation-regulated kinase (DYRK) phosphorylates many substrates involved in signaling pathways, cell survival, cell cycle control, differentiation, and neuronal development. However, little is known about the cellular function of DYRK3, one of the five members of the DYRK family. Interestingly, it was observed that the expression of DYRK3, as well as p62 (a multifunctional signaling protein), is highly enhanced in most melanoma cell lines. This study aimed to investigate whether DYRK3 interacts with p62, and how this affects melanoma progression, particularly in melanoma cell lines. We found that DYRK3 directly phosphorylates p62 at the Ser-207 and Thr-269 residue. Phosphorylation at Thr-269 of p62 by DYRK3 increased the interaction of p62 with TRAF6, an already known activator of mTORC1 in the mTOR-involved signaling pathways. Moreover, the phosphorylation of p62 at Thr-269 promoted the activation of mTORC1. We also found that DYRK3-mediated phosphorylation of p62 at Thr-269 enhanced the growth of melanoma cell lines and melanoma progression. Conversely, DYRK3 knockdown or blockade of p62-T269 phosphorylation inhibited melanoma growth, colony formation, and cell migration. In conclusion, we demonstrated that DYRK3 phosphorylates p62, positively modulating the p62-TRAF6-mTORC1 pathway in melanoma cells. This finding suggests that DYRK3 suppression may be a novel therapy for preventing melanoma progression by regulating the mTORC1 pathway.

PMID:38519031 | DOI:10.1016/j.jbc.2024.107206

Cell Rep. 2024 Mar 21;43(4):113895. doi: 10.1016/j.celrep.2024.113895. Online ahead of print.

ABSTRACT

Topologically associated domains (TADs) are interaction subnetworks of chromosomal regions in 3D genomes. TAD boundaries frequently coincide with genome breaks while boundary deletion is under negative selection, suggesting that TADs may facilitate genome rearrangements and evolution. We show that genes co-localize by evolutionary age in humans and mice, resulting in TADs having different proportions of younger and older genes. We observe a major transition in the age co-localization patterns between the genes born during vertebrate whole-genome duplications (WGDs) or before and those born afterward. We also find that genes recently duplicated in primates and rodents are more frequently essential when they are located in old-enriched TADs and interact with genes that last duplicated during the WGD. Therefore, the evolutionary relevance of recent genes may increase when located in TADs with established regulatory networks. Our data suggest that TADs could play a role in organizing ancestral functions and evolutionary novelty.

PMID:38517894 | DOI:10.1016/j.celrep.2024.113895

Cell Rep. 2024 Mar 21;43(4):113979. doi: 10.1016/j.celrep.2024.113979. Online ahead of print.

ABSTRACT

Bacterial polyhydroxyalkanoates (PHAs) have emerged as promising eco-friendly alternatives to petroleum-based plastics since they are synthesized from renewable resources and offer exceptional properties. However, their production is limited to the stationary growth phase under nutrient-limited conditions, requiring customized strategies and costly two-phase bioprocesses. In this study, we tackle these challenges by employing a model-driven approach to reroute carbon flux and remove regulatory constraints using synthetic biology. We construct a collection of Pseudomonas putida-overproducing strains at the expense of plastics and lignin-related compounds using growth-coupling approaches. PHA production was successfully achieved during growth phase, resulting in the production of up to 46% PHA/cell dry weight while maintaining a balanced carbon-to-nitrogen ratio. Our strains are additionally validated under an upcycling scenario using enzymatically hydrolyzed polyethylene terephthalate as a feedstock. These findings have the potential to revolutionize PHA production and address the global plastic crisis by overcoming the complexities of traditional PHA production bioprocesses.

PMID:38517887 | DOI:10.1016/j.celrep.2024.113979

Cell Rep. 2024 Mar 21;43(4):113988. doi: 10.1016/j.celrep.2024.113988. Online ahead of print.

