A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells.

Tumor-infiltrating myeloid cells (TIMs) are key regulators in tumor progression, but the similarity and distinction of their fundamental properties across different tumors remain elusive. Here, by performing a pan-cancer analysis of single myeloid cells from 210 patients across 15 human cancer types, we identified distinct features of TIMs across cancer types. Mast cells in nasopharyngeal cancer were found to be associated with better prognosis and exhibited an anti-tumor phenotype with a high ratio of TNF+/VEGFA+ cells. Systematic comparison between cDC1- and cDC2-derived LAMP3+ cDCs revealed their differences in transcription factors and external stimulus. Additionally, pro-angiogenic tumor-associated macrophages (TAMs) were characterized with diverse markers across different cancer types, and the composition of TIMs appeared to be associated with certain features of somatic mutations and gene expressions. Our results provide a systematic view of the highly heterogeneous TIMs and suggest future avenues for rational, targeted immunotherapies.

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.

A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.

Redox State Controls Phase Separation of the Yeast Ataxin-2 Protein via Reversible Oxidation of Its Methionine-Rich Low-Complexity Domain.

Yeast ataxin-2, also known as Pbp1, senses the activity state of mitochondria in order to regulate TORC1. A domain of Pbp1 required to adapt cells to mitochondrial activity is of low sequence complexity. The low-complexity (LC) domain of Pbp1 forms labile, cross-ß polymers that facilitate phase transition of the protein into liquid-like or gel-like states. Phase transition for other LC domains is reliant upon widely distributed aromatic amino acids. In place of tyrosine or phenylalanine residues prototypically used for phase separation, Pbp1 contains 24 similarly disposed methionine residues. Here, we show that the Pbp1 methionine residues are sensitive to hydrogen peroxide (H2O2)-mediated oxidation in vitro and in living cells. Methionine oxidation melts Pbp1 liquid-like droplets in a manner reversed by methionine sulfoxide reductase enzymes. These observations explain how reversible formation of labile polymers by the Pbp1 LC domain enables the protein to function as a sensor of cellular redox state.

Yeast Ataxin-2 Forms an Intracellular Condensate Required for the Inhibition of TORC1 Signaling during Respiratory Growth.

Yeast ataxin-2, also known as Pbp1 (polyA binding protein-binding protein 1), is an intrinsically disordered protein implicated in stress granule formation, RNA biology, and neurodegenerative disease. To understand the endogenous function of this protein, we identify Pbp1 as a dedicated regulator of TORC1 signaling and autophagy under conditions that require mitochondrial respiration. Pbp1 binds to TORC1 specifically during respiratory growth, but utilizes an additional methionine-rich, low complexity (LC) region to inhibit TORC1. This LC region causes phase separation, forms reversible fibrils, and enables self-association into assemblies required for TORC1 inhibition. Mutants that weaken phase separation in vitro exhibit reduced capacity to inhibit TORC1 and induce autophagy. Loss of Pbp1 leads to mitochondrial dysfunction and reduced fitness during nutritional stress. Thus, Pbp1 forms a condensate in response to respiratory status to regulate TORC1 signaling.

Transcription factors TCF-1 and GATA3 are key factors for the epigenetic priming of early innate lymphoid progenitors toward distinct cell fates.

The differentiation of innate lymphoid cells (ILCs) from hematopoietic stem cells needs to go through several multipotent progenitor stages. However, it remains unclear whether the fates of multipotent progenitors are predefined by epigenetic states. Here, we report the identification of distinct accessible chromatin regions in all lymphoid progenitors (ALPs), EILPs, and ILC precursors (ILCPs). Single-cell MNase-seq analyses revealed that EILPs contained distinct subpopulations epigenetically primed toward either dendritic cell lineages or ILC lineages. We found that TCF-1 and GATA3 co-bound to the lineage-defining sites for ILCs (LDS-Is), whereas PU.1 binding was enriched in the LDSs for alternative dendritic cells (LDS-As). TCF-1 and GATA3 were indispensable for the epigenetic priming of LDSs at the EILP stage. Our results suggest that the multipotency of progenitor cells is defined by the existence of a heterogeneous population of cells epigenetically primed for distinct downstream lineages, which are regulated by key transcription factors.

