Host traits shape virome composition and virus transmission in wild small mammals.

Bats, rodents, and shrews are the most important animal sources of human infectious diseases. However, the evolution and transmission of viruses among them remain largely unexplored. Through the meta-transcriptomic sequencing of internal organ and fecal samples from 2,443 wild bats, rodents, and shrews sampled from four Chinese habitats, we identified 669 viruses, including 534 novel viruses, thereby greatly expanding the mammalian virome. Our analysis revealed high levels of phylogenetic diversity, identified cross-species virus transmission events, elucidated virus origins, and identified cases of invertebrate viruses in mammalian hosts. Host order and sample size were the most important factors impacting virome composition and patterns of virus spillover. Shrews harbored a high richness of viruses, including many invertebrate-associated viruses with multi-organ distributions, whereas rodents carried viruses with a greater capacity for host jumping. These data highlight the remarkable diversity of mammalian viruses in local habitats and their ability to emerge in new hosts.

Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.

Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.

Editing DNA Methylation in the Mammalian Genome.

Mammalian DNA methylation is a critical epigenetic mechanism orchestrating gene expression networks in many biological processes. However, investigation of the functions of specific methylation events remains challenging. Here, we demonstrate that fusion of Tet1 or Dnmt3a with a catalytically inactive Cas9 (dCas9) enables targeted DNA methylation editing. Targeting of the dCas9-Tet1 or -Dnmt3a fusion protein to methylated or unmethylated promoter sequences caused activation or silencing, respectively, of an endogenous reporter. Targeted demethylation of the BDNF promoter IV or the MyoD distal enhancer by dCas9-Tet1 induced BDNF expression in post-mitotic neurons or activated MyoD facilitating reprogramming of fibroblasts into myoblasts, respectively. Targeted de novo methylation of a CTCF loop anchor site by dCas9-Dnmt3a blocked CTCF binding and interfered with DNA looping, causing altered gene expression in the neighboring loop. Finally, we show that these tools can edit DNA methylation in mice, demonstrating their wide utility for functional studies of epigenetic regulation.

ATR inhibition induces synthetic lethality in mismatch repair-deficient cells and augments immunotherapy.

The mismatch repair (MMR) deficiency of cancer cells drives mutagenesis and offers a useful biomarker for immunotherapy. However, many MMR-deficient (MMR-d) tumors do not respond to immunotherapy, highlighting the need for alternative approaches to target MMR-d cancer cells. Here, we show that inhibition of the ATR kinase preferentially kills MMR-d cancer cells. Mechanistically, ATR inhibitor (ATRi) imposes synthetic lethality on MMR-d cells by inducing DNA damage in a replication- and MUS81 nuclease-dependent manner. The DNA damage induced by ATRi is colocalized with both MSH2 and PCNA, suggesting that it arises from DNA structures recognized by MMR proteins during replication. In syngeneic mouse models, ATRi effectively reduces the growth of MMR-d tumors. Interestingly, the antitumor effects of ATRi are partially due to CD8+ T cells. In MMR-d cells, ATRi stimulates the accumulation of nascent DNA fragments in the cytoplasm, activating the cGAS-mediated interferon response. The combination of ATRi and anti-PD-1 antibody reduces the growth of MMR-d tumors more efficiently than ATRi or anti-PD-1 alone, showing the ability of ATRi to augment the immunotherapy of MMR-d tumors. Thus, ATRi selectively targets MMR-d tumor cells by inducing synthetic lethality and enhancing antitumor immunity, providing a promising strategy to complement and augment MMR deficiency-guided immunotherapy.

Mapping subcellular localizations of unannotated microproteins and alternative proteins with MicroID.

