Frontiers in Genetics

Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and metagenomics data. Microbial protein and genomic sequences, like natural languages, form a language of life , enabling the adoption of LLMs to extract useful insights from complex microbial ecologies. In this paper, we review applications of deep learning and language models in analyzing microbiome and metagenomics data. We focus on problem formulations, necessary datasets, and the integration of language modeling techniques. We provide an extensive overview of protein/genomic language modeling and their contributions to microbiome studies. We also discuss applications such as novel viromics language modeling, biosynthetic gene cluster prediction, and knowledge integration for metagenomics studies.

Objectives: Unlike other diseases, cancer is not just a genome disease but should broadly be viewed as a disease of the cellular machinery. Therefore, integrative multifaceted approaches are crucial to understanding the complex nature of cancer biology. Bcl-2 (B-cell lymphoma 2), encoded by the human BCL-2 gene, is an anti-apoptotic molecule that plays a key role in apoptosis and genetic variation of Bcl-2 proteins and is vital in disrupting the apoptotic machinery. Single nucleotide polymorphisms (SNPs) are considered viable diagnostic and therapeutic biomarkers for various cancers. Therefore, this study explores the association between SNPs in Bcl-2 and the structural, functional, protein-protein interactions (PPIs), drug binding and dynamic characteristics. Methods: Comprehensive cross-validated bioinformatics tools and molecular dynamics (MD) simulations. Multiple sequence, genetic, structural and disease phenotype analyses were applied in this study. Results: Analysis revealed that out of 130 mutations, approximately 8.5% of these mutations were classified as pathogenic. Furthermore, two particular variants, namely, Bcl-2G101V and Bcl-2F104L, were found to be the most deleterious across all analyses. Following 500 ns, MD simulations showed that these mutations caused a significant distortion in the protein conformational, protein-protein interactions (PPIs), and drug binding landscape compared to Bcl-2WT. Conclusion: Despite being a predictive study, the findings presented in this report would offer a perspective insight for further experimental investigation, rational drug design, and cancer gene therapy.

Facioscapulohumeral muscular dystrophy type 1 (FSHD1) and Becker muscular dystrophy (BMD) are distinct disorders caused by different genetic variations and exhibiting different inheritance patterns. The co-occurrence of both conditions within the same family is rare. In this case report, the proband was a 10 year-old boy who presented with eye and mouth orbicular muscles, shoulder and proximal upper and lower limbs weakness. Genetic testing showed that the number of D4Z4 repeat units in the sub-terminal region 4qA of chromosome 4q35 in the proband was only 4 (normal value ≥ 11) and, at the same time, a heterozygous deletion was found in exons 13–29 of DMD gene in the proband, thus the diagnosis was clinically and genetically compatible with both FSHD1 and BMD. Pedigree investigation revealed that his maternal grandmother, mother, aunt and cousin also had muscle weakness in the face, shoulders and limbs. Genetic testing confirmed that each of the four relatives had four D4Z4 repeats in the 4qA region, and all of them carried a heterozygous deletion in exons 13–29 of DMD . Based on the X-linked features of DMD/BMD, the maternal grandmother, mother, and aunt were diagnosed with FSHD1 combined with DMD deletion carriers, and the male cousin was diagnosed with FSHD1 combined with BMD. This study identifies a family with a co-occurrence of clinically overt FSHD1 and BMD, which has important reference value for the diagnosis and treatment of hereditary myopathies.

Introduction Typical adolescent neurodevelopment is marked by decreases in grey matter (GM) volume, increases in myelination, measured by fractional anisotropy (FA), and improvement in cognitive performance. Methods To understand how epigenetic changes, methylation (DNAm) in particular, may be involved during this phase of development, we studied cognitive assessments, DNAm from saliva, and neuroimaging data from a longitudinal cohort of normally developing adolescents, aged nine to fourteen. We extracted networks of methylation with patterns of correlated change using a weighted gene correlation network analysis (WCGNA). Modules from these analyses, consisting of co-methylation networks, were then used in multivariate analyses with GM, FA, and cognitive measures to assess the nature of their relationships with cognitive improvement and brain development in adolescence. Results This longitudinal exploration of co-methylated networks revealed an increase in correlated epigenetic changes as subjects progressed into adolescence. Co-methylation networks enriched for pathways involved in neuronal systems, potassium channels, neurexins and neuroligins were both conserved across time as well as associated with maturation patterns in GM, FA, and cognition. Discussion Our research shows that correlated changes in the DNAm of genes in neuronal processes involved in adolescent brain development that were both conserved across time and related to typical cognitive and brain maturation, revealing possible epigenetic mechanisms driving this stage of development.

