Perspectives on label-free microscopy of heterogeneous and dynamic biological systems

Significance

Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging).

Aim

This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs.

Approach

Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended.

Conclusion

Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

The complex roles of m 6 A modifications in neural stem cell proliferation, differentiation, and self-renewal and implications for memory and neurodegenerative diseases

N6-methyladenosine (m 6 A), the most prevalent and conserved RNA modification in eukaryotic cells, profoundly influences virtually all aspects of mRNA metabolism. mRNA plays crucial roles in neural stem cell genesis and neural regeneration, where it is highly concentrated and actively involved in these processes. Changes in m 6 A modification levels and the expression levels of related enzymatic proteins can lead to neurological dysfunction and contribute to the development of neurological diseases. Furthermore, the proliferation and differentiation of neural stem cells, as well as nerve regeneration, are intimately linked to memory function and neurodegenerative diseases. This paper presents a comprehensive review of the roles of m 6 A in neural stem cell proliferation, differentiation, and self-renewal, as well as its implications in memory and neurodegenerative diseases. m 6 A has demonstrated divergent effects on the proliferation and differentiation of neural stem cells. These observed contradictions may arise from the time-specific nature of m 6 A and its differential impact on neural stem cells across various stages of development. Similarly, the diverse effects of m 6 A on distinct types of memory could be attributed to the involvement of specific brain regions in memory formation and recall. Inconsistencies in m 6 A levels across different models of neurodegenerative disease, particularly Alzheimer's disease and Parkinson's disease, suggest that these disparities are linked to variations in the affected brain regions. Notably, the opposing changes in m 6 A levels observed in Parkinson's disease models exposed to manganese compared to normal Parkinson's disease models further underscore the complexity of m 6 A's role in neurodegenerative processes. The roles of m 6 A in neural stem cell proliferation, differentiation, and self-renewal, and its implications in memory and neurodegenerative diseases, appear contradictory. These inconsistencies may be attributed to the time-specific nature of m 6 A and its varying effects on distinct brain regions and in different environments.

Therapeutic potential of exercise-hormone irisin in Alzheimer's disease

Irisin is a myokine that is generated by cleavage of the membrane protein fibronectin type III domain-containing protein 5 (FNDC5) in response to physical exercise. Studies reveal that irisin/FNDC5 has neuroprotective functions against Alzheimer's disease, the most common form of dementia in the elderly, by improving cognitive function and reducing amyloid-β and tau pathologies as well as neuroinflammation in cell culture or animal models of Alzheimer's disease. Although current and ongoing studies on irisin/FNDC5 show promising results, further mechanistic studies are required to clarify its potential as a meaningful therapeutic target for alleviating Alzheimer's disease. We recently found that irisin treatment reduces amyloid-β pathology by increasing the activity/levels of amyloid-β-degrading enzyme neprilysin secreted from astrocytes. Herein, we present an overview of irisin/FNDC5's protective roles and mechanisms against Alzheimer's disease.

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

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.

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.

Small extracellular vesicles derived from human induced pluripotent stem cell-differentiated neural progenitor cells mitigate retinal ganglion cell degeneration in a mouse model of optic nerve injury

JOURNAL/nrgr/04.03/01300535-202502000-00034/figure1/v/2024-05-28T214302Z/r/image-tiff Several studies have found that transplantation of neural progenitor cells (NPCs) promotes the survival of injured neurons. However, a poor integration rate and high risk of tumorigenicity after cell transplantation limits their clinical application. Small extracellular vesicles (sEVs) contain bioactive molecules for neuronal protection and regeneration. Previous studies have shown that stem/progenitor cell-derived sEVs can promote neuronal survival and recovery of neurological function in neurodegenerative eye diseases and other eye diseases. In this study, we intravitreally transplanted sEVs derived from human induced pluripotent stem cells (hiPSCs) and hiPSCs-differentiated NPCs (hiPSC-NPC) in a mouse model of optic nerve crush. Our results show that these intravitreally injected sEVs were ingested by retinal cells, especially those localized in the ganglion cell layer. Treatment with hiPSC-NPC-derived sEVs mitigated optic nerve crush-induced retinal ganglion cell degeneration, and regulated the retinal microenvironment by inhibiting excessive activation of microglia. Component analysis further revealed that hiPSC-NPC derived sEVs transported neuroprotective and anti-inflammatory miRNA cargos to target cells, which had protective effects on RGCs after optic nerve injury. These findings suggest that sEVs derived from hiPSC-NPC are a promising cell-free therapeutic strategy for optic neuropathy.