Widespread macromolecular interaction perturbations in human genetic disorders.

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

A lanthanide-rich kilonova in the aftermath of a long gamma-ray burst.

Observationally, kilonovae are astrophysical transients powered by the radioactive decay of nuclei heavier than iron, thought to be synthesized in the merger of two compact objects1-4. Over the first few days, the kilonova evolution is dominated by a large number of radioactive isotopes contributing to the heating rate2,5. On timescales of weeks to months, its behaviour is predicted to differ depending on the ejecta composition and the merger remnant6-8. Previous work has shown that the kilonova associated with gamma-ray burst 230307A is similar to kilonova AT2017gfo (ref. 9), and mid-infrared spectra revealed an emission line at 2.15 micrometres that was attributed to tellurium. Here we report a multi-wavelength analysis, including publicly available James Webb Space Telescope data9 and our own Hubble Space Telescope data, for the same gamma-ray burst. We model its evolution up to two months after the burst and show that, at these late times, the recession of the photospheric radius and the rapidly decaying bolometric luminosity (Lbol ∝ t-2.7±0.4, where t is time) support the recombination of lanthanide-rich ejecta as they cool.

Tumour-selective activity of RAS-GTP inhibition in pancreatic cancer.

Broad-spectrum RAS inhibition has the potential to benefit roughly a quarter of human patients with cancer whose tumours are driven by RAS mutations1,2. RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS and NRAS, with affinity for both mutant and wild-type variants3. More than 90% of cases of human pancreatic ductal adenocarcinoma (PDAC) are driven by activating mutations in KRAS4. Here we assessed the therapeutic potential of RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumour activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo. Pharmacological analyses revealed divergent responses to RMC-7977 in tumour versus normal tissues. Treated tumours exhibited waves of apoptosis along with sustained proliferative arrest, whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC mouse model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumours identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro. Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance.

A long-duration gamma-ray burst with a peculiar origin.

It is generally believed that long-duration gamma-ray bursts (GRBs) are associated with massive star core collapse1, whereas short-duration GRBs are associated with mergers of compact star binaries2. However, growing observations3-6 have suggested that oddball GRBs do exist, and several criteria (prompt emission properties, supernova/kilonova associations and host galaxy properties) rather than burst duration only are needed to classify GRBs physically7. A previously reported long-duration burst, GRB 060614 (ref. 3), could be viewed as a short GRB with extended emission if it were observed at a larger distance8 and was associated with a kilonova-like feature9. As a result, it belongs to the type I (compact star merger) GRB category and is probably of binary neutron star (NS) merger origin. Here we report a peculiar long-duration burst, GRB 211211A, whose prompt emission properties in many aspects differ from all known type I GRBs, yet its multiband observations suggest a non-massive-star origin. In particular, substantial excess emission in both optical and near-infrared wavelengths has been discovered (see also ref. 10), which resembles kilonova emission, as observed in some type I GRBs. These observations point towards a new progenitor type of GRBs. A scenario invoking a white dwarf (WD)-NS merger with a post-merger magnetar engine provides a self-consistent interpretation for all the observations, including prompt gamma rays, early X-ray afterglow, as well as the engine-fed11,12 kilonova emission.

Structured community transitions explain the switching capacity of microbial systems.

Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space-which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions: one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.

Pbp1 associates with Puf3 and promotes translation of its target mRNAs involved in mitochondrial biogenesis.

Pbp1 (poly(A)-binding protein-binding protein 1) is a cytoplasmic stress granule marker that is capable of forming condensates that function in the negative regulation of TORC1 signaling under respiratory conditions. Polyglutamine expansions in its mammalian ortholog ataxin-2 lead to spinocerebellar dysfunction due to toxic protein aggregation. Here, we show that loss of Pbp1 in S. cerevisiae leads to decreased amounts of mRNAs and mitochondrial proteins which are targets of Puf3, a member of the PUF (Pumilio and FBF) family of RNA-binding proteins. We found that Pbp1 supports the translation of Puf3-target mRNAs in respiratory conditions, such as those involved in the assembly of cytochrome c oxidase and subunits of mitochondrial ribosomes. We further show that Pbp1 and Puf3 interact through their respective low complexity domains, which is required for Puf3-target mRNA translation. Our findings reveal a key role for Pbp1-containing assemblies in enabling the translation of mRNAs critical for mitochondrial biogenesis and respiration. They may further explain prior associations of Pbp1/ataxin-2 with RNA, stress granule biology, mitochondrial function, and neuronal health.