Proteogenomic identification of translated small open reading frames has revealed thousands of previously unannotated, largely uncharacterized microproteins, or polypeptides of less than 100 amino acids, and alternative proteins (alt-proteins) that are co-encoded with canonical proteins and are often larger. The subcellular localizations of microproteins and alt-proteins are generally unknown but can have significant implications for their functions. Proximity biotinylation is an attractive approach to define the protein composition of subcellular compartments in cells and in animals. Here, we developed a high-throughput technology to map unannotated microproteins and alt-proteins to subcellular localizations by proximity biotinylation with TurboID (MicroID). More than 150 microproteins and alt-proteins are associated with subnuclear organelles. One alt-protein, alt-LAMA3, localizes to the nucleolus and functions in pre-rRNA transcription. We applied MicroID in a mouse model, validating expression of a conserved nuclear microprotein, and establishing MicroID for discovery of microproteins and alt-proteins in vivo.

Induction of pluripotency in mouse somatic cells with lineage specifiers.

The reprogramming factors that induce pluripotency have been identified primarily from embryonic stem cell (ESC)-enriched, pluripotency-associated factors. Here, we report that, during mouse somatic cell reprogramming, pluripotency can be induced with lineage specifiers that are pluripotency rivals to suppress ESC identity, most of which are not enriched in ESCs. We found that OCT4 and SOX2, the core regulators of pluripotency, can be replaced by lineage specifiers that are involved in mesendodermal (ME) specification and in ectodermal (ECT) specification, respectively. OCT4 and its substitutes attenuated the elevated expression of a group of ECT genes, whereas SOX2 and its substitutes curtailed a group of ME genes during reprogramming. Surprisingly, the two counteracting lineage specifiers can synergistically induce pluripotency in the absence of both OCT4 and SOX2. Our study suggests a "seesaw model" in which a balance that is established using pluripotency factors and/or counteracting lineage specifiers can facilitate reprogramming.

VIBRANT: spectral profiling for single-cell drug responses.

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Pharmacological expansion of type 2 alveolar epithelial cells promotes regenerative lower airway repair.

Type 2 alveolar epithelial cells (AEC2s) are stem cells in the adult lung that contribute to lower airway repair. Agents that promote the selective expansion of these cells might stimulate regeneration of the compromised alveolar epithelium, an etiology-defining event in several pulmonary diseases. From a high-content imaging screen of the drug repurposing library ReFRAME, we identified that dipeptidyl peptidase 4 (DPP4) inhibitors, widely used type 2 diabetes medications, selectively expand AEC2s and are broadly efficacious in several mouse models of lung damage. Mechanism of action studies revealed that the protease DPP4, in addition to processing incretin hormones, degrades IGF-1 and IL-6, essential regulators of AEC2 expansion whose levels are increased in the luminal compartment of the lung in response to drug treatment. To selectively target DPP4 in the lung with sufficient drug exposure, we developed NZ-97, a locally delivered, lung persistent DPP4 inhibitor that broadly promotes efficacy in mouse lung damage models with minimal peripheral exposure and good tolerability. This work reveals DPP4 as a central regulator of AEC2 expansion and affords a promising therapeutic approach to broadly stimulate regenerative repair in pulmonary disease.

Machine Learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD).

Nonalcoholic fatty liver disease (NAFLD) has emerged as the predominant chronic liver condition globally, and underdiagnosis is common, particularly in mild cases, attributed to the asymptomatic nature and traditional ultrasonography's limited sensitivity to detect early-stage steatosis. Consequently, patients may experience progressive liver pathology. The objective of this research is to ascertain the efficacy of serum glycan glycopatterns as a potential diagnostic biomarker, with a particular focus on the disease's early stages. We collected a total of 170 serum samples from volunteers with mild-NAFLD (Mild), severe-NAFLD (Severe), and non-NAFLD (None). Examination via lectin microarrays has uncovered pronounced disparities in serum glycopatterns identified by 19 distinct lectins. Following this, we employed four distinct machine learning algorithms to categorize the None, Mild, and Severe groups, drawing on the alterations observed in serum glycopatterns. The gradient boosting decision tree (GBDT) algorithm outperformed other models in diagnostic accuracy within the validation set, achieving an accuracy rate of 95% in differentiating the None group from the Mild group. Our research indicates that employing lectin microarrays to identify alterations in serum glycopatterns, when integrated with advanced machine learning algorithms, could constitute a promising approach for the diagnosis of NAFLD, with a special emphasis on its early detection.