Microglia lactylation in relation to central nervous system diseases

The development of neurodegenerative diseases is closely related to the disruption of central nervous system homeostasis. Microglia, as innate immune cells, play important roles in the maintenance of central nervous system homeostasis, injury response, and neurodegenerative diseases. Lactate has been considered a metabolic waste product, but recent studies are revealing ever more of the physiological functions of lactate. Lactylation is an important pathway in lactate function and is involved in glycolysis-related functions, macrophage polarization, neuromodulation, and angiogenesis and has also been implicated in the development of various diseases. This review provides an overview of the lactate metabolic and homeostatic regulatory processes involved in microglia lactylation, histone versus non-histone lactylation, and therapeutic approaches targeting lactate. Finally, we summarize the current research on microglia lactylation in central nervous system diseases. A deeper understanding of the metabolic regulatory mechanisms of microglia lactylation will provide more options for the treatment of central nervous system diseases.

From the cauldron of conflict: Endogenous gene regulation by piRNA and other modes of adaptation enabled by selfish transposable elements

Transposable elements (TEs) provide a prime example of genetic conflict because they can proliferate in genomes and populations even if they harm the host. However, numerous studies have shown that TEs, though typically harmful, can also provide fuel for adaptation. This is because they code functional sequences that can be useful for the host in which they reside. In this review, I summarize the "how" and "why" of adaptation enabled by the genetic conflict between TEs and hosts. In addition, focusing on mechanisms of TE control by small piwi-interacting RNAs (piRNAs), I highlight an indirect form of adaptation enabled by conflict. In this case, mechanisms of host defense that regulate TEs have been redeployed for endogenous gene regulation. I propose that the genetic conflict released by meiosis in early eukaryotes may have been important because, among other reasons, it spurred evolutionary innovation on multiple interwoven trajectories - on the part of hosts and also embedded genetic parasites. This form of evolution may function as a complexity generating engine that was a critical player in eukaryotic evolution.

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.

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.

Vaccines targeting ESR1 activating mutations elicit anti-tumor immune responses and suppress estrogen signaling in therapy resistant ER+ breast cancer

ER+ breast cancers (BC) are characterized by the elevated expression and signaling of estrogen receptor alpha (ESR1), which renders them sensitive to anti-endocrine therapy. While these therapies are clinically effective, prolonged treatment inevitably results in therapeutic resistance, which can occur through the emergence of gain-of-function mutations in ESR1. The central importance of ESR1 and development of mutated forms of ESR1 suggest that vaccines targeting these proteins could potentially be effective in preventing or treating endocrine resistance. To explore the potential of this approach, we developed several recombinant vaccines encoding different mutant forms of ESR1 (ESR1mut) and validated their ability to elicit ESR1-specific T cell responses. We then developed novel ESR1mut-expressing murine mammary cancer models to test the anti-tumor potential of ESR1mut vaccines. We found that these vaccines could suppress tumor growth, ESR1mut expression and estrogen signaling in vivo. To illustrate the applicability of these findings, we utilize HPLC to demonstrate the presentation of ESR1 and ESR1mut peptides on human ER+ BC cell MHC complexes. We then show the presence of human T cells reactive to ESR1mut epitopes in an ER+ BC patient. These findings support the development of ESR1mut vaccines, which we are testing in a Phase I clinical trial.

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.

