Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment

JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.

Salsolinol as an RNA m6A methylation inducer mediates dopaminergic neuronal death by regulating YAP1 and autophagy

JOURNAL/nrgr/04.03/01300535-202503000-00032/figure1/v/2024-06-17T092413Z/r/image-tiff Salsolinol (1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline, Sal) is a catechol isoquinoline that causes neurotoxicity and shares structural similarity with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, an environmental toxin that causes Parkinson's disease. However, the mechanism by which Sal mediates dopaminergic neuronal death remains unclear. In this study, we found that Sal significantly enhanced the global level of N6-methyladenosine (m6A) RNA methylation in PC12 cells, mainly by inducing the downregulation of the expression of m6A demethylases fat mass and obesity-associated protein (FTO) and alkB homolog 5 (ALKBH5). RNA sequencing analysis showed that Sal downregulated the Hippo signaling pathway. The m6A reader YTH domain-containing family protein 2 (YTHDF2) promoted the degradation of m6A-containing Yes-associated protein 1 (YAP1) mRNA, which is a downstream key effector in the Hippo signaling pathway. Additionally, downregulation of YAP1 promoted autophagy, indicating that the mutual regulation between YAP1 and autophagy can lead to neurotoxicity. These findings reveal the role of Sal on m6A RNA methylation and suggest that Sal may act as an RNA methylation inducer mediating dopaminergic neuronal death through YAP1 and autophagy. Our results provide greater insights into the neurotoxic effects of catechol isoquinolines compared with other studies and may be a reference for assessing the involvement of RNA methylation in the pathogenesis of Parkinson's disease.

Pro-resolving lipid mediator reduces amyloid-β42-induced gene expression in human monocyte-derived microglia

JOURNAL/nrgr/04.03/01300535-202503000-00031/figure1/v/2024-06-17T092413Z/r/image-tiff Specialized pro-resolving lipid mediators including maresin 1 mediate resolution but the levels of these are reduced in Alzheimer's disease brain, suggesting that they constitute a novel target for the treatment of Alzheimer's disease to prevent/stop inflammation and combat disease pathology. Therefore, it is important to clarify whether they counteract the expression of genes and proteins induced by amyloid-β. With this objective, we analyzed the relevance of human monocyte-derived microglia for in vitro modeling of neuroinflammation and its resolution in the context of Alzheimer's disease and investigated the pro-resolving bioactivity of maresin 1 on amyloid-β42-induced Alzheimer's disease-like inflammation. Analysis of RNA-sequencing data and secreted proteins in supernatants from the monocyte-derived microglia showed that the monocyte-derived microglia resembled Alzheimer's disease-like neuroinflammation in human brain microglia after incubation with amyloid-β42. Maresin 1 restored homeostasis by down-regulating inflammatory pathway related gene expression induced by amyloid-β42 in monocyte-derived microglia, protection of maresin 1 against the effects of amyloid-β42 is mediated by a re-balancing of inflammatory transcriptional networks in which modulation of gene transcription in the nuclear factor-kappa B pathway plays a major part. We pinpointed molecular targets that are associated with both neuroinflammation in Alzheimer's disease and therapeutic targets by maresin 1. In conclusion, monocyte-derived microglia represent a relevant in vitro microglial model for studies on Alzheimer's disease-like inflammation and drug response for individual patients. Maresin 1 ameliorates amyloid-β42-induced changes in several genes of importance in Alzheimer's disease, highlighting its potential as a therapeutic target for Alzheimer's disease.

Toward understanding the role of genomic repeat elements in neurodegenerative diseases

Neurodegenerative diseases cause great medical and economic burdens for both patients and society; however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

Combined biochar and wheat-derived endophytic bacteria reduces cadmium uptake in wheat grains in a metal-polluted soil

In this study, two wheat-derived cadmium (Cd)-immobilizing endophytic Pseudomonas paralactis M14 and Priestia megaterium R27 were evaluated for their effects on wheat tissue Cd uptake under hydroponic conditions. Then, the impacts of the biochar (BC), M14+R27 (MR), and BC+MR treatments on wheat Cd uptake and the mechanisms involved were investigated at the jointing, heading, and mature stages of wheat plants under field-plot conditions. A hydroponic experiment showed that the MR treatment significantly decreased the above-ground tissue Cd content compared with the M14 or R27 treatment. The BC+MR treatment reduced the grain Cd content by 51.5%-67.7% and Cd translocation factor at the mature stage of wheat plants and increased the organic matter-bound Cd content by 31%-75% in the rhizosphere soils compared with the BC or MR treatment. Compared with the BC or MR treatment, the relative abundances of the biomarkers associated with Gemmatimonas, Altererythrobacter, Gammaproteobacteria, Xanthomonadaceae, Phenylobacterium, and Nocardioides in the BC+MR-treated rhizosphere microbiome decreased and negatively correlated with the organic matter-bound Cd contents. In the BC+MR-treated root interior microbiome, the relative abundance of the biomarker belonging to Exiguobacterium increased and negatively correlated with the Cd translocation factor, while the relative abundance of the biomarker belonging to Pseudonocardiaceae decreased and positively correlated with the Cd translocation factor. Our findings suggested that the BC+MR treatment reduced Cd availability and Cd transfer through affecting the abundances of these specific biomarkers in the rhizosphere soil and root interior microbiomes, leading to decreased wheat grain Cd uptake in the contaminated soil.

Metagenomic perspectives on antibiotic resistance genes in tap water: The environmental characteristic, potential mobility and health threat

As an emerging environmental contaminant, antibiotic resistance genes (ARGs) in tap water have attracted great attention. Although studies have provided ARG profiles in tap water, research on their abundance levels, composition characteristics, and potential threat is still insufficient. Here, 9 household tap water samples were collected from the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. Additionally, 75 sets of environmental sample data (9 types) were downloaded from the public database. Metagenomics was then performed to explore the differences in the abundance and composition of ARGs. 221 ARG subtypes consisting of 17 types were detected in tap water. Although the ARG abundance in tap water was not significantly different from that found in drinking water plants and reservoirs, their composition varied. In tap water samples, the three most abundant classes of resistance genes were multidrug, fosfomycin and MLS (macrolide-lincosamide-streptogramin) ARGs, and their corresponding subtypes ompR, fosX and macB were also the most abundant ARG subtypes. Regarding the potential mobility, vanS had the highest abundance on plasmids and viruses, but the absence of key genes rendered resistance to vancomycin ineffective. Generally, the majority of ARGs present in tap water were those that have not been assessed and are currently not listed as high-threat level ARG families based on the World Health Organization Guideline. Although the current potential threat to human health posed by ARGs in tap water is limited, with persistent transfer and accumulation, especially in pathogens, the potential danger to human health posed by ARGs should not be ignored.

