J Cyst Fibros. 2025 Jan 14:S1569-1993(25)00010-4. doi: 10.1016/j.jcf.2025.01.010. Online ahead of print.

ABSTRACT

BACKGROUND: Whether improvements in gastrointestinal (GI) symptoms observed with Elexacaftor/Tezacaftor/Ivacaftor (ETI) treatment are sustained in the longer-term requires exploration. This study investigated how GI-symptoms change with longer-term ETI use in pancreatic insufficient adults with cystic fibrosis (awCF).

METHODS: Participants completed up to three abdominal symptom questionnaires, employing the validated CFAbd-Score. Changes in total CFAbd-Score and its five domains, pain, gastroesophageal reflux-disease (GERD), disorders of bowel movement (DBM), disorders of appetite (DA) and quality of life (QOL), were analysed pre-ETI (T0) and at ≤1.5 years (T1) and 2-4 years of ETI-therapy (T2).

RESULTS: A total of 165 CFAbd-Scores from 68 participants were analysed (median age: 34 years; IQR: 28-39). Total CFAbd-Score significantly (p < 0.05) and clinically meaningfully decreased from 20.4 ± 1.6 pre-ETI (median:40 weeks pre-treatment) to 15.3 ± 1.9 and 16.8 ± 1.6 at T1 (median: 25 weeks of ETI) and T2 (median: 148 weeks of ETI), respectively. The CFAbd-Score´s domains DA and QoL only significantly decreased between T0 and T1, whereas DBM only significantly decreased after 2-4 years of ETI therapy (T2). GERD scores were significantly lower at both T1 and T2.

CONCLUSION: While GI symptoms in awCF significantly improve within the first 1.5 years of ETI-therapy, they appear to somewhat wane with longer-term use, despite GI-symptom burden still being lower compared to pre-ETI. However, we cannot differentiate whether this results from reduced adherence, a decrease in ETI effects, or long-term changes in diet, gut microbiota or symptom perception. The longer-term impact of ETI and other potential modulator therapies on GI symptoms requires ongoing monitoring.

PMID:39814671 | DOI:10.1016/j.jcf.2025.01.010

Exp Clin Endocrinol Diabetes. 2025 Jan 15. doi: 10.1055/a-2460-6977. Online ahead of print.

ABSTRACT

The issue of a possible association between Shwachman-Diamond Syndrome and diabetes has been debated for many years. This review updates the Italian Shwachman-Diamond registry, confirming our previous findings that suggest that these patients might be at higher risk of developing diabetes, particularly type 1. These data are of relevance in the clinical follow-up of patients in everyday life, emphasizing the need for early diagnosis and timely intervention.

PMID:39814041 | DOI:10.1055/a-2460-6977

BioData Min. 2025 Jan 16;18(1):4. doi: 10.1186/s13040-024-00419-4.

ABSTRACT

BACKGROUND: Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures. Leveraging this progress, we developed a novel multi-view learning model.

RESULTS: We introduce a graph-integrated model that captures both local and global structural features of chemical compounds. In our model, graph attention layers are employed to effectively capture essential local structures by jointly considering atom and bond features, while multi-head attention layers extract important global features. We evaluated our model on nine MoleculeNet datasets, encompassing both classification and regression tasks, and compared its performance with state-of-the-art methods. Our model achieved an average area under the receiver operating characteristic (AUROC) of 0.822 and a root mean squared error (RMSE) of 1.133, representing a 3% improvement in AUROC and a 7% improvement in RMSE over state-of-the-art models in extensive seed testing.

CONCLUSION: MultiChem highlights the importance of integrating both local and global structural information in predicting molecular properties, while also assessing the stability of the models across multiple datasets using various random seed values.

IMPLEMENTATION: The codes are available at https://github.com/DMnBI/MultiChem .

PMID:39815309 | DOI:10.1186/s13040-024-00419-4

Respir Res. 2025 Jan 15;26(1):17. doi: 10.1186/s12931-024-03093-6.

