Classification and characterisation of extracellular vesicles-related tuberculosis subgroups and immune cell profiles.

Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism of Mycobacterium tuberculosis (Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs-related differentially expressed genes (DEGs) were identified, and 17 EVs-related DEGs were up-regulated and three DEGs were down-regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs-related gene signature was identified and two EVs-related subclusters were defined. The single-cell RNA sequence (scRNA-seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs-related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high-risk group had significantly enriched immune-related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs-related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.

Enhanced resolution of marine viruses with violet side scatter.

Marine viruses make up an essential compartment of the marine ecosystem. They are the most abundant organisms and represent one of the biggest sources of unknown biodiversity. Viruses also have an important impact on bacterial and algal mortality in the ocean, and as such have a major influence on microbial diversity and biogeochemical cycling. However, little is known about the abundance and distribution patterns of viruses across the oceans and seas. Over the last 20 years, flow cytometry has been the technique of choice to detect and count the viral particles in natural samples. Nevertheless, due to their small size, the detection of marine viruses is still extremely challenging. In this article we describe how a new generation of flow cytometer which uses the side scatter (SSC) of violet photons from a 405 nm laser beam helps to improve the resolution for detecting marine viruses. To the best of our knowledge, this is the first report where virioplankton has been detected in aquatic samples using flow cytometry with a 405 nm violet SSC instead of a 488 nm blue SSC.

Characterization of metabolism-associated molecular patterns in prostate cancer.

BACKGROUND: Metabolism is a hallmark of cancer and it involves in resistance to antitumor treatment. Therefore, the purposes of this study are to classify metabolism-related molecular pattern and to explore the molecular and tumor microenvironment characteristics for prognosis predicting in prostate cancer. METHODS: The mRNA expression profiles and the corresponding clinical information for prostate cancer patients from TCGA, cBioPortal, and GEO databases. Samples were classified using unsupervised non-negative matrix factorization (NMF) clustering based on differentially expressed metabolism-related genes (MAGs). The characteristics of disease-free survival (DFS), clinicopathological characteristics, pathways, TME, immune cell infiltration, response to immunotherapy, and sensitivity to chemotherapy between subclusters were explored. A prognostic signature was constructed by LASSO cox regression analysis based on differentially expressed MAGs and followed by the development for prognostic prediction. RESULTS: A total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer. The significant differences in clinical characteristics (age, T/N stage, Gleason) and DFS between two subclusters. Cluster 1 was associated with cell cycle and metabolism-related pathways, and epithelial-mesenchymal transition (EMT), etc., involved in cluster 2. Moreover, lower ESTIMATE/immune/stromal scores, lower expression of HLAs and immune checkpoint-related genes, and lower half-maximal inhibitory concentration (IC50) values in cluster 1 compared with cluster 2. The 10 MAG signature was identified and constructed a risk model for DFS predicting. The patients with high-risk scores showed poorer DFS. The area under the curve (AUC) values for 1-, 3-, 5-year DFS were 0.744, 0.731, 0.735 in TCGA-PRAD dataset, and 0.668, 0.712, 0.809 in GSE70768 dataset, 0.763, 0.802, 0.772 in GSE70769 dataset. Besides, risk score and Gleason score were identified as independent factors for DFS predicting, and the AUC values of risk score and Gleason score were respectively 0.743 and 0.738. The nomogram showed a favorable performance in DFS predicting. CONCLUSION: Our data identified two metabolism-related molecular subclusters for prostate cancer that were distinctly characterized in prostate cancer. Metabolism-related risk profiles were also constructed for prognostic prediction.

A characterization of the molecular phenotype and inflammatory response of schizophrenia patient-derived microglia-like cells.

Different lines of evidence support a causal role for microglia in the pathogenesis of schizophrenia. However, how schizophrenia patient-derived microglia are affected at the phenotypic and functional level is still largely unknown. We used a recently described model to induce patient-derived microglia-like cells and used this to analyze changes in the molecular phenotype and function of myeloid cells in schizophrenia. We isolated monocytes from twenty recent-onset schizophrenia patients and twenty non-psychiatric controls. We cultured the cells towards an induced microglia-like phenotype (iMG), analyzed the phenotype of the cells by RNA sequencing and mass cytometry, and their response to LPS. Mass cytometry showed a high heterogeneity of iMG in cells derived from patients as well as controls. The prevalence of two iMG clusters was significantly higher in schizophrenia patients (adjusted p-value < 0.001). These subsets are characterized by expression of ApoE, Ccr2, CD18, CD44, and CD95, as well as IRF8, P2Y12, Cx3cr1 and HLA-DR. In addition, we found that patient-derived iMG show an enhanced response to LPS, with increased secretion of TNF-α. Further studies are needed to replicate these findings, to determine whether similar subclusters are present in schizophrenia patients in vivo, and to address how these subclusters are related to the increased response to LPS, as well as other microglial functions.

Diversity and natural selection on the thrombospondin-related adhesive protein (TRAP) gene of Plasmodium knowlesi in Malaysia.

BACKGROUND: Plasmodium knowlesi a parasite of the macaques is currently the most common cause of human malaria in Malaysia. The thrombospondin-related adhesive protein (TRAP) gene is pre-erythrocytic stage antigen. It is a well-characterized vaccine candidate in Plasmodium vivax and Plasmodium falciparum, however, no study has been done in the orthologous gene of P. knowlesi. This study investigates nucleotide diversity, haplotypes, natural selection and population differentiation of full-length pktrap genes in clinical samples from Malaysia. METHODS: Forty full-length pktrap sequences from clinical isolates of Malaysia along with the reference H-strain were downloaded from published databases. Genetic diversity, polymorphism, haplotype and natural selection were determined using DnaSP 5.10 software. McDonald-Kreitman test was conducted using P. vivax and Plasmodium coatneyi as ortholog sequence in DnaSP 5.10 software. Population genetic differentiation index (FST) of parasite populations was determined using Arlequin v3.5. Phylogenetic relationships between trap ortholog genes were determined using MEGA 5.0 software. RESULTS: Comparison of 40 full-length pktrap sequences along with the H-strain identified 74 SNPs (53 non-synonymous and 21 synonymous substitutