Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel in vitro tubular model to recapitulate features of distal airways: the bronchioid

Background Airflow limitation is the hallmark of obstructive pulmonary diseases. While numerous in vitro airway models already exist, chronic obstructives diseases currently lack an 3D culture system enabling to unveil the developmental processes underlying airway remodelling and the associated constrictive limitation. Here, we aimed to engineer a model of small airways, which would overcome the limitations of current culture systems. Methods Working with the Cellular Capsule Technology, we developed a so-called bronchioid model, using an innovative tubular cell-based assay and human bronchial adult stem cells derived from clinical samples. Results We produced a tubular scaffold made of alginate gel, that drives the spontaneous self-organisation of epithelial cells. Fine control of the level of contraction is required to obtain a model of distal bronchiole, with physiologically relevant shape and size. 3D imaging, gene expression and single-cell RNA-seq analysis of bronchioids made of bronchial epithelial cells demonstrates the tubular organization, the formation of epithelial junctions, as well as differentiation into ciliated and goblet cells. Ciliary beating is observed, at a decreased frequency in bronchioids made of cells from COPD patients. The bronchioid can be infected by rhinovirus. An air-liquid interface is introduced, that modulates gene expression. Conclusion We provide here a proof of concept of a perfusable bronchioid, with proper mucociliary and contractile functions. Key advantages of our approach, such as the air-liquid interface, the lumen accessibility, the recapitulation of pathological features and possible assessment of clinically pertinent endpoints, will make our pulmonary organoid-like model a powerful tool for pre-clinical studies.
Deciphering abdominal aortic diseases through T-cell clonal repertoire of perivascular adipose tissue

Recent studies suggested that immune-mediated inflammation of perivascular adipose tissue (PVAT) of abdominal aortic aneurysms (AAA) contributes to disease development and progression. Whether the PVAT of AAA is characterized by a specific adaptive immune signature remains unknown. To investigate this hypothesis, we sequenced the T-cell receptor β-chain (TCRβ) in the PVAT of AAA patients and compared with patients with aortic occlusive disease (AOD), who share with the former anatomical site of the lesion, risk factors but differ in pathogenic mechanisms. Our results demonstrate that AAA patients have a lower repertoire diversity than those with AOD and significant differences in V/J gene segment usage. Furthermore, we identified a set of 7 public TCRβ clonotypes that distinguished AAA and AOD with very high accuracy. We also found that the TCRβ repertoire differentially characterizes small and large AAA (aortic diameter <55 mm and ≥55 mm, respectively). This work supports the hypothesis that T-cell-mediated immunity is fundamental in AAA pathogenesis and opens up new clinical perspectives. Summary Different immune mechanisms may play a key role in the pathogenesis of distal aortic aneurysm and aortoiliac occlusive disease. The TCRβ repertoire of perivascular adipose tissue differs between the two pathologic conditions, suggesting the involvement of specific antigen-specific immune responses.
A Small Genome Amidst the Giants: Evidence of Genome Reduction in a Small Tubulinid Free-Living Amoeba

This study investigates the genomic characteristics of Echinamoeba silvestris, a small-sized amoeba within the Tubulinea clade of the Amoebozoa supergroup. Despite Tubulinea’s significance in various fields, genomic data for this clade have been scarce. E. silvestris presents the smallest free-living amoeba genome within Tubulinea and Amoebozoa to date. Comparative analysis reveals intriguing parallels with parasitic lineages in terms of genome size and predicted gene numbers, emphasizing the need to understand the consequences of reduced genomes in free-living amoebae. Functional categorization of predicted genes in E. silvestris shows similar percentages of ortholog groups to other amoebae in various categories, but a distinctive feature is the extensive gene contraction in orphan (ORFan) genes and those involved in biological processes. Notably, among the few genes that underwent expansion, none are related to cellular components, suggesting adaptive processes that streamline biological processes and cellular components for efficiency and energy conservation. The investigation delves into genomic structural evidence, including gene content and repetitive elements, illuminating the distinctive genomic traits of E. silvestris and providing reinforcement for its compact genome size. Overall, this research underscores the diversity within Tubulinea, highlights knowledge gaps in Amoebozoa genomics, and positions E. silvestris as a valuable addition to genomic datasets, prompting further exploration of complexities in Amoebozoa diversity and genome evolution.
Unified Multi-caller Ensemble (UME) generates an unbiased maize haplotype map for variable coverage whole genome data

