PFOS impairs cardiac function and energy metabolism under high-fat diet: Insights into role of circulating macrophage emphasized by exposure distribution.

Per- and polyfluoroalkyl substances (PFAS), widely utilized in consumer products, have been linked to an increased risk of cardiovascular disease (CVD). With the increasing prevalence of high-fat diet, a common risk factor for CVD, the PFAS exposed populations who consume a high-fat diet will inevitably grow and may have a higher CVD risk. However, the potential toxic effect and mode of action remain elusive. We constructed a mouse model orally exposed to perfluorooctane sulfonate (PFOS), a prototypical PFAS, and fed a high-fat diet. PFOS exposure induced cardiomyopathy and structural abnormalities in the mice heart. Moreover, a characteristic of energy metabolism remodeling from aerobic to anaerobic process was observed. Interestingly, PFOS was rarely detected in heart but showed high level in serum, suggesting an indirect route of action for PFOS-caused cardiac toxicity. We further demonstrated that PFOS-caused circulating inflammation promoted metabolic remodeling and contractile dysfunction in cardiomyocytes. Wherein, PFOS stimulated the release of IL-1ß from circulating proinflammatory macrophages mediated by NF-κB and caspase-1. This study provides valuable data on PFAS-induced cardiac risks associated with exposed populations with increasing high-fat diet consumption, highlighting the significance of indirect pathways in PFOS's impact on the heart, based on the distribution of internal exposure.

Minimally-invasive implantable device enhances brain cancer suppression.

Current brain tumor treatments are limited by the skull and BBB, leading to poor prognosis and short survival for glioma patients. We introduce a novel minimally-invasive brain tumor suppression (MIBTS) device combining personalized intracranial electric field therapy with in-situ chemotherapeutic coating. The core of our MIBTS technique is a wireless-ultrasound-powered, chip-sized, lightweight device with all functional circuits encapsulated in a small but efficient "Swiss-roll" structure, guaranteeing enhanced energy conversion while requiring tiny implantation windows ( ~ 3 × 5 mm), which favors broad consumers acceptance and easy-to-use of the device. Compared with existing technologies, competitive advantages in terms of tumor suppressive efficacy and therapeutic resolution were noticed, with maximum ~80% higher suppression effect than first-line chemotherapy and 50-70% higher than the most advanced tumor treating field technology. In addition, patient-personalized therapy strategies could be tuned from the MIBTS without increasing size or adding circuits on the integrated chip, ensuring the optimal therapeutic effect and avoid tumor resistance. These groundbreaking achievements of MIBTS offer new hope for controlling tumor recurrence and extending patient survival.

Variability in soybean yield responses to elevated atmospheric CO2: Insights from non-structural carbohydrate remobilisation during seed filling.

The increasing atmospheric CO2 concentration (e[CO2]) has mixed effects on soybean most varieties' yield. This study elucidated the effect of e[CO2] on soybean yield and the underlying mechanisms related to photosynthetic capacity, non-structural carbohydrate (NSC) accumulation, and remobilisation. Four soybean cultivars were cultivated in open-top chambers at two CO2 levels. Photosynthesis rates were determined from R2 to R6. Plants were sampled at R5 and R8 to determine carbohydrate concentrations. There were significant variations in yield responses among the soybean cultivars under e[CO2], from no change in DS1 to a 22% increase in SN14. DS1 and SN14 had the smallest and largest increase, respectively, in daily carbon assimilation capacity. Under e[CO2], DS1, MF5, and XHJ had an increase in Ci, at which point the transition from Rubisco-limited to ribulose-1,5-bisphosphate regeneration-limited photosynthesis occurred, in contrast with SN14. Thus, the cultivars might have distinct mechanisms that enhance photosynthesis under e[CO2] conditions. A positive correlation was between daily carbon assimilation response to e[CO2] and soybean yield, emphasising the importance of enhanced photosynthate accumulation before the R5 stage in determining yield response to e[CO2]. E[CO2] significantly influenced NSC accumulation in vegetative organs at R5, with variation among cultivars. There was enhanced NSC remobilisation during seed filling, indicating cultivar-specific responses to the remobilisation of sucrose and soluble sugars, excluding sucrose and starch. A positive correlation was between leaf and stem NSC remobilisation and yield response to e[CO2], emphasising the role of genetic differences in carbohydrate remobilisation mechanisms in determining soybean yield variation under elevated CO2 levels.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data.

BACKGROUND: Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. RESULTS: We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . CONCLUSIONS: Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.

Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.

sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.

Genome-wide association studies (GWAS) have identified thousands of variants in the human genome with autoimmune diseases. However, identifying functional regulatory variants associated with autoimmune diseases remains challenging, largely because of insufficient experimental validation data. We adopt the concept of semi-supervised learning by combining labeled and unlabeled data to develop a deep learning-based algorithm framework, sscNOVA, to predict functional regulatory variants in autoimmune diseases and analyze the functional characteristics of these regulatory variants. Compared to traditional supervised learning methods, our approach leverages more variants' data to explore the relationship between functional regulatory variants and autoimmune diseases. Based on the experimentally curated testing dataset and evaluation metrics, we find that sscNOVA outperforms other state-of-the-art methods. Furthermore, we illustrate that sscNOVA can help to improve the prioritization of functional regulatory variants from lead single-nucleotide polymorphisms and the proxy variants in autoimmune GWAS data.

Toxic metal contamination effects mediated by hotspot intensity of soil enzymes and microbial community structure.

Metal contamination from mine waste is a widespread threat to soil health. Understanding of the effects of toxic metals from mine waste on the spatial patterning of rhizosphere enzymes and the rhizosphere microbiome remains elusive. Using zymography and high-throughput sequencing, we conducted a mesocosm experiment with mine-contaminated soil, to compare the effects of different concentrations of toxic metals on exoenzyme kinetics, microbial communities, and maize growth. The negative effects of toxic metals exerted their effects largely on enzymatic hotspots in the rhizosphere zone, affecting both resistance and the area of hotspots. This study thus revealed the key importance of such hotspots in overall changes in soil enzymatic activity under metal toxicity. Statistical and functional guild analysis suggested that these enzymatic changes and associated microbial community changes were involved in the inhibition of maize growth. Keystone species of bacteria displayed negative correlations with toxic metals and positive correlations with the activity of enzymatic hotspots, suggesting a potential role. This study contributes to an emerging paradigm, that changes both in the activity of soil enzymes and soil biota - whether due to substrate addition or in this case toxicity - are largely confined to enzymatic hotspot areas.

Inferring single-cell copy number profiles through cross-cell segmentation of read counts.

BACKGROUND: Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data. RESULTS: By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation-maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna . CONCLUSIONS: Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.