Colorectal cancer is a major global health burden, with significant heterogeneity in clinical outcomes among patients. Identifying robust prognostic gene expression signatures can help stratify patients, guide treatment decisions, and improve clinical management. This review provides an overview of current prognostic gene expression profiles in colorectal cancer research. We have synthesized evidence from numerous published studies investigating the association between tumor gene expression patterns and patient survival outcomes. The reviewed literature reveals several promising gene signatures that have demonstrated the ability to predict disease-free survival and overall survival in CRC patients, independent of standard clinicopathological risk factors. These genes are crucial in fundamental biological processes, including cell cycle control, epithelial-mesenchymal transition, and immune regulation. The implementation of prognostic gene expression tests in clinical practice holds great potential for enabling more personalized management strategies for colorectal cancer.

Tumors are associated with the highest mortality rates worldwide. For more than a decade, research has focused on the genetic involvement of proteins in cancer; however, a complete class of molecular non‑coding (nc)RNAs have been discovered in recent years, and these are considered to be associated with cancer. Notably, ncRNAs are highly conserved and multifunctional. These interact with multiple signaling pathways, influencing cell cycle progression and various physiological processes. Therefore, the present review aimed to investigate ncRNA, microRNA, transfer RNA‑derived small RNA, PIWI‑interacting RNA and long non‑coding RNA to further understand the associated generation processes, functional mechanisms and therapeutic roles in tumors. The present review demonstrated the critical role of ncRNAs in tumors, and may provide a novel theoretical basis for the role of ncRNAs as biomarkers or therapeutic tools in the treatment of cancer.

A comprehensive overview of food mycotoxins, their toxicity, and contemporary detoxification techniques is given in this article. Mycotoxins, which are harmful secondary metabolites generated by a variety of fungi, including Fusarium, Aspergillus, and Penicillium, provide serious health concerns to humans and animals. These include hepatotoxicity, neurotoxicity, and carcinogenicity. Mycotoxins are commonly found in basic food products, as evidenced by recent studies, raising worries about public health and food safety. The article discusses detection techniques such as enzyme-linked immunosorbent assays (ELISA), and quick strip tests. Moreover, the use of various control systems associated with the detoxification of mycotoxinis highlighted. In addition, novel detoxification strategies such as nanotechnology, plant extracts, and omics studies were also discussed. When taken as a whole, this analysis helps to clarify the pressing need for efficient management and monitoring techniques to prevent mycotoxin contamination in the food chain.

Neurodevelopmental studies employing animal models encounter challenges due to interspecies differences and ethical concerns. Maturing neurons of human origin, undergoing several developmental stages, present a powerful alternative. In this study, human embryonic stem cell (H9 cell line) was differentiated into neural stem cells and subsequently matured into neurons over 30 days. Ion AmpliSeq™ was used for transcriptomic characterization of human stem cell-derived neurons at multiple time points. Data analysis revealed a progressive increase of markers associated with neuronal development and astrocyte markers, indicating the establishment of a co-culture accommodating both glial and neurons. Transcriptomic and pathway enrichment analysis also revealed a more pronounced GABAergic phenotype in the neurons, signifying their specialization toward this cell type. The findings confirm the robustness of these cells across different passages and demonstrate detailed progression through stages of development. The model is intended for neurodevelopmental applications and can be adapted to investigate how genetic modifications or exposure to chemicals, pharmaceuticals, and other environmental factors influence neurons and glial maturation.

The complexity of the mammalian brain must arise from a comparably small number of genes. Proteins with moonlighting functions, i.e. entirely different functions in different compartments or cell types, contribute to multiply functional diversity. Here we review examples of such proteins with moonlighting functions during neurogenesis and in neuronal maturation. These range from cytoskeletal proteins acting as transcriptional regulators or synaptic proteins or exon junction proteins binding to and regulating the cytoskeleton to immediate early gene transcription factors regulating lipid metabolism in the endoplasmic reticulum. We further discuss how proteins with such moonlighting functions contribute to the heterogeneity of organelles shaping cell-type diversity in the brain.

Douglas Hanahan added "non-mutational epigenetic reprogramming" and "unlocking phenotypic plasticity" as new hallmarks of cancer, proposing that cancer cells possess fundamental features that are not directly linked to their genetic abnormalities. In vivo reprogramming studies have demonstrated that non-mutational epigenetic regulation can cause cellular reprogramming, leading to cancer development at the organismal level. Given that epigenetic regulation functions as an interface between the cellular environment and gene expression, these results suggest that intercellular communications in the tumor microenvironment play a critical role in cancer development. This review first introduces genetic aberrations that cause cancer development. Then, it illustrates the impact of epigenetic abnormalities in cancer, especially with reference to studies that use in vivo reprogramming technologies. Finally, it discusses the importance of histological evaluations of tumor tissue to understand non-cell-autonomous epigenetic regulation that establishes cancer hallmarks.

Supported by rapid technological advancements, biocatalytic applications have matured into sustainable, scalable, and cost-competitive alternatives to established chemical catalysis. This review presents the most recent examples of enzyme-based solutions for the manufacturing of molecules with extended carbon-carbon frameworks and multiple stereogenic centers at commercial scale, including peptide building blocks, (rare) sugars, synthetic (oligo)nucleotides, and terpenoids, such as (-)-Ambrox®. Novel enzyme classes are highlighted along with their potential applications-the synthesis of DNA/RNA, the depolymerization of synthetic plastics, or fully enzymatic protection/deprotection schemes-pointing toward the diversification and broader industrial utilization of biocatalysis-based processes.

Repeated sequences spread throughout the genome play important roles in shaping the structure of chromosomes and facilitating the generation of new genomic variation through structural rearrangements. Several mechanisms of structural variation formation use shared nucleotide similarity between repeated sequences as substrate for ectopic recombination. We performed genome-wide analyses of direct and inverted intrachromosomal repeated sequence pairs with 200 bp or more and 80% or greater sequence identity in three human genome assemblies, GRCh37, GRCh38, and T2T-CHM13. Overall, the composition and distribution of direct and inverted repeated sequences identified was similar among the three assemblies involving 13%-15% of the haploid genome, with an increased, albeit not significant, number of repeated sequences in T2T-CHM13. Interestingly, the majority of repeated sequences are below 1 kb in length with a median of 84.2% identity, highlighting the potential relevance of smaller, less identical repeats, such as Alu-Alu pairs, for ectopic recombination. We cross-referenced the identified repeated sequences with protein-coding genes to identify those at risk for being involved in genomic rearrangements. Olfactory receptors and immune response genes were enriched among those impacted.

Abnormalities in X chromosomes, either numerical or structural, cause X-linked disorders, such as Duchenne muscular dystrophy (DMD). Recent molecular and cytogenetic techniques can help identify DMD gene mutations. The accurate diagnosis of Duchenne is crucial, directly impacting patient treatment management, genetics, and the establishment of effective prevention strategies. This review provides an overview of X chromosomal disorders affecting Duchenne and discusses how mutations in Dystrophin domains can impact detection accuracy. Firstly, the efficiency and use of cytogenetic and molecular techniques for the genetic diagnosis of Duchenne disease have, thus, become increasingly important. Secondly, artificial intelligence (AI) will be instrumental in developing future therapies by enabling the aggregation and synthesis of extensive and heterogeneous datasets, thereby elucidating underlying molecular mechanisms. However, despite advances in diagnostic technology, understanding the role of Dystrophin in Duchenne disease remains a challenge. Therefore, this review aims to synthesize this complex information to significantly advance the understanding of DMD and how it could affect patient care.

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and genes play a vital role in understanding the AD and developing effective prevention and treatment strategies. Recent works combine imaging and genetic data, and leverage the strengths of both modalities to achieve better classification results. In this work, we propose MCA-GCN, a Multi-stream Cross-Attention and Graph Convolutional Network-based classification method for AD patients. It first constructs a brain region-gene association network based on brain region fMRI time series and gene SNP data. Then it integrates the absolute and relative positions of the brain region time series to obtain a new brain region time series containing temporal information. Then long-range and local association features between brain regions and genes are sequentially aggregated by multi-stream cross-attention and graph convolutional networks. Finally, the learned brain region and gene features are input to the fully connected network to predict AD types. Experimental results on the ADNI dataset show that our model outperforms other methods in AD classification tasks. Moreover, MCA-GCN designed a multi-stage feature scoring process to extract high-risk genes and brain regions related to disease classification.

Phenotypic modifications and their effects on cellular functions through the up-regulation of target gene expression have frequently been observed in genetic studies, but the unique roles of cell lines and their introduced plasmids in influencing these functions have not been fully revealed. In this research, we developed two distinct cell lines derived from the A549 cell line: one that stably overexpresses GFP and another that is a polyclonal stable line overexpressing both GFP and P2RY12. We then utilized transcriptome sequencing (RNA-seq) technology to screen out differentially expressed genes (DEGs) and genes with differential transcript usage (gDTUs) after GFP overexpression (GFP-OE) and P2RY12 overexpression (P2RY12-OE). We found that, compared with A549, there were more than 1700 differentially expressed genes (DEGs) in both GFP-OE and P2RY12-OE cells, while only 866 DEGs were identified in GFP-OE and P2RY12-OE cells. Notably, the differences in transcript usage were relatively minor, with only over 400 genes exhibiting changes across all three groups. The functional analysis of DEGs and gDTUs showed that they were both highly enriched in the pathways associated with cell proliferation and migration. In summary, we performed an extensive analysis of the transcriptome profile of gene expression and alternative splicing with GFP-OE and P2RY12-OE, enhancing our comprehension of how genes function within cells and the processes that control gene expression.