ABSTRACT

H3 lysine 4 trimethylation (H3K4me3) modification and related regulators extensively regulate various crucial transcriptional courses in health and disease. However, the regulatory relationship between H3K4me3 modification and anti-tumor immunity has not been fully elucidated. We identified 72 independent prognostic genes of lung adenocarcinoma (LUAD) whose transcriptional expression were closely correlated with known 27 H3K4me3 regulators. We constructed three H3K4me3 modification patterns utilizing the expression profiles of the 72 genes, and patients classified in each pattern exhibited unique tumor immune infiltration characteristics. Using the principal component analysis (PCA) of H3K4me3-related patterns, we constructed a H3K4me3 risk score (H3K4me3-RS) system. The deep learning analysis using 12,159 cancer samples from 26 cancer types and 725 cancer samples from 5 immunotherapy cohorts revealed that H3K4me3-RS was significantly correlated with cancer immune tolerance and sensitivity. Importantly, this risk-score system showed satisfactory predictive performance for the ICB therapy responses of patients suffering from several cancer types, and we identified that SLAMF9 was one of the immunosuppressive phenotype and immunotherapy resistance-determined genes of H3K4me3-RS. The mice melanoma model showed Slamf9 knockdown remarkably restrained cancer progression and enhanced the efficacy of anti-CTLA-4 and anti-PD-L1 therapies by elevating CD8 + T cell infiltration. This study provided a new H3K4me3-associated biomarker system to predict tumor immunotherapy response and suggested the preclinical rationale for investigating the roles of SLAMF9 in cancer immunity regulation and treatment.

PMID:39815269 | DOI:10.1186/s12931-024-03093-6

BMC Med Inform Decis Mak. 2025 Jan 15;25(1):26. doi: 10.1186/s12911-025-02859-2.

ABSTRACT

PURPOSE: Identifying patients who may benefit from multiple drilling are crucial. Hence, the purpose of the study is to utilize radiomics and deep learning for predicting no-collapse survival in patients with femoral head osteonecrosis.

METHODS: Patients who underwent multiple drilling were enrolled. Radiomics and deep learning features were extracted from pelvic radiographs and selected by LASSO-COX regression, radiomics and DL signature were then built. The clinical variables were selected through univariate and multivariate Cox regression analysis, and the clinical, radiomics, DL and DLRC model were constructed. Model performance was evaluated using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curves, and Decision Curve Analysis (DCA).

RESULTS: A total of 144 patients (212 hips) were included in the study. ARCO classification, bone marrow edema, and combined necrotic angle were identified as independent risk factors for collapse. The DLRC model exhibited superior discrimination ability with higher C-index of 0.78 (95%CI: 0.73-0.84) and AUC values (0.83 and 0.87) than other models. The DLRC model demonstrated superior predictive performance with a higher C-index of 0.78 (95% CI: 0.73-0.84) and area under the curve (AUC) values of 0.83 for 3-year survival and 0.87 for 5-year survival, outperforming other models. The DLRC model also exhibited favorable calibration and clinical utility, with Kaplan-Meier survival curves revealing significant differences in survival rates between high-risk and low-risk cohorts.

CONCLUSION: This study introduces a novel approach that integrates radiomics and deep learning techniques and demonstrates superior predictive performance for no-collapse survival after multiple drilling. It offers enhanced discrimination ability, favorable calibration, and strong clinical utility, making it a valuable tool for stratifying patients into high-risk and low-risk groups. The model has the potential to provide personalized risk assessment, guiding treatment decisions and improving outcomes in patients with osteonecrosis of the femoral head.

PMID:39815247 | DOI:10.1186/s12911-025-02859-2

BMC Bioinformatics. 2025 Jan 16;26(1):17. doi: 10.1186/s12859-024-06020-0.

ABSTRACT

BACKGROUND: Pacific Biosciences (PacBio) circular consensus sequencing (CCS), also known as high fidelity (HiFi) technology, has revolutionized modern genomics by producing long (10 + kb) and highly accurate reads. This is achieved by sequencing circularized DNA molecules multiple times and combining them into a consensus sequence. Currently, the accuracy and quality value estimation provided by HiFi technology are more than sufficient for applications such as genome assembly and germline variant calling. However, there are limitations in the accuracy of the estimated quality scores when it comes to somatic variant calling on single reads.

RESULTS: To address the challenge of inaccurate quality scores for somatic variant calling, we introduce TopoQual, a novel tool designed to enhance the accuracy of base quality predictions. TopoQual leverages techniques including partial order alignments (POA), topologically parallel bases, and deep learning algorithms to polish consensus sequences. Our results demonstrate that TopoQual corrects approximately 31.9% of errors in PacBio consensus sequences. Additionally, it validates base qualities up to q59, which corresponds to one error in 0.9 million bases. These improvements will significantly enhance the reliability of somatic variant calling using HiFi data.