Nascent alt-protein chemoproteomics reveals a pre-60S assembly checkpoint inhibitor.

Many unannotated microproteins and alternative proteins (alt-proteins) are coencoded with canonical proteins, but few of their functions are known. Motivated by the hypothesis that alt-proteins undergoing regulated synthesis could play important cellular roles, we developed a chemoproteomic pipeline to identify nascent alt-proteins in human cells. We identified 22 actively translated alt-proteins or N-terminal extensions, one of which is post-transcriptionally upregulated by DNA damage stress. We further defined a nucleolar, cell-cycle-regulated alt-protein that negatively regulates assembly of the pre-60S ribosomal subunit (MINAS-60). Depletion of MINAS-60 increases the amount of cytoplasmic 60S ribosomal subunit, upregulating global protein synthesis and cell proliferation. Mechanistically, MINAS-60 represses the rate of late-stage pre-60S assembly and export to the cytoplasm. Together, these results implicate MINAS-60 as a potential checkpoint inhibitor of pre-60S assembly and demonstrate that chemoproteomics enables hypothesis generation for uncharacterized alt-proteins.

Using new indices to predict metabolism dysfunction-associated fatty liver disease (MAFLD): analysis of the national health and nutrition examination survey database.

BACKGROUND: Metabolism dysfunction-associated fatty liver disease (MAFLD), is the most common chronic liver disease. Few MAFLD predictions are simple and accurate. We examined the predictive performance of the albumin-to-glutamyl transpeptidase ratio (AGTR), plasma atherogenicity index (AIP), and serum uric acid to high-density lipoprotein cholesterol ratio (UHR) for MAFLD to design practical, inexpensive, and reliable models. METHODS: The National Health and Nutrition Examination Survey (NHANES) 2007-2016 cycle dataset, which contained 12,654 participants, was filtered and randomly separated into internal validation and training sets. This study examined the relationships of the AGTR and AIP with MAFLD using binary multifactor logistic regression. We then created a MAFLD predictive model using the training dataset and validated the predictive model performance with the 2017-2018 NHANES and internal datasets. RESULTS: In the total population, the predictive ability (AUC) of the AIP, AGTR, UHR, and the combination of all three for MAFLD showed in the following order: 0.749, 0.773, 0.728 and 0.824. Further subgroup analysis showed that the AGTR (AUC1 = 0.796; AUC2 = 0.690) and the combination of the three measures (AUC1 = 0.863; AUC2 = 0.766) better predicted MAFLD in nondiabetic patients. Joint prediction outperformed the individual measures in predicting MAFLD in the subgroups. Additionally, the model better predicted female MAFLD. Adding waist circumference and or BMI to this model improves predictive performance. CONCLUSION: Our study showed that the AGTR, AIP, and UHR had strong MAFLD predictive value, and their combination can increase MAFLD predictive performance. They also performed better in females.

DARS-AS1: A Vital Oncogenic LncRNA Regulator with Potential for Cancer Prognosis and Therapy.

DARS-AS1, short for Aspartyl-tRNA synthetase antisense RNA 1, has emerged as a pivotal player in cancers. Upregulation of this lncRNA is a recurrent phenomenon observed across various cancer types, where it predominantly assumes oncogenic roles, exerting influence on multiple facets of tumor cell biology. This aberrant expression of DARS-AS1 has triggered extensive research investigations, aiming to unravel its roles and clinical values in cancer. In this review, we elucidate the significant correlation between dysregulated DARS-AS1 expression and adverse survival prognoses in cancer patients, drawing from existing literature and pan-cancer analyses from The Cancer Genome Atlas (TCGA). Additionally, we provide comprehensive insights into the diverse functions of DARS-AS1 in various cancers. Our review encompasses the elucidation of the molecular mechanisms, ceRNA networks, functional mediators, and signaling pathways, as well as its involvement in therapy resistance, coupled with the latest advancements in DARS-AS1-related cancer research. These recent updates enrich our comprehensive comprehension of the pivotal role played by DARS-AS1 in cancer, thereby paving the way for future applications of DARS-AS1-targeted strategies in tumor prognosis evaluation and therapeutic interventions. This review furnishes valuable insights to advance the ongoing efforts in combating cancer effectively.