We present a novel diversity-focused haplotype map (HapMap) that characterizes over 64.5 million maize (Zea mays ssp. mays) single nucleotide polymorphisms (SNPs) genotyped across 818 individuals from diverse backgrounds. This HapMap aims to balance the variation obtained from domesticated landraces and inbred lines, outgroup Zea spp. and more distant Tripsacum spp. in order to minimize ascertainment bias for diversity studies. Included individuals derive from public data from various experimental setups and coverages, which is challenging for standard SNP callers to accommodate. We provide evidence of coverage biases associated with standard callers that influence resulting variation and introduce a novel approach called Unified Multi-Caller Ensemble (UME), which enhances variant calling accuracy in low-coverage and mixed-coverage genomic datasets. UME corrects for coverage bias resulting from inter-sample coverage heterogeneity by leveraging evidence from variant callers with orthogonal strategies, re-calibrating the error probabilities across callers to minimize the impact of error biases inherent to a given caller. It outperforms individual strategies and excels in de novo variant calling, taking advantage of instances of higher depth reads, even in low coverage individuals, while preserving biologically informative variant relationships across coverage levels. An important feature of UME is the independence from population allele frequencies in the discovery panel, thus avoiding ascertainment bias resulting from unbalanced input genetic diversity. Discovered variants are unbiased because no population filtering is used, and the full diversity of SNPs is retained in the final variant call set to maximize the utility of the dataset for production calling newly sequenced samples. We present a strategy for filtering the recalibrated error profiles that relies on maximizing demographic signals to retain genetic relationships within the population while reducing sequencing error. After the variant discovery phase, we employ the UME production stage, which enriches genotype calling across all coverage levels, benefiting low-coverage samples. Error introduced in this process is removed through subsequent filtering. Using this approach, we generated a coverage bias-controlled maize HapMap database, providing a comprehensive representation of maize accessions and emphasizing landrace diversity. This diverse panel of domesticated maize and outgroups from across the Americas enables accurate genotyping in low-coverage samples while offering crucial context for interpreting diversity, particularly for natural diversity and paleogenomic analyses.
Large-Scale Information Retrieval and Correction of Noisy Pharmacogenomic Datasets through Residual Thresholded Deep Matrix Factorization

Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in molecularly characterized cancer cell lines (CCLs), resulting in a number of open drug sensitivity datasets for drug biomarker discovery. However, a significant inconsistency in drug sensitivity values among these datasets has been noted. Such inconsistency indicates the presence of substantial noise, subsequently hindering downstream analyses. To address the noise in drug sensitivity data, we introduce a robust and scalable deep learning framework, Residual Thresholded Deep Matrix Factorization (RT-DMF). This method takes a single drug sensitivity data matrix as its sole input and outputs a corrected and imputed matrix. Deep Matrix Factorization (DMF) excels at uncovering subtle patterns, due to its minimal reliance on data structure assumptions. This attribute significantly boosts DMF’s ability to identify complex hidden patterns among nuisance effects in the data, thereby facilitating the detection of signals that are therapeutically relevant. Furthermore, RT-DMF incorporates an iterative residual thresholding (RT) procedure, which plays a crucial role in retaining signals more likely to hold therapeutic importance. Validation using simulated datasets and real pharmacogenomics datasets demonstrates the effectiveness of our approach in correcting noise and imputing missing data in drug sensitivity datasets (open source package available at https://github.com/tomwhoooo/rtdmf).
ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers

Protein design holds immense potential for optimizing naturally occurring proteins, with broad applications in drug discovery, material design, and sustainability. How-ever, computational methods for protein engineering are confronted with significant challenges, such as an expansive design space, sparse functional regions, and a scarcity of available labels. These issues are further exacerbated in practice by the fact most real-life design scenarios necessitate the simultaneous optimization of multiple properties. In this work, we introduce ProteinNPT, a non-parametric trans-former variant tailored to protein sequences and particularly suited to label-scarce and multi-task learning settings. We first focus on the supervised fitness prediction setting and develop several cross-validation schemes which support robust perfor-mance assessment. We subsequently reimplement prior top-performing baselines, introduce several extensions of these baselines by integrating diverse branches of the protein engineering literature, and demonstrate that ProteinNPT consistently outperforms all of them across a diverse set of protein property prediction tasks. Finally, we demonstrate the value of our approach for iterative protein design across extensive in silico Bayesian optimization and conditional sampling experiments.
A proteogenomic surfaceome study identifies DLK1 as an immunotherapeutic target in neuroblastoma