CONCLUSION: TopoQual represents a significant advancement in genomics by improving the accuracy of base quality predictions for PacBio HiFi sequencing data. By correcting a substantial proportion of errors and achieving high base quality validation, TopoQual enables confident and accurate somatic variant calling. This tool not only addresses a critical limitation of current HiFi technology but also opens new possibilities for precise genomic analysis in various research and clinical applications.

PMID:39815230 | DOI:10.1186/s12859-024-06020-0

BMC Med Res Methodol. 2025 Jan 15;25(1):10. doi: 10.1186/s12874-024-02430-z.

ABSTRACT

PURPOSE: The process of searching for and selecting clinical evidence for systematic reviews (SRs) or clinical guidelines is essential for researchers in Traditional Chinese medicine (TCM). However, this process is often time-consuming and resource-intensive. In this study, we introduce a novel precision-preferred comprehensive information extraction and selection procedure to enhance both the efficiency and accuracy of evidence selection for TCM practitioners.

METHODS: We integrated an established deep learning model (Evi-BERT combined rule-based method) with Boolean logic algorithms and an expanded retrieval strategy to automatically and accurately select potential evidence with minimal human intervention. The selection process is recorded in real-time, allowing researchers to backtrack and verify its accuracy. This innovative approach was tested on ten high-quality, randomly selected systematic reviews of TCM-related topics written in Chinese. To evaluate its effectiveness, we compared the screening time and accuracy of this approach with traditional evidence selection methods.

RESULTS: Our finding demonstrated that the new method accurately selected potential literature based on consistent criteria while significantly reducing the time required for the process. Additionally, in some cases, this approach identified a broader range of relevant evidence and enabled the tracking of selection progress for future reference. The study also revealed that traditional screening methods are often subjective and prone to errors, frequently resulting in the inclusion of literature that does not meet established standards. In contrast, our method offers a more accurate and efficient way to select clinical evidence for TCM practitioners, outperforming traditional manual approaches.

CONCLUSION: We proposed an innovative approach for selecting clinical evidence for TCM reviews and guidelines, aiming to reduce the workload for researchers. While this method showed promise in improving the efficiency and accuracy of evidence-based selection, its full potential required further validation. Additionally, it may serve as a useful tool for editors to assess manuscript quality in the future.

PMID:39815209 | DOI:10.1186/s12874-024-02430-z

Mol Imaging Biol. 2025 Jan 15. doi: 10.1007/s11307-025-01981-x. Online ahead of print.

ABSTRACT

PURPOSE: We aim to perform radiogenomic profiling of breast cancer tumors using dynamic contrast magnetic resonance imaging (MRI) for the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) genes.

METHODS: The dataset used in the current study consists of imaging data of 922 biopsy-confirmed invasive breast cancer patients with ER, PR, and HER2 gene mutation status. Breast MR images, including a T1-weighted pre-contrast sequence and three post-contrast sequences, were enrolled for analysis. All images were corrected using N4 bias correction algorithms. Based on all images and tumor masks, a bounding box of 128 × 128 × 68 was chosen to include all tumor regions. All networks were implemented in 3D fashion with input sizes of 128 × 128 × 68, and four images were input to each network for multi-channel analysis. Data were randomly split into train/validation (80%) and test set (20%) with stratification in class (patient-wise), and all metrics were reported in 20% of the untouched test dataset.

RESULTS: For ER prediction, SEResNet50 achieved an AUC mean of 0.695 (CI95%: 0.610-0.775), a sensitivity of 0.564, and a specificity of 0.787. For PR prediction, ResNet34 achieved an AUC mean of 0.658 (95% CI: 0.573-0.741), a sensitivity of 0.593, and a specificity of 0.734. For HER2 prediction, SEResNext101 achieved an AUC mean of 0.698 (95% CI: 0.560-0.822), a sensitivity of 0.750, and a specificity of 0.625.

CONCLUSION: The current study demonstrated the feasibility of imaging gene-phenotype decoding in breast tumors using MR images and deep learning algorithms with moderate performance.

PMID:39815134 | DOI:10.1007/s11307-025-01981-x

J Gastroenterol. 2025 Jan 15. doi: 10.1007/s00535-024-02209-5. Online ahead of print.

ABSTRACT

BACKGROUND: The automated classification of Helicobacter pylori infection status is gaining attention, distinguishing among uninfected (no history of H. pylori infection), current infection, and post-eradication. However, this classification has relatively low performance, primarily due to the intricate nature of the task. This study aims to develop a new multistage deep learning method for automatically classifying H. pylori infection status.