Vaccine approaches to treat urothelial cancer

Bladder cancer (BC) accounts for about 4% of all malignancies. Non-muscle-invasive BC, 75% of cases, is treated with transurethral resection and adjuvant intravesical instillation, while muscle-invasive BC warrants cisplatin-based perioperative chemotherapy. Although immune-checkpoint inhibitors, antibody drug conjugates and targeted agents have provided dramatic advances, metastatic BC remains a generally incurable disease and clinical trials continue to vigorously evaluate novel molecules. Cancer vaccines aim at activating the patient's immune system against tumor cells. Several means of delivering neoantigens have been developed, including peptides, antigen-presenting cells, virus, or nucleic acids. Various improvements are constantly being explored, such as adjuvants use and combination strategies. Nucleic acids-based vaccines are increasingly gaining attention in recent years, with promising results in other malignancies. However, despite the recent advantages, numerous obstacles persist. This review is aimed at describing the different types of cancer vaccines, their evaluations in UC patients and the more recent innovations in this field.

Roles of TRPM channels in glioma

Gliomas are the most common type of primary brain tumor. Despite advances in treatment, it remains one of the most aggressive and deadly tumor of the central nervous system (CNS). Gliomas are characterized by high malignancy, heterogeneity, invasiveness, and high resistance to radiotherapy and chemotherapy. It is urgent to find potential new molecular targets for glioma. The TRPM channels consist of TRPM1-TPRM8 and play a role in many cellular functions, including proliferation, migration, invasion, angiogenesis, etc. More and more studies have shown that TRPM channels can be used as new therapeutic targets for glioma. In this review, we first introduce the structure, activation patterns, and physiological functions of TRPM channels. Additionally, the pathological mechanism of glioma mediated by TRPM2, 3, 7, and 8 and the related signaling pathways are described. Finally, we discuss the therapeutic potential of targeting TRPM for glioma.

Causal relationship between iron status and preeclampsia-eclampsia: a Mendelian randomization analysis

BACKGROUND

Preeclampsia/eclampsia is a severe pregnancy-related disorder associated with hypertension and organ damage. While observational studies have suggested a link between maternal iron status and preeclampsia/eclampsia, the causal relationship remains unclear. The aim of this study was to investigate the genetic causality between iron status and preeclampsia/eclampsia using large-scale genome-wide association study (GWAS) summary data and Mendelian randomization (MR) analysis.

METHODS

Summary data for the GWAS on preeclampsia/eclampsia and genetic markers related to iron status were obtained from the FinnGen Consortium and the IEU genetic databases. The "TwoSampleMR" software package in R was employed to test the genetic causality between these markers and preeclampsia/eclampsia. The inverse variance weighted (IVW) method was primarily used for MR analysis. Heterogeneity, horizontal pleiotropy, and potential outliers were evaluated for the MR analysis results.

RESULTS

The random-effects IVW results showed that ferritin (OR = 1.11, 95% CI: .89-1.38, p = .341), serum iron (OR = .90, 95% CI: .75-1.09, p = .275), TIBC (OR = .98, 95% CI: .89-1.07, p = .613), and TSAT (OR = .94, 95% CI: .83-1.07, p = .354) have no genetic causal relationship with preeclampsia/eclampsia. There was no evidence of heterogeneity, horizontal pleiotropy, or possible outliers in our MR analysis (p > .05).

CONCLUSIONS

Our study did not detect a genetic causal relationship between iron status and preeclampsia/eclampsia. Nonetheless, this does not rule out a relationship between the two at other mechanistic levels.