Nitrogen-cycling processes under long-term compound heavy metal(loids) pressure around a gold mine: Stimulation of nitrite reduction

Mining and tailings deposition can cause serious heavy metal(loids) pollution to the surrounding soil environment. Soil microorganisms adapt their metabolism to such conditions, driving alterations in soil function. This study aims to elucidate the response patterns of nitrogen-cycling microorganisms under long-term heavy metal(loids) exposure. The results showed that the diversity and abundance of nitrogen-cycling microorganisms showed negative feedback to heavy metal(loids) concentrations. Denitrifying microorganisms were shown to be the dominant microorganisms with over 60% of relative abundance and a complex community structure including 27 phyla. Further, the key bacterial species in the denitrification process were calculated using a random forest model, where the top three key species (Pseudomonas stutzei, Sphingobium japonicum and Leifsonia rubra) were found to play a prominent role in nitrite reduction. Functional gene analysis and qPCR revealed that nirK, which is involved in nitrite reduction, significantly accumulated in the most metal-rich soil with the increase of absolute abundance of 63.86%. The experimental results confirmed that the activity of nitrite reductase (Nir) encoded by nirK in the soil was increased at high concentrations of heavy metal(loids). Partial least squares-path model identified three potential modes of nitrite reduction processes being stimulated by heavy metal(loids), the most prominent of which contributed to enhanced nirK abundance and soil Nir activity through positive stimulation of key species. The results provide new insights and preliminary evidence on the stimulation of nitrite reduction processes by heavy metal(loids).

Efficacy and pharmacodynamic effect of anti-CD73 and anti-PD-L1 monoclonal antibodies in combination with cytotoxic therapy: observations from mouse tumor models

CD73 is a cell surface 5'nucleotidase (NT5E) and key node in the catabolic process generating immunosuppressive adenosine in cancer. Using a murine monoclonal antibody surrogate of Oleclumab, we investigated the effect of CD73 inhibition in concert with cytotoxic therapies (chemotherapies as well as fractionated radiotherapy) and PD-L1 blockade. Our results highlight improved survival in syngeneic tumor models of colorectal cancer (CT26 and MC38) and sarcoma (MCA205). This therapeutic outcome was in part driven by cytotoxic CD8 T-cells, as evidenced by the detrimental effect of CD8 depleting antibody treatment of MCA205 tumor bearing mice treated with anti-CD73, anti-PD-L1 and 5-Fluorouracil+Oxaliplatin (5FU+OHP). We hypothesize that the improved responses are tumor microenvironment (TME)-driven, as suggested by the lack of anti-CD73 enhanced cytopathic effects mediated by 5FU+OHP on cell lines in vitro. Pharmacodynamic analysis, using imaging mass cytometry and RNA-sequencing, revealed noteworthy changes in specific cell populations like cytotoxic T cells, B cells and NK cells in the CT26 TME. Transcriptomic analysis highlighted treatment-related modulation of gene profiles associated with an immune response, NK and T-cell activation, T cell receptor signaling and interferon (types 1 & 2) pathways. Inclusion of comparator groups representing the various components of the combination allowed deconvolution of contribution of the individual therapeutic elements; highlighting specific effects mediated by the anti-CD73 antibody with respect to immune-cell representation, chemotaxis and myeloid biology. These pre-clinical data reflect complementarity of adenosine blockade with cytotoxic therapy, and T-cell checkpoint inhibition, and provides new mechanistic insights in support of combination therapy.

Design and evaluation of a multi-epitope DNA vaccine against HPV16

Cervical cancer, among the deadliest cancers affecting women globally, primarily arises from persistent infection with high-risk human papillomavirus (HPV). To effectively combat persistent infection and prevent the progression of precancerous lesions into malignancy, a therapeutic HPV vaccine is under development. This study utilized an immunoinformatics approach to predict epitopes of cytotoxic T lymphocytes (CTLs) and helper T lymphocytes (HTLs) using the E6 and E7 oncoproteins of the HPV16 strain as target antigens. Subsequently, through meticulous selection of T-cell epitopes and other necessary elements, a multi-epitope vaccine was constructed, exhibiting good immunogenic, physicochemical, and structural characteristics. Furthermore, in silico simulations showed that the vaccine not only interacted well with toll-like receptors (TLR2/TLR3/TLR4), but also induced a strong innate and adaptive immune response characterized by elevated Th1-type cytokines, such as interferon-gamma (IFN-γ) and interleukin-2 (IL2). Additionally, our study investigated the effects of different immunization intervals on immune responses, aiming to optimize a time-efficient immunization program. In animal model experiments, the vaccine exhibited robust immunogenic, therapeutic, and prophylactic effects. Administered thrice, it consistently induced the expansion of specific CD4 and CD8 T cells, resulting in substantial cytokines release and increased proliferation of memory T cell subsets in splenic cells. Overall, our findings support the potential of this multi-epitope vaccine in combating HPV16 infection and signify its candidacy for future HPV vaccine development.

Drought-induced molecular changes in crown of various barley phytohormone mutants

One of the main signal transduction pathways that modulate plant growth and stress responses, including drought, is the action of phytohormones. Recent advances in omics approaches have facilitated the exploration of plant genomes. However, the molecular mechanisms underlying the response in the crown of barley, which plays an essential role in plant performance under stress conditions and regeneration after stress treatment, remain largely unclear. The objective of the present study was the elucidation of drought-induced molecular reactions in the crowns of different barley phytohormone mutants. We verified the hypothesis that defects of gibberellins, brassinosteroids, and strigolactones action affect the transcriptomic, proteomic, and hormonal response of barley crown to the transitory drought influencing plant development under stress. Moreover, we assumed that due to the strong connection between strigolactones and branching the hvdwarf14.d mutant, with dysfunctional receptor of strigolactones, manifests the most abundant alternations in crowns and phenotype under drought. Finally, we expected to identify components underlying the core response to drought which are independent of the genetic background. Large-scale analyses were conducted using gibberellins-biosynthesis, brassinosteroids-signaling, and strigolactones-signaling mutants, as well as reference genotypes. Detailed phenotypic evaluation was also conducted. The obtained results clearly demonstrated that hormonal disorders caused by mutations in the HvGA20ox2, HvBRI1, and HvD14 genes affected the multifaceted reaction of crowns to drought, although the expression of these genes was not induced by stress. The study further detected not only genes and proteins that were involved in the drought response and reacted specifically in mutants compared to the reaction of reference genotypes and vice versa, but also the candidates that may underlie the genotype-universal stress response. Furthermore, candidate genes involved in phytohormonal interactions during the drought response were identified. We also found that the interplay between hormones, especially gibberellins and auxins, as well as strigolactones and cytokinins may be associated with the regulation of branching in crowns exposed to drought. Overall, the present study provides novel insights into the molecular drought-induced responses that occur in barley crowns.

Mutually exclusive teams-like patterns of gene regulation characterize phenotypic heterogeneity along the noradrenergic-mesenchymal axis in neuroblastoma

Neuroblastoma is the most frequent extracranial pediatric tumor and leads to 15% of all cancer-related deaths in children. Tumor relapse and therapy resistance in neuroblastoma are driven by phenotypic plasticity and heterogeneity between noradrenergic (NOR) and mesenchymal (MES) cell states. Despite the importance of this phenotypic plasticity, the dynamics and molecular patterns associated with these bidirectional cell-state transitions remain relatively poorly understood. Here, we analyze multiple RNA-seq datasets at both bulk and single-cell resolution, to understand the association between NOR- and MES-specific factors. We observed that NOR-specific and MES-specific expression patterns are largely mutually exclusive, exhibiting a "teams-like" behavior among the genes involved, reminiscent of our earlier observations in lung cancer and melanoma. This antagonism between NOR and MES phenotypes was also associated with metabolic reprogramming and with immunotherapy targets PD-L1 and GD2 as well as with experimental perturbations driving the NOR-MES and/or MES-NOR transition. Further, these "teams-like" patterns were seen only among the NOR- and MES-specific genes, but not in housekeeping genes, possibly highlighting a hallmark of network topology enabling cancer cell plasticity.

Unraveling the copper-death connection: Decoding COVID-19's immune landscape through advanced bioinformatics and machine learning approaches

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1's pivotal role validated. FDX1's high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1's connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

Establishment of a novel tumor neoantigen prediction tool for personalized vaccine design

The personalized neoantigen nanovaccine (PNVAC) platform for patients with gastric cancer we established previously exhibited promising anti-tumor immunoreaction. However, limited by the ability of traditional neoantigen prediction tools, a portion of epitopes failed to induce specific immune response. In order to filter out more neoantigens to optimize our PNVAC platform, we develop a novel neoantigen prediction model, NUCC. This prediction tool trained through a deep learning approach exhibits better neoantigen prediction performance than other prediction tools, not only in two independent epitope datasets, but also in a totally new epitope dataset we construct from scratch, including 25 patients with advance gastric cancer and 150 candidate mutant peptides, 13 of which prove to be neoantigen by immunogenicity test in vitro. Our work lay the foundation for the improvement of our PNVAC platform for gastric cancer in the future.

Effects of high-temperature stress on gene expression related to photosynthesis in two jujube (Ziziphus jujuba Mill.) varieties

Elevated temperatures critically impact crop growth, development, and yield, with photosynthesis being the most temperature-sensitive physiological process in plants. This study focused on assessing the photosynthetic response and genetic adaptation of two different heat-resistant jujube varieties 'Junzao' (J) and 'Fucuimi' (F), to high-temperature stress (42°C Day/30°C Night). Comparative analyses of leaf photosynthetic indices, microstructural changes, and transcriptome sequencing were conducted. Results indicated superior high-temperature adaptability in F, evidenced by alterations in leaf stomatal behavior - particularly in J, where defense cells exhibited significant water loss, shrinkage, and reduced stomatal opening, alongside a marked increase in stomatal density. Through transcriptome sequencing 13,884 differentially expressed genes (DEGs) were identified, significantly enriched in pathways related to plant-pathogen interactions, amino acid biosynthesis, starch and sucrose metabolism, and carbohydrate metabolism. Key findings include the identification of photosynthetic pathway related DEGs and HSFA1s as central regulators of thermal morphogenesis and heat stress response. Revealing their upregulation in F and downregulation in J. The results indicate that these genes play a crucial role in improving heat tolerance in F. This study unveils critical photosynthetic genes involved in heat stress, providing a theoretical foundation for comprehending the molecular mechanisms underlying jujube heat tolerance.

Neuroendocrine gene subsets are uniquely dysregulated in prostate adenocarcinoma

Prostate cancer has heterogeneous growth patterns, and its prognosis is the poorest when it progresses to a neuroendocrine phenotype. Using bioinformatic analysis, we evaluated RNA expression of neuroendocrine genes in a panel of five different cancer types: prostate adenocarcinoma, breast cancer, kidney chromophobe, kidney renal clear cell carcinoma and kidney renal papillary cell carcinoma. Our results show that specific neuroendocrine genes are significantly dysregulated in these tumors, suggesting that they play an active role in cancer progression. Among others, synaptophysin (SYP), a conventional neuroendocrine marker, is upregulated in prostate adenocarcinoma (PRAD) and breast cancer (BRCA). Our analysis shows that SYP is enriched in small extracellular vesicles (sEVs) derived from plasma of PRAD patients, but it is absent in sEVs derived from plasma of healthy donors. Similarly, classical sEV markers are enriched in sEVs derived from plasma of prostate cancer patients, but weakly detectable in sEVs derived from plasma of healthy donors. Overall, our results pave the way to explore new strategies to diagnose these diseases based on the neuroendocrine gene expression in patient tumors or plasma sEVs.