Cancer immunotherapies have produced remarkable results in B-cell malignancies; however, optimal cell surface targets for many solid cancers remain elusive. Here, we present an integrative proteomic, transcriptomic, and epigenomic analysis of tumor specimens along with normal tissues to identify biologically relevant cell surface proteins that can serve as immunotherapeutic targets for neuroblastoma, an often-fatal childhood cancer of the developing nervous system. We apply this approach to human-derived cell lines (N=9) and cell/patient-derived xenograft (N=12) models of neuroblastoma. Plasma membrane-enriched mass spectrometry identified 1,461 cell surface proteins in cell lines and 1,401 in xenograft models, respectively. Additional proteogenomic analyses revealed 60 high-confidence candidate immunotherapeutic targets and we prioritized Delta-like canonical notch ligand 1 (DLK1) for further study. High expression of DLK1 directly correlated with the presence of a super-enhancer spanning the DLK1 locus. Robust cell surface expression of DLK1 was validated by immunofluorescence, flow cytometry, and immunohistochemistry. Short hairpin RNA mediated silencing of DLK1 in neuroblastoma cells resulted in increased cellular differentiation. ADCT-701, a DLK1-targeting antibody-drug conjugate (ADC), showed potent and specific cytotoxicity in DLK1-expressing neuroblastoma xenograft models. Moreover, DLK1 is highly expressed in several adult cancer types, including adrenocortical carcinoma (ACC), pheochromocytoma/paraganglioma (PCPG), hepatoblastoma, and small cell lung cancer (SCLC), suggesting potential clinical benefit beyond neuroblastoma. Taken together, our study demonstrates the utility of comprehensive cancer surfaceome characterization and credentials DLK1 as an immunotherapeutic target. Highlights Plasma membrane enriched proteomics defines surfaceome of neuroblastoma Multi-omic data integration prioritizes DLK1 as a candidate immunotherapeutic target in neuroblastoma and other cancers DLK1 expression is driven by a super-enhancer DLK1 silencing in neuroblastoma cells results in cellular differentiation ADCT-701, a DLK1-targeting antibody-drug conjugate, shows potent and specific cytotoxicity in DLK1-expressing neuroblastoma preclinical models
Improved mRNA electroporation into immature oocytes to examine protein dynamics during development

One of the major cause of oocyte quality deterioration along with aging, chromosome segregation abnormalities occur mainly during meiosis I. However, currently, there is a technical limitation in the introduction of mRNA into premature oocytes without impairing embryonic developmental ability. In this study, we established a low-invasive electroporation (EP) method to introduce mRNA into pre-ovulatory, germinal vesicle (GV) mouse oocytes in an easier manner than the traditional microinjection method. The EP method with an optimized impedance value resulted in the efficient introduction of mRNAs encoding enhanced green fluorescent protein (EGFP) into the GV oocytes surrounded by cumulus cells at a survival rate of 95.0%. Furthermore, the introduction of histone H2B-EGFP mRNA into the GV oocytes labeled most of the oocytes without affecting the blastocyst development rate, indicating the feasibility of the visualization of oocyte chromosomal dynamics that enable us to assay chromosomal integrity in oocyte maturation and cell count in embryonic development. The establishment of this EP method offers extensive assays to select pre-implantation embryos and enables the surveying of essential factors for mammalian oocyte quality determination. Summary blurb This study introduces a low-invasive electroporation method with high survival rate and developmental ability, offering a potential breakthrough in preimplantation embryo treatment and assessment.
Predictive Power of Machine Learning Models for Relapse Outcomes in Acute Myeloid Leukemia: Unveiling Key Genes and Pathways for Improved Patient Management

Acute Myeloid Leukemia (AML) is a challenging form of blood cancer requiring accurate relapse prediction for effective therapy and patient management. In this study, we applied multiple machine learning techniques to a dataset of AML patients in order to develop a reliable model for predicting relapse and guiding treatment decisions. We utilized various feature selection methods to identify the most relevant features associated with relapse. Additionally, we investigated gene ontology using the Gene Ontology (GO) database to gain insights into the biological processes and KEGG pathways related to the selected features. Our findings revealed key genes and pathways implicated in AML relapse. Among the machine learning models, Decision Tree (DT) showed the highest accuracy in predicting relapse outcomes. Furthermore, we compared the performance of DT models across different feature selections, highlighting the significance of specific factors such as MCL1, WBC, HGB, and BAD.p112 in relapse prediction. The results of our study have important implications for tailoring treatment plans and improving patient outcomes in AML. By accurately identifying patients at high risk of relapse, our model can aid in early interventions and personalized therapies. Ultimately, our research contributes to advancing the field of machine learning in AML and lays the foundation for developing effective strategies to combat relapse in this disease.
DNA-terminus-dependent transcription by T7 RNA polymerase and its C-helix mutants