Small RNA-Seq and real time rt-qPCR reveal islet miRNA released under stress conditions

Replacement of beta cells through transplantation is a potential therapeutic approach for individuals with pancreas removal or poorly controllable type 1 diabetes. However, stress and death of beta cells pose significant challenges. Circulating miRNA has emerged as potential biomarkers reflecting early beta cell stress and death, allowing for timely intervention. The aim of this study was to identify miRNAs as potential biomarkers for beta cell health. Literature review combined with small RNA sequencing was employed to select islet-enriched miRNA. The release of those miRNA was assessed by RT-qPCR in vivo, using a streptozotocin induced diabetes mouse model and in vitro, through mouse and human islets exposed to varying degrees of hypoxic and cytokine stressors. Utilizing the streptozotocin induced model, we identified 18 miRNAs out of 39 candidate islet-enriched miRNA to be released upon islet stress in vivo. In vitro analysis of culture supernatants from cytokine and/or hypoxia stressed islets identified the release of 45 miRNAs from mouse and 8 miRNAs from human islets. Investigation into the biological pathways targeted by the cytokine- and/or hypoxia-induced miRNA suggested the involvement of MAPK and PI3K-Akt signaling pathways in both mouse and human islets. We have identified miRNAs associated with beta cell health and stress. The findings allowed us to propose a panel of 47 islet-related human miRNA that is potentially valuable for application in clinical contexts of beta cell transplantation and presymptomatic early-stage type 1 diabetes.

A computational study of gene expression patterns in head and neck squamous cell carcinoma using TCGA data

Aim: Head and Neck squamous cell carcinoma (HNSCC) is the second most prevalent cancer in Pakistan. Methods: Gene expression data from TCGA and GETx for normal genes to analyze Differentially Expressed Genes (DEGs). Data was further investigated using the Enrichr tool to perform Gene Ontology (GO). Results: Our analysis identified most significantly differentially expressed genes and explored their established cellular functions as well as their potential involvement in tumor development. We found that the highly expressed Keratin family and S100A9 genes. The under-expressed genes KRT4 and KRT13 provide instructions for the production of keratin proteins. Conclusion: Our study suggests that factors such as poor oral hygiene and smokeless tobacco can result in oral stress and cellular damage and cause cancer.

Mechanism investigation of Forsythoside A against esophageal squamous cell carcinoma in vitro and in vivo

CONTEXT

Forsythoside A (FSA) was extracted from Forsythia suspensa, a traditional Chinese medicine, which has been demonstrated to exert anti-inflammatory, antibacterial, and other pharmacological effects. However, the anticancer effect of FSA in esophageal squamous cell carcinoma (ESCC) has not been documented.

OBJECTIVE

The present study aimed to elucidate the mechanism of FSA against ESCC.

MATERIALS AND METHODS

Network pharmacology and molecular docking were employed to predict the mechanism. FSA was utilized to treat ESCC cell lines KYSE450 and KYSE30, followed by CCK-8 assay, cell cloning formation assay, flow cytometry, Western blot, RNA-seq analysis, and subsequent in vivo experiments.

RESULTS

Network pharmacology and molecular docking predicted that the therapeutic effect of FSA in ESCC is mediated through proteins such as BCL2 and BAX, influencing KEGG pathways associated with apoptosis. In vitro experiments showed that FSA inhibited cell proliferation and plate clone formation, promoted cell apoptosis and impacted the cell cycle distribution of G2/M phase by regulating BCL2, BAX, and p21. Further RNA-seq in KYSE450 cells showed that FSA regulated the expression of 223 genes, specifically affecting the biological process of epidermal development. In vivo experiments showed that gastric administration of FSA resulted in notable reductions in both tumor volume and weight by regulating BCL2, BAX, and p21. 16S rRNA sequencing showed that FSA led to significant changes of beta diversity. Abundance of 11 specific bacterial taxa were considerably changed following administration of FSA.

CONCLUSIONS

This study presents a novel candidate drug against ESCC and establishes a foundation for future clinical application.

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

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.