High-altitude adaptation is a remarkable example of natural selection, yet the genomic and physiological adaptation mechanisms of Ethiopian highlanders remain poorly understood compared to their Andean and Tibetan counterparts. Ethiopian populations, such as the Amhara and Oromo, exhibit unique adaptive strategies characterized by moderate hemoglobin levels and enhanced arterial oxygen saturation, indicating distinct mechanisms of coping with chronic hypoxia. This review synthesizes current genomic insights into Ethiopian high-altitude adaptation, identifying key candidate genes involved in hypoxia tolerance and examining the influence of genetic diversity and historical admixture on adaptive responses. Furthermore, the review highlights significant research gaps, particularly the underrepresentation of Ethiopian populations in global genomic studies, the lack of comprehensive genotype-phenotype analyses, and inconsistencies in research methodologies. Addressing these gaps is crucial for advancing our understanding of the genetic basis of human adaptation to extreme environments and for developing a more complete picture of human physiological resilience. This review offers a comparative perspective with Tibetan and Andean highlanders, emphasizing the need for expanding genomic representation and refining methodologies to uncover the genetic mechanisms underlying high-altitude adaptation in Ethiopian populations.

Introduction The deficiency of estrogen correlates with a range of diseases, notably Postmenopausal osteoporosis (PMO) and Parkinson’s disease (PD). There is a possibility that PMO and PD may share underlying molecular mechanisms that are pivotal in their development and progression. The objective of this study was to identify critical genes and potential mechanisms associated with PMO by examining co-expressed genes linked to PD. Methods Initially, pertinent data concerning PMO and PD were obtained from the GWAS database, followed by conducting a Bayesian colocalization analysis. Subsequently, co-expressed genes from the PMO dataset (GSE35956) and the PD dataset (GSE20164) were identified and cross-referenced with estrogen-related genes (ERGs). Differentially expressed genes (DEGs) among PMO, PD, and ERGs were subjected to an array of bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses, in addition to protein-protein interaction (PPI) network analysis. The study also involved constructing TF-gene interactions, TF-microRNA coregulatory networks, interactions of hub genes with diseases, and validation through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results The colocalization analysis uncovered shared genetic variants between PD and osteoporosis, with a posterior probability of colocalization (PPH4) measured at 0.967. Notably, rs3796661 was recognized as a shared genetic variant. A total of 11 genes were classified as DEGs across PMO, PD, and ERGs. Five principal KEGG pathways were identified, which include the p53 signaling pathway, TGF-beta signaling pathway, cell cycle, FoxO signaling pathway, and cellular senescence. Additionally, three hub genes—WT1, CCNB1, and SMAD7—were selected from the PPI network utilizing Cytoscape software. These three hub genes, which possess significant diagnostic value for PMO and PD, were further validated using GEO datasets. Interactions between transcription factors and genes, as well as between microRNAs and hub genes, were established. Ultimately, the expression trends of the identified hub genes were confirmed through qRT-PCR validation. Conclusions This study is anticipated to offer innovative approaches for identifying potential biomarkers and important therapeutic targets for both PMO and PD.

Background Delays in diagnosing rare genetic disorders often arise due to limited awareness and systemic challenges in primary care. This case highlights the importance of a holistic approach to patient care, encompassing timely detection and comprehensive evaluation of clinical features. Methods We report the case of a 21-year-old Ecuadorian male with facial and hand dysmorphias, cardiomegaly, pulmonary hypertension, and patent ductus arteriosus (PDA). Whole-exome sequencing, performed using the Illumina NextSeq platform. We extensively analyzed over 100 genes linked to congenital structural heart diseases. Results The genetic findings provided a definitive diagnosis of Craniofacial-Deafness-Hand Syndrome, an extremely rare autosomal dominant condition, but found no variants that explain the patient’s cardiac phenotype. We identified a novel pathogenic missense variant in the PAX3 gene (c.A91C, p. T31P). Discussion and conclusions This case underscores the necessity of integrating genetic testing into routine clinical practice to enhance diagnostic precision for rare diseases. It also highlights the need for multidisciplinary collaboration and a holistic care model to improve patient outcomes. The unique association of Craniofacial-Deafness-Hand Syndrome with cardiovascular anomalies due to a PAX3 variation provides valuable insights into the genetic underpinnings of this rare condition.