Genetic information will be increasingly integrated into clinical eye care within the current generation of ophthalmologists. For monogenic diseases such as retinoblastoma, genetic studies have been relatively straightforward as these conditions result from pathogenic variants in a single gene resulting in large physiological effects. However, most eye diseases result from the cumulative effects of multiple genetic variants and environmental factors. In such diseases, because each variant usually has an individually small effect, genetic studies for complex diseases are comparatively more challenging. This article aims to provide an overview of three genetic epidemiology methods for polygenic (or complex) diseases: genome-wide association studies (GWAS), Polygenic Risk Scores (PRS) and Mendelian randomisation (MR). A GWAS systematically conducts association analyses of a trait of interest against millions of genetic variants, usually in the form of single nucleotide polymorphisms, across the genome. GWAS findings can then be used for PRS construction and MR analyses. To construct a PRS, the cumulative effect of many genetic variants associated with a trait from a prior GWAS is calculated and taken as a quantitative representation of an individual's genetic risk of a complex disease. MR studies analyse an outcome measure against the genetic variants of an exposure, and are particularly useful in investigating causal relations between two traits where randomised controlled trials are not possible or ethical. In addition to explaining the principles of these three genetic epidemiology concepts, this article provides a minimally technical description of their basic methodology that is accessible to the non-expert reader.

Colorectal cancer (CRC) is identified as the second leading cause of cancer-associated deaths worldwide. Therefore, there is ongoing research to discover new potential biomarkers enabling early and noninvasive diagnosis of the disease. YRNAs, a novel class of non-coding RNAs, have been identified as a new player in carcinogenesis and an independent class of clinical biomarkers in various malignancies. Nevertheless, the role of plasma YRNAs in CRC diagnosis and prognosis remains unknown. Therefore, the current study aimed to investigate the clinical significance of plasma YRNAs as a noninvasive biomarker for CRC.

Plasma YRNAs expression was assessed in 50 newly diagnosed CRC patients as well as 50 age- and sex-matched healthy controls using quantitative reverse transcription polymerase chain reaction.

All plasma YRNAs expression levels were significantly higher in CRC patients than in controls. A significant correlation was observed between YRNA1 and YRNA3, and between YRNA1 and YRNA4. However, no significant correlation between YRNA1 and YRNA5 was identified. Plasma YRNA1 expression showed the highest diagnostic performance for the detection of CRC using the receiver operating characteristic curve analysis, with a sensitivity of 92% and a specificity of 90%. Nevertheless, when the four YRNAs were combined in a single ROC analysis, sensitivity decreased to 80%, while the specificity remained virtually unchanged. Moreover, significant association was observed between plasma YRNA1 and YRNA3 and tumor stage, grade, lymph node presence, metastasis, and lymphovascular invasion.

Plasma YRNA may serve as a potential noninvasive biomarker for the diagnosis and prognosis of CRC with high sensitivity and specificity vs. healthy controls.

Primary sclerosing cholangitis (PSC) is a chronic biliary inflammation associated with periductular fibrosis of the intrahepatic and extrahepatic bile ducts leading to strictures, bacterial cholangitis, decompensated liver disease and need for liver transplantation. This rare focal liver disease affects all races and ages, with a predominance of young males. There is an up to 88% association with inflammatory bowel disease. Although the aetiology is unknown and the pathophysiology is poorly understood, PSC is regarded as an autoimmune liver disease based on a strong immunogenetic background. Further, the associated risk for various malignancies, particularly cholangiocellular carcinoma, is also poorly understood. No medical therapy has been approved so far nor has been shown to improve transplant-free survival. However, ursodeoxycholic acid is widely used since it improves the biochemical parameters of cholestasis and is safe at low doses. MRI of the biliary tract is the primary imaging technology for diagnosis. Endoscopic interventions of the bile ducts should be limited to clinically relevant strictures for balloon dilatation, biopsy and brush cytology. End-stage liver disease with decompensation is an indication for liver transplantation with recurrent PSC in up to 38% of patients. Several novel therapeutic strategies are in various stages of development, including apical sodium-dependent bile acid transporter and ileal bile acid transporter inhibitors, integrin inhibitors, peroxisome proliferator-activated receptor agonists, CCL24 blockers, recombinant FGF19, CCR2/CCR5 inhibitors, farnesoid X receptor bile acid receptor agonists, and nor-ursodeoxycholic acid. Manipulation of the gut microbiome includes faecal microbiota transplantation. This article summarizes present knowledge and defines unmet medical needs to improve quality of life and survival.

Autoimmune diseases arise when the immune system attacks healthy tissues, losing tolerance for self-tissues. Normally, the immune system recognizes and defends against pathogens like bacteria and viruses. The cGAS-STING pathway, activated by pattern-recognition receptors (PRRs), plays a key role in autoimmune responses. The cGAS protein senses pathogenic DNA and synthesizes cGAMP, which induces conformational changes in STING, activating kinases IKK and TBK1 and leading to the expression of interferon genes or inflammatory mediators. This pathway is crucial in immunotherapy, activating innate immunity, enhancing antigen presentation, modulating the tumor microenvironment, and integrating into therapeutic strategies. Modulation strategies include small molecule inhibitors, oligonucleotide therapies, protein and antibody therapies, genetic and epigenetic regulation, cytokine and metabolite modulation, and nanoscale delivery systems. Post-translational modifications (PTMs) of the cGAS-STING pathway, such as phosphorylation, acetylation, ubiquitination, methylation, palmitoylation, and glycosylation, fine-tune immune responses by regulating protein activity, stability, localization, and interactions. These modifications are interconnected and collectively influence pathway functionality. We summarize the functions of cGAS-STING and its PTMs in immune and non-immune cells across various diseases, and explore potential clinical applications.