Survival analysis of patients with extrahepatic cholangiocarcinoma: a nomogram for clinical and MRI features.

BACKGROUND: This study aimed to establish a predictive model to estimate the postoperative prognosis of patients with extrahepatic cholangiocarcinoma (ECC) based on preoperative clinical and MRI features. METHODS: A total of 104 patients with ECC confirmed by surgery and pathology were enrolled from January 2013 to July 2021, whose preoperative clinical, laboratory, and MRI data were retrospectively collected and examined, and the effects of clinical and imaging characteristics on overall survival (OS) were analyzed by constructing Cox proportional hazard regression models. A nomogram was constructed to predict OS, and calibration curves and time-dependent receiver operating characteristic (ROC) curves were employed to assess OS accuracy. RESULTS: Multivariate regression analyses revealed that gender, DBIL, ALT, GGT, tumor size, lesion's position, the signal intensity ratio of liver to paraspinal muscle (SIRLiver/Muscle), and the signal intensity ratio of spleen to paraspinal muscle (SIRSpleen/Muscle) on T2WI sequences were significantly associated with OS, and these variables were included in a nomogram. The concordance index of nomogram for predicting OS was 0.766, and the AUC values of the nomogram predicting 1-year and 2-year OS rates were 0.838 and 0.863, respectively. The calibration curve demonstrated good agreement between predicted and observed OS. 5-fold and 10-fold cross-validation show good stability of nomogram predictions. CONCLUSIONS: Our nomogram based on clinical, laboratory, and MRI features well predicted OS of ECC patients, and could be considered as a convenient and personalized prediction tool for clinicians to make decisions.

Alterations of mannosylated glycopatterns recognized by Hippeastrum hybrid lectin in saliva of patients with lung cancer.

OBJECTIVES: Lung cancer (LC) is the malignant tumor with the highest mortality rate worldwide, and precise early diagnosis can improve patient prognosis. The purpose of this study was to investigate whether alterations in the glycopatterns recognized by the Hippeastrum hybrid lectin (HHL) in salivary proteins are associated with the development of LC. MATERIALS AND METHODS: First, we collected saliva samples from LC (15 lung adenocarcinoma (ADC); 15 squamous cell carcinoma (SCC); 15 small cell lung cancer (SCLC)) and 15 benign pulmonary disease (BPD) for high-throughput detection of abundance levels of HHL-recognized glycopatterns using protein microarrays, and then validated the pooled samples from each group with lectin blotting analysis. Finally, the N-glycan profiles of salivary glycoproteins isolated from the pooled samples using HHL-magnetic particle conjugates were characterized separately using MALDI-TOF/TOF-MS. RESULTS: The results showed that the abundance level of glycopatterns recognized by HHL in salivary proteins was elevated in LC compared to BPD. The proportion of mannosylated N-glycans was notably higher in ADC (31.7%), SCC (39.0%), and SCLC (46.6%) compared to BPD (23.3%). CONCLUSIONS: The altered salivary glycopatterns such as oligomannose, Manα1-3Man, or Manα1-6Man N-glycans recognized by HHL might serve as potential biomarkers for the diagnosis of LC patients. CLINICAL RELEVANCE: This study provides crucial information for studying changes in salivary to differentiate between BPD and LC and facilitate the discovery of biomarkers for LC diagnosis based on precise alterations of mannosylated N-glycans in saliva.

Studies on the Biomimetic Synthesis of Marine Ladder Polyethers via Endo-Selective Epoxide-to-Epoxonium Ring-Opening Cascades.

