Spatial epigenome-transcriptome co-profiling of mammalian tissues.

Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1-5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.

Post-implantation analysis of genomic variations in the progeny from developing fetus to birth.

The analysis of genomic variations in offspring after implantation has been infrequently studied. In this study, we aim to investigate the extent of de novo mutations in humans from developing fetus to birth. Using high-depth whole-genome sequencing, 443 parent-offspring trios were studied to compare the results of de novo mutations (DNMs) between different groups. The focus was on fetuses and newborns, with DNA samples obtained from the families' blood and the aspirated embryonic tissues subjected to deep sequencing. It was observed that the average number of total DNMs in the newborns group was 56.26 (54.17-58.35), which appeared to be lower than that the multifetal reduction group, which was 76.05 (69.70-82.40) (F = 2.42, P = 0.12). However, after adjusting for parental age and maternal pre-pregnancy body mass index (BMI), significant differences were found between the two groups. The analysis was further divided into single nucleotide variants (SNVs) and insertion/deletion of a small number of bases (indels), and it was discovered that the average number of de novo SNVs associated with the multifetal reduction group and the newborn group was 49.89 (45.59-54.20) and 51.09 (49.22-52.96), respectively. No significant differences were noted between the groups (F = 1.01, P = 0.32). However, a significant difference was observed for de novo indels, with a higher average number found in the multifetal reduction group compared to the newborn group (F = 194.17, P < 0.001). The average number of de novo indels among the multifetal reduction group and the newborn group was 26.26 (23.27-29.05) and 5.17 (4.82-5.52), respectively. To conclude, it has been observed that the quantity of de novo indels in the newborns experiences a significant decrease when compared to that in the aspirated embryonic tissues (7-9 weeks). This phenomenon is evident across all genomic regions, highlighting the adverse effects of de novo indels on the fetus and emphasizing the significance of embryonic implantation and intrauterine growth in human genetic selection mechanisms.

CyVerse: Cyberinfrastructure for open science.

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

Injectable Oxygen-Carrying Microsphere Hydrogel for Dynamic Regulation of Redox Microenvironment of Wounds.

The delayed healing of infected wounds can be attributed to the increased production of reactive oxygen species (ROS) and consequent damages to vascellum and tissue, resulting in a hypoxic wound environment that further exacerbates inflammation. Current clinical treatments including hyperbaric oxygen therapy and antibiotic treatment fail to provide sustained oxygenation and drug-free resistance to infection. To propose a dynamic oxygen regulation strategy, this study develops a composite hydrogel with ROS-scavenging system and oxygen-releasing microspheres in the wound dressing. The hydrogel itself reduces cellular damage by removing ROS derived from immune cells. Simultaneously, the sustained release of oxygen from microspheres improves cell survival and migration in hypoxic environments, promoting angiogenesis and collagen regeneration. The combination of ROS scavenging and oxygenation enables the wound dressing to achieve drug-free anti-infection through activating immune modulation, inhibiting the secretion of pro-inflammatory cytokines interleukin-6, and promoting tissue regeneration in both acute and infected wounds of rat skins. Thus, the composite hydrogel dressing proposed in this work shows great potential for dynamic redox regulation of infected wounds and accelerates wound healing without drugs.

AtlasXplore: a web platform for visualizing and sharing spatial epigenome data.

MOTIVATION: In recent years, a growing number of spatial epigenome datasets have been generated, presenting rich opportunities for studying the regulation mechanisms in solid tissue sections. However, visual exploration of these datasets requires extensive computational processing of raw data, presenting a challenge for researchers without advanced computational skills to fully explore and analyze such datasets. RESULTS: Here, we introduce AtlasXplore, a web-based platform that enables scientists to interactively navigate a growing collection of spatial epigenome data using an expanding set of tools. AVAILABILITY AND IMPLEMENTATION: https://web.atlasxomics.com.

Can Artificial Intelligence Chatbots Improve Mental Health?: A Scoping Review.