Understanding the cellular and molecular effect of proton radiation, particularly the increased DNA damage complexity at the distal end of the Bragg curve, is current topic of investigation. This work aims to study in vitro clonogenic survival and DNA damage foci kinetics of a head and neck squamous cell carcinoma cell line at various positions along a double passively scattered Bragg curve. Complementary in silico studies are conducted to gain insights into the link between cell survival variations, experimentally yielded foci and the number and complexity of double strand breaks (DSBs).

Proton irradiations are performed at the HollandPTC R&D proton beamline, using a double passively scattered setup. A custom water phantom setup is employed to accurately position the samples within the Bragg curve. FaDu cells are irradiated at the proximal 36 % point of the Bragg peak, (P36), proximal 80 % point of the Bragg peak (P80) and distal 20 % point of the Bragg peak (D20), with dose-averaged mean lineal energies (y D ¯ ) of 1.10 keV/μm, 1.80 keV/μm and 7.25 keV/μm, respectively.

Clonogenic survival correlates strongly withy D ¯ , showing similar survival for P36 (D37%=3.0 Gy) and P80 (D37%=2.9 Gy), but decreased survival for D20 (D37% = 1.6 Gy). D20 irradiated samples exhibit increased 53BP1 foci shortly after irradiation, slower resolution of the foci, and larger residual 53BP1 foci after 24 h, indicating unrepaired complex breaks. These experimental observations are supported by the in silico study which demonstrates that irradiation at D20 leads to a 1.7-fold increase in complex DSBs with respect to the total number of strand breaks compared to P36 and P80.

This combined approach provides valuable insights into the cellular and molecular effect of proton radiation, emphasizing the increased DNA damage complexity at the distal end of the Bragg curve, and has the potential to enhance the efficacy of proton therapy.

The unique characteristics of biological structures depend on the behavior of DNA sequences confined in a microscale cell under environmental fluctuations and dissipation. Here, we report a prominent difference in fluorescence from dye-modified single-stranded DNA in a light-induced assembly of DNA-functionalized heterogeneous probe particles in a microwell of several microliters in volume. Strong optical forces from the Mie scattering of microparticles accelerated hybridization, and the photothermal effect from the localized surface plasmons in gold nanoparticles enhanced specificity to reduce the fluorescence intensity of dye-modified DNA to a few %, even in a one-base mismatched sequence, enabling us to clearly highlight the single nucleotide polymorphisms in DNA. Fluorescence intensity was positively correlated with complementary DNA concentrations ranging in several tens fg/μL after only 5 min of laser irradiation. Remarkably, a total amount of DNA in an optically assembled structure of heterogeneous probe particles was estimated between 2.36 ymol (2.36 × 10-24 mol) and 2.36 amol (2.36 × 10-18 mol) in the observed concentration range. These findings can promote an innovative production method of nanocomposite structures via biological molecules and biological sensing with simple strategies avoiding genetic amplification in a PCR-free manner.

Pediatric AML prognosis research has advanced significantly, yet gaps in understanding genetic and molecular interactions persist. Despite improved outcomes, relapse/refractory cases and personalized treatment integration remain critical clinical challenges.

To analyze the global research landscape on pediatric AML prognosis, highlight influential components and collaborations, and identify major potential research trends.

Publications on pediatric AML prognosis research from 1999 to 2023 were retrieved from the Clarivate Analytics Web of Science Core Collection (WoSCC) database. Bibliometric analysis was conducted using CiteSpace and VOSviewer to identify leading countries, prominent institutions, high-impact journals, key research categories, influential authors, and emerging research topics.

The bibliometric analysis encompassed 924 publications, with St. Jude Children's Research Hospital emerging as the most prolific institution. The United States leads globally in terms of countries, institutions, journals, and authors. Todd A. Alonzo ranks highest in publication volume, while U. Creutzig leads in citations. The top research categories were Oncology, Hematology, and Pediatrics. Key research topics included genomics, transcriptomics, epigenomics, targeted therapies, immune therapy, and integrative diagnostic approaches.

This bibliometric analysis highlights significant advancements in pediatric AML prognosis over the past 25 years, driven by the integration of genetic markers, immunological insights, transcriptomics, and epigenomics, which have collectively transformed risk stratification and treatment strategies. Overcoming challenges, such as discovering new therapeutic targets and enhancing treatment combinations, will depend on global collaboration and advanced technologies to propel the field forward.