An optimized caries model of Streptococcus mutans in rats and its application for evaluating prophylactic vaccines

Dental caries is a prevalent oral disease that mainly results from Streptococcus mutans. Susceptibility to S. mutans decreased rapidly after weaning in a well-known rat model. However, owing to the lack of time to establish protective immunity ahead of challenge, the weaning rat model is suboptimal for assessing prophylactic vaccines against S. mutans infection. In this study, we found that, in adult rats, S. mutans cultured under air-restricted conditions showed dramatically increased colonization efficacy and accelerated development of dental caries compared with those cultured under air-unrestricted conditions. We propose that S. mutans cultured under air-restricted conditions can be used to develop an optimal caries model, especially for the evaluation of prophylactic efficacy against S. mutans. Therefore, we used the anti-caries vaccine, KFD2-rPAc, to reevaluate the protection against the challenge of S. mutans. In immunized rats, rPAc-specific protective antibodies were robustly elicited by KFD2-rPAc before the challenge. In addition to inhibiting the initial and long-term colonization of S. mutans in vivo, KFD2-rPAc immunization showed an 83% inhibitory efficacy against the development of caries, similar to that previously evaluated in a weaning rat model. These results demonstrate that culturing under air-restricted conditions can promote S. mutans infection in adult rats, thereby helping establish a rat infection model to evaluate the prophylactic efficacy of vaccines and anti-caries drugs.

Expression responses of XTH genes in tomato and potato to environmental mechanical forces: focus on behavior in response to rainfall, wind and touch

Rainfall, wind and touch, as mechanical forces, were mimicked on 6-week-old soil-grown tomato and potato under controlled conditions. Expression level changes of xyloglucan endotransglucosylase/hydrolase genes (XTHs) of tomato (Solanum lycopersicum L. cv. Micro Tom; SlXTHs) and potato (Solanum tuberosum L. cv. Desirée; StXTHs) were analyzed in response to these mechanical forces. Transcription intensity of every SlXTHs of tomato was altered in response to rainfall, while the expression intensity of 72% and 64% of SlXTHs was modified by wind and touch, respectively. Ninety-one percent of StXTHs (32 out of 35) in potato responded to the rainfall, while 49% and 66% of the StXTHs were responsive to the wind and touch treatments, respectively. As previously demonstrated, all StXTHs were responsive to ultrasound treatment, and all were sensitive to one or more of the environmental mechanical factors examined in the current study. To our best knowledge, this is the first study to demonstrate that these ubiquitous mechanical environmental cues, such as rainfall, wind and touch, influence the transcription of most XTHs examined in both species.

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.

The proliferation/migration ability mediated by CD151/PI3K/AKT pathway determines the therapeutic effect of hUC-MSCs transplantation on rheumatoid arthritis

OBJECTIVE

To elucidate the underlying mechanism by which the proliferation and migration abilities of human umbilical cord mesenchymal stem cells (hUC-MSCs) determine their therapeutic efficacy in rheumatoid arthritis treatment.

METHODS

The DBA/1J mice were utilized to establish a collagen-induced RA (CIA) mouse model and to validate the therapeutic efficacy of hUC-MSCs transfected with CD151 siRNA. RNA-seq, QT-PCR and western blotting were utilized to evaluate the mRNA and protein levels of the PI3K/AKT pathway, respectively.

RESULTS

IFN-γ significantly enhanced the proliferation and migration abilities of hUC-MSCs, up-regulating the expression of CD151, a gene related to cell proliferation and migration. Effective inhibition of this effect was achieved through CD151 siRNA treatment. However, IFN-γ did not affect hUC-MSCs differentiation or changes in cell surface markers. Additionally, transplantation of CD151-interfered hUC-MSCs (siRNA-CD151-hUC-MSCs) resulted in decreased colonization in the toes of CIA mice and worse therapeutic effects compared to empty vector treatment (siRNA-NC-hUC-MSCs).

CONCLUSION

IFN-γ facilitates the proliferation and migration of hUC-MSCs through the CD151/PI3K/AKT pathway. The therapeutic efficacy of siRNA-CD151-hUC-MSCs was found to be inferior to that of siRNA-NC-hUC-MSCs.

MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells by binding ITGB4/LAMB3 to activate the AKT signaling pathway

Colorectal cancer (CRC) is one of the most lethal cancers. Single-cell RNA sequencing (scRNA-seq) and protein-protein interactions (PPIs) have enabled the systematic study of CRC. In our research, the activation of the AKT pathway in CRC was analyzed by KEGG using single-cell sequencing data from the GSE144735 dataset. The correlation and PPIs of MDFI and ITGB4/LAMB3 were examined. The results were verified in the TCGA and CCLE and further tested by coimmunoprecipitation experiments. The effect of MDFI on the AKT pathway via ITGB4/LAMB3 was validated by knockdown and lentiviral overexpression experiments. The effect of MDFI on oxaliplatin/fluorouracil sensitivity was probed by colony formation assay and CCK8 assay. We discovered that MDFI was positively associated with ITGB4/LAMB3. In addition, MDFI was negatively associated with oxaliplatin/fluorouracil sensitivity. MDFI upregulated the AKT pathway by directly interacting with LAMB3 and ITGB4 in CRC cells, and enhanced the proliferation of CRC cells via the AKT pathway. Finally, MDFI reduced the sensitivity of CRC cells to oxaliplatin and fluorouracil. In conclusion, MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells, partially through the activation of the AKT signaling pathway by the binding to ITGB4/LAMB3. Our findings provide a possible molecular target for CRC therapy.

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.

Maintenance of stem cell self-renewal by sex chromosomal zinc-finger transcription factors

In this Editorial review, we would like to focus on a very recent discovery showing the global autosomal gene regulation by Y- and inactivated X-chromosomal transcription factors, zinc finger gene on the Y chromosome (ZFY) and zinc finger protein X-linked (ZFX). ZFX and ZFY are both zinc-finger proteins that encode general transcription factors abundant in hematopoietic and embryonic stem cells. Although both proteins are homologs, interestingly, the regulation of self-renewal by these transcriptional factors is almost exclusive to ZFX. This fact implies that there are some differential roles between ZFX and ZFY in regulating the maintenance of self-renewal activity in stem cells. Besides the maintenance of stemness, ZFX overexpression or mutations may be linked to certain cancers. Although cancers and stem cells are double-edged swords, there is no study showing the link between ZFX activity and the telomere. Thus, stemness or cancers with ZFX may be linked to other molecules, such as Oct4, Sox2, Klf4, and others. Based on very recent studies and a few lines of evidence in the past decade, it appears that the ZFX is linked to the canonical Wnt signaling, which is one possible mechanism to explain the role of ZFX in the self-renewal of stem cells.

Comparative global profiling of Perilla leaf and stem via transcriptomics and metabolomics

Perilla (Perilla frutescens L.) is a time-honored herbal plant with widespread applications in both medicine and culinary practices around the world. Profiling the essential organs and tissues with medicinal significance on a global scale offers valuable insights for enhancing the yield of desirable compounds in Perilla and other medicinal plants. In the present study, genome-wide RNA-sequencing (RNA-seq) and assessing the global spectrum of metabolites were carried out in the two major organs/tissues of stem (PfST) and leaf (PfLE) in Perilla. The results showed a total of 18,490 transcripts as the DEGs (differentially expressed genes) and 144 metabolites as the DAMs (differentially accumulated metabolites) through the comparative profiling of PfST vs PfLE, and all the DEGs and DAMs exhibited tissue-specific trends. An association analysis between the transcriptomics and metabolomics revealed 14 significantly enriched pathways for both DEGs and DAMs, among which the pathways of Glycine, serine and threonine metabolism (ko00260), Glyoxylate and dicarboxylate metabolism (ko00630), and Glucagon signaling pathway (ko04922) involved relatively more DEGs and DAMs. The results of qRT-PCR assays of 18 selected DEGs confirmed the distinct tissue-specific characteristics of all identified DEGs between PfST and PfLE. Notably, all eight genes associated with the flavonoid biosynthesis/metabolism pathways exhibited significantly elevated expression levels in PfLE compared to PfST. This observation suggests a heightened accumulation of metabolites related to flavonoids in Perilla leaves. The findings of this study offer a comprehensive overview of the organs and tissues in Perilla that have medicinal significance.

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.