BACKGROUND AND OBJECTIVES: Mental health disorders, including anxiety and depression, are the leading causes of global health-related burden and have increased dramatically since the 1990s. Delivering mental healthcare using artificial intelligence chatbots may be one option for closing the gaps in mental healthcare access. The overall aim of this scoping review was to describe the use, efficacy, and advantages/disadvantages of using an artificial intelligence chatbot for mental healthcare (stress, anxiety, depression). METHODS: PubMed, PsycINFO, CINAHL, and Web of Science databases were searched. When possible, Medical Subject Headings terms were searched in combination with keywords. Two independent reviewers reviewed a total of 5768 abstracts. RESULTS: Fifty-four articles were chosen for further review, with 10 articles included in the final analysis. Regarding quality assessment, the overall quality of the evidence was lower than expected. Overall, most studies showed positive trends in improving anxiety, stress, and depression. DISCUSSION: Overall, using an artificial intelligence chatbot for mental health has some promising effects. However, many studies were done using rudimentary versions of artificial intelligence chatbots. In addition, lack of guardrails and privacy issues were identified. More research is needed to determine the effectiveness of artificial intelligence chatbots and to describe undesirable effects.

Enhancing the Photocatalytic Degradation Efficiency of Dyes of Copper-Based Metal-Organic Frameworks through a Dimension-Induced Structural Strategy.

Photocatalytic degradation of pollutants is an effective environment purification strategy. Metal-organic frameworks (MOFs) have attracted extensive attention in the field of photocatalysis owing to their structural diversity, uniform cavity, and large specific surface area. However, poor electrical conductivity, light absorption, and water stability restrict their development. The tailorable structure of MOFs may effectively overcome these limitations. Herein, three Cu-based MOFs (complexes 1-3) with one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) structures, respectively, were successfully prepared by introducing different uncoordinated ligands and adjusting the ligand/metal salt ratio. Among them, complex 1 with a 1D chain was constructed as a typical J-type aggregation by π-π stacking interactions between adjacent naphthalene rings. This intermolecular aggregation mode enhances strong exciton coupling between conjugated rings, reduces the transition energy, expands the intrinsic light absorption edge, and provides a channel for electron transport, thus improving the charge-separation efficiency. As expected, complex 1 with a 1D chain structure exhibited excellent Fenton-like catalytic activity. The apparent reaction rates were 3.2 and 2.0 times higher than those of 2D and 3D MOFs, respectively.

Inhibition of soluble epoxide hydrolase prevents diabetic retinopathy.

Diabetic retinopathy is an important cause of blindness in adults, and is characterized by progressive loss of vascular cells and slow dissolution of inter-vascular junctions, which result in vascular leakage and retinal oedema. Later stages of the disease are characterized by inflammatory cell infiltration, tissue destruction and neovascularization. Here we identify soluble epoxide hydrolase (sEH) as a key enzyme that initiates pericyte loss and breakdown of endothelial barrier function by generating the diol 19,20-dihydroxydocosapentaenoic acid, derived from docosahexaenoic acid. The expression of sEH and the accumulation of 19,20-dihydroxydocosapentaenoic acid were increased in diabetic mouse retinas and in the retinas and vitreous humour of patients with diabetes. Mechanistically, the diol targeted the cell membrane to alter the localization of cholesterol-binding proteins, and prevented the association of presenilin 1 with N-cadherin and VE-cadherin, thereby compromising pericyte-endothelial cell interactions and inter-endothelial cell junctions. Treating diabetic mice with a specific sEH inhibitor prevented the pericyte loss and vascular permeability that are characteristic of non-proliferative diabetic retinopathy. Conversely, overexpression of sEH in the retinal Müller glial cells of non-diabetic mice resulted in similar vessel abnormalities to those seen in diabetic mice with retinopathy. Thus, increased expression of sEH is a key determinant in the pathogenesis of diabetic retinopathy, and inhibition of sEH can prevent progression of the disease.

The Role of lncRNA-miR-26a-mRNA Network in Cancer Progression and Treatment.

The role of non-coding RNAs in regulating biological processes associated with cancer progression, such as proliferation, migration, and apoptosis, has been extensively studied. Long non-coding RNAs (lncRNAs) play a role in regulating these processes through various mechanisms, including transcriptional and post-transcriptional modifications. In post-transcriptional regulation, lncRNAs can bind to specific miRNAs and affect their function, which can either promote or inhibit cancer development. The interaction between lncRNAs, miRNAs, and mRNAs forms a network known as competitive endogenous RNA (ceRNA), which is involved in cancer progression or inhibition. One specific miRNA called miR-26a-5p has been identified as having tumor-suppressive properties. However, when lncRNAs bind to and inhibit miR-26a-5p, it can lead to cancer progression. Therefore, targeting this ceRNA network could be a promising strategy for preventing cancer development. This review will first discuss the anticancer effects of miR-26a-5p and then explore the involvement of the lncRNA-miR26a-5p-mRNA axis in cancer progression and potential targeted therapies.

Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.

BACKGROUND: Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling. OBJECTIVE: This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival. METHODS: The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance. RESULTS: A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival. CONCLUSIONS: This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.

BACKGROUND: Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden. METHODS: Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model. RESULTS: Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F1 score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F1 score by 33.7%. CONCLUSIONS: Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data.

Reference Genes Screening and Gene Expression Patterns Analysis Involved in Gelsenicine Biosynthesis under Different Hormone Treatments in Gelsemium elegans.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is an accurate method for quantifying gene expression levels. Choosing appropriate reference genes to normalize the data is essential for reducing errors. Gelsemium elegans is a highly poisonous but important medicinal plant used for analgesic and anti-swelling purposes. Gelsenicine is one of the vital active ingredients, and its biosynthesis pathway remains to be determined. In this study, G. elegans leaf tissue with and without the application of one of four hormones (SA, MeJA, ETH, and ABA) known to affect gelsenicine synthesis, was analyzed using ten candidate reference genes. The gene stability was evaluated using GeNorm, NormFinder, BestKeeper, ∆CT, and RefFinder. The results showed that the optimal stable reference genes varied among the different treatments and that at least two reference genes were required for accurate quantification. The expression patterns of 15 genes related to the gelsenicine upstream biosynthesis pathway was determined by RT-qPCR using the relevant reference genes identified. Three genes 8-HGO, LAMT, and STR, were found to have a strong correlation with the amount of gelsenicine measured in the different samples. This research is the first study to examine the reference genes of G. elegans under different hormone treatments and will be useful for future molecular analyses of this medically important plant species.

Effects of greenness in university campuses on test anxiety among Chinese university students during COVID-19 lockdowns: a correlational and mediation analysis.

The purpose of this study is to investigate the correlations of greenness exposure with test anxiety among university students during COVID-19 lockdowns and to explore their mechanisms. We conducted a cross-sectional study with 2609 university students in Anhui and Shandong provinces, China. We assessed perceived campus greenness using a five-point Likert scale for quality, visibility, abundance, usage, and accessibility. Objective greenness was estimated via average normalised difference vegetation index (NDVI) in 1,000-, 1,500-, and 2,000-m radius zones around each of the campuses. A generalised linear mixed model examined the associations between greenness and test anxiety and to evaluate the mediation effects of physical activity, body mass index (BMI), and air pollution. Results showed that higher NDVI1500-m correlated with lower test anxiety (OR = 0.871; 95% CI: 0.851, 0.891), physical activity may partially mediate this association. Increased campus greenness may alleviate test anxiety among Chinese university students.

[Effects of family dignity intervention on male infertility patients and their spouses].

OBJECTIVE: To investigate the effects of family dignity intervention (FDI) on anxiety, depression, hope level and quality of life (QOL) of male infertility patients and their spouses. METHODS: Using quasi-experimental design, we selected male infertility patients and their spouses undergoing human-assisted reproductive technology (ART) in our Center of Reproductive Medicine from June to December 2022 and divided them into an intervention group (38 couples) and a control group (40 couples). The former underwent a four-stage FDI, including ovulation promotion cycle assessment, family sharing, pre-transplantation interview and post-transplantation follow-up, while the latter received routine nursing. Using Hospital Anxiety and Depression Scale, Herth Hope Index and Fertility Quality of Life Scale, we evaluated the effects of FDI before and after transplantation. RESULTS: After FDI, the anxiety and depression scores were significantly lower (P < 0.05) and the total scores on the hope level and all other dimensions remarkably higher in the intervention group than in the control (P < 0.05). The self-confidence of the couples in the intervention groups in ART treatment was markedly increased in comparison with that of the controls, and their scores on physical and mental health were significantly higher than those of the latter (P < 0.05). CONCLUSION: FDI can effectively relieve the anxiety and depression, raise the hope level and improve the quality of life of both male infertility patients and their spouses.

Construction and evaluation of an integrated predictive model for chronic kidney disease based on the random forest and artificial neural network approaches.