ABSTRACT

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

PMID:38517886 | DOI:10.1016/j.celrep.2024.113988

Syst Biol Reprod Med. 2024 Dec;70(1):73-90. doi: 10.1080/19396368.2024.2317804. Epub 2024 Mar 22.

ABSTRACT

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

PMID:38517373 | DOI:10.1080/19396368.2024.2317804

Hum Vaccin Immunother. 2024 Dec 31;20(1):2322795. doi: 10.1080/21645515.2024.2322795. Epub 2024 Mar 22.

ABSTRACT

Coronavirus disease 2019 (COVID-19) was extraordinarily harmful, with high rates of infection and hospitalization. This study aimed to evaluate the impact of COVID-19 vaccination status and other factors on hospitalization and disease severity, using data from Nagasaki Prefecture, Japan. Confirmed cases of COVID-19 infection with vaccination status were included and the differences in characteristics between different vaccination statuses, hospitalization or not, and patients with varying levels of disease severity were analyzed. Furthermore, logistic regression was used to calculate odds ratio (ORs) and 95% confidence intervals (CI) to evaluate the association of various factors with hospitalization and disease severity. From March 14, 2020 to August 31, 2022, 23,139 patients were unvaccinated 13,668 vaccinated the primary program with one or two doses, and 4,575 completed the booster. Vaccination reduced the risk of hospitalization with an odd ratio of 0.759 (95% CI: 0.654-0.881) and the protective effect of completed booster vaccination was more pronounced (OR: 0.261, 95% CI: 0.207-0.328). Similarly, vaccination significantly reduced the risk of disease severity (vaccinated primary program: OR: 0.191, 95% CI: 0.160-0.228; completed booster vaccination: OR: 0.129, 95% CI: 0.099-0.169). Overall, unvaccinated, male, elderly, immunocompromised, obese, and patients with other severe illness factors were all risk factors for COVID-19-related hospitalization and disease severity. Vaccination was associated with a decreased risk of hospitalization and disease severity, and highlighted the benefits of completing booster.

PMID:38517220 | DOI:10.1080/21645515.2024.2322795

Front Plant Sci. 2024 Mar 7;15:1274610. doi: 10.3389/fpls.2024.1274610. eCollection 2024.

ABSTRACT

Although rice is one of the main sources of calories for most of the world, nearly 60% of rice is grown in soils that are low in phosphorus especially in Asia and Africa. Given the limitations of bioavailable inorganic phosphate (Pi) in soils, it is important to develop crops tolerant to low phosphate in order to boost food security. Due to the immobile nature of Pi, plants have developed complex molecular signalling pathways that allow them to discern changes in Pi concentrations in the environment and adapt their growth and development. Recently, in rice, it was shown that a specific serine-threonine kinase known as Phosphorus-starvation tolerance 1 (PSTOL1) is important for conferring low phosphate tolerance in rice. Nonetheless, knowledge about the mechanism underpinning PSTOL1 activity in conferring low Pi tolerance is very limited in rice. Post-translation modifications (PTMs) play an important role in plants in providing a conduit to detect changes in the environment and influence molecular signalling pathways to adapt growth and development. In recent years, the PTM SUMOylation has been shown to be critical for plant growth and development. It is known that plants experience hyperSUMOylation of target proteins during phosphate starvation. Here, we demonstrate that PSTOL1 is SUMOylated in planta, and this affects its phosphorylation activity. Furthermore, we also provide new evidence for the role of SUMOylation in regulating PSTOL1 activity in plant responses to Pi starvation in rice and Arabidopsis. Our data indicated that overexpression of the non-SUMOylatable version of OsPSTOL1 negatively impacts total root length and total root surface area of rice grown under low Pi. Interestingly, our data also showed that overexpression of OsPSTOL1 in a non-cereal species, Arabidopsis, also positively impacts overall plant growth under low Pi by modulating root development. Taken together our data provide new evidence for the role of PSTOL1 SUMOylation in mediating enhanced root development for tolerating phosphate-limiting conditions.