Endogenous cAMP elevation in Brassica napus causes changes in phytohormone levels

In higher plants, the regulatory roles of cAMP (cyclic adenosine 3',5'-monophosphate) signaling remain elusive until now. Cellular cAMP levels are generally much lower in higher plants than in animals and transiently elevated for triggering downstream signaling events. Moreover, plant adenylate cyclase (AC) activities are found in different moonlighting multifunctional proteins, which may pose additional complications in distinguishing a specific signaling role for cAMP. Here, we have developed rapeseed (Brassica napus L.) transgenic plants that overexpress an inducible plant-origin AC activity for generating high AC levels much like that in animal cells, which served the genetic model disturbing native cAMP signaling as a whole in plants. We found that overexpression of the soluble AC activity had significant impacts on the contents of indole-3-acetic acid (IAA) and stress phytohormones, i.e. jasmonic acid (JA), abscisic acid (ABA), and salicylic acid (SA) in the transgenic plants. Acute induction of the AC activity caused IAA overaccumulation, and upregulation of TAA1 and CYP83B1 in the IAA biosynthesis pathways, but also simultaneously the hyper-induction of PR4 and KIN2 expression indicating activation of JA and ABA signaling pathways. We observed typical overgrowth phenotypes related to IAA excess in the transgenic plants, including significant increases in plant height, internode length, width of leaf blade, petiole length, root length, and fresh shoot biomass, as well as the precocious seed development, as compared to wild-type plants. In addition, we identified a set of 1465 cAMP-responsive genes (CRGs), which are most significantly enriched in plant hormone signal transduction pathway, and function mainly in relevance to hormonal, abiotic and biotic stress responses, as well as growth and development. Collectively, our results support that cAMP elevation impacts phytohormone homeostasis and signaling, and modulates plant growth and development. We proposed that cAMP signaling may be critical in configuring the coordinated regulation of growth and development in higher plants.

Unveiling kiwifruit TCP genes: evolution, functions, and expression insights

The TEOSINTE-BRANCHED1/CYCLOIDEA/PROLEFERATING-CELL-FACTORS (TCP) gene family is a plant-specific transcriptional factor family involved in leaf morphogenesis and senescence, lateral branching, hormone crosstalk, and stress responses. To date, a systematic study on the identification and characterization of the TCP gene family in kiwifruit has not been reported. Additionally, the function of kiwifruit TCPs in regulating kiwifruit responses to the ethylene treatment and bacterial canker disease pathogen (Pseudomonas syringae pv. actinidiae, Psa) has not been investigated. Here, we identified 40 and 26 TCP genes in Actinidia chinensis (Ac) and A. eriantha (Ae) genomes, respectively. The synteny analysis of AcTCPs illustrated that whole-genome duplication accounted for the expansion of the TCP family in Ac. Phylogenetic, conserved domain, and selection pressure analysis indicated that TCP family genes in Ac and Ae had undergone different evolutionary patterns after whole-genome duplication (WGD) events, causing differences in TCP gene number and distribution. Our results also suggested that protein structure and cis-element architecture in promoter regions of TCP genes have driven the function divergence of duplicated gene pairs. Three and four AcTCP genes significantly affected kiwifruit responses to the ethylene treatment and Psa invasion, respectively. Our results provided insight into general characters, evolutionary patterns, and functional diversity of kiwifruit TCPs.

ShF5H1 overexpression increases syringyl lignin and improves saccharification in sugarcane leaves

The agricultural sugarcane residues, bagasse and straws, can be used for second-generation ethanol (2GE) production by the cellulose conversion into glucose (saccharification). However, the lignin content negatively impacts the saccharification process. This polymer is mainly composed of guaiacyl (G), hydroxyphenyl (H), and syringyl (S) units, the latter formed in the ferulate 5-hydroxylase (F5H) branch of the lignin biosynthesis pathway. We have generated transgenic lines overexpressing ShF5H1 under the control of the C4H (cinnamate 4-hydroxylase) rice promoter, which led to a significant increase of up to 160% in the S/G ratio and 63% in the saccharification efficiency in leaves. Nevertheless, the content of lignin was unchanged in this organ. In culms, neither the S/G ratio nor sucrose accumulation was altered, suggesting that ShF5H1 overexpression would not affect first-generation ethanol production. Interestingly, the bagasse showed a significantly higher fiber content. Our results indicate that the tissue-specific manipulation of the biosynthetic branch leading to S unit formation is industrially advantageous and has established a foundation for further studies aiming at refining lignin modifications. Thus, the ShF5H1 overexpression in sugarcane emerges as an efficient strategy to improve 2GE production from straw.

Martínez-Castilla L, García-Sandoval R, Ballester P, Ferrándiz C, Sanchez MDLP, García-Ponce B, Garay-Arroyo A, R. Álvarez-Buylla E. HDACs MADS-domain protein interaction: a case study of HDA15 and XAL1 in Arabidopsis thaliana

Cellular behavior, cell differentiation and ontogenetic development in eukaryotes result from complex interactions between epigenetic and classic molecular genetic mechanisms, with many of these interactions still to be elucidated. Histone deacetylase enzymes (HDACs) promote the interaction of histones with DNA by compacting the nucleosome, thus causing transcriptional repression. MADS-domain transcription factors are highly conserved in eukaryotes and participate in controlling diverse developmental processes in animals and plants, as well as regulating stress responses in plants. In this work, we focused on finding out putative interactions of Arabidopsis thaliana HDACs and MADS-domain proteins using an evolutionary perspective combined with bioinformatics analyses and testing the more promising predicted interactions through classic molecular biology tools. Through bioinformatic analyses, we found similarities between HDACs proteins from different organisms, which allowed us to predict a putative protein-protein interaction between the Arabidopsis thaliana deacetylase HDA15 and the MADS-domain protein XAANTAL1 (XAL1). The results of two-hybrid and Bimolecular Fluorescence Complementation analysis demonstrated in vitro and in vivo HDA15-XAL1 interaction in the nucleus. Likely, this interaction might regulate developmental processes in plants as is the case for this type of interaction in animals.

