Ribosome biogenesis is essential for hemogenic endothelial cells to generate hematopoietic stem cells.

Undergoing endothelial-to-hematopoietic transition, a small fraction of embryonic aortic endothelial cells specializes into hemogenic endothelial cells (HECs) and eventually gives rise to hematopoietic stem cells (HSCs). Previously we have found that the activity of ribosome biogenesis (RiBi) is highly enriched in the HSC-primed HECs as compared with adjacent arterial endothelial cells, however, whether RiBi is required in HECs for the generation of HSC remain to be determined. Here, we found that robust RiBi was markedly augmented from HEC stage along the HSC ontogeny. Pharmacological inhibition of RiBi completely impeded the generation of HSCs in explant cultures. Moreover, disrupting RiBi selectively interrupted the HSC generation potential of HECs rather than T1 pre-HSCs, which was in line with its influence on cell cycle activity. Further investigation revealed that upon HEC specification the master transcription factor Runx1 dramatically bound to the loci of genes involved in RiBi, thereby facilitating this biological process. Taken together, our study provided functional evidence showing the indispensable role of RiBi in HECs to generate HSCs, providing novel insights that may contribute to improving HSC regeneration strategies.

Graphormer supervised de novo protein design method and function validation.

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling.

The biological function of proteins is determined not only by their static structures but also by the dynamic properties of their conformational ensembles. Numerous high-accuracy static structure prediction tools have been recently developed based on deep learning; however, there remains a lack of efficient and accurate methods for exploring protein dynamic conformations. Traditionally, studies concerning protein dynamics have relied on molecular dynamics (MD) simulations, which incur significant computational costs for all-atom precision and struggle to adequately sample conformational spaces with high energy barriers. To overcome these limitations, various enhanced sampling techniques have been developed to accelerate sampling in MD. Traditional enhanced sampling approaches like replica exchange molecular dynamics (REMD) and frontier expansion sampling (FEXS) often follow the MD simulation approach and still cost a lot of computational resources and time. Variational autoencoders (VAEs), as a classic deep generative model, are not restricted by potential energy landscapes and can explore conformational spaces more efficiently than traditional methods. However, VAEs often face challenges in generating reasonable conformations for complex proteins, especially intrinsically disordered proteins (IDPs), which limits their application as an enhanced sampling method. In this study, we presented a novel deep learning model (named Phanto-IDP) that utilizes a graph-based encoder to extract protein features and a transformer-based decoder combined with variational sampling to generate highly accurate protein backbones. Ten IDPs and four structured proteins were used to evaluate the sampling ability of Phanto-IDP. The results demonstrate that Phanto-IDP has high fidelity and diversity in the generated conformation ensembles, making it a suitable tool for enhancing the efficiency of MD simulation, generating broader protein conformational space and a continuous protein transition path.

Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.

MOTIVATION: Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial role, and numerous successful methods have been developed. Notably, deep learning-based protein sequence design methods have experienced significant advancements in recent years. However, a comprehensive and systematic comparison and evaluation of these methods have been lacking, with indicators provided by different methods often inconsistent or lacking effectiveness. RESULTS: To address this gap, we have designed a diverse set of indicators that cover several important aspects, including sequence recovery, diversity, root-mean-square deviation of protein structure, secondary structure, and the distribution of polar and nonpolar amino acids. In our evaluation, we have employed an improved weighted inferiority-superiority distance method to comprehensively assess the performance of eight widely used deep learning-based protein sequence design methods. Our evaluation not only provides rankings of these methods but also offers optimization suggestions by analyzing the strengths and weaknesses of each method. Furthermore, we have developed a method to select the best temperature parameter and proposed solutions for the common issue of designing sequences with consecutive repetitive amino acids, which is often encountered in protein design methods. These findings can greatly assist users in selecting suitable protein sequence design methods. Overall, our work contributes to the field of protein sequence design by providing a comprehensive evaluation system and optimization suggestions for different methods.

Characteristic patterns of functional connectivity-mediated cerebral small vessel disease-related cognitive impairment and depression.