PMID:38516661 | PMC:PMC10954814 | DOI:10.3389/fpls.2024.1274610

Front Genome Ed. 2024 Mar 6;6:1380682. doi: 10.3389/fgeed.2024.1380682. eCollection 2024.

ABSTRACT

This minireview examines the current state and challenges of genome editing in macroalgae. Despite the ecological and economic significance of this group of organisms, genome editing has seen limited applications. While CRISPR functionality has been established in two brown (Ectocarpus species 7 and Saccharina japonica) and one green seaweed (Ulva prolifera), these studies are limited to proof-of-concept demonstrations. All studies also (co)-targeted ADENINE PHOSPHORIBOSYL TRANSFERASE to enrich for mutants, due to the relatively low editing efficiencies. To advance the field, there should be a focus on advancing auxiliary technologies, particularly stable transformation, so that novel editing reagents can be screened for their efficiency. More work is also needed on understanding DNA repair in these organisms, as this is tightly linked with the editing outcomes. Developing efficient genome editing tools for macroalgae will unlock the ability to characterize their genes, which is largely uncharted terrain. Moreover, given their economic importance, genome editing will also impact breeding campaigns to develop strains that have better yields, produce more commercially valuable compounds, and show improved resilience to the impacts of global change.

PMID:38516199 | PMC:PMC10955705 | DOI:10.3389/fgeed.2024.1380682

Plant Pathol. 2023 Apr;72(3):536-547. doi: 10.1111/ppa.13684. Epub 2023 Jan 10.

ABSTRACT

Crop diseases can cause major yield losses, so the ability to detect and identify them in their early stages is important for disease control. Deep learning methods have shown promise in classifying multiple diseases; however, many studies do not use datasets that represent real field conditions, necessitating either further image processing or reducing their applicability. In this paper, we present a dataset of wheat images taken in real growth situations, including both field and glasshouse conditions, with five categories: healthy plants and four foliar diseases, yellow rust, brown rust, powdery mildew and Septoria leaf blotch. This dataset was used to train a deep learning model. The resulting model, named CerealConv, reached a 97.05% classification accuracy. When tested against trained pathologists on a subset of images from the larger dataset, the model delivered an accuracy score 2% higher than the best-performing pathologist. Image masks were used to show that the model was using the correct information to drive its classifications. These results show that deep learning networks are a viable tool for disease detection and classification in the field, and disease quantification is a logical next step.

PMID:38516179 | PMC:PMC10953319 | DOI:10.1111/ppa.13684

Front Microbiol. 2024 Mar 7;15:1304044. doi: 10.3389/fmicb.2024.1304044. eCollection 2024.

ABSTRACT

INTRODUCTION: Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria.

METHODS: N-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness.

RESULTS: The models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models.

DISCUSSION: Hydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.

PMID:38516021 | PMC:PMC10955071 | DOI:10.3389/fmicb.2024.1304044

Front Microbiol. 2024 Mar 7;15:1304682. doi: 10.3389/fmicb.2024.1304682. eCollection 2024.

ABSTRACT

Lettuce is an economically major leafy vegetable that is affected by numerous diseases. One of the most devastating diseases of lettuce is white mold caused by Sclerotinia sclerotiorum. Control methods for this fungus are limited due to the development of genetic resistance to commonly used fungicides, the large number of hosts and the long-term survival of sclerotia in soil. To elaborate a new and more sustainable approach to contain this pathogen, 1,210 Pseudomonas strains previously isolated from agricultural soils in Canada were screened for their antagonistic activity against S. sclerotiorum. Nine Pseudomonas strains showed strong in vitro inhibition in dual-culture confrontational assays. Whole genome sequencing of these strains revealed their affiliation with four phylogenomic subgroups within the Pseudomonas fluorescens group, namely Pseudomonas corrugata, Pseudomonas asplenii, Pseudomonas mandelii, and Pseudomonas protegens. The antagonistic strains harbor several genes and gene clusters involved in the production of secondary metabolites, including mycin-type and peptin-type lipopeptides, and antibiotics such as brabantamide, which may be involved in the inhibitory activity observed against S. sclerotiorum. Three strains also demonstrated significant in planta biocontrol abilities against the pathogen when either inoculated on lettuce leaves or in the growing substrate of lettuce plants grown in pots. They however did not impact S. sclerotiorum populations in the rhizosphere, suggesting that they protect lettuce plants by altering the fitness and the virulence of the pathogen rather than by directly impeding its growth. These results mark a step forward in the development of biocontrol products against S. sclerotiorum.