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

Proteome-wide analysis reveals G protein-coupled receptor-like proteins in rice (Oryza sativa)

G protein-coupled receptors (GPCRs) constitute the largest family of transmembrane proteins in metazoans that mediate the regulation of various physiological responses to discrete ligands through heterotrimeric G protein subunits. The existence of GPCRs in plant is contentious, but their comparable crucial role in various signaling pathways necessitates the identification of novel remote GPCR-like proteins that essentially interact with the plant G protein α subunit and facilitate the transduction of various stimuli. In this study, we identified three putative GPCR-like proteins (OsGPCRLPs) (LOC_Os06g09930.1, LOC_Os04g36630.1, and LOC_Os01g54784.1) in the rice proteome using a stringent bioinformatics workflow. The identified OsGPCRLPs exhibited a canonical GPCR 'type I' 7TM topology, patterns, and biologically significant sites for membrane anchorage and desensitization. Cluster-based interactome mapping revealed that the identified proteins interact with the G protein α subunit which is a characteristic feature of GPCRs. Computational results showing the interaction of identified GPCR-like proteins with G protein α subunit and its further validation by the membrane yeast-two-hybrid assay strongly suggest the presence of GPCR-like 7TM proteins in the rice proteome. The absence of a regulator of G protein signaling (RGS) box in the C- terminal domain, and the presence of signature motifs of canonical GPCR in the identified OsGPCRLPs strongly suggest that the rice proteome contains GPCR-like proteins that might be involved in signal transduction.

Identification and expression profiling of microRNAs in leaf tissues of Foeniculum vulgare Mill. under salinity stress

Foeniculum vulgare Mill. commonly known as fennel, is a globally recognized aromatic medicinal plant and culinary herb with widespread popularity due to its antimicrobial, antioxidant, carminative, and diuretic properties, among others. Although the phenotypic effects of salinity stress have been previously explored in fennel, the molecular mechanisms underlying responses to elevated salinity in this plant remain elusive. MicroRNAs (miRNAs) are tiny, endogenous, and extensively conserved non-coding RNAs (ncRNAs) typically ranging from 20 to 24 nucleotides (nt) in length that play a major role in a myriad of biological functions. In fact, a number of miRNAs have been extensively associated with responses to abiotic stress in plants. Consequently, employing computational methodologies and rigorous filtering criteria, 40 putative miRNAs belonging to 25 different families were characterized from fennel in this study. Subsequently, employing the psRNATarget tool, a total of 67 different candidate target transcripts for the characterized fennel miRNAs were predicted. Additionally, the expression patterns of six selected fennel miRNAs (i.e. fvu-miR156a, fvu-miR162a-3p, fvu-miR166a-3p, fvu-miR167a-5p, fvu-miR171a-3p, and fvu-miR408-3p) were analyzed under salinity stress conditions via qPCR. This article holds notable significance as it identifies not only 40 putative miRNAs in fennel, a non-model plant, but also pioneers the analysis of their expression under salinity stress conditions.

Impacts of AlaAT3 transgenic poplar on rhizosphere soil chemical properties, enzyme activity, bacterial community, and metabolites under two nitrogen conditions

Poplar stands as one of the primary afforestation trees globally. We successfully generated transgenic poplar trees characterized by enhanced biomass under identical nutrient conditions, through the overexpression of the pivotal nitrogen assimilation gene, pxAlaAT3. An environmental risk assessment was conducted for investigate the potential changes in rhizosphere soil associated with these overexpressing lines (OL). The results show that acid phosphatase activity was significantly altered under ammonium in OL compared to the wild-type control (WT), and a similar difference was observed for protease under nitrate. 16SrDNA sequencing indicated no significant divergence in rhizosphere soil microbial community diversity between WT and OL. Metabolomics analysis revealed that the OL caused minimal alterations in the metabolites of the rhizosphere soil, posing no potential harm to the environment. With these findings in mind, we anticipate that overexpressed plants will not adversely impact the surrounding soil environment.

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

Unveiling molecular mechanisms of pigment synthesis in gardenia (Gardenia jasminoides) fruits through integrative transcriptomics and metabolomics analysis

This study conducted a combined transcriptomics and metabolomics analysis in premature and mature developmental stages of Gardenia jasminoides Ellis fruits to identify the molecular mechanisms of pigment synthesis. The transcriptomics data produced high-quality clean data amounting to 46.98 gigabytes, exhibiting a mapping ratio of 86.36% to 91.43%. Transcriptomics analysis successfully identified about 3,914 differentially expressed genes which are associated with pivotal biological processes, including photosynthesis, chlorophyll, biosynthetic processes, and protein-chromophore linkage pathways. Functional diversity was clarified by the Clusters of Orthologous Groups (COG) classification, which focused mainly on pigment synthesis functions. Pathways analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) revealed critical pathways affecting pigment development. Metabolomics studies were carried out utilizing Ultra Performance Liquid Chromatography and mass spectrometry (UPLC-MS). About 480 metabolites were detected via metabolomics investigation, the majority of that were significantly involved in pigment synthesis. Cluster and pathway analyses revealed the importance of pathways such as plant secondary metabolite biosynthesis, biosynthesis of phenylpropanoids and plant hormone signal transduction in pigment synthesis. Current research advances our comprehension of the underlying mechanisms at the molecular level governing pigment synthesis in gardenia fruits, furnishing valuable insights for subsequent investigations.

DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures

RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion's code and data are available at http://bioinfo.org/deepfusion/.

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Deep immunoglobulin repertoire sequencing depicts a comprehensive atlas of spike-specific antibody lineages shared among COVID-19 convalescents

Neutralizing antibodies are a key component in protective humoral immunity against SARS-CoV-2. Currently, available technologies cannot track epitope-specific antibodies in global antibody repertoires. Thus, the comprehensive repertoire of spike-specific neutralizing antibodies elicited by SARS-CoV-2 infection is not fully understood. We therefore combined high-throughput immunoglobulin heavy chain (IgH) repertoire sequencing, and structural and bioinformatics analysis to establish an antibodyomics pipeline, which enables tracking spike-specific antibody lineages that target certain neutralizing epitopes. We mapped the neutralizing epitopes on the spike and determined the epitope-preferential antibody lineages. This analysis also revealed numerous overlaps between immunodominant neutralizing antibody-binding sites and mutation hotspots on spikes as observed so far in SARS-CoV-2 variants. By clustering 2677 spike-specific antibodies with 360 million IgH sequences that we sequenced, a total of 329 shared spike-specific antibody clonotypes were identified from 33 COVID-19 convalescents and 24 SARS-CoV-2-naïve individuals. Epitope mapping showed that the shared antibody responses target not only neutralizing epitopes on RBD and NTD but also non-neutralizing epitopes on S2. The immunodominance of neutralizing antibody response is determined by the occurrence of specific precursors in human naïve B-cell repertoires. We identified that only 28 out of the 329 shared spike-specific antibody clonotypes persisted for at least 12 months. Among them, long-lived IGHV3-53 antibodies are likely to evolve cross-reactivity to Omicron variants through accumulating somatic hypermutations. Altogether, we created a comprehensive atlas of spike-targeting antibody lineages in COVID-19 convalescents and antibody precursors in human naïve B cell repertoires, providing a valuable reference for future vaccine design and evaluation.