Chronic kidney disease (CKD) is recognized as a serious global health problem due to its high prevalence and all-cause mortality. The aim of this research was to identify critical biomarkers and construct an integrated model for the early prediction of CKD. By using existing RNA-seq data and clinical information from CKD patients from the Gene Expression Omnibus (GEO) database, we applied a computational technique that combined the random forest (RF) and artificial neural network (ANN) approaches to identify gene biomarkers and construct an early diagnostic model. We generated ROC curves to compare the model with other markers and evaluated the associations of selected genes with various clinical properties of CKD. Moreover, we highlighted two biomarkers involved in energy metabolism pathways: pyruvate dehydrogenase kinase 4 (PDK4) and zinc finger protein 36 (ZFP36). The downregulation of the identified key genes was subsequently confirmed in both unilateral ureteral obstruction (UUO) and ischemia reperfusion injury (IRI) mouse models, accompanied by decreased energy metabolism. In vitro experiments and single-cell sequencing analysis proved that these key genes were related to the energy metabolism of proximal tubule cells and were involved in the development of CKD. Overall, we constructed a composite prediction model and discovered key genes that might be used as biomarkers and therapeutic targets for CKD.

BSAseq: an interactive and integrated web-based workflow for identification of causal mutations in bulked F2 populations.

SUMMARY: With the advance of next-generation sequencing technologies and reductions in the costs of these techniques, bulked segregant analysis (BSA) has become not only a powerful tool for mapping quantitative trait loci but also a useful way to identify causal gene mutations underlying phenotypes of interest. However, due to the presence of background mutations and errors in sequencing, genotyping, and reference assembly, it is often difficult to distinguish true causal mutations from background mutations. In this study, we developed the BSAseq workflow, which includes an automated bioinformatics analysis pipeline with a probabilistic model for estimating the linked region (the region linked to the causal mutation) and an interactive Shiny web application for visualizing the results. We deeply sequenced a sorghum male-sterile parental line (ms8) to capture the majority of background mutations in our bulked F2 data. We applied the workflow to 11 bulked sorghum F2 populations and 1 rice F2 population and identified the true causal mutation in each population. The workflow is intuitive and straightforward, facilitating its adoption by users without bioinformatics analysis skills. We anticipate that the BSAseq workflow will be broadly applicable to the identification of causal mutations for many phenotypes of interest. AVAILABILITY AND IMPLEMENTATION: BSAseq is freely available on https://www.sciapps.org/page/bsa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential expression of enzymes in thymidylate biosynthesis in zebrafish at different developmental stages: implications for dtymk mutation-caused neurodegenerative disorders.

BACKGROUND: Deoxythymidine triphosphate (dTTP) is an essential building block of DNA, and defects in enzymes involved in dTTP synthesis cause neurodegenerative disorders. For instance, mutations in DTYMK, the gene coding for thymidylate kinase (TMPK), cause severe microcephaly in human. However, the mechanism behind this is not well-understood. Here we used the zebrafish model and studied (i) TMPK, an enzyme required for both the de novo and the salvage pathways of dTTP synthesis, and (ii) thymidine kinases (TK) of the salvage pathway in order to understand their role in neuropathology. RESULTS: Our findings reveal that maternal-stored dNTPs are only sufficient for 6 cell division cycles, and the levels of dNTPs are inversely correlated to cell cycle length during early embryogenesis. TMPK and TK activities are prominent in the cytosol of embryos, larvae and adult fish and brain contains the highest TMPK activity. During early development, TMPK activity increased gradually from 6 hpf and a profound increase was observed at 72 hpf, and TMPK activity reached its maximal level at 96 hpf, and remained at high level until 144 hpf. The expression of dtymk encoded Dtymk protein correlated to its mRNA expression and neuronal development but not to the TMPK activity detected. However, despite the high TMPK activity detected at later stages of development, the Dtymk protein was undetectable. Furthermore, the TMPK enzyme detected at later stages showed similar biochemical properties as the Dtymk enzyme but was not recognized by the Dtymk specific antibody. CONCLUSIONS: Our results suggest that active dNTP synthesis in early embryogenesis is vital and that Dtymk is essential for neurodevelopment, which is supported by a recent study of dtymk knockout zebrafish with neurological disorder and lethal outcomes. Furthermore, there is a novel TMPK-like enzyme expressed at later stages of development.

Actively targeted delivery of SN38 by ultrafine iron oxide nanoparticle for treating pancreatic cancer.