Broader functions of TIR domains in Arabidopsis immunity.

TIR domains are NAD-degrading enzymes that function during immune signaling in prokaryotes, plants, and animals. In plants, most TIR domains are incorporated into intracellular immune receptors termed TNLs. In Arabidopsis, TIR-derived small molecules bind and activate EDS1 heterodimers, which in turn activate RNLs, a class of cation channel-forming immune receptors. RNL activation drives cytoplasmic Ca2+ influx, transcriptional reprogramming, pathogen resistance, and host cell death. We screened for mutants that suppress an RNL activation mimic allele and identified a TNL, SADR1. Despite being required for the function of an autoactivated RNL, SADR1 is not required for defense signaling triggered by other tested TNLs. SADR1 is required for defense signaling initiated by some transmembrane pattern recognition receptors and contributes to the unbridled spread of cell death in lesion simulating disease 1. Together with RNLs, SADR1 regulates defense gene expression at infection site borders, likely in a non-cell autonomous manner. RNL mutants that cannot sustain this pattern of gene expression are unable to prevent disease spread beyond localized infection sites, suggesting that this pattern corresponds to a pathogen containment mechanism. SADR1 potentiates RNL-driven immune signaling not only through the activation of EDS1 but also partially independently of EDS1. We studied EDS1-independent TIR function using nicotinamide, an NADase inhibitor. Nicotinamide decreased defense induction from transmembrane pattern recognition receptors and decreased calcium influx, pathogen growth restriction, and host cell death following intracellular immune receptor activation. We demonstrate that TIR domains can potentiate calcium influx and defense and are thus broadly required for Arabidopsis immunity.

A statistical physics approach for disease module detection.

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation in specific neighborhoods of the human protein-protein interaction (PPI) network (also known as the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency. Here, we developed a statistical physics approach based on the random-field Ising model (RFIM) for disease module detection, which is both mathematically rigorous and computationally efficient. We applied our RFIM approach to genome-wide association studies (GWAS) of ten complex diseases to examine its performance for disease module detection. We found that our RFIM approach outperforms existing methods in terms of computational efficiency, connectivity of disease modules, and robustness to the interactome incompleteness.

MPCLCDA: predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning.

Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between human diseases and circRNA can help in disease prevention, diagnosis and treatment. Traditional methods are time consuming and laborious. Meanwhile, computational models can effectively predict potential circRNA-disease associations (CDAs), but are restricted by limited data, resulting in data with high dimension and imbalance. In this study, we propose a model based on automatically selected meta-path and contrastive learning, called the MPCLCDA model. First, the model constructs a new heterogeneous network based on circRNA similarity, disease similarity and known association, via automatically selected meta-path and obtains the low-dimensional fusion features of nodes via graph convolutional networks. Then, contrastive learning is used to optimize the fusion features further, and obtain the node features that make the distinction between positive and negative samples more evident. Finally, circRNA-disease scores are predicted through a multilayer perceptron. The proposed method is compared with advanced methods on four datasets. The average area under the receiver operating characteristic curve, area under the precision-recall curve and F1 score under 5-fold cross-validation reached 0.9752, 0.9831 and 0.9745, respectively. Simultaneously, case studies on human diseases further prove the predictive ability and application value of this method.

NSRGRN: a network structure refinement method for gene regulatory network inference.

The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology's optimisation and the global topology's maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.

The role of ncRNA regulatory mechanisms in diseases-case on gestational diabetes.

Non-coding RNAs (ncRNAs) are a class of RNA molecules that do not have the potential to encode proteins. Meanwhile, they can occupy a significant portion of the human genome and participate in gene expression regulation through various mechanisms. Gestational diabetes mellitus (GDM) is a pathologic condition of carbohydrate intolerance that begins or is first detected during pregnancy, making it one of the most common pregnancy complications. Although the exact pathogenesis of GDM remains unclear, several recent studies have shown that ncRNAs play a crucial regulatory role in GDM. Herein, we present a comprehensive review on the multiple mechanisms of ncRNAs in GDM along with their potential role as biomarkers. In addition, we investigate the contribution of deep learning-based models in discovering disease-specific ncRNA biomarkers and elucidate the underlying mechanisms of ncRNA. This might assist community-wide efforts to obtain insights into the regulatory mechanisms of ncRNAs in disease and guide a novel approach for early diagnosis and treatment of disease.