Introduction Fritillaria taipaiensis P. Y. Li is a plant used to treat respiratory diseases such as pneumonia, bronchitis, and influenza. Its wild resources have become increasingly scarce, and the demand for efficient artificial cultivation has increased significantly in recent years. Phosphorus-solubilizing fungi can promote the dissolution of insoluble phosphate complex, which benefits plant nutrition. Another strategy for efficiently cultivating traditional Chinese medicine plants is to combine the soil with phosphorus-solubilizing fungi to provide nutrients and other desired features. This study aimed to investigate the effects of different phosphorus-solubilizing fungi and their combinations on photosynthesis, physiological and biochemical characteristics, and expression of protective enzyme system–related genes, and to find a reference strain suitable for the artificial cultivation and industrial development of F. taipaiensis P. Y. Li. In this study, the phosphorus-solubilizing fungi isolated from the rhizosphere soil of F. taipaiensis P. Y. Li were applied to the cultivation of F. taipaiensis P. Y. Li for the first time. Methods In this study, seven treatment groups (S1-S7) and one control group were set up using indoor pots as follows: S1 (inoculation with Aspergillus tubingensis ), S2 (inoculation with A. niger ), S3 (inoculation with Aspergillus nigerfunigatus ) and S4 (inoculation with A. tubingensis and A. niger ), S5 (inoculation with A. tubingensis and A. nigerfunigatus ), S6 (inoculation with A. niger and A. nigerfunigatus ), S7 (inoculation with A. tubingensis , A. niger , and A. nigerfunigatus ), and CK (control group). These strains were inoculated into pots containing F. taipaiensis P. Y. Li bulbs,and the effects of different phosphorus-solubilizing fungi and combinations on the photosynthetic characteristics, basic physiological and biochemical indicators, and differential gene expression of protective enzyme systems in F. taipaiensis P. Y. Li leaves were determined. Results Most growth indexes showed significant differences in the fungal treatment groups compared with the CK group ( P < 0.05). The stem diameter and plant height in the S5 group were the highest, which were 58.23% and 62.49% higher than those in the CK group, respectively. The leaf area in the S7 group was the largest, which increased by 141.34% compared with that in the CK group. Except for intercellular CO 2 concentration (Ci), the contents of photosynthetic pigments, photosynthetic parameters, and amounts of osmoregulatory substances increased to varying degrees in the fungal treatment groups ( P < 0.05). Among these, the S5 group had the highest stomatal conductance index and soluble sugar and free proline contents, whereas S6 had the highest chlorophyll a and soluble protein contents. In addition, the malondialdehyde (MDA) content in all inoculation groups was lower than that in the CK group. The MDA content was the lowest in S7, about 44.83% of that in the CK group. The activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) were higher in all inoculation groups than those in the CK group; the changes in SOD and CAT activities were significant ( P < 0.05). The expression levels of FtSOD , FtPOD , and FtCAT in the S5 group were the highest, which were 8.67, 7.65, and 6.08 times of those in the CK group, respectively. Conclusion Various combinations of phosphorus-solubilizing fungi exhibit differential capacities to enhance plant growth indices (including leaf area, plant height, and stem diameter), promote the accumulation of photosynthetic pigments, regulate osmotic pressure, and elevate antioxidant activity. Notably, The three fungal combinations (S7) were prone to cause a certain degree of antagonism, leading to suboptimal performances in certain biochemical indicators, such as free proline and POD levels. Our study pointed out that the S5 group inoculated with A. tubingensis and A. niger had the best overall effect. These experimental results provided a theoretical basis for the selection and development of artificial cultivation of F. taipaiensis P. Y. Li.

Purpose Clinical genetic testing is increasingly integrated in managing and diagnosing cardiac conditions and disease. It is important to identify ongoing challenges. This study aimed to better understand how genetic testing is integrated into pediatric cardiac care and identify barriers and opportunities for improvement. Methods We conducted qualitative interviews with pediatric cardiology clinicians (N = 12). Following a journey mapping approach to data analysis, we described genetic testing workflow phases, participants’ thoughts and behaviors within each phase, and barriers and opportunities for improvement. Results Participants described several challenges across the genetic testing workflow, from identifying patients for testing to disclosing results to the patients. Testing logistics, decision-making, and collaboration emerged as the most prominent challenges. Variation remains in the utilization of genetic testing, partially driven by case complexity and type of testing and attributable to other factors, like the level of interaction with genetics experts and inconsistent processes within the electronic medical record. Conclusion Clinical genetic pediatric cardiology requires more systematic integration of genetic testing and transparent processes. Major opportunities include the interplay between clinicians, genetic experts, and the EMR. Incorporating process mapping results into clinical logistics may eradicate some barriers experienced by pediatric cardiologists and increase clinical efficiency.