Heterochromatin in plant meiosis

Heterochromatin is an organizational property of eukaryotic chromosomes, characterized by extensive DNA and histone modifications, that is associated with the silencing of transposable elements and repetitive sequences. Maintaining heterochromatin is crucial for ensuring genomic integrity and stability during the cell cycle. During meiosis, heterochromatin is important for homologous chromosome synapsis, recombination, and segregation, but our understanding of meiotic heterochromatin formation and condensation is limited. In this review, we focus on the dynamics and features of heterochromatin and how it condenses during meiosis in plants. We also discuss how meiotic heterochromatin influences the interaction and recombination of homologous chromosomes during prophase I.

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.

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.

PANoptosis signaling enables broad immune response in psoriasis: From pathogenesis to new therapeutic strategies

Background

Accumulating evidence suggests that regulated cell death, such as pyroptosis, apoptosis, and necroptosis, is deeply involved in the pathogenesis of psoriasis. As a newly recognized form of systematic cell death, PANoptosis is involved in a variety of inflammatory disorders through amplifying inflammatory and immune cascades, but its role in psoriasis remains elusive.

Objectives

To reveal the role of PANoptosis in psoriasis for a potential therapeutic strategy.

Methods

Multitranscriptomic analysis and experimental validation were used to identify PANoptosis signaling in psoriasis. RNA-seq and scRNA-seq analyses were performed to establish a PANoptosis-mediated immune response in psoriasis, which revealed hub genes through WGCNA and predicted disulfiram as a potential drug. The effect and mechanism of disulfiram were verified in imiquimod (IMQ)-induced psoriasis.

Results

Here, we found a highlighted PANoptosis signature in psoriasis patients through multitranscriptomic analysis and experimental validation. Based on this, two distinct PANoptosis patterns (non/high) were identified, which were the options for clinical classification. The high-PANoptosis-related group had a higher response rate to immune cell infiltration (such as M1 macrophages and keratinocytes). Subsequently, WGCNA showed the hub genes (e.g., S100A12, CYCS, NOD2, STAT1, HSPA4, AIM2, MAPK7), which were significantly associated with clinical phenotype, PANoptosis signature, and identified immune response in psoriasis. Finally, we explored disulfiram (DSF) as a candidate drug for psoriasis through network pharmacology, which ameliorated IMQ-mediated psoriatic symptoms through antipyroptosis-mediated inflammation and enhanced apoptotic progression. By analyzing the specific ligand-receptor interaction pairs within and between cell lineages, we speculated that DSF might exert its effects by targeting keratinocytes directly or targeting M1 macrophages to downregulate the proliferation of keratinocytes.

Conclusions

PANoptosis with its mediated immune cell infiltration provides a roadmap for research on the pathogenesis and therapeutic strategies of psoriasis.

"Active carbon" is more advantageous to the bacterial community in the rice rhizosphere than "stable carbon"

Carbon materials are commonly used for soil carbon sequestration and fertilization, which can also affect crop growth by manipulating the rhizosphere bacterial community. However, the comparison of the differences between active carbon (e.g., organic fertilizers) and stable carbon (e.g., biochar) on rhizosphere microdomains is still unclear. Hence, a trial was implemented to explore the influence of control (CK, no fertilizer; NPK, chemical fertilizer), organic fertilizer (CF-O, organic fertilizer; CF-BO, biochar-based organic fertilizer) and biochar material (CF-B, perishable garbage biochar; CF-PMB, pig manure biochar) on the diversity, composition, and interaction of rice rhizosphere bacterial community through 16 S rRNA gene high-throughput sequencing. Our results demonstrate that organic fertilizer increases bacterial alpha-diversity compared to no-carbon supply treatment to the extend, whereas biochar has the opposite effect. The rhizosphere bacterial community composition showed pronounced variations among the various fertilization treatments. The relative abundance in Firmicutes decreased with organic fertilizer application, whereas that in Chloroflexi and Actinobacteria decreased with biochar application. Bacterial network analysis demonstrate that organic fertilizer enhances the complexity and key taxa of bacterial interactions, while biochar exhibits an opposing trend. The findings of our study indicate that organic fertilizer may contribute to a positive and advantageous impact on bacterial diversity and interaction in rice rhizosphere, whereas the influence of biochar is not as favorable and constructive. This study lays the foundation for elucidating the fate of the rhizosphere bacterial community following different carbon material inputs in the context of sustainable agricultural development.