Genomic epidemiology and ceftazidime-avibactam high-level resistance mechanisms of Pseudomonas aeruginosa in China from 2010 to 2022

Ceftazidime-avibactam (CZA) resistance is a huge threat in the clinic; however, the underlying mechanism responsible for high-level CZA resistance in Pseudomonas aeruginosa (PA) isolates remains unknown. In this study, a total of 5,763 P. aeruginosa isolates were collected from 2010 to 2022 to investigate the ceftazidime-avibactam (CZA) high-level resistance mechanisms of Pseudomonas aeruginosa (PA) isolates in China. Fifty-six PER-producing isolates were identified, including 50 isolates carrying blaPER-1 in PA, and 6 isolates carrying blaPER-4. Of these, 82.1% (46/56) were classified as DTR-PA isolates, and 76.79% (43/56) were resistant to CZA. Importantly, blaPER-1 and blaPER-4 overexpression led to 16-fold and >1024-fold increases in the MICs of CZA, respectively. WGS revealed that the blaPER-1 gene was located in two different transferable IncP-2-type plasmids and chromosomes, whereas blaPER-4 was found only on chromosomes and was carried by a class 1 integron embedded in a Tn6485-like transposon. Overexpression of efflux pumps may be associated with high-level CZA resistance in blaPER-1-positive strains. Kinetic parameter analysis revealed that PER-4 exhibited a similar kcat/Km with ceftazidime and a high (∼3359-fold) IC50 value with avibactam compared to PER-1. Our study found that overexpression of PER-1 combined with enhanced efflux pump expression and the low affinity of PER-4 for avibactam contributes to high-level resistance to CZA. Additionally, the Tn6485-like transposon plays a significant role in disseminating blaPER. Urgent active surveillance is required to prevent the further spread of high-level CZA resistance in DTR-PA isolates.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Structural and functional analyses of nematode-derived antimicrobial peptides support the occurrence of direct mechanisms of worm-microbiota interactions

The complex relationships between gastrointestinal (GI) nematodes and the host gut microbiota have been implicated in key aspects of helminth disease and infection outcomes. Nevertheless, the direct and indirect mechanisms governing these interactions are, thus far, largely unknown. In this proof-of-concept study, we demonstrate that the excretory-secretory products (ESPs) and extracellular vesicles (EVs) of key GI nematodes contain peptides that, when recombinantly expressed, exert antimicrobial activity in vitro against Bacillus subtilis. In particular, using time-lapse microfluidics microscopy, we demonstrate that exposure of B. subtilis to a recombinant saposin-domain containing peptide from the 'brown stomach worm', Teladorsagia circumcincta, and a metridin-like ShK toxin from the 'barber's pole worm', Haemonchus contortus, results in cell lysis and significantly reduced growth rates. Data from this study support the hypothesis that GI nematodes may modulate the composition of the vertebrate gut microbiota directly via the secretion of antimicrobial peptides, and pave the way for future investigations aimed at deciphering the impact of such changes on the pathophysiology of GI helminth infection and disease.

Prion forensics: a multidisciplinary approach to investigate CWD at an illegal deer carcass disposal site

Infectious prions are resistant to degradation and remain infectious in the environment for several years. Chronic wasting disease (CWD) has been detected in cervids inhabiting North America, the Nordic countries, and South Korea. CWD-prion spread is partially attributed to carcass transport and disposal. We employed a forensic approach to investigate an illegal carcass dump site connected with a CWD-positive herd. We integrated anatomic, genetic, and prion amplification methods to discover CWD-positive remains from six white-tailed deer (Odocoileus virginianus) and, using microsatellite markers, confirmed a portion originated from the CWD-infected herd. This approach provides a foundation for future studies of carcass prion transmission risk.

Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Identification of cancer driver genes based on hierarchical weak consensus model

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-024-00279-6.

RNA-Seq and miRNA-Seq data from Epstein-Barr virus-infected tree shrews reveal a ceRNA network contributing to immune microenvironment regulation

Epstein-Barr virus (EBV) infection in humans is ubiquitous and associated with various diseases. Remodeling of the immune microenvironment is the primary cause of EBV infection and pathogenesis; however, the underlying mechanism has not been fully elucidated. In this study, we used whole-transcriptome RNA-Seq to detect mRNAs, long non-coding RNAs (lncRNA), and microRNA (miRNA) profiles in the control group, 3 days, and 28 days after EBV infection, based on the tree shrew model that we reported previously. First, we estimated the proportion of 22 cell types in each sample using CIBERSORT software and identified 18 high-confidence DElncRNAs related to immune microenvironment regulation after EBV infection. Functional enrichment analysis of these differentially expressed lncRNAs primarily focused on the autophagy, endocytosis, and ferroptosis signalling pathways. Moreover, EBV infection affects miRNA expression patterns, and many miRNAs are silenced. Finally, three competing endogenous RNA regulatory networks were built using lncRNAs that significantly correlated with immune cell types, miRNAs that responded to EBV infection, and potentially targeted the mRNA of the miRNAs. Among them, MRPL42-AS-5 might act as an hsa-miR-296-5p "sponge" and compete with target mRNAs, thus increasing mRNA expression level, which could induce immune cell infiltration through the cellular senescence signalling pathway against EBV infection. Overall, we conducted a complete transcriptomic analysis of EBV infection in vivo for the first time and provided a novel perspective for further investigation of EBV-host interactions.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Rational design of monomeric IL37 variants guided by stability and dynamical analyses of IL37 dimers