Marine ladder polyethers have attracted the attention of chemists and biologists because of their potent biological activities. Synthetic chemists have attempted to construct their polyether frameworks by epoxide ring-opening cascades, as Nakanishi hypothesis describes. However, Baldwin's rules of ring closure state that exo-selective intramolecular cyclization of epoxy alcohols is preferred over endo-selective cyclization. Herein, we investigated epoxide ring-opening cascades of polyepoxy alcohols in [EMIM]BF4/PFTB (1-ethyl-3-methylimidazolium tetrafluoroborate /perfluoro-tert-butyl alcohol) and found that all-endo products were formed via epoxide-to-epoxonium ring-opening cyclizations (not restricted by Baldwin's rules, which only apply to intramolecular hydroxyl-to-epoxide cyclizations). We determined that the key factor enabling polyepoxy alcohols to undergo a high proportion of all-endo-selective cyclizations was inhibition of exo-selective hydroxyl-to-epoxide cyclization starting from the terminal hydroxyl group of a polyepoxy alcohol. By introducing a slow-release protecting group to the terminal hydroxyl group, we could markedly increase the cyclization yields of polyether fragments with hydrogen atoms at the ring junctions. For the first time, we constructed consecutively fused six-membered-ring and fused seven-, eight-, and nine-membered-ring polyether fragments by epoxide-to-epoxonium ring-opening cyclizations through the addition of a suitable Lewis acid. We also suggest that the biosynthesis of marine ladder polyethers may proceed via epoxide-to-epoxonium ring-opening cyclization of polyepoxide.

Programmable Biomolecule-Mediated Processors.

Programmable biomolecule-mediated computing is a new computing paradigm as compared to contemporary electronic computing. It employs nucleic acids and analogous biomolecular structures as information-storing and -processing substrates to tackle computational problems. It is of great significance to investigate the various issues of programmable biomolecule-mediated processors that are capable of automatically processing, storing, and displaying information. This Perspective provides several conceptual designs of programmable biomolecule-mediated processors and provides some insights into potential future research directions for programmable biomolecule-mediated processors.

MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network.

BACKGROUND: Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI. METHODS: We recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated. RESULTS: Model 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively. CONCLUSION: The DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

Long-term outcomes of ultrasound guided high intensity focused ultrasound ablation for patients with uterine fibroids classified by T2WI: a multicenter retrospective study.

OBJECTIVE: To evaluate the long-term outcomes of ultrasound-guided high-intensity focused ultrasound (USgHIFU) ablation of uterine fibroids classified by T2-weighted magnetic resonance imaging (T2WI-MRI). MATERIALS AND METHODS: The data of 1427 premenopausal women with symptomatic uterine fibroids who underwent USgHIFU at four teaching hospitals in China were analyzed retrospectively. The uterine fibroids were classified based on their T2WI-MRI signal intensities relative to that of skeletal muscle, myometrium and endometrium as: hypointense, isointense, heterogeneous hyperintense fibroids (HHF), slightly HHF (sHHF) and markedly HHF (mHHF), respectively. The rates of symptom relief and reintervention post-USgHIFU ablation were compared between the classified groups. RESULTS: A total of 1303 patients were followed up for 44 (40, 49) months. The symptom relief rate of the hypointense and isointense fibroids was 83.3% and 79.5%, respectively, which were significantly higher (p < .05) compared to that of HHF, sHHF and mHHF (58.3%, 44.2% and 60.4%), respectively. sHHF had the lowest symptom relief rate (p < .05). The cumulative reintervention rate for hypointense, isointense, HHF, sHHF and mHHF types were 8.8%, 10.8%, 21.4%, 39.9% and 19.8%, respectively. The reintervention rate of hypointense/isointense fibroids was significantly lower than that of HHF/mHHF/sHHF (p < .01), while sHHF had the highest re-intervention rate (p < .01). Thus, reintervention rate is inversely correlated to the rate of symptom relief. CONCLUSIONS: USgHIFU ablation is effective for hypointense, isointense, HHF and mHHF with acceptable long-term follow-up outcomes. However, sHHF is associated with a higher reintervention rate.