PMID:38516010 | PMC:PMC10955138 | DOI:10.3389/fmicb.2024.1304682

Front Digit Health. 2024 Mar 7;6:1349595. doi: 10.3389/fdgth.2024.1349595. eCollection 2024.

ABSTRACT

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

PMID:38515550 | PMC:PMC10955144 | DOI:10.3389/fdgth.2024.1349595

Glia. 2024 Mar 21. doi: 10.1002/glia.24526. Online ahead of print.

ABSTRACT

Tamoxifen-inducible systems are widely used in research to control Cre-mediated gene deletion in genetically modified animals. Beyond Cre activation, tamoxifen also exerts off-target effects, whose consequences are still poorly addressed. Here, we investigated the impact of tamoxifen on lipopolysaccharide (LPS)-induced neuroinflammatory responses, focusing on the neurogenic activity in the adult mouse dentate gyrus. We demonstrated that a four-day LPS treatment led to an increase in microglia, astrocytes and radial glial cells with concomitant reduction of newborn neurons. These effects were counteracted by a two-day tamoxifen pre-treatment. Through selective microglia depletion, we elucidated that both LPS and tamoxifen influenced astrogliogenesis via microglia mediated mechanisms, while the effects on neurogenesis persisted even in a microglia-depleted environment. Notably, changes in radial glial cells resulted from a combination of microglia-dependent and -independent mechanisms. Overall, our data reveal that tamoxifen treatment per se does not alter the balance between adult neurogenesis and astrogliogenesis but does modulate cellular responses to inflammatory stimuli exerting a protective role within the adult hippocampal neurogenic niche.

PMID:38515286 | DOI:10.1002/glia.24526

J Chem Inf Model. 2024 Mar 21. doi: 10.1021/acs.jcim.3c01486. Online ahead of print.

ABSTRACT

Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.

PMID:38514966 | DOI:10.1021/acs.jcim.3c01486

Nat Immunol. 2024 Mar 21. doi: 10.1038/s41590-024-01782-4. Online ahead of print.

ABSTRACT

Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.

PMID:38514887 | DOI:10.1038/s41590-024-01782-4

Nat Biotechnol. 2024 Mar 21. doi: 10.1038/s41587-024-02173-8. Online ahead of print.

ABSTRACT

Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.

PMID:38514799 | DOI:10.1038/s41587-024-02173-8

Nat Plants. 2024 Mar 21. doi: 10.1038/s41477-024-01654-7. Online ahead of print.

ABSTRACT

Beneficial interactions with microorganisms are pivotal for crop performance and resilience. However, it remains unclear how heritable the microbiome is with respect to the host plant genotype and to what extent host genetic mechanisms can modulate plant-microbiota interactions in the face of environmental stresses. Here we surveyed 3,168 root and rhizosphere microbiome samples from 129 accessions of locally adapted Zea, sourced from diverse habitats and grown under control and different stress conditions. We quantified stress treatment and host genotype effects on the microbiome. Plant genotype and source environment were predictive of microbiome abundance. Genome-wide association analysis identified host genetic variants linked to both rhizosphere microbiome abundance and source environment. We identified transposon insertions in a candidate gene linked to both the abundance of a keystone bacterium Massilia in our controlled experiments and total soil nitrogen in the source environment. Isolation and controlled inoculation of Massilia alone can contribute to root development, whole-plant biomass production and adaptation to low nitrogen availability. We conclude that locally adapted maize varieties exert patterns of genetic control on their root and rhizosphere microbiomes that follow variation in their home environments, consistent with a role in tolerance to prevailing stress.