Machine learning in infectious diseases: potential applications and limitations

Infectious diseases are a major threat for human and animal health worldwide. Artificial Intelligence (AI) combined algorithms including Machine Learning and Big Data analytics have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseases. In this commentary we explore the potential applications and limitations of ML to management of infectious disease. It explores challenges in key areas such as outbreak prediction, pathogen identification, drug discovery, and personalized medicine. We propose potential solutions to mitigate these hurdles and applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases. In addition to use of ML for management of infectious diseases, potential applications are based on catastrophic evolution events for the identification of biomolecular targets to reduce risks for infectious diseases and vaccinomics for discovery and characterization of vaccine protective antigens using intelligent Big Data analytics techniques. These considerations set a foundation for developing effective strategies for managing infectious diseases in the future.

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.

Integration of miRNA expression analysis of purified leukocytes and whole blood reveals blood-borne candidate biomarkers for lung cancer

Changes in leukocyte populations may confound the disease-associated miRNA signals in the blood of cancer patients. We aimed to develop a method to detect differentially expressed miRNAs from lung cancer whole blood samples that are not influenced by variations in leukocyte proportions. The Ref-miREO method identifies differential miRNAs unaffected by changes in leukocyte populations by comparing the within-sample relative expression orderings (REOs) of miRNAs from healthy leukocyte subtypes and those from lung cancer blood samples. Over 77% of the differential miRNAs observed between lung cancer and healthy blood samples overlapped with those between myeloid-derived and lymphoid-derived leukocytes, suggesting the potential impact of changes in leukocyte populations on miRNA profile. Ref-miREO identified 16 differential miRNAs that target 19 lung adenocarcinoma-related genes previously linked to leukocytes. These miRNAs showed enrichment in cancer-related pathways and demonstrated high potential as diagnostic biomarkers, with the LASSO regression models effectively distinguishing between healthy and lung cancer blood or serum samples (all AUC > 0.85). Additionally, 12 of these miRNAs exhibited significant prognostic correlations. The Ref-miREO method offers valuable candidates for circulating biomarker detection in cancer that are not affected by changes in leukocyte populations.

16S rRNA Sequencing Reveals Alterations of Gut Bacteria in Hirschsprung-Associated Enterocolitis

Hirschsprung-associated enterocolitis (HAEC) stands as most common and serious complication of Hirschsprung's disease. Variations in the microbiota composition may account for the differences observed between HAEC and healthy individuals, offering crucial insights into the disease's pathogenesis. Here, we performed a study to changes in the gut microbiome using 16sRNA amplicon sequencing in a cohort of HAEC patients ( n  = 16) and healthy controls ( n  = 14). Our result revealed a significant disparity in beta diversity between the two groups. Following correction for false discovery rate, a rank-sum test at the genus level indicated a notable decrease in the relative abundance of Bifidobacterium , Lactobacillus , and Veillonella , whereas the Enterococcus genus exhibited a substantial increase in HAEC, a finding further supported by additional linear discriminant analysis effect size analysis. Functional analysis showed that putative transport and catabolism, digestive system, and metabolism of cofactors and vitamins were proved to be some abundant KOs (Kyoto Encyclopedia of Genes and Genomes [KEGG] orthologs) in healthy group, whereas infectious disease, membrane transport, and carbohydrate metabolism were the three KOs with the higher abundance in the HAEC group. Our data increased our insight into the HAEC, which may shed further light on HAEC pathogenesis. Our study firstly demonstrated the difference between fecal microbiota of HAEC patients and healthy individuals, which made a step forward in the understanding of the pathophysiology of HAEC.

Investigation of in silico studies for cytochrome P450 isoforms specificity

Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.

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.

Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

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.

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.

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.