Background: Cancer rates are rising rapidly, causing global mortality. According to the World Health Organization (WHO), 9.9 million people died from cancer in 2020. Machine learning (ML) helps identify cancer early, reducing deaths. An MLbased cancer diagnostic model can use the patient’s genetic information, such as microarray data. Microarray data are high dimensional, which can degrade the performance of the ML-based models. For this, feature selection becomes essential. Methods: Swarm Optimization Algorithm (SSA), Improved Maximum Relevance and Minimum Redundancy (IMRMR), and Boruta form the basis of this work’s MLbased model BIMSSA. The BIMSSA model implements a pipelined feature selection method to effectively handle high-dimensional microarray data. Initially, Boruta and IMRMR were applied to extract relevant gene expression aspects. Then, SSA was implemented to optimize feature size. To optimize feature space, five separate machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), Extreme Learning Machine (ELM), AdaBoost, and XGBoost, were applied as the base learners. Then, majority voting was used to build an ensemble of the top three algorithms. The ensemble ML-based model BIMSSA was evaluated using microarray data from four different cancer types: Adult acute lymphoblastic leukemia and Acute myelogenous leukemia (ALL-AML), Lymphoma, Mixed-lineage leukemia (MLL), and Small round blue cell tumors (SRBCT). Results: In terms of accuracy, the proposed BIMSSA (Boruta + IMRMR + SSA) achieved 96.7% for ALL-AML, 96.2% for Lymphoma, 95.1% for MLL, and 97.1% for the SRBCT cancer datasets, according to the empirical evaluations. Conclusion: The results show that the proposed approach can accurately predict different forms of cancer, which is useful for both physicians and researchers.

Background The realization of many protein functions requires binding with ligands. As a significant protein-binding ligand, ATP plays a crucial role in various biological processes. Currently, the precise prediction of ATP binding residues remains challenging. Methods Based on the sequence information, this paper introduces a method called S-DCNN for predicting ATP binding residues, utilizing a deep convolutional neural network (DCNN) enhanced with the synthetic minority over-sampling technique (SMOTE). Results The incorporation of additional feature parameters such as dihedral angles, energy, and propensity factors into the standard parameter set resulted in a significant enhancement in prediction accuracy on the ATP-289 dataset. The S-DCNN achieved the highest Matthews correlation coefficient value of 0.5031 and an accuracy rate of 97.06% on an independent test set. Furthermore, when applied to the ATP-221 and ATP-388 datasets for validation, the S-DCNN outperformed existing methods on ATP-221 and performed comparably to other methods on ATP-388 during independent testing. Conclusion Our experimental results underscore the efficacy of the S-DCNN in accurately predicting ATP binding residues, establishing it as a potent tool in the prediction of ATP binding residues.

The Ogura cytoplasmic male sterility (CMS) line of Brassica napus has gained significant attention for its use in harnessing heterosis. It remains unaffected by temperature and environment and is thorough and stable. The Ogura cytoplasmic restorer line of Brassica napus is derived from the distant hybridization of Raphanus sativus L. and B. napus , but it carried a large number of radish fragments into Brassica napus , because there is no homologous allele of the restorer gene in B. napus , transferring it becomes challenging. In this study, the double haploid induction line in B. napus was used as the male parent for hybridization with the Ogura CMS of B. napus. Surprisingly, fertile plants appeared in the offspring. Further analysis revealed that the cytoplasmic type, ploidy, and chromosome number of the fertile offspring were consistent with the sterile female parent. Moreover, the mitochondrial genome similarity between the fertile offspring and the sterile female parent was 97.7% indicates that the cytoplasm of the two is the same, while the nuclear gene difference between fertile offspring and sterile female parent was only 10.33%, indicates that new genes appeared in the offspring. To further investigate and locate the restorer gene, the BSA method was employed to construct extreme mixed pools. As a result, the restorer gene was mapped to three positions: A09 chromosome 10.99–17.20 Mb, C03 chromosome 5.07–5.34 Mb, and C09 chromosome 18.78–36.60 Mb. The experimental results have proved that induction does produce restorer genes. The induction of the Ogura CMS restorer gene through DH induction line provides a promising new approach for harnessing heterosis in B. napus .