IL37 plays important roles in the regulation of innate immunity and its oligomeric status is critical to these roles. In its monomeric state, IL37 can effectively inhibit the inflammatory response of IL18 by binding to IL18Rα, a capacity lost in its dimeric form, underlining the pivotal role of the oligomeric status of IL37 in its anti-inflammatory action. Until now, two IL37 dimer structures have been deposited in PDB, reflecting a substantial difference in their dimer interfaces. Given this discrepancy, we analyzed the PDB structures of the IL37 dimer (PDB IDs: 6ncu, 5hn1) along with a AF2-multimer prediction by molecular dynamics (MD) simulations. Results showed that the 5hn1 and AF2-predicted dimers have the same interface and stably maintained their conformations throughout simulations, while the recent IL37 dimer (PDB ID: 6ncu) with a different interface did not, proposing a possible issue with the recent IL37 dimer structure (6ncu). Next, focusing on the stable dimer structures, we have identified five critical positions of V71/Y85/I86/E89/S114, three new positions compared to the literature, that would reduce dimer stability without affecting the monomer structure. Two quintuple mutants were tested by MD simulations and showed partial or complete dissociation of the dimer. Overall, the insights gained from this study reinforce the validity of the 5hn1 and AF2 multimer structures, while also advancing our understanding of the IL37 dimer interface through the generation of monomer-locked IL37 variants.

A novel small RNA PhaS contributes to polymyxin B-heteroresistance in carbapenem-resistant Klebsiella pneumoniae

In recent years, polymyxin has been used as a last-resort therapy for carbapenem-resistant bacterial infections. The emergence of heteroresistance (HR) to polymyxin hampers the efficacy of polymyxin treatment by amplifying resistant subpopulation. However, the mechanisms behind polymyxin HR remain unclear. Small noncoding RNAs (sRNAs) play an important role in regulating drug resistance. The purpose of this study was to investigate the effects and mechanisms of sRNA on polymyxin B (PB)-HR in carbapenem-resistant Klebsiella pneumoniae. In this study, a novel sRNA PhaS was identified by transcriptome sequencing. PhaS expression was elevated in the PB heteroresistant subpopulation. Overexpression and deletion of PhaS were constructed in three carbapenem-resistant K. pneumoniae strains. Population analysis profiling, growth curve, and time-killing curve analysis showed that PhaS enhanced PB-HR. In addition, we verified that PhaS directly targeted phoP through the green fluorescent protein reporter system. PhaS promoted the expression of phoP, thereby encouraging the expression of downstream genes pmrD and arnT. This upregulation of arnT promoted the 4-amino-4-deoxyL-arabinosaccharide (L-Ara4N) modification of lipid A in PhaS overexpressing strains, thus enhancing PB-HR. Further, within the promoter region of PhaS, specific PhoP recognition sites were identified. ONPG assays and RT-qPCR analysis confirmed that PhaS expression was positively modulated by PhoP and thus up-regulated by PB stimulation. To sum up, a novel sRNA enhancing PB-HR was identified and a positive feedback regulatory pathway of sRNA-PhoP/Q was demonstrated in the study. This helps to provide a more comprehensive and clear understanding of the underlying mechanisms behind polymyxin HR in carbapenem-resistant K. pneumoniae.

Whole genome sequencing unravels cryptic circulation of divergent dengue virus lineages in the rainforest region of Nigeria

Dengue is often misclassified and underreported in Africa due to inaccurate differential diagnoses of nonspecific febrile illnesses such as malaria, sparsity of diagnostic testing and poor clinical and genomic surveillance. There are limited reports on the seroprevalence and genetic diversity of dengue virus (DENV) in humans and vectors in Nigeria. In this study, we investigated the epidemiology and genetic diversity of dengue in the rainforest region of Nigeria. We screened 515 febrile patients who tested negative for malaria and typhoid fever in three hospitals in Oyo and Ekiti States in southern Nigeria with a combination of anti-dengue IgG/IgM/NS1 rapid test kits and metagenomic sequencing. We found that approximately 28% of screened patients had previous DENV exposure, with the highest prevalence in persons over sixty. Approximately 8% of the patients showed evidence of recent or current infection, and 2.7% had acute infection. Following sequencing of sixty samples, we assembled twenty DENV-1 genomes (3 complete and 17 partial). We found that all assembled genomes belonged to DENV-1 genotype III. Our phylogenetic analyses showed evidence of prolonged cryptic circulation of divergent DENV lineages in Oyo state. We were unable to resolve the source of DENV in Nigeria owing to limited sequencing data from the region. However, our sequences clustered closely with sequences in Tanzania and sequences reported in Chinese with travel history to Tanzania in 2019. This may reflect the wider unsampled bidirectional transmission of DENV-1 in Africa, which strongly emphasizes the importance of genomic surveillance in monitoring ongoing DENV transmission in Africa.

Association of hyaluronan and proteoglycan link protein 1 gene with the need of home oxygen therapy in premature Japanese infants with bronchopulmonary dysplasia

BACKGROUND

Bronchopulmonary dysplasia (BPD) has a lasting effect on the respiratory function of infants, imposing chronic health burdens. BPD is influenced by various prenatal, postnatal, and genetic factors. This study explored the connection between BPD and home oxygen therapy (HOT), and then we examined the association between HOT and a specific single-nucleotide polymorphism (SNP) in the hyaluronan and proteoglycan link protein 1 (HAPLN1) gene among premature Japanese infants.

MATERIALS AND METHODS

Prenatal and postnatal data from 212 premature infants were collected and analyzed by four SNPs (rs975563, rs10942332, rs179851, and rs4703570) around HAPLN1 using the TaqMan polymerase chain reaction method. The clinical characteristics and genotype frequencies of HAPLN1 were assessed and compared between HOT and non-HOT groups.

RESULTS

Individuals with AA/AC genotypes in the rs4703570 SNP exhibited significantly higher HOT rates at discharge than those with CC homozygotes (odds ratio, 1.20, 95% confidence interval, 1.07-1.35, p = .038). A logistic regression analysis determined that CC homozygotes in the rs4703570 SNP did not show a statistically significant independent association with HOT at discharge.

CONCLUSIONS

Although our study did not reveal a correlation between HAPLN1 and the onset of BPD, we observed that individuals with CC homozygosity at the rs4703570 SNP exhibit a reduced risk of HOT.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research

Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow. We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.