Cerebral small vessel disease is common in most individuals aged 60 years or older, and it is associated with cognitive dysfunction, depression, anxiety disorder, and mobility problems. Currently, many cerebral small vessel disease patients have both cognitive impairment and depressive symptoms, but the relationship between the 2 is unclear. The present research combined static and dynamic functional network connectivity methods to explore the patterns of functional networks in cerebral small vessel disease individuals with cognitive impairment and depression (cerebral small vessel disease-mild cognitive impairment with depression) and their relationship. We found specific functional network patterns in the cerebral small vessel disease-mild cognitive impairment with depression individuals (P < 0.05). The cerebral small vessel disease individuals with depression exhibited unstable dynamic functional network connectivity states (transitions likelihood: P = 0.040). In addition, we found that the connections within the lateral visual network between the sensorimotor network and ventral attention network could mediate white matter hyperintensity-related cognitive impairment (indirect effect: 0.064; 95% CI: 0.003, 0.170) and depression (indirect effect: -0.415; 95% CI: -1.080, -0.011). Cognitive function can negatively regulate white matter hyperintensity-related depression. These findings elucidate the association between cognitive impairment and depression and provide new insights into the underlying mechanism of cerebral small vessel disease-related cognitive dysfunction and depression.

Transcriptome-wide subtyping of pediatric and adult T cell acute lymphoblastic leukemia in an international study of 707 cases.

T cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy of T cell progenitors, known to be a heterogeneous disease in pediatric and adult patients. Here we attempted to better understand the disease at the molecular level based on the transcriptomic landscape of 707 T-ALL patients (510 pediatric, 190 adult patients, and 7 with unknown age; 599 from published cohorts and 108 newly investigated). Leveraging the information of gene expression enabled us to identify 10 subtypes (G1­G10), including the previously undescribed one characterized by GATA3 mutations, with GATA3R276Q capable of affecting lymphocyte development in zebrafish. Through associating with T cell differentiation stages, we found that high expression of LYL1/LMO2/SPI1/HOXA (G1­G6) might represent the early T cell progenitor, pro/precortical/cortical stage with a relatively high age of disease onset, and lymphoblasts with TLX3/TLX1 high expression (G7­G8) could be blocked at the cortical/postcortical stage, while those with high expression of NKX2-1/TAL1/LMO1 (G9­G10) might correspond to cortical/postcortical/mature stages of T cell development. Notably, adult patients harbored more cooperative mutations among epigenetic regulators, and genes involved in JAK-STAT and RAS signaling pathways, with 44% of patients aged 40 y or above in G1 bearing DNMT3A/IDH2 mutations usually seen in acute myeloid leukemia, suggesting the nature of mixed phenotype acute leukemia.

Map conformational landscapes of intrinsically disordered proteins with polymer physics quantities.

Disordered proteins are conformationally flexible proteins that are biologically important and have been implicated in devastating diseases such as Alzheimer's disease and cancer. Unlike stably folded structured proteins, disordered proteins sample a range of different conformations that needs to be accounted for. Here, we treat disordered proteins as polymer chains, and compute a dimensionless quantity called instantaneous shape ratio (Rs), as Rs = Ree2/Rg2, where Ree is end-to-end distance and Rg is radius of gyration. Extended protein conformations tend to have high Ree compared with Rg, and thus have high Rs values, whereas compact conformations have smaller Rs values. We use a scatter plot of Rs (representing shape) against Rg (representing size) as a simple map of conformational landscapes. We first examine the conformational landscape of simple polymer models such as Random Walk, Self-Avoiding Walk, and Gaussian Walk (GW), and we notice that all protein/polymer maps lie within the boundaries of the GW map. We thus use the GW map as a reference and, to assess conformational diversity, we compute the fraction of the GW conformations (fC) covered by each protein/polymer. Disordered proteins all have high fC scores, consistent with their disordered nature. Each disordered protein accesses a different region of the reference map, revealing differences in their conformational ensembles. We additionally examine the conformational maps of the nonviral gene delivery vector polyethyleneimine at various protonation states, and find that they resemble disordered proteins, with coverage of the reference map decreasing with increasing protonation state, indicating decreasing conformational diversity. We propose that our method of combining Rs and Rg in a scatter plot generates a simple, meaningful map of the conformational landscape of a disordered protein, which in turn can be used to assess conformational diversity of disordered proteins.

PTMint database of experimentally verified PTM regulation on protein-protein interaction.