Glioma is one of the most malignancy and prevalent tumor in the human central nervous system, which is associated with severe morbidity and high mortality. Numerous studies have explained the clear correlation between abnormal expression of lncRNA and progression of Glioma. LncRNA small nucleolar RNA host gene 4 (SNHG4) have been proved to play oncogenesis roles in various tumors, however, the underlying mechanism remains to be explored deeply. In this study, by analysis of the public database, we found that SNHG4 was upregulated in multiple cancer tissues, including glioma. Subsequently, the functional roles of SNHG4 were investigated, and we found that knockdown of SNHG4 remarkedly inhibited cell proliferation, migration. While, overexpression of SNHG4 enhanced these functions of glioma cells in vitro. Meanwhile, as the in vivo tool, zebrafish xenograft model was used to verify the functions of SNHG4 in glioma cells. Mechanically, we identified that SNHG4 or MYO1B could bind with miR-367-3p by the luciferase reporter assays. Furthermore, the rescue experiments showed that the inhibition of miR-367-3p or the expression of MYO1B partially rescue the inhibition effects of SNHG4 in glioma cells. Our study reveals that SNHG4 promotes the proliferation, migration of glioma via regulating miR-367-3p/MYO1B axis.

Intergenerational transmission of inequality is a central question in the social sciences. We use one trait, beauty, to infer how much parents' physical characteristics transmit inequality across generations. Analyses of a large-scale longitudinal dataset in the United States, and a much smaller dataset of Chinese parents and children, show that increases in parents' looks are associated with increases in their child's looks. A large dataset of U.S. siblings shows a positive correlation of their beauty. The appropriate weighted average from the three samples shows that a one SD increase in ratings of both parents' looks is associated with a 0.25 SD increase in their child's. Coupling these estimates with those from large literatures measuring the impact of beauty on earnings and the intergenerational elasticity of income suggests that a one SD difference in parents' looks is correlated with a 0.074 SD difference in their adult child's earnings, about U.S. $2,170 annually.

The Human Pangenome Reference Consortium, the Chinese Pangenome Consortium, and other plant and animal pangenome projects have announced the completion of pilot work aimed at constructing high-quality, haplotype-resolved reference graph genomes representative of global ethno-linguistically different populations or different plant and animal species. These graph-based, gapless pangenome references, which are enriched in terms of genomic diversity, completeness, and contiguity, have the potential for enhancing long-read sequencing (LRS)-based genomic research, as well as improving mappability and variant genotyping on traditional short-read sequencing platforms. We comprehensively discuss the advancements in pangenome-based genomic integrative genomic discoveries across forensic-related species (humans, animals, and plants) and summarize their applications in variant identification and forensic genomics, epigenetics, transcriptomics, and microbiome research. Recent developments in multiplexed array sequencing have introduced a highly efficient and programmable technique to overcome the limitations of short forensic marker lengths in LRS platforms. This technique enables the concatenation of short RNA transcripts and DNA fragments into LRS-optimal molecules for sequencing, assembly, and genotyping. The integration of new pangenome reference coordinates and corresponding computational algorithms will benefit forensic integrative genomics by facilitating new marker identification, accurate genotyping, high-resolution panel development, and the updating of statistical algorithms. This review highlights the necessity of integrating LRS-based platforms, pangenome-based study designs, and graph-based pangenome references in short-read mapping and LRS-based innovations to achieve precision forensic science.

The melanocortin-3 receptor (MC3R) was recognized for its critical role in energy metabolism and inflammatory responses in mammals; however, its functions in fish remain poorly understood. This study characterized the mc3r gene in goldfish, investigating its sequence, tissue distribution, and pharmacological responses. The coding sequence of goldfish mc3r was 975 bp, translating to a 325-amino-acid protein typical of G protein-coupled receptors, with notable conservation across cyprinids. Quantitative PCR analysis revealed high expression levels in the brain. Luciferase assays demonstrated that various agonists, particularly NDP-MSH and ACTH (1-24), effectively activated the cAMP and MAPK/ERK signaling pathways. Furthermore, all agonists tested (α-MSH, β-MSH, ACTH (1-24), and NDP-MSH) significantly inhibited NF-κB signaling, correlating with their activation of cAMP. These findings enhanced our understanding of the melanocortin system's role in regulating energy metabolism and inflammatory processes in teleost fish.

Given the presence of highly repetitive genomic regions such as subtelomeric regions, understanding human genomic evolution remains challenging. Recently, long-read sequencing technology has facilitated the identification of complex genetic variants, including structural variants (SVs), at the single-nucleotide level. Here, we resolved SVs and their underlying DNA damage-repair mechanisms in subtelomeric regions, which are among the most uncharted genomic regions. We generated ∼20 × high-fidelity long-read sequencing data from three Korean individuals and their partially phased high-quality de novo genome assemblies (contig N50: 6.3-58.2 Mb). We identified 131 138 deletion and 121 461 insertion SVs, 41.6% of which were prevalent in the East Asian population. The commonality of the SVs identified among the Korean population was examined by short-read sequencing data from 103 Korean individuals, providing the first comprehensive SV set representing the population based on the long-read assemblies. Manual investigation of 19 large subtelomeric SVs (≥5 kb) and their associated repair signatures revealed the potential repair mechanisms leading to the formation of these SVs. Our study provides mechanistic insight into human telomere evolution and can facilitate our understanding of human SV formation.

Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. While these inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumour phylogenies reconstruct ground truth DNA-based tumour phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumour phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.