PMID:38514787 | DOI:10.1038/s41477-024-01654-7

Nat Genet. 2024 Mar 21. doi: 10.1038/s41588-024-01689-8. Online ahead of print.

ABSTRACT

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC-seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene-peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

PMID:38514783 | DOI:10.1038/s41588-024-01689-8

Gynecol Oncol. 2024 Mar 20:S0090-8258(24)00158-6. doi: 10.1016/j.ygyno.2024.03.013. Online ahead of print.

NO ABSTRACT

PMID:38514301 | DOI:10.1016/j.ygyno.2024.03.013

J Ethnopharmacol. 2024 Mar 19:118073. doi: 10.1016/j.jep.2024.118073. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Medicinal mushrooms belonging to the Lignosus spp., colloquially known as Tiger Milk mushrooms (TMMs), are used as traditional medicine by communities across various regions of China and Southeast Asia to enhance immunity and to treat various diseases. At present, three Lignosus species have been identified in Malaysia: L. rhinocerus, L. tigris, and L. cameronensis. Similarities in their macroscopic morphologies and the nearly indistinguishable appearance of their sclerotia often lead to interchangeability between them. Hence, substantiation of their traditional applications via identification of their individual bioactive properties is imperative in ensuring that they are safe for consumption. L. tigris was first identified in 2013. Thus far, studies on L. tigris cultivar sclerotia (Ligno TG-K) have shown that it possesses significant antioxidant activities and has greater antiproliferative action against selected cancer cells in vitro compared to its sister species, L. rhinocerus TM02®. Our previous genomics study also revealed significant genetic dissimilarities between them. Further -omics investigations on Ligno TG-K hold immense potential in facilitating the identification of its bioactive compounds and their associated bioactivities.

AIM OF STUDY: The overall aim of this study was to investigate the gene expression profile of Ligno TG-K via de novo RNA-seq and pathway analysis. We also aimed to identify highly expressed genes encoding compounds that contribute to its cytotoxic and antioxidant properties, as well as perform a comparative transcriptomics analysis between Ligno TG-K and its sister species, L. rhinocerus TM02®.

MATERIALS AND METHODS: Total RNA from fresh 3-month-old cultivated L. tigris sclerotia (Ligno TG-K) was extracted and analyzed via de novo RNA sequencing. Expressed genes were analyzed using InterPro and NCBI-Nr databases for domain identification and homology search. Functional categorization based on gene functions and pathways was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Clusters of Orthologous Genes (COG) databases. Selected genes were subsequently subjected to phylogenetic analysis.

RESULTS: Our transcriptomics analysis of Ligno TG-K revealed that 68.06% of its genes are expressed in the sclerotium; 80.38% of these were coding transcripts. Our analysis identified highly expressed transcripts encoding proteins with prospective medicinal properties. These included serine proteases (FPKM = 7356.68), deoxyribonucleases (FPKM = 3777.98), lectins (FPKM = 3690.87), and fungal immunomodulatory proteins (FPKM = 2337.84), all of which have known associations with anticancer activities. Transcripts linked to proteins with antioxidant activities, such as superoxide dismutase (FPKM = 1161.69) and catalase (FPKM = 1905.83), were also highly expressed. Results of our sequence alignments revealed that these genes and their orthologs can be found in other mushrooms. They exhibit significant sequence similarities, suggesting possible parallels in their anticancer and antioxidant bioactivities.

CONCLUSION: This study is the first to provide a reference transcriptome profile of genes expressed in the sclerotia of L. tigris. The current study also presents distinct COG profiles of highly expressed genes in Ligno TG-K and L. rhinocerus TM02®, highlighting that any distinctions uncovered may be attributed to their interspecies variations and inherent characteristics that are unique to each species. Our findings suggest that Ligno TG-K contains bioactive compounds with prospective medicinal properties that warrant further investigations.

CLASSIFICATION: Systems biology and omics.

PMID:38513780 | DOI:10.1016/j.jep.2024.118073