Prostate cancer (PCa) is a common and serious health issue among older men globally. Metabolic reprogramming, particularly involving lactate and mitochondria, plays a key role in PCa progression, but studies linking these factors to prognosis are limited. To identify novel prognostic markers of PCa based on lactate-mitochondria-related genes (LMRGs), RNA sequencing data and clinical information of PCa from The Cancer Genome Atlas (TCGA) and the cBioPortal database were used to construct a lactate-mitochondria-related risk signature. Here, we established a novel nine-LMRG risk signature for PCa, and Kaplan-Meier curves confirmed a worse prognosis for high-risk subgroups in the TCGA dataset. Meanwhile, a nomogram that effectively predicts the prognosis of PCa patients was also constructed. Next, close associations between the lactate-mitochondria-related signature and the immune microenvironment were examined to clarify the role of LMRGs in shaping the immune landscape. Furthermore, as the only lactate-related gene among the nine key prognostic risk genes, myeloperoxidase (MPO) was identified as a key factor that mediates lactate production in vitro and in vivo through attenuation of the glycolytic pathway. More importantly, MPO significantly inhibited PCa cell migration, invasion, and epithelial–mesenchymal transition (EMT), indicating its potential as an anticancer gene. Additionally, PCa with high MPO expression is highly sensitive to chemotherapeutic agents and mitochondrial inhibitors, highlighting its potential as an improved therapeutic strategy for PCa management.

Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and it plays an important role in omics research. Therefore, this study aimed to analyze RNA-seq data by ML method to identify differentially expressed genes (DEGs) affecting IMF deposition in pigs. In this study, a total of 74 RNA-seq data from muscle tissue samples were used. A total of 155 DEGs were identified using a limma package between the two groups. 100 and 11 significant genes were identified by support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) models, respectively. A total of six intersecting genes were in both models. KEGG pathway enrichment analysis of the intersecting genes revealed that these genes were enriched in pathways associated with lipid deposition. These pathways include α-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism. Four key genes affecting intramuscular fat deposition, PLA2G6, MPV17, NUDT2 , and ND4L , were identified based on significant pathways. The results of this study are important for the elucidation of the molecular regulatory mechanism of intramuscular fat deposition and the effective improvement of IMF content in pigs.

Social media sites like X (formerly Twitter) increasingly serve as spaces for the public to discuss controversial topics. Social media can spark extreme viewpoints and spread biased or inaccurate information while simultaneously allowing for debate around policy-relevant topics. The arrest of Joseph J. DeAngelo in April 2018 ignited a barrage of social media conversations on how DNA and genetic genealogy led to the suspect. These conversations continued over the following years as policies changed and as the use of the approach expanded. We examined social media coverage of investigative genetic genealogy (IGG) to characterize the volume and temporal patterns in the topics and sentiments of these public conversations. First, using a data analytics tool Brandwatch Consumer Research, we built flexible search strings to collect tweets from the social media platform Twitter/X for IGG-relevant content published from 2018 to 2022, resulting in 24,209 tweets. Second, we applied informatics tools to the dataset to generate topic clusters and analyze trends in cluster volume and distribution over time to define the top 25 peaks in tweet volume, representing the 25 events that generated the highest volume of conversation over the 5-year period. Third, drawing on the contextual framework of key IGG events, we selected three of the top ten events to code for sentiment along with a randomly sampled subset of tweets across the timeframe. Qualitative coding for position on IGG revealed a majority of tweets were supportive of the use of IGG, but key concerns were also voiced about the ethics of IGG. Over a third of conversations on Twitter/X were on either cases solved or suggestions for use of IGG. We archived the social media data for future research. These data highlight key areas of public support and concern within IGG processes and across application contexts.

Echinococcosis is a zoonotic parasitic disease caused by the larvae of echinococcus tapeworms infesting the human body. Drug combination therapy is highly valued for the treatment of echinococcosis because of its potential to overcome resistance and enhance the response to existing drugs. Traditional methods of identifying drug combinations via biological experimentation is costly and time-consuming. Besides, the scarcity of existing drug combinations for echinococcosis hinders the development of computational methods. In this study, we propose a transfer learning-based model, namely TransferBAN-Syn, to identify synergistic drug combinations against echinococcosis based on abundant information of drug combinations against parasitic diseases. To the best of our knowledge, this is the first work that leverages transfer learning to improve prediction accuracy with limited drug combination data in echinococcosis treatment. Specifically, TransferBAN-Syn contains a drug interaction feature representation module, a disease feature representation module, and a prediction module, where the bilinear attention network is employed in the drug interaction feature representation module to deeply extract the fusion feature of drug combinations. Besides, we construct a special dataset with multi-source information and drug combinations for parasitic diseases, including 21 parasitic diseases and echinococcosis. TransferBAN-Syn is designed and initially trained on the abundant data from the 21 parasitic diseases, which serves as the source domain. The parameters in the feature representation modules of drug interactions and diseases are preserved from this source domain, and those in the prediction module are then fine-tuned to specifically identify the synergistic drug combinations for echinococcosis in the target domain. Comparison experiments have shown that TransferBAN-Syn not only improves the accuracy of predicting echinococcosis drug combinations but also enhances generalizability. Furthermore, TransferBAN-Syn identifies potential drug combinations that hold promise in the treatment of echinococcosis. TransferBAN-Syn not only offers new synergistic drug combinations for echinococcosis but also provides a novel approach for predicting potential drug pairs for diseases with limited combination data.

