Search

IntroductionSystemic dimorphic fungi pose a significant public health challenge, causing over one million new infections annually. The dimorphic transition between saprophytic mycelia and pathogenic yeasts is strongly associated with the pathogenesis of dimorphic fungi. However, despite the dynamic nature of dimorphic transition, the current omics studies focused on dimorphic transition primarily employ static strategies, partly due to the lack of suitable dynamic analytical methods.MethodsWe conducted time-course transcriptional profiling during the dimorphic transition of Talaromyces marneffei, a model organism for thermally dimorphic fungi. To capture non-uniform and nonlinear transcriptional changes, we developed DyGAM-NS (dynamic optimized generalized additive model with natural cubic smoothing). The performance of DyGAM-NS was evaluated by comparison with seven other commonly used time-course analysis methods. Based on dimorphic transition induced genes (DTIGs) identified by DyGAM-NS, cluster analysis was utilized to discern distinct gene expression patterns throughout dimorphic transitions of T. marneffei. Simultaneously, a gene expression regulatory network was constructed to probe pivotal regulatory elements governing the dimorphic transitions.ResultsBy using DyGAM-NS, model, we identified 5,223 DTIGs of T. marneffei. Notably, the DyGAM-NS model showcases performance on par with or superior to other commonly used models, achieving the highest F1 score in our assessment. Moreover, the DyGAM-NS model also demonstrates potential in predicting gene expression levels throughout temporal processes. The cluster analysis of DTIGs suggests divergent gene expression patterns between mycelium-to-yeast and yeast-to-mycelium transitions, indicating the asymmetrical nature of two transition directions. Additionally, leveraging the identified DTIGs, we constructed a regulatory network for the dimorphic transition and identified two zinc finger-containing transcription factors that potentially regulate dimorphic transition in T. marneffei.DiscussionOur study elucidates the dynamic transcriptional profile changes during the dimorphic transition of T. marneffei. Furthermore, it offers a novel perspective for unraveling the underlying mechanisms of fungal dimorphism, emphasizing the importance of dynamic analytical methods in understanding complex biological processes.

Abstract Background Human endogenous retroviruses (HERVs), the remnants of ancient retroviruses, account for 8% of the human genome, but most have lost their transcriptional abilities under physiological conditions. However, mounting evidence shows that several expressed HERVs do exert biological functions. Here, we systematically characterize physiologically expressed HERVs and examine whether they may give insight into the molecular fundamentals of human development and disease. Results We systematically identify 13,889 expressed HERVs across normal body sites and demonstrate that they are expressed in body site-specific patterns and also by sex, ethnicity, and age. Analyzing cis-ERV-related quantitative trait loci, we find that 5435 hervRNAs are regulated by genetic variants. Combining this with a genome-wide association study, we elucidate that the dysregulation of expressed HERVs might be associated with various complex diseases, particularly neurodegenerative and psychiatric diseases. We further find that physiologically activated hervRNAs are associated with histone modifications rather than DNA demethylation. Conclusions Our results present a locus-specific landscape of physiologically expressed hervRNAs, which represent a hidden layer of genetic architecture in development and disease.

Extracting precise stellar labels is crucial for large spectroscopic surveys like the Sloan Digital Sky Survey (SDSS) and APOGEE. In this paper, we report the newest implementation of StellarGAN, a data-driven method based on generative adversarial networks (GANs). Using 1D operators like convolution, the 2D GAN is modified into StellarGAN. This allows it to learn the relevant features of 1D stellar spectra without needing labels for specific stellar types. We test the performance of StellarGAN on different stellar spectra trained on SDSS and APOGEE data sets. Our result reveals that StellarGAN attains the highest overall F1-score on SDSS data sets (F1-score = 0.82, 0.77, 0.74, 0.53, 0.51, 0.61, and 0.55, for O-type, B-type, A-type, F-type, G-type, K-type, and M-type stars) when the signal-to-noise ratio (S/N) is low (90% of the spectra have an S/N < 50), with 1% of labeled spectra used for training. Using 50% of the labeled spectral data for training, StellarGAN consistently demonstrates performance that surpasses or is comparable to that of other data-driven models, as evidenced by the F1-scores of 0.92, 0.77, 0.77, 0.84, 0.84, 0.80, and 0.67. In the case of APOGEE (90% of the spectra have an S/N < 500), our method is also superior regarding its comprehensive performance (F1-score = 0.53, 0.60, 0.56, 0.56, and 0.78 for A-type, F-type, G-type, K-type, and M-type stars) with 1% of labeled spectra for training, manifesting its learning ability out of a limited number of labeled spectra. Our proposed method is also applicable to other types of data that need to be classified (such as gravitational-wave signals, light curves, etc.).