This article reviews existing work and future opportunities in neuroevolution, an area of machine learning in which evolutionary optimization methods such as genetic algorithms are used to construct neural networks to achieve desired behavior. The article takes a neuroscience perspective, identifying where neuroevolution can lead to insights about the structure, function, and developmental and evolutionary origins of biological neural circuitry that can be studied in further neuroscience experiments. It proposes optimization under environmental constraints as a unifying theme and suggests the evolution of language as a grand challenge whose time may have come.

Precision cancer medicine is widely established, and numerous molecularly targeted drugs for various tumor entities are approved or are in development. Personalized pharmacotherapy in oncology has so far been based primarily on tumor characteristics, for example, somatic mutations. However, the response to drug treatment also depends on pharmacological processes summarized under the term ADME (absorption, distribution, metabolism, and excretion). Variations in ADME genes have been the subject of intensive research for >5 decades, considering individual patients' genetic makeup, referred to as pharmacogenomics (PGx). The combined impact of a patient's tumor and germline genome is only partially understood and often not adequately considered in cancer therapy. This may be attributed, in part, to the lack of methods for combined analysis of both data layers. Optimized personalized cancer therapies should, therefore, aim to integrate molecular information, which derives from both the tumor and the germline genome, and taking into account existing PGx guidelines for drug therapy. Moreover, such strategies should provide the opportunity to consider genetic variants of previously unknown functional significance. Bioinformatic analysis methods and corresponding algorithms for data interpretation need to be developed to integrate PGx data in cancer therapy with a special meaning for interdisciplinary molecular tumor boards, in which cancer patients are discussed to provide evidence-based recommendations for clinical management based on individual tumor profiles. SIGNIFICANCE STATEMENT: The era of personalized oncology has seen the emergence of drugs tailored to genetic variants associated with cancer biology. However, the full potential of targeted therapy remains untapped owing to the predominant focus on acquired tumor-specific alterations. Optimized cancer care must integrate tumor and patient genomes, guided by pharmacogenomic principles. An essential prerequisite for realizing truly personalized drug treatment of cancer patients is the development of bioinformatic tools for comprehensive analysis of all data layers generated in modern precision oncology programs.

DNA methylation is an essential epigenetic mechanism for regulation of gene expression, through which many physiological (X-chromosome inactivation, genetic imprinting, chromatin structure and miRNA regulation, genome defense, silencing of transposable elements) and pathological processes (cancer and repetitive sequences-associated diseases) are regulated. Nanopore sequencing has emerged as a novel technique that can analyze long strands of DNA (long-read sequencing) without chemically treating the DNA. Interestingly, nanopore sequencing can also extract epigenetic status of the nucleotides (including both 5-Methylcytosine and 5-hydroxyMethylcytosine), and a large variety of bioinformatic tools have been developed for improving its detection properties. Out of all genomic regions, long read sequencing provides advantages in studying repetitive elements, which are difficult to characterize through other sequencing methods. Transposable elements are repetitive regions of the genome that are silenced and usually display high levels of DNA methylation. Their demethylation and activation have been observed in many cancers. Due to their repetitive nature, it is challenging to accurately estimate DNA methylation levels within transposable elements using short sequencing technologies. The advantage to sequence native DNA (without PCR amplification biases or harsh bisulfite treatment) and long and ultra long reads coupled with epigenetic states of the DNA allows to accurately estimate DNA methylation levels in transposable elements. This is a big step forward for epigenomic studies, and unsolved questions regarding gene expression and transposable elements silencing through DNA methylation can now be answered.

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Oxford Nanopore Technologies sequencing, also referred to as Nanopore sequencing, stands at the forefront of a revolution in clinical genetics, offering the potential for rapid, long read, and real-time DNA and RNA sequencing. This technology is currently making sequencing more accessible and affordable. In this comprehensive review, we explore its potential regarding precision cancer diagnostics and treatment. We encompass a critical analysis of clinical cases where Nanopore sequencing was successfully applied to identify point mutations, splice variants, gene fusions, epigenetic modifications, non-coding RNAs, and other pivotal biomarkers that defined subsequent treatment strategies. Additionally, we address the challenges of clinical applications of Nanopore sequencing and discuss the current efforts to overcome them.

Programs for preclinical testing of targeted cancer agents in murine models of childhood cancers have been supported by the National Cancer Institute (NCI) since 2004. These programs were established to work collaboratively with industry partners to address the paucity of targeted agents for pediatric cancers compared with the large number of agents developed and approved for malignancies primarily affecting adults. The distinctive biology of pediatric cancers and the relatively small numbers of pediatric cancer patients are major challenges for pediatric oncology drug development. These factors are exacerbated by the division of cancers into multiple subtypes that are further sub-classified by their genomic properties. The imbalance between the large number of candidate agents and small patient populations requires careful prioritization of agents developed for adult cancers for clinical evaluation in children with cancer. The NCI-supported preclinical pediatric programs have published positive and negative results of efficacy testing for over 100 agents to aid the pediatric research community in identifying the most promising candidates to move forward for clinical testing in pediatric oncology. Here, we review and summarize lessons learned from two decades of experience with the design and execution of preclinical trials of antineoplastic agents in murine models of childhood cancers.