METHODS: The proposed multistage deep learning method was developed using a training set of 538 subjects, then tested on a validation set of 146 subjects. The classification performance of this new method was compared with the findings of four physicians.

RESULTS: The accuracy of our method was 87.7%, 83.6%, and 95.9% for uninfected, post-eradication, and currently infected cases, respectively, whereas that of the physicians was 81.7%, 76.5%, and 90.3%, respectively. When including the patient's H. pylori eradication history information, the classification accuracy of the method was 92.5%, 91.1%, and 98.6% for uninfected, post-eradication, and currently infected cases, respectively, whereas that of the physicians was 85.6%, 85.1%, and 97.4%, respectively.

CONCLUSION: The new multistage deep learning method shows potential for an innovative approach to gastric cancer screening. It can evaluate individual subjects' cancer risk based on endoscopic images and reduce the burden of physicians.

PMID:39815116 | DOI:10.1007/s00535-024-02209-5

Eur J Hum Genet. 2025 Jan 15. doi: 10.1038/s41431-025-01787-z. Online ahead of print.

ABSTRACT

The facial gestalt (overall facial morphology) is a characteristic clinical feature in many genetic disorders that is often essential for suspecting and establishing a specific diagnosis. Therefore, publishing images of individuals affected by pathogenic variants in disease-associated genes has been an important part of scientific communication. Furthermore, medical imaging data is also crucial for teaching and training deep-learning models such as GestaltMatcher. However, medical data is often sparsely available, and sharing patient images involves risks related to privacy and re-identification. Therefore, we explored whether generative neural networks can be used to synthesize accurate portraits for rare disorders. We modified a StyleGAN architecture and trained it to produce artificial condition-specific portraits for multiple disorders. In addition, we present a technique that generates a sharp and detailed average patient portrait for a given disorder. We trained our GestaltGAN on the 20 most frequent disorders from the GestaltMatcher database. We used REAL-ESRGAN to increase the resolution of portraits from the training data with low-quality and colorized black-and-white images. To augment the model's understanding of human facial features, an unaffected class was introduced to the training data. We tested the validity of our generated portraits with 63 human experts. Our findings demonstrate the model's proficiency in generating photorealistic portraits that capture the characteristic features of a disorder while preserving patient privacy. Overall, the output from our approach holds promise for various applications, including visualizations for publications and educational materials and augmenting training data for deep learning.

PMID:39815041 | DOI:10.1038/s41431-025-01787-z

Geroscience. 2025 Jan 15. doi: 10.1007/s11357-024-01502-8. Online ahead of print.

ABSTRACT

Aging is influenced by a complex interplay of multifarious factors, including an individual's genetics, environment, and lifestyle. Notably, high altitude may impact aging and age-related diseases through exposures such as hypoxia and ultraviolet (UV) radiation. To investigate this, we mined risk exposure data (summary exposure value), disease burden data (disability-adjusted life years (DALYs)), and death rates and life expectancy from the Global Health Data Exchange (GHDx) and National Data Management Center for Health of Ethiopia for each subnational region of Ethiopia, a country with considerable differences in the living altitude. We conducted a cross-sectional clinical trial involving 227 highland and 202 lowland dwellers from the Tigray region in Northern Ethiopia to gain a general insight into the biological aging at high altitudes. Notably, we observed significantly lower risk exposure rates and a reduced disease burden as well as increased life expectancy by lower mortality rates in higher-altitude regions of Ethiopia. When assessing biological aging using facial photographs, we found a faster rate of aging with increasing elevation, likely due to greater UV exposure. Conversely, analysis of nuclear morphologies of peripheral blood mononuclear cells (PBMCs) in blood smears with five different senescence predictors revealed a significant decrease in DNA damage-induced senescence in both monocytes and lymphocytes with increasing elevation. Overall, our findings suggest that disease and DNA damage-induced senescence decreases with altitude in agreement with the idea that oxidative stress may drive aging.

PMID:39815037 | DOI:10.1007/s11357-024-01502-8

Nature. 2025 Jan 15. doi: 10.1038/s41586-024-08435-4. Online ahead of print.