Polatuzumab vedotin in previously untreated DLBCL: an Asia subpopulation analysis from the phase 3 POLARIX trial.

In the phase 3 POLARIX study in previously untreated diffuse large B-cell lymphoma, polatuzumab vedotin combined with rituximab plus cyclophosphamide, doxorubicin, and prednisone (Pola-R-CHP) significantly improved progression-free survival (PFS) compared with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) with similar safety. Patients were randomized 1:1 to 6 cycles of Pola-R-CHP or R-CHOP plus 2 cycles of rituximab alone. For registration of POLARIX in China, consistency of PFS in an Asia subpopulation (defined as ≥50% of the risk reduction in PFS expected in the global population) was evaluated. Overall, 281 patients were analyzed: 160 patients from Asia in the intention-to-treat (ITT) population of the global study and 121 from an ITT China extension cohort. Of these, 141 were randomized to Pola-R-CHP and 140 to R-CHOP. At data cutoff (28 June 2021; median follow-up 24.2 months), PFS met the consistency definition with the global population, and was superior with Pola-R-CHP vs R-CHOP (hazard ratio, 0.64; 95% confidence interval [CI], 0.40-1.03). Two-year PFS was 74.2% (95% CI, 65.7-82.7) and 66.5% (95% CI, 57.3-75.6) with Pola-R-CHP and R-CHOP, respectively. Safety was comparable between Pola-R-CHP and R-CHOP, including rates of grade 3 to 4 adverse events (AEs; 72.9% vs 66.2%, respectively), serious AEs (32.9% vs 32.4%), grade 5 AEs (1.4% vs 0.7%), AEs leading to study treatment discontinuation (5.0% vs 7.2%), and any-grade peripheral neuropathy (44.3% vs 50.4%). These findings demonstrate consistent efficacy and safety of Pola-R-CHP vs R-CHOP in the Asia and global populations in POLARIX. This trial was registered at https://clinicaltrials.gov/ct2/home as # NCT03274492.

RDscan: Extracting RNA-disease relationship from the literature based on pre-training model.

With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at https://cellknowledge.com.cn/RDscan/.

Hippocampal excitation-inhibition balance underlies the 5-HT2C receptor in modulating depressive behaviours.

The implication of 5-hydroxytryptamine 2C receptor (5-HT2CR) in depression is a topic of debate, and the underlying mechanisms remain largely unclear. We now elucidate hippocampal excitation-inhibition (E/I) balance underlies the regulatory effects of 5-HT2CR in depression. Molecular biological analyses showed that chronic mild stress (CMS) reduced the expression of 5-HT2CR in hippocampus. We revealed that inhibition of 5-HT2CR induced depressive-like behaviors, reduced GABA release and shifted the E/I balance towards excitation in CA3 pyramidal neurons by using behavioral analyses, microdialysis coupled with mass spectrum, and electrophysiological recording. Moreover, 5-HT2CR modulated neuronal nitric oxide synthase (nNOS)-carboxy-terminal PDZ ligand of nNOS (CAPON) interaction through influencing intracellular Ca2+ release, as determined by fiber photometry and coimmunoprecipitation. Notably, disruption of nNOS-CAPON by specific small molecule compound ZLc-002 or AAV-CMV-CAPON-125C-GFP, abolished 5-HT2CR inhibition-induced depressive-like behaviors, as well as the impairment in soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex assembly-mediated GABA vesicle release and a consequent E/I imbalance. Importantly, optogenetic inhibition of CA3 GABAergic neurons prevented the effects of AAV-CMV-CAPON-125C-GFP on depressive behaviors in the presence of 5-HT2CR antagonist. Conclusively, our findings disclose the regulatory role of 5-HT2CR in depressive-like behaviors and highlight the hippocampal nNOS-CAPON coupling-triggered E/I imbalance as a pivotal cellular event underpinning the behavioral consequences of 5-HT2CR inhibition.