Backfat thickness (BFT) and feed conversion ratio (FCR) are important commercial traits in the pig industry. With the increasing demand for human health and meat production, identifying functional genomic regions and genes associated with these commercial traits is critical for enhancing production efficiency. In this research, we conducted a genome-wide association study (GWAS) on a Landrace population comprising 4,295 individuals with chip data for BFT and FCR. Our analysis revealed a total of 118 genome-wide significant signals located on chromosomes SSC1, SSC2, SSC7, SSC12, and SSC13, respectively. Furthermore, we identified 10 potential regions associated with the two traits and annotated the genes within these regions. In addition, enrichment analysis was also performed. Notably, candidate genes such as SHANK2 , KCNQ1 , and ABL1 were found to be associated with BFT, whereas NAP1L4 , LSP1 , and PPFIA1 genes were related to the FCR. Our findings provide valuable insights into the genetic architecture of these two traits and offer guidance for future pig breeding efforts.

Background Triple-negative breast cancer (TNBC) is a heterogeneous disease with a worse prognosis. Despite ongoing efforts, existing therapeutic approaches show limited success in improving early recurrence and survival outcomes for TNBC patients. Therefore, there is an urgent need to discover novel and targeted therapeutic strategies, particularly those focusing on the immune infiltrate in TNBC, to enhance diagnosis and prognosis for affected individuals. Methods The gene co-expression network and gene ontology analyses were used to identify the differential modules and their functions based on the GEO dataset of GSE76275. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to describe the correlation patterns among genes across multiple samples. Subsequently, we identified key genes in TNBC by assessing genes with an absolute correlation coefficient greater than 0.80 within the eigengene of the enriched module that were significantly associated with breast cancer subtypes. The diagnostic potential of these key genes was evaluated using receiver operating characteristic (ROC) curve analysis with three-fold cross-validation. Furthermore, to gain insights into the prognostic implications of these key genes, we performed relapse-free survival (RFS) analysis using the Kaplan-Meier plotter online tool. CIBERSORT analysis was used to characterize the composition of immune cells within complex tissues based on gene expression data, typically derived from bulk RNA sequencing or microarray datasets. Therefore, we explored the immune microenvironment differences between TNBC and non-TNBC by leveraging the CIBERSORT algorithm. This enabled us to estimate the immune cell compositions in the breast cancer tissue of the two subtypes. Lastly, we identified key transcription factors involved in macrophage infiltration and polarization in breast cancer using transcription factor enrichment analysis integrated with orthogonal omics. Results The gene co-expression network and gene ontology analyses revealed 19 modules identified using the dataset GSE76275. Of these, modules 5, 11, and 12 showed significant differences between in breast cancer tissue between TNBC and non-TNBC. Notably, module 11 showed significant enrichment in the WNT signaling pathway, while module 12 demonstrated enrichment in lipid/fatty acid metabolism pathways. Subsequently, we identified SHC4/KCNK5 and ABCC11/ABCA12 as key genes in module 11 and module 12, respectively. These key genes proved to be crucial in accurately distinguishing between TNBC and non-TNBC, as evidenced by the promising average AUC value of 0.963 obtained from the logistic regression model based on their combinations. Furthermore, we found compelling evidence indicating the prognostic significance of three key genes, KCNK5, ABCC11, and ABCA12, in TNBC. Finally, we also identified the immune cell compositions in breast cancer tissue between TNBC and non-TNBC. Our findings revealed a notable increase in M0 and M1 macrophages in TNBC compared to non-TNBC, while M2 macrophages exhibited a significant reduction in TNBC. Particularly intriguing discovery emerged with respect to the transcription factor FOXM1, which demonstrated a significant regulatory role in genes positively correlated with the proportions of M0 and M1 macrophages, while displaying a negative correlation with the proportion of M2 macrophages in breast cancer tissue. Conclusion Our research provides new insight into the biomarkers and immune infiltration of TNBC, which could be useful for clinical diagnosis of TNBC.