The remarkable success of mRNA-based vaccines has underscored their potential as a novel biotechnology platform for vaccine development and therapeutic protein delivery. However, the single-subunit RNA polymerase from bacteriophage T7 widely used for in vitro transcription is well known to generate double-stranded RNA (dsRNA) byproducts that strongly stimulate the mammalian innate immune response. The dsRNA was reported to be originated from self-templated RNA extension or promoter-independent transcription. Here, we identified that the primary source of the full-length dsRNA during in vitro transcription is the DNA-terminus-initiated transcription by T7 RNA polymerase. Guanosines or cytosines at the end of DNA templates enhance the DNA-terminus-initiated transcription. Moreover, we found that aromatic residues located at position 47 in the C-helix interfere with the binding of T7 RNA polymerase to DNA termini, leading to a significant reduction in the production of full-length dsRNA. As a result, the mRNA synthesized using the T7 RNA polymerase G47W mutant exhibits higher expression efficiency and lower immunogenicity compared to the mRNA produced using the wild-type T7 RNA polymerase.
Endogenous opioid receptor system mediates costly altruism in the human brain

Functional neuroimaging studies suggest that a large-scale brain network transforms others’ pain into its vicarious representation in the observer, potentially modulating helping behaviour. The neuromolecular basis of individual differences in vicarious pain and helping, however, have remain poorly understood. Here we investigated the role of the endogenous μ-opioid receptor (MOR) system – known for its role in analgesia and sociability – in altruistic costly helping. MOR density was measured with high-affinity agonist radioligand [11C]carfentanil. In a separate fMRI experiment, participants could choose to donate money to reduce the pain of the confederate who was subjected to electric shocks of varying intensity. We found that subjects were in general willing to engage in costly helping, and haemodynamic activity in amydala, anterior insula, anterior cingulate cortex, striatum, primary motor cortex, primary somatosensory cortex, and thalamus increased when participants witnessed the pain of others. These haemodynamic responses were negatively associated with MORs availability in the striatolimbic and cortical emotion circuits. In turn, haemodynamic responses during helping were positively associated with MOR availability in the anterior cingulate cortex and hippocampus. Altogether these data suggest that the endogenous MOR system modulates the processing of altruistic behaviour and costly helping in the human brain.
The origins and diversification of Holarctic brown bear populations inferred from genomes of past and present populations

The brown bear (Ursus arctos) is one of the survivors of the Late Quaternary megafauna extinctions. However, despite being widely distributed across the Holarctic, brown bears have experienced extensive range reductions, and even extirpations in some geographic regions. Previous research efforts utilising genetic data have provided valuable insights into their evolutionary history. However, most studies have been limited to contemporary individuals or mitochondrial DNA, limiting insights into population processes that preceded the present. Here we present genomic data from two Late Pleistocene brown bears from Honshu, Japan, and eastern Siberia, and combine them with published contemporary and ancient genomes from across the Holarctic range of brown bears to investigate the evolutionary relationships among brown bear populations through time and space. By including genomic data from Late Pleistocene and Holocene individuals sampled outside the current distribution range we uncover diversity not present in the contemporary populations. Notably, although contemporary individuals display geographically structured populations most likely driven by isolation-by-distance, this pattern varies among the ancient samples across different regions. The inclusion of ancient brown bears in our analysis provides novel insights into the evolutionary history of brown bears and contributes to understanding the populations and diversity lost during the Late Quaternary.
Faithful Model Evaluation for Model-Based Metrics

Statistical significance testing is used in natural language processing (NLP) to determine whether the results of a study or experiment are likely to be due to chance or if they reflect a genuine relationship. A key step in significance testing is the estimation of confidence interval which is a function of sample variance. Sample variance calculation is straightforward when evaluating against ground truth. However, in many cases, a metric model is often used for evaluation. For example, to compare toxicity of two large language models, a toxicity classifier is used for evaluation. Existing works usually do not consider the variance change due to metric model errors, which can lead to wrong conclusions. In this work, we establish the mathematical foundation of significance testing for model-based metrics. With experiments on public benchmark datasets and a production system, we show that considering metric model errors to calculate sample variances for model-based metrics changes the conclusions in certain experiments.
Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients

Fine-tuning all parameters of large language models (LLMs) requires significant computational resources and is time-consuming. Recent parameter-efficient tuning methods such as Adapter tuning, Prefix tuning, and LoRA allow updating a small subset of parameters in large language models. However, they can only save approximately 30% of the training memory requirements because gradient computation and backpropagation are still necessary for these methods. This paper proposes a novel parameter-efficient tuning method for LLMs without calculating their gradients. Leveraging the discernible similarities between the parameter-efficient modules of the same task learned by both large and small language models, we put forward a strategy for transferring the parameter-efficient modules derived initially from small language models to much larger ones. To ensure a smooth and effective adaptation process, we introduce a Bridge model to guarantee dimensional consistency while stimulating a dynamic interaction be-tween the models. We demonstrate the effectiveness of our method using the T5 and GPT-2 series of language models on the SuperGLUE benchmark. Our method achieves comparable performance to fine-tuning and parameter-efficient tuning on large language models without needing gradient-based optimization. Additionally, our method achieves up to 5.7 × memory reduction compared to parameter-efficient tuning.