Pancreatic cancer remains one of the most lethal cancers largely due to the inefficient delivery of therapeutics. Nanomaterials have been extensively investigated as drug delivery platforms, showing improved drug pharmacodynamics and pharmacokinetics. However, their applications in pancreatic cancer have not yet been successful due to limited tumor delivery caused by dense tumor stroma and distorted tumor vasculatures. Meanwhile, smaller-sized nanomaterials have shown improved tumor delivery and retention in various tumors, including pancreatic tumors, suggesting their potential in enhancing drug delivery. An ultrafine iron oxide nanoparticle (uIONP) was used to encapsulate 7-ethyl-10-hydroxyl camptothecin (SN38), the water-insoluble active metabolite of pancreatic cancer chemotherapy drug irinotecan. Insulin-like growth factor 1 (IGF-1) was conjugated to uIONP as a ligand for targeting pancreatic cancer cells overexpressing IGF-1 receptor (IGF1R). The SN38 loading and release profile were characterized. The pancreatic cancer cell targeting using IGF1-uIONP/SN38 and subsequently induced cell apoptosis were also investigated. IGF1-uIONP/SN38 demonstrated a stable drug loading in physiological pH with the loading efficiency of 68.2 ± 3.5% (SN38/Fe, wt%) and < 7% release for 24 h. In tumor-interstitial- and lysosomal-mimicking pH (6.5 and 5.5), 52.2 and 91.3% of encapsulated SN38 were released over 24 h. The IGF1-uIONP/SN38 exhibited specific receptor-mediated cell targeting and cytotoxicity Ato MiaPaCa-2 and Panc02 pancreatic cancer cells with IC50 of 11.8 ± 2.3 and 20.8 ± 3.5 nM, respectively, but not to HEK293 human embryonic kidney cells. IGF1-uIONP significantly improved the targeted SN38 delivery to pancreatic cancer cells, holding the potential for in vivo theranostic applications.

DTYMK is essential for genome integrity and neuronal survival.

Nucleotide metabolism is a complex pathway regulating crucial cellular processes such as nucleic acid synthesis, DNA repair and proliferation. This study shows that impairment of the biosynthesis of one of the building blocks of DNA, dTTP, causes a severe, early-onset neurodegenerative disease. Here, we describe two unrelated children with bi-allelic variants in DTYMK, encoding dTMPK, which catalyzes the penultimate step in dTTP biosynthesis. The affected children show severe microcephaly and growth retardation with minimal neurodevelopment. Brain imaging revealed severe cerebral atrophy and disappearance of the basal ganglia. In cells of affected individuals, dTMPK enzyme activity was minimal, along with impaired DNA replication. In addition, we generated dtymk mutant zebrafish that replicate this phenotype of microcephaly, neuronal cell death and early lethality. An increase of ribonucleotide incorporation in the genome as well as impaired responses to DNA damage were observed in dtymk mutant zebrafish, providing novel pathophysiological insights. It is highly remarkable that this deficiency is viable as an essential component for DNA cannot be generated, since the metabolic pathway for dTTP synthesis is completely blocked. In summary, by combining genetic and biochemical approaches in multiple models we identified loss-of-function of DTYMK as the cause of a severe postnatal neurodegenerative disease and highlight the essential nature of dTTP synthesis in the maintenance of genome stability and neuronal survival.

ADAMTS18 regulates early branching morphogenesis of lacrimal gland and has a significant association with the risk of dry eye in mice.

ADAMTS18 is an orphan member of the ADAMTS family of metalloproteinase. ADAMTS18 mutation has been linked to developmental eye disorders, such as retinal dystrophies and ectopia lentis. Here, we report a new function of ADAMTS18 in modulating the lacrimal gland (LG) branching morphogenesis, and an association with dry eye in mice. Adamts18 mRNA was found to be enriched in the epithelium of branching tips of embryonic (E) LG, but its expression was barely detectable after 2 weeks of birth. Histological analyses of E16.5-E17.5 LG showed that ADAMTS18 deficiency resulted in a significant reduction of epithelial branching in embryonic LG. In vitro culture of E15.5 LG explants showed that the numbers of epithelial buds and branches in Adamts18 knockout (Adamts18-/-) LGs were significantly decreased when compared to those of wild type (Adamts18+/+) LGs after 0 h, 24 h, and 48 h of culture. Increased fibronectin deposition was detected in LG mesenchyme of E16.5 Adamts18-/- mice. At 14 months of age, Adamts18-/- mice manifested multiple LG pathological changes, including acinar atrophy and irregular duct ectasis with periductal fibrosis. The tear volume was significantly decreased in Adamts18-/- mice at 4 months of age, which corresponds to early adulthood in humans.