The Darwin Tree of Life (DToL) project aims to generate high-quality reference genomes for all eukaryotic organisms in Britain and Ireland. At the time of writing, PacBio HiFi reads are generated for all samples using the Sequel IIe systems by the Wellcome Sanger Institute’s Scientific Operations teams, however we expect lessons from this work to apply directly to the Revio system too, as core principles of SMRT sequencing remain the same. We observed that HiFi yield is highly variable for DToL samples. We have investigated what drives this variation, and potential mitigations. To support these investigations a number of controls were evaluated to ensure that the library and sequencing preparation procedures, reagents, consumables, and Sequel IIe instruments, were performing as expected. Our findings support that a primary factor driving variability in HiFi yield is the quality of the DNA prior to library construction, e.g., purity, size, and damage. We investigated whether quality assessment assays could link measurable DNA damage or purity to sequencing yield. Some correlation could be established, however no assay was predictive of sequencing yield for all samples, indicating that the variability is driven by multiple factors that may interact. We demonstrate that contaminants present in some samples are the cause of very low HiFi yield, and show that these contaminants can negatively affect the PacBio internal sequencing control and samples multiplexed on the same SMRT Cell. We found that consistently high yields could be obtained if an amplification workflow was utilised, namely PacBio’s ultra-low input library preparation protocol.

Marfan syndrome (MFS) is an inherited disorder that affects the connective tissues and mainly presents in the bones, eyes, and cardiovascular system, etc. Aortic pathology is the leading cause of death in patients with Marfan syndrome. The fibrillin-1 gene ( FBN1 ) is a major gene involved in the pathogenesis of MFS. It has been shown that the aortic pathogenesis of MFS is associated with the imbalances of the transforming growth factor-beta (TGF-β) signaling pathway. However, the exact molecular mechanism of MFS is unclear. Animal models may partially mimic MFS and are vital to the study of MFS. Several species of animals have been used for MFS studies, including chicks, cattle, mice, pigs, zebrafishes, Caenorhabditis elegans , and rabbits. These models were developed spontaneously or in combination with genetic engineering techniques. This review is to describe the TGF-β signaling pathway in MFS and the potential application of animal models to provide new therapeutic strategies for patients with MFS.

Introduction Stem cells derived from adipose tissue are gaining popularity in the field of regenerative medicine due to their adaptability and clinical potential. Their rapid growth, ability to differentiate, and easy extraction with minimal complications make adipose-derived stem cells (ADSCs) a promising option for many treatments, particularly those targeting bone-related diseases. This study analyzed gene expression in canine ADSCs subjected to long-term culture and osteogenic differentiation. Methods ADSCs were isolated from discarded surgical waste and cultured for 14 days with and without differentiation media to assess osteogenic changes. RNA sequencing (RNA-seq) and bioinformatical analysis were performed to obtain comprehensive transcriptomic data. A total of 17793 genes were detected and GO enrichment analysis was performed on the differentially expressed genes to identify significantly up- and downregulated Biological Process (BP) GO terms across each comparison. Results The upregulation of apoptosis-regulating genes and genes related to circulatory system development suggest an induction of these processes, while the downregulation of neurogenesis and gliogenesis genes points to reciprocal regulation during osteogenic differentiation of canine ADSCs. Discussion These findings underscore the potential of ADSCs in bone regeneration and offer valuable insights for advancing tissue engineering, however further studies, including proteomic analyses, are needed to confirm these patterns and their biological significance.

Background Endometriosis, a prevalent chronic gynecological condition, is frequently associated with infertility and pelvic pain. Despite numerous studies indicating a correlation between epigenetic regulation and endometriosis, its precise genetic etiology remains elusive. Methyltransferase-like 14 (METTL14), a crucial component of the N6-methyladenosine (m⁶A) RNA methyltransferase complex and an RNA binding scaffold, is known to play a pivotal role in various human diseases. The possibility that single nucleotide polymorphisms (SNPs) in the METTL14 gene contribute to susceptibility of endometriosis has not been thoroughly investigated. Methods We assessed the genotype frequencies of five potential functional METTL14 SNPs (rs298982 G>A, rs62328061A>G, rs9884978G>A, rs4834698C>T, and rs1064034A>T) in a Chinese population consisting of 458 patients with ovarian endometriosis and 462 healthy controls. We employed unconditional logistic regression and stratified analyses to evaluate their genotypic associations with the risk of ovarian endometriosis. Results Among the five SNPs examined, we found that the rs298982 A allele was significantly associated with increased risk, whereas the rs62328061 G allele was linked to a decreased risk of ovarian endometriosis. Individuals harboring two unfavorable genotypes demonstrated a significantly elevated risk of ovarian endometriosis (adjusted odds ratio (AOR) = 1.57, 95% confidence interval (CI) = 1.16–2.13, P = 0.004) compared with those with no risk genotypes. Stratified analysis revealed the risk effect of rs298982 GA/AA genotypes in the gravidity≤1, parity≤1, rASRM stage I, and rASRM stage II + III + IVsubgroups. Haplotype analysis showed that individuals with the GATAA haplotype were at higher risk of ovarian endometriosis (AOR = 5.54, 95% CI = 1.63–18.87, P = 0.006), whereas the AGTTG haplotype exhibited protective effects (AOR = 0.55, 95% CI = 0.31–0.97, P = 0.039) compared with wild-type GACAG haplotype carriers. Additionally, Bayesian false discovery probability and false positive report probability analysis confirmed the robustness of the significant findings. Expression quantitative trait loci analysis revealed a significant association between the rs9884978 GA/AA genotypes and elevated METTL14 mRNA levels in fibroblasts and adrenal gland. Conversely, the rs298982 GA/GG genotypes were significantly associated with reduced METTL14 mRNA levels in the nucleus accumbens and frontal cortex. Conclusion Our results demonstrate that METTL14 polymorphisms are associated with susceptibility to ovarian endometriosis among Chinese women.