Emerging evidence showed that lncRNAs play important roles in a wide range of biological processes of fungi such as Saccharomyces cerevisiae. However, systemic identification of lncRNAs in non-model fungi is a challenging task as the efficiency of rRNA removal has been proved to be affected by mismatches of universal rRNA-targeting probes of commercial kits, which forces deeper sequencing depth and increases costs. Here, we developed a low-cost and simple rRNA depletion method (rProbe) that could efficiently remove more than 99% rRNA in both yeast and mycelium samples of Talaromyces marneffei. The efficiency and robustness of rProbe were demonstrated to outperform the Illumina Ribo-Zero kit. Using rProbe RNA-seq, we identified 115 differentially expressed lncRNAs and constructed lncRNA-mRNA co-expression network related to dimorphic switch of T. marneffei. Our rRNA removal method has the potential to be a useful tool to explore non-coding transcriptomes of non-model fungi by adjusting rRNA probe sequences species specifically.

Circular RNAs (circRNAs) act through multiple mechanisms via their sequence features to fine-tune gene expression networks. Due to overlapping sequences with linear cognates, identifying internal sequences of circRNAs remains a challenge, which hinders a comprehensive understanding of circRNA functions and mechanisms. Here, based on rolling circular reverse transcription and nanopore sequencing, we developed circFL-seq, a full-length circRNA sequencing method, to profile circRNA at the isoform level. With a customized computational pipeline to directly identify full-length sequences from rolling circular reads, we reconstructed 77,606 high-quality circRNAs from seven human cell lines and two human tissues. circFL-seq benefits from rolling circles and long-read sequencing, and the results showed more than tenfold enrichment of circRNA reads and advantages for both detection and quantification at the isoform level compared to those for short-read RNA sequencing. The concordance of the RT-qPCR and circFL-seq results for the identification of differential alternative splicing suggested wide application prospects for functional studies of internal variants in circRNAs. Moreover, the detection of fusion circRNAs at the omics scale may further expand the application of circFL-seq. Taken together, the accurate identification and quantification of full-length circRNAs make circFL-seq a potential tool for large-scale screening of functional circRNAs.

Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches.

Exophiala spinifera, a capsule-producing black yeast, is overrepresented as agent of disseminated infection in humans with inherited dysfunction of the CARD9 gene. In a review of published caspase recruitment domain-containing protein 9 (CARD9) deficiency cases, black fungi were linked to mutations other than those prevalent in yeast and dermatophyte cases, and were found to respond to a larger panel of cytokines. Here, we sequenced and annotated the genomes of BMU 08022 from a patient with CARD9 deficiency and two environmental strains, BMU 00051 and BMU 00047. We performed genomic and transcriptomic analysis for these isolates including published black yeasts genomes, using a combination of long-read (PACBIO) and short-read (Illumina) sequencing technologies with a hybrid assembly strategy. We identified the virulence factors, fitness, and the major genetic and gene expression differences between the strains with RNAseq technology. Genome assembly reached sub-chromosome level with between 12,043 and 12,130 predicted genes. The number of indels identified in the clinical strain was higher than observed in environmental strains. We identify a relatively large core genome of 9,887 genes. Moreover, substantial syntenic rearrangements of scaffolds I and III in the CARD9-related isolate were detected. Seventeen gene clusters were involved in the production of secondary metabolites. PKS-cluster 17 was consistently found to be absent in the clinical strain. Comparative transcriptome analysis demonstrated that 16 single-copy genes were significantly differentially expressed upon incubation in brain-heart infusion broth vs. Sabouraud glucose broth. Most of the single-copy genes upregulated with Brain Heart Infusion (BHI) were transporters. There were 48 unique genes differentially expressed exclusively to the clinical strain in two different media, including genes from various metabolic processes and transcriptional regulation. Up-regulated genes in the clinical strain with Gene Ontology (GO) enrichment are mainly involved in transmembrane transport, biosynthetic process and metabolic process. This study has provided novel insights into understanding of strain-differences in intrinsic virulence of the species and indicated that intraspecific variability may be related to habitat choice. This indicates that strains of E. spinifera are differentially prone to cause infection in susceptible patient populations, and provides clues for future studies exploring the mechanisms of pathogenic and adaptive strategies of black yeasts in immunodeficient patients.