The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides

Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role in innate immune defense against pathogens. Their mechanism of action involves the disruption of bacterial cell membranes, causing leakage of cellular contents and ultimately leading to cell death. While AMPs typically lack a defined structure in solution, they often assume a defined conformation when interacting with bacterial membranes. Given this structural flexibility, we investigated whether intrinsically disordered regions (IDRs) with AMP-like properties could exhibit antimicrobial activity. We tested 14 peptides from different IDRs predicted to have antimicrobial activity and found that nearly all of them did not display the anticipated effects. These peptides failed to adopt a defined secondary structure and had compromised membrane interactions, resulting in a lack of antimicrobial activity. We hypothesize that evolutionary constraints may prevent IDRs from folding, even in membrane-like environments, limiting their antimicrobial potential. Moreover, our research reveals that current antimicrobial predictors fail to accurately capture the structural features of peptides when dealing with intrinsically unstructured sequences. Hence, the results presented here may have far-reaching implications for designing and improving antimicrobial strategies and therapies against infectious diseases.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Transcriptomic profiling of adding cobalt chloride to improve dendrobine-type total alkaloid production

Trichoderma longibrachiatum UN32 is known for its efficient production of dendrobine-type total alkaloids (DTTAs). This study aimed to determine the optimal medium composition for the UN32 strain using response surface methodology. Key factors, including glucose, beef extract, and CoCl2, were selected through the Plackett-Burman design. Subsequently, a factorial optimization approach was employed using the steepest ascent design, and 17 trial sets were completed via the Box-Behnken design. The optimal medium composition was found to consist of 29.4 g/L of glucose, 17.3 g/L of beef extract, and 0.28 mmol/L of CoCl2. This optimized medium resulted in an impressive 80.8% increase in mycelial dry weight (reaching 12.303 g/L) and a substantial 76.4% boost in DTTA production (reaching 541.63 ± 46.95 μg). Furthermore, the fermentation process was scaled up to a 5-L bioreactor, leading to a DTTA production approximately 1.95 times than the control. Transcriptome analysis of strain UN32 in response to CoCl2 supplementation revealed significant changes in the expression of critical genes associated with the TCA cycle and L-valine, L-leucine, and L-isoleucine biosynthesis changed. These alterations resulted in a heightened influx of acetyl-CoA into DTTA production. Additionally, the expression of genes related to antioxidant enzymes was modified to maintain homeostasis of reactive oxygen species (ROS). A potential mechanism for the accumulation of DTTAs based on ROS as a signal transduction was proposed. These findings provide valuable insights into the regulatory mechanisms of DTTA biosynthesis, potentially offering a method to enhance the production of secondary metabolites in the UN32 strain. KEY POINTS: • After the RSM optimization, there is a substantial increase of 80.8% in biomass production and a significant 76.4% rise in DTTA production. • Transcriptome analysis revealed that the inclusion of CoCl2 supplements resulted in an enhanced influx of acetyl-CoA. • Proposed a mechanism for the accumulation of DTTAs for the role of ROS as a signal transduction pathway.

Genes controlling hydrolysate toxin tolerance identified by QTL analysis of the natural Saccharomyces cerevisiae BCC39850

Lignocellulosic material can be converted to valorized products such as fuels. Pretreatment is an essential step in conversion, which is needed to increase the digestibility of the raw material for microbial fermentation. However, pretreatment generates by-products (hydrolysate toxins) that are detrimental to microbial growth. In this study, natural Saccharomyces strains isolated from habitats in Thailand were screened for their tolerance to synthetic hydrolysate toxins (synHTs). The Saccharomyces cerevisiae natural strain BCC39850 (toxin-tolerant) was crossed with the laboratory strain CEN.PK2-1C (toxin-sensitive), and quantitative trait locus (QTL) analysis was performed on the segregants using phenotypic scores of growth (OD600) and glucose consumption. VMS1, DET1, KCS1, MRH1, YOS9, SYO1, and YDR042C were identified from QTLs as candidate genes associated with the tolerance trait. CEN.PK2-1C knockouts of the VMS1, YOS9, KCS1, and MRH1 genes exhibited significantly greater hydrolysate toxin sensitivity to growth, whereas CEN.PK2-1C knock-ins with replacement of VMS1 and MRH1 genes from the BCC39850 alleles showed significant increased ethanol production titers compared with the CEN.PK2-1C parental strain in the presence of synHTs. The discovery of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin tolerance in S. cerevisiae indicates the roles of the endoplasmic-reticulum-associated protein degradation pathway, plasma membrane protein association, and the phosphatidylinositol signaling system in this trait. KEY POINTS: • QTL analysis was conducted using a hydrolysate toxin-tolerant S. cerevisiae natural strain • Deletion of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin-sensitivity • Replacement of VMS1 and MRH1 with natural strain alleles increased ethanol production titers in the presence of hydrolysate toxins.

Methanogenic partner influences cell aggregation and signalling of Syntrophobacterium fumaroxidans

For several decades, the formation of microbial self-aggregates, known as granules, has been extensively documented in the context of anaerobic digestion. However, current understanding of the underlying microbial-associated mechanisms responsible for this phenomenon remains limited. This study examined morphological and biochemical changes associated with cell aggregation in model co-cultures of the syntrophic propionate oxidizing bacterium Syntrophobacterium fumaroxidans and hydrogenotrophic methanogens, Methanospirillum hungatei or Methanobacterium formicicum. Formerly, we observed that when syntrophs grow for long periods with methanogens, cultures tend to form aggregates visible to the eye. In this study, we maintained syntrophic co-cultures of S. fumaroxidans with either M. hungatei or M. formicicum for a year in a fed-batch growth mode to stimulate aggregation. Millimeter-scale aggregates were observed in both co-cultures within the first 5 months of cultivation. In addition, we detected quorum sensing molecules, specifically N-acyl homoserine lactones, in co-culture supernatants preceding the formation of macro-aggregates (with diameter of more than 20 μm). Comparative transcriptomics revealed higher expression of genes related to signal transduction, polysaccharide secretion and metal transporters in the late-aggregation state co-cultures, compared to the initial ones. This is the first study to report in detail both biochemical and physiological changes associated with the aggregate formation in syntrophic methanogenic co-cultures. KEYPOINTS: • Syntrophic co-cultures formed mm-scale aggregates within 5 months of fed-batch cultivation. • N-acyl homoserine lactones were detected during the formation of aggregates. • Aggregated co-cultures exhibited upregulated expression of adhesins- and polysaccharide-associated genes.

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.