Background Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by a dismal prognosis. Treatment outcomes exhibit substantial variability across patients, underscoring the urgent need for robust predictive models to effectively estimate survival probabilities and therapeutic responses in PDAC. Methods Metabolic and immune-related genes exhibiting differential expression were identified using the TCGA-PDAC and GTEx datasets. A genetic prognostic model was developed via univariable Cox regression analysis on a training cohort. Predictive accuracy was assessed using Kaplan-Meier (K-M) curves, calibration plots, and ROC curves. Additional analyses, including GSAE and immune cell infiltration studies, were conducted to explore relevant biological mechanisms and predict therapeutic efficacy. Results An 8-gene prognostic model (AK2, CXCL11, TYK2, ANGPT4, IL20RA, MET, ENPP6, and CA12) was established. Three genes (AK2, ENPP6, and CA12) were associated with metabolism, while the others were immune-related. Most genes correlated with poor prognosis. Validation in TCGA-PDAC and GSE57495 datasets demonstrated robust performance, with AUC values for 1-, 3-, and 5-year OS exceeding 0.7. The model also effectively predicted responses to adjuvant therapy. Conclusion This 8-gene signature enhances prognostic accuracy and therapeutic decision-making in PDAC, offering valuable insights for clinical applications and personalized treatment strategies.

Introduction The blue mussel is one of the major aquaculture species worldwide. In France, this species faces a significant threat from infectious disease outbreaks in both mussel farms and the natural environment over the past decade. Diseases caused by various pathogens, particularly Vibrio spp., have posed a significant challenge to the mussel industry. Genetic improvement of disease resistance can be an effective approach to overcoming this issue. Methods In this work, we tested genomic selection in the blue mussel (Mytilus edulis) to understand the genetic basis of resistance to one pathogenic strain of Vibrio splendidus (strain 14/053 2T1) and to predict the accuracy of selection using both pedigree and genomic information. Additionally, we performed a genome-wide association study (GWAS) to identify putative QTLs underlying disease resistance. We conducted an experimental infection involving 2,280 mussels sampled from 24 half-sib families containing each two full-sib families which were injected with V. splendidus. Dead and survivor mussels were all sampled, and among them, 348 dead and 348 surviving mussels were genotyped using a recently published multi-species medium-density 60K SNP array. Results From potentially 23.5K SNPs for M. edulis present on the array, we identified 3,406 high-quality SNPs, out of which 2,204 SNPs were successfully mapped onto the recently published reference genome. Heritability for resistance to V. splendidus was moderate ranging from 0.22 to 0.31 for a pedigree-based model and from 0.28 to 0.36 for a genomic-based model. Discussion GWAS revealed the polygenic architecture of the resistance trait in the blue mussel. The genomic selection models studied showed overall better performance than the pedigree-based model in terms of accuracy of breeding values prediction. This work provides insights into the genetic basis of resistance to V. splendidus and exemplifies the potential of genomic selection in family-based breeding programs in M. edulis.

Background KBG syndrome (KBGS, OMIM: 148050) is a rare genetic disorder characterized by macrodontia, short stature, skeletal abnormalities, and neurological manifestations. The objective of this study is to investigate a case of KBG syndrome caused by a novel frameshift mutation in ANKRD11. Methods and results We present the case of an 18-year-old Chinese male exhibiting characteristic features including a triangular face, micrognathia, hypertelorism, macrodontia, bushy eyebrows, prominent ears, short stature, low hairline, delayed cognitive development, and scoliosis. Whole exome sequencing identified a novel frameshift variant in the ANKRD11 gene which ultimately led to the diagnosis of KBG syndrome. Conclusion In this study we have identified a previously unreported frameshift variant (NM_013275.6:c.2589dup) in ANKRD11 that causes KBG syndrome. This finding expands both the molecular and clinical spectrum of this rare genetic disease.