Here we intend to shift the "DNA- and information-centric" conception of biological inheritance, with the accompanying exclusion of any non-DNA matter, to a "poly-matter network" framework which, in addition to DNA, considers the action of other cellular membranous constituents. These cellular structures, in particular organelles and plasma membranes, express "landscapes" of specific topologies at their surfaces, which may become altered in response to certain environmental factors. These so-called "membranous environmental landscapes" (MELs), which replicate by self-organization / autopoiesis rather than self-assembly, are transferred from donor to acceptor cells by various - vesicular and non-vesicular - mechanisms and exert novel features in the acceptor cells. The "DNA-centric" conception may be certainly explanatorily sufficient for the transfer of heritable phenotype variation to acceptor cells following the copying of DNA in donor cells and thereby for the phenomenon of biological inheritance of traits. However, it is not causally sufficient. With the observation of phenotype variation, as initially manifested during bacterial transformation, the impact of environmental factors, such as nutrition and stress, in the differential regulation of gene expression has been widely accepted and resulted in intense efforts to resolve the underlying epigenetic mechanisms. However, these are explained under a conceptual frame where the DNA (and associated proteins) are the only matter of inheritance. In contrast, it is our argumentation that inheritance can only be adequately understood as the transfer of DNA in concert with non-DNA matter in a "poly-matter network" conception. The adequate inclusion of the transfer of non-DNA matter is still a desideratum of future genetic research, which may pave the way for the experimental elucidation not only of how DNA and membrane matter act in concert to enable the inheritance of innate traits, but also whether they interact for that of acquired biological traits. Moreover, the "poly-matter network" conception may open new perspectives for an understanding of the pathogenesis of "common complex" diseases.

Most of the genome is transcribed into RNA but only 2% of the sequence codes for proteins. Non-coding RNA transcripts include a very large number of long noncoding RNAs (lncRNAs). A growing number of identified lncRNAs operate in cellular stress responses, for example in response to hypoxia, genotoxic stress, and oxidative stress. Additionally, lncRNA plays important roles in epigenetic mechanisms operating at chromatin and in maintaining chromatin architecture. Here, we address three lncRNA topics that have had significant recent advances. The first is an emerging role for many lncRNAs in cellular stress responses. The second is the development of high throughput screening assays to develop causal relationships between lncRNAs across the genome with cellular functions. Finally, we turn to recent advances in understanding the role of lncRNAs in regulating chromatin architecture and epigenetics, advances that build on some of the earliest work linking RNA to chromatin architecture.

T-cell Acute Lymphoblastic Leukemia (T-ALL) is a highly aggressive form of ALL with at least 25% relapse rates. The high relapse rates are often linked to poor prognoses. More detailed studies for novel therapeutic targets for the treatment of T-ALL are required as the genetic and transcriptomic data currently available on T-ALL pathophysiology is insufficient. Long non-coding RNAs are emerging as important players in the regulation of tumour proliferation and metastasis. Studies on various cancers have revealed their potential as biomarkers and therapeutic targets in treatment. This review describes the characterization, biosynthesis, and role of long non-coding RNA in T-ALL and highlights their potential as next generation molecule in development of promising diagnostic, prognostic and/or therapeutic markers.

Powerful new genomic technologies are transforming the way healthcare is delivered, shaping medical practice across all specialties. In this rapidly changing landscape, there is an urgent need to equip the clinical workforce with knowledge and skills to navigate the new healthcare terrain. Co-design of healthcare resources with end users is increasingly gaining traction as a method of ensuring that educational content and delivery are tailored to users' needs, increasing likelihood of use and resulting in better outcomes for patients. Here we describe the co-design and ongoing co-creation of GeNotes - an NHS England National Genomics Education flagship online resource providing genomics education at the point of patient care.

To understand the barriers to implementation of genomic medicine and the training needs of the diverse NHS workforce, we adopted a co-design approach with clinicians from both primary and secondary care who are uniquely placed to understand the context in which they are working and identify their own training needs. Concept design, initial user research and subsequent 'alpha' and 'private beta' phase user research was conducted in a series of co-design iterations employing a mixed methodology integrating quantitative and qualitative data collection and analysis.

User evaluation data demonstrated excellent feedback across the tested domains (content, navigation, likelihood of use and recommendation to colleagues). We identified several key themes from user testing that shaped the resource's development.

The co-design approach to the development of this point-of-care genomics education resource for clinicians has allowed insight into the education needs, challenges and learning styles of end-users. The utility of this approach was supported by excellent user feedback across the tested domains, and we recommend it to others involved in developing healthcare resources in a fast-paced environment.

