An miR-200 Cluster on Chromosome 23 Regulates Sperm Motility in Zebrafish

Besides its well-documented roles in cell proliferation, apoptosis, and carcinogenesis, the function of the p53-microRNA axis has been recently revealed in the reproductive system. Recent studies indicated that miR-200 family members are dysregulated in nonobstructive azoospermia patients, whereas their functions remain poorly documented. The aim of this study was to investigate the function of the miR-200 family on zebrafish testis development and sperm activity. There was no substantial difference in testis morphology and histology between wild-type (WT) and knockout zebrafish with deletion of miR-200 cluster on chromosome 6 (chr6-miR-200-KO) or on chromosome 23 (chr23-miR-200-KO). Interestingly, compared with WT zebrafish, the chr6-miR-200-KO zebrafish had no difference on sperm motility, whereas chr23-miR-200-KO zebrafish showed significantly improved sperm motility. Consistently, ectopic expression of miR-429a, miR-200a, and miR-200b, which are located in the miR-200 cluster on chromosome 23, significantly reduced motility traits of sperm. Several sperm motility-related genes, such as amh, wt1a, and srd5a2b have been confirmed as direct targets of miR-200s on chr23. 17a-ethynylestradiol (EE2) exposure resulted in upregulated expression of p53 and miR-429a in testis and impairment of sperm motility. Strikingly, in p53 mutant zebrafish testis, the expression levels of miR-200s on chr23 were significantly reduced and accompanied by a stimulation of sperm motility. Moreover, the upregulation of miR-429a associated with EE2 treatment was abolished in testis with p53 mutation. And the impairment of sperm activity by EE2 treatment was also eliminated when p53 was mutated. Together, our results reveal that miR-200 cluster on chromosome 23 controls sperm motility in a p53-dependent manner.</p

Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

The latest biological findings observe that the traditional motionless 'lock-and-key' theory is not generally applicable because the receptor and ligand are constantly moving. Nonetheless, remarkable changes in associated atomic sites and binding pose can provide vital information in understanding the process of drug binding. Based on this mechanism, molecular dynamics (MD) simulations were invented as a useful tool for investigating the dynamic properties of a molecular system. However, the computational expenditure limits the growth and application of protein trajectory-related studies, thus hindering the possibility of supervised learning. To tackle this obstacle, we present a novel spatial-temporal pre-training method based on the modified Equivariant Graph Matching Networks (EGMN), dubbed ProtMD, which has two specially designed self-supervised learning tasks: an atom-level prompt-based denoising generative task and a conformation-level snapshot ordering task to seize the flexibility information inside MD trajectories with very fine temporal resolutions. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen, i.e., the binding affinity prediction and the ligand efficacy prediction, to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. We observe a huge improvement from current state-of-the-art methods, with a decrease of 4.3% in RMSE for the binding affinity problem and an average increase of 13.8% in AUROC and AUPRC for the ligand efficacy problem. The results demonstrate valuable insight into a strong correlation between the magnitude of conformation's motion in the 3D space (i.e., flexibility) and the strength with which the ligand binds with its receptor

Identification of A Novel Capsular Polysaccharide Cluster in Lactiplantibacillus plantarum YZH81

In order to investigate the mechanism of extracellular polysaccharide (EPS) synthesis in Lactobacillus plantarum, a strain of Lactobacillus plantarum YZH81 with high EPS production was selected for the study of extracellular polysaccharide synthesis gene cluster (cps). After whole-genome sequencing, alignment and analysis, it was determined that the genome of YZH81 contained two cps gene clusters, one of which (cps1) had yet to be identified in terms of structure and function, while the other (cps2) was highly homologous to other reported Lactobacillus plantarum strains. The conclusions in this study, the cps1 gene cluster resulted 52.28% reduction in EPS production, accelerated self-aggregation, reduced adhesion and 32.42% reduction in the ability to generated 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH·) in the YZH81∆cps1 strain compared to the wild-type YZH81 strain. The results suggested that the cps1 gene cluster of strain YZH81 was associated with EPS synthesis and established favorable conditions for further studies on the mechanism of EPS biosynthesis in this strain

A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications.

Disease-disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease-disease relationships by assembling four biological networks: disease-miRNA, miRNA-gene, disease-gene, and the human protein-protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease-disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease-disease relationships with miRNA-mediated pathobiological pathways

The Epitope Study on the SARS-CoV Nucleocapsid Protein

The nucleocapsid protein (N protein) has been found to be an antigenic protein in a number of coronaviruses. Whether the N protein in severe acute respiratory syndrome-associated coronavirus (SARS-CoV) is antigenic remains to be elucidated. Using Western blot and Enzyme-linked Immunosorbent Assay (ELISA), the recombinant N proteins and the synthesized peptides derived from the N protein were screened in sera from SARS patients. All patient sera in this study displayed strong positive immunoreactivities against the recombinant N proteins, whereas normal sera gave negative immunoresponses to these proteins, indicating that the N protein of SARS-CoV is an antigenic protein. Furthermore, the epitope sites in the N protein were determined by competition experiments, in which the recombinant proteins or the synthesized peptides competed against the SARS-CoV proteins to bind to the antibodies raised in SARS sera. One epitope site located at the C-terminus was confirmed as the most antigenic region in this protein. A detailed screening of peptide with ELISA demonstrated that the amino sequence from Codons 371 to 407 was the epitope site at the C-terminus of the N protein. Understanding of the epitope sites could be very significant for developing an effective diagnostic approach to SARS

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Improving molecular representation learning with metric learning-enhanced optimal transport

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