ABSTRACT

Molecular recognition events between proteins drive biological processes in living systems1. However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules2-5. Despite recent advances, computational tools for the design of new chemically induced protein interactions have remained a challenging task for the field6,7. Here we present a computational strategy for the design of proteins that target neosurfaces, that is, surfaces arising from protein-ligand complexes. To develop this strategy, we leveraged a geometric deep learning approach based on learned molecular surface representations8,9 and experimentally validated binders against three drug-bound protein complexes: Bcl2-venetoclax, DB3-progesterone and PDF1-actinonin. All binders demonstrated high affinities and accurate specificities, as assessed by mutational and structural characterization. Remarkably, surface fingerprints previously trained only on proteins could be applied to neosurfaces induced by interactions with small molecules, providing a powerful demonstration of generalizability that is uncommon in other deep learning approaches. We anticipate that such designed chemically induced protein interactions will have the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells for innovative drug-controlled cell-based therapies10.

PMID:39814890 | DOI:10.1038/s41586-024-08435-4

Probiotics Antimicrob Proteins. 2025 Jan 16. doi: 10.1007/s12602-025-10451-3. Online ahead of print.

ABSTRACT

The emergence of multidrug-resistant pathogens presents a significant global health challenge, which is primarily fuelled by overuse and misuse of antibiotics. Bacteria-derived antimicrobial metabolites offer a promising alternative strategy for combating antimicrobial resistance issues. Bacillus velezensis PD9 (BvPD9), isolated from stingless bee propolis, has been reported to have antibacterial activities against methicillin-resistant Staphylococcus aureus (MRSA). This study aimed to characterise and identify the antimicrobial compounds (AMCs) synthesised by BvPD9 through integration of genome mining and liquid chromatography-mass spectrometry (LC-MS) analysis. The whole-genome sequence of BvPD9 contained 4,263,351 base pairs and 4101 protein-coding sequences, with 12 potential AMC biosynthetic gene clusters. Comparative genomic analysis highlighted the unique profile of BvPD9 that possesses the largest number of unknown proteins, indicating significant potential for further exploration. The combined genomics-metabolic profiling uncovered five AMCs in BvPD9 extract, including bacillibactin, bacilysin, surfactin A, fengycin A, and bacillomycin D. The extract exhibited a broad antibacterial spectrum against 25 pathogenic bacteria, including both Gram-positive and Gram-negative bacteria, with the lowest minimum inhibitory concentration (MIC, 0.032 mg/ml) against S. epidermidis ATCC 12228, and the lowest minimum bactericidal concentration (MBC; 0.128 mg/ml) against MRSA ATCC 700699 and Aeromonas hydrophilia. The robust stability of BvPD9 extract was demonstrated at high temperatures, over a wide range of pH conditions (6 to 12) and in the presence of various hydrolytic enzymes. Additionally, the extract showed 50% haemolytic and cytotoxicity activity at 0.158 and 0.250 mg/ml, respectively. These characteristics suggest potential applications of BvPD9 metabolites for tackling antimicrobial resistance and its applicability across diverse industries.

PMID:39815115 | DOI:10.1007/s12602-025-10451-3

Nature. 2025 Jan 15. doi: 10.1038/s41586-024-08522-6. Online ahead of print.

NO ABSTRACT

PMID:39814901 | DOI:10.1038/s41586-024-08522-6

Nature. 2025 Jan 15. doi: 10.1038/s41586-024-08426-5. Online ahead of print.

ABSTRACT

Tertiary lymphoid structures (TLSs) are de novo ectopic lymphoid aggregates that regulate immunity in chronically inflamed tissues, including tumours. Although TLSs form due to inflammation-triggered activation of the lymphotoxin (LT)-LTβ receptor (LTβR) pathway1, the inflammatory signals and cells that induce TLSs remain incompletely identified. Here we show that interleukin-33 (IL-33), the alarmin released by inflamed tissues2, induces TLSs. In mice, Il33 deficiency severely attenuates inflammation- and LTβR-activation-induced TLSs in models of colitis and pancreatic ductal adenocarcinoma (PDAC). In PDAC, the alarmin domain of IL-33 activates group 2 innate lymphoid cells (ILC2s) expressing LT that engage putative LTβR+ myeloid organizer cells to initiate tertiary lymphoneogenesis. Notably, lymphoneogenic ILC2s migrate to PDACs from the gut, can be mobilized to PDACs in different tissues and are modulated by gut microbiota. Furthermore, we detect putative lymphoneogenic ILC2s and IL-33-expressing cells within TLSs in human PDAC that correlate with improved prognosis. To harness this lymphoneogenic pathway for immunotherapy, we engineer a recombinant human IL-33 protein that expands intratumoural lymphoneogenic ILC2s and TLSs and demonstrates enhanced anti-tumour activity in PDAC mice. In summary, we identify the molecules and cells of a druggable pathway that induces inflammation-triggered TLSs. More broadly, we reveal a lymphoneogenic function for alarmins and ILC2s.