Diabetic retinopathy (DR) is a microvascular complication of diabetes that leads to vision loss. The striking features of DR are hard exudate, cotton-wool spots, hemorrhage, and neovascularization. The dysregulated retinal cells, encompassing microvascular endothelial cells, pericytes, Müller cells, and adjacent retinal pigment epithelial cells, are involved in the pathological processes of DR. According to recent research, oxidative stress, inflammation, ferroptosis, pyroptosis, apoptosis, and angiogenesis contribute to DR. Recent advancements have highlighted that noncoding RNAs could regulate diverse targets in pathological processes that contribute to DR. Noncoding RNAs, including long noncoding RNAs, microRNAs (miRNA), and circular RNAs, are dysregulated in DR, and interact with miRNA, mRNA, or proteins to control the pathological processes of DR. Hence, modulation of noncoding RNAs may have therapeutic effects on DR. Small extracellular vesicles may be valuable tools for transferring noncoding RNAs and regulating the genes involved in progression of DR. However, the roles of noncoding RNA in developing DR are not fully understood; it is critical to summarize the mechanisms for noncoding RNA regulation of pathological processes and pathways related to DR. This review provides a fundamental understanding of the relationship between noncoding RNAs and DR, exploring the mechanism of how noncoding RNA modulates different signaling pathways, and pave the way for finding potential therapeutic strategies for DR.

In clinical practice, timely and accurate diagnosis can effectively reduce unnecessary treatment, avoid high medical costs, and prevent adverse prognoses. However, some patients with malignant tumors and those with infection often exhibit similar symptoms, which are difficult to distinguish, posing challenges in accurate clinical diagnosis. Metagenomic next-generation sequencing (mNGS) technology has been widely applied to confirm the source of infection. Recent studies have shown that for pathogen detection, mNGS technology can be used to perform chromosomal copy number variations (CNVs) analysis in two different analytical pipelines using the same wet test. mNGS technology has further demonstrated its utility in not only the determination of pathogenic microorganisms but also of CNVs, thereby facilitating early differential diagnosis for malignant tumors. In this review, we aim to analyze the diagnostic performance of mNGS technology in the simultaneous detection of pathogenic microorganisms and CNVs in current clinical practice and discuss the advantages and limitations of mNGS-CNV dual-omics detection technology. Our review highlights the need for more large-scale prospective research data on current mNGS-CNV dual-omics detection technology to provide more evidence-based results for researchers and clinicians and to promote the greater role of this technology in future clinical practice.

Asthma has become one of the most serious chronic respiratory diseases threatening people's lives worldwide. The pathogenesis of asthma is complex and driven by numerous cells and their interactions, which contribute to its genetic and phenotypic heterogeneity. The clinical characteristic is insufficient for the precision of patient classification and therapies; thus, a combination of the functional or pathophysiological mechanism and clinical phenotype proposes a new concept called "asthma endophenotype" representing various patient subtypes defined by distinct pathophysiological mechanisms. High-throughput omics approaches including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiome enable us to investigate the pathogenetic heterogeneity of diverse endophenotypes and the underlying mechanisms from different angles. In this review, we provide a comprehensive overview of the roles of diverse cell types in the pathophysiology and heterogeneity of asthma and present a current perspective on their contribution into the bidirectional interaction between airway inflammation and airway remodeling. We next discussed how integrated analysis of multi-omics data via machine learning can systematically characterize the molecular and biological profiles of genetic heterogeneity of asthma phenotype. The current application of multi-omics approaches on patient stratification and therapies will be described. Integrating multi-omics and clinical data will provide more insights into the key pathogenic mechanism in asthma heterogeneity and reshape the strategies for asthma management and treatment.

Reference genomes are foundational to modern genomics. Our growing understanding of genome structure leads to continual improvements in reference genomes and new genome "builds" with incompatible coordinate systems. We quantified the impact of genome build on germline and somatic variant calling by analyzing tumour-normal whole-genome pairs against the two most widely used human genome builds. The average individual had a build-discordance of 3.8% for germline SNPs, 8.6% for germline SVs, 25.9% for somatic SNVs and 49.6% for somatic SVs. Build-discordant variants are not simply false-positives: 47% were verified by targeted resequencing. Build-discordant variants were associated with specific genomic and technical features in variant- and algorithm-specific patterns. We leveraged these patterns to create StableLift, an algorithm that predicts cross-build stability with AUROCs of 0.934 ± 0.029. These results call for significant caution in cross-build analyses and for use of StableLift as a computationally efficient solution to mitigate inter-build artifacts.

Diet has a pivotal role in shaping the composition, function and diversity of the gut microbiome, with various diets having a profound impact on the stability, functionality and diversity of the microbial community within our gut. Understanding the profound impact of varied diets on the microbiome is crucial, as it will enable us not only to make well-informed dietary decisions for better metabolic and intestinal health, but also to prevent and slow the onset of specific diet-related diseases that stem from suboptimal diets. In this Review, we explore how geographical location affects the gut microbiome and how different diets shape its composition and function. We examine the mechanisms by which whole dietary regimes, such as the Mediterranean diet, high-fibre diet, plant-based diet, high-protein diet, ketogenic diet and Western diet, influence the gut microbiome. Furthermore, we underscore the need for exhaustive studies to better understand the causal relationship between diet, host and microorganisms for the development of precision nutrition and microbiome-based therapies.