MOTIVATION: Post-translational modification (PTM) is an important biochemical process. which includes six most well-studied types: phosphorylation, acetylation, methylation, sumoylation, ubiquitylation and glycosylation. PTM is involved in various cell signaling pathways and biological processes. Abnormal PTM status is closely associated with severe diseases (such as cancer and neurologic diseases) by regulating protein functions, such as protein-protein interactions (PPIs). A set of databases was constructed separately for PTM sites and PPI; however, the resource of regulation for PTM on PPI is still unsolved. RESULTS: Here, we firstly constructed a public accessible database of PTMint (PTMs that are associated with PPIs) (https://ptmint.sjtu.edu.cn/) that contains manually curated complete experimental evidence of the PTM regulation on PPIs in multiple organisms, including Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Schizosaccharomyces pombe. Currently, the first version of PTMint encompassed 2477 non-redundant PTM sites in 1169 proteins affecting 2371 protein-protein pairs involving 357 diseases. Various annotations were systematically integrated, such as protein sequence, structure properties and protein complex analysis. PTMint database can help to insight into disease mechanism, disease diagnosis and drug discovery associated with PTM and PPI. AVAILABILITY AND IMPLEMENTATION: PTMint is freely available at: https://ptmint.sjtu.edu.cn/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Real-world efficacy, safety data and predictive clinical parameters for treatment outcomes in advanced soft tissue sarcoma treated with combined immunotherapy and antiangiogenic therapy.

BACKGROUND: The combination of immunotherapy and antiangiogenic therapy has shown potential in the treatment of numerous malignant tumors, but limited evidence was available for soft tissue sarcomas (STS). Therefore, the aim of the present study is to assess the efficacy and safety of immunotherapy in conjunction with antiangiogenic therapy in patients diagnosed with advanced STS (aSTS). METHODS: The study enrolled patients with aSTS from January 2014 to October 2022. Eligible participants had previously received anthracycline-based chemotherapy, presented with an anthracycline-resistant sarcoma subtype, or were ineligible for anthracycline treatment due to medical conditions. Following enrollment, these patients received a combination of immunotherapy and antiangiogenic therapy. The primary endpoints were the objective response rate (ORR) and progression-free survival (PFS), while the secondary endpoints included the disease control rate (DCR), overall survival (OS), and the incidence of adverse events. RESULTS: Fifty-one patients were included in this cohort study. The median duration of follow-up was 15.8 months. The ORR and DCR were 17.6%, and 76.5%, respectively. The median PFS (mPFS) was 5.8 months (95% CI: 4.8-6.8) for all patients, and the median OS had not been reached as of the date cutoff. Multivariate analysis indicated that Eastern Cooperative Oncology Group performance status of 0-1 and ≤ second-line treatment were positive predictors for both PFS and OS. Patients with alveolar soft part sarcoma or clear cell sarcoma had longer mPFS (16.2 months, 95% CI: 7.8-25.6) when compared to those with other subtypes of STS (4.4 months, 95% CI: 1.4-7.5, P < 0.001). Among the observed adverse events, hypertension (23.5%), diarrhea (17.6%), and proteinuria (17.6%) were the most common, with no treatment-related deaths reported. CONCLUSION: The combination of immunotherapy and antiangiogenic agents showed promising efficacy and acceptable toxicity in patients with aSTS, especially those with alveolar soft part sarcoma or clear cell sarcoma.

Systemic lupus erythematosus with chronic persistent intracranial hypertension: A case report.

OBJECTIVE: The aim is to investigate the clinical characteristics of systemic lupus erythematosus with intracranial hypertension. METHODS: The clinical characteristics of one case of systemic lupus erythematosus with chronic persistent intracranial hypertension were analyzed, and related literature was reviewed by searching Medline and Wanfang databases. RESULTS: Intracranial hypertension in SLE patients may occur at the onset or during the course of the disease. Our patient was diagnosed with IH 3 years after the onset of SLE. Headache and papilledema were the most common symptoms of intracranial hypertension, followed by nausea or vomiting, vision changes, and cerebral palsy. Our patient had a headache and cranial hypertension that lasted for years, but no papilledema was found. Corticosteroid is currently the mainstay of the treatment of IIH in patients with SLE, and immunosuppressive agents, acetazolamide, intravenous mannitol and furosemide are also used. However, our patient did not respond to these treatments and presents the characteristics of chronic persistent intracranial hypertension. CONCLUSION: Systemic lupus erythematosus with intracranial hypertension is a rare manifestation of SLE, which is not completely parallel to SLE activity. Headache and papilledema were the most common presenting symptoms. Different from previous reported cases, our patient had poor response to treatments, showing chronic and persistent characteristics.