The opportunistic human pathogen Talaromyces marneffei exhibits a temperature-dependent dimorphic transition, which is closely related with its pathogenicity. This species grows as multinucleate mycelia that produce infectious conidia at 25°C, while undergoes a dimorphic transition to generate uninucleate yeast form cells at 37°C. The mechanisms of phenotype switching are not fully understood. The transcription factor madsA gene is a member of the MADS-box gene family. Previously, it was found that overexpression of madsA gene resulted in mycelial growth instead of yeast form at 37°C. In the current study, the madsA deletion mutant (ΔmadsA) and complemented strain (CMA) were constructed by genetic manipulation. We compared the phenotypes, growth, conidiation, conidial germination and susceptibility to stresses (including osmotic and oxidative) of the ΔmadsA with the wild-type (WT) and CMA strains. The results showed that the ΔmadsA displayed a faster process of the yeast-to-mycelium transition than the WT and CMA. In addition, the deletion of madsA led to a delay in conidia production and conidial germination. The tolerance of ΔmadsA conidia to hydrogen peroxide was better than that of the WT and CMA strains. Then, RNA-seq was performed to identify differences in gene expression between the ΔmadsA mutant and WT strain during the yeast phase, mycelium phase, yeast-to-mycelium transition and mycelium-to-yeast transition, respectively. Gene ontology functional enrichment analyses indicated that some important processes such as transmembrane transport, oxidation-reduction process, protein catabolic process and response to oxidative stress were affected by the madsA deletion. Together, our results suggest that madsA functions as a global regulator involved in the conidiation and germination, especially in the dimorphic transition of T. marneffei. Its roles in the survival, pathogenicity and transmission of T. marneffei require further investigation.

Long non-coding RNAs (lncRNAs) are pivotal controllers of gene expression through epigenetic mechanisms, Methylation, a prominent area of study in epigenetics, significantly impacts cellular processes. Various RNA base methylations, including m6A, m5C, m1A, and 2’-O-methylation, profoundly influence lncRNA folding, interactions, and stability, thereby shaping their functionality. LncRNAs and methylation significantly contribute to tumor development, especially in lung cancer. Their roles encompass cell differentiation, proliferation, the generation of cancer stem cells, and modulation of immune responses. Recent studies have suggested that dysregulation of lncRNA methylation can contribute to lung cancer development. Furthermore, methylation modifications of lncRNAs hold potential for clinical application in lung cancer. Dysregulated lncRNA methylation can promote lung cancer progression and may offer insights into potential biomarker or therapeutic target. This review summarizes the current knowledge of lncRNA methylation in lung cancer and its implications for RNA epigenetics and pulmonary diseases.

Abstract Background As a subclass of endogenous stable noncoding RNAs, circular RNAs are beginning to be appreciated for their potential as tumor therapeutics. However, the functions and mechanisms by which circRNAs exert protective functions in non-small cell lung cancer (NSCLC) remain largely elusive. Methods The prognostic role of circGUCY1A2 was explored in lung adenocarcinoma specimens. The overexpressed and knockdown plasmids were used to evaluate the effect of circGUCY1A2 on NSCLC cell proliferation and apoptosis efficacy. Luciferase reporter system is used to prove that circGUCY1A2 could bind to miRNA. Chip-PCR was used to prove that circGUCY1A2 could be initiated by transcription factors ARNTL. Subcutaneous tumorigenicity grafts models were established to validate findings in vivo. Results The expression of circGUCY1A2 were significantly reduced (P

Abstract Vasculogenic mimicry (VM) gives rise to tumor neovascularization that is critical for tumor growth and metastasis. Long non-coding RNAs (lncRNAs) have been implicated in diverse and fundamental biological processes. LINC00312 is associated with lung adenocarcinoma. In this study, we found that LINC00312 induced migration, invasion and VM of lung cancer cells by direct binding to the transcription factor Y-Box Binding Protein 1 (YBX1). Moreover, we demonstrated that YBX1 is associated with different fragments within 0–2410 nt 5’region of LINC00312. In addition, LINC00312 is associated with VM in 124 lung adenocarcinoma clinical specimens. The results suggest that LINC00312 is a promising therapeutic and diagnostic target for lung adenocarcinoma.