PMID:39814891 | DOI:10.1038/s41586-024-08426-5

Nat Commun. 2025 Jan 15;16(1):684. doi: 10.1038/s41467-025-56017-3.

NO ABSTRACT

PMID:39814735 | DOI:10.1038/s41467-025-56017-3

Nat Commun. 2025 Jan 15;16(1):709. doi: 10.1038/s41467-025-56128-x.

ABSTRACT

Programmed-cell death is an antimicrobial defense mechanism that promotes clearance of intracellular pathogens. Toxoplasma counteracts host immune defenses by secreting effector proteins into host cells; however, how the parasite evades lytic cell death and the effectors involved remain poorly characterized. We identified ROP55, a rhoptry protein that promotes parasite survival by preventing lytic cell death in absence of IFN-γ stimulation. RNA-Seq analysis revealed that ROP55 acts as a repressor of host pro-inflammatory responses. In THP-1 monocytes ΔROP55 infection increased NF-κB p65 nuclear translocation, IL-1β production, and GSDMD cleavage compared to wild type or complemented parasites. ΔROP55 infection also induced RIPK3-dependent necroptosis in human and mouse primary macrophages. Moreover, ΔROP55 parasites were significantly impaired in virulence in female mice and prevented NF-κB activation and parasite clearance in mBMDM. These findings place ROP55 as a major virulence factor, dampening lytic cell death and enabling Toxoplasma to evade clearance from infected cells.

PMID:39814722 | DOI:10.1038/s41467-025-56128-x

Nat Commun. 2025 Jan 16;16(1):710. doi: 10.1038/s41467-025-55928-5.

ABSTRACT

Zygotic genome activation (ZGA) is crucial for maternal to zygotic transition at the 2-8-cell stage in order to overcome silencing of genes and enable transcription from the zygotic genome. In humans, ZGA is induced by DUX4, a pioneer factor that drives expression of downstream germline-specific genes and retroelements. Here we show that herpesviruses from all subfamilies, papillomaviruses and Merkel cell polyomavirus actively induce DUX4 expression to promote viral transcription and replication. Analysis of single-cell sequencing data sets from patients shows that viral DUX4 activation is of relevance in vivo. Herpes-simplex virus 1 (HSV-1) immediate early proteins directly induce expression of DUX4 and its target genes, which mimics zygotic genome activation. Upon HSV-1 infection, DUX4 directly binds to the viral genome and promotes viral transcription. DUX4 is functionally required for infection, since genetic depletion by CRISPR/Cas9 as well as degradation of DUX4 by nanobody constructs abrogates HSV-1 replication. Our results show that DNA viruses including herpesviruses mimic an embryonic-like transcriptional program that prevents epigenetic silencing of the viral genome and facilitates herpesviral gene expression.

PMID:39814710 | DOI:10.1038/s41467-025-55928-5

Nat Commun. 2025 Jan 15;16(1):700. doi: 10.1038/s41467-024-55735-4.

NO ABSTRACT

PMID:39814709 | DOI:10.1038/s41467-024-55735-4

Nat Commun. 2025 Jan 15;16(1):698. doi: 10.1038/s41467-025-56059-7.

ABSTRACT

The Drosophila visual system is a powerful model to study the development of neural circuits. Lobula columnar neurons-LCNs are visual output neurons that encode visual features relevant to natural behavior. There are ~20 classes of LCNs forming non-overlapping synaptic optic glomeruli in the brain. To address their origin, we used single-cell mRNA sequencing to define the transcriptome of LCN subtypes and identified lines that are expressed throughout their development. We show that LCNs originate from stem cells in four distinct brain regions exhibiting different modes of neurogenesis, including the ventral and dorsal tips of the outer proliferation center, the ventral superficial inner proliferation center and the central brain. We show that this convergence of similar neurons illustrates the complexity of generating neuronal diversity, and likely reflects the evolutionary origin of each subtype that detects a specific visual feature and might influence behaviors specific to each species.

PMID:39814708 | DOI:10.1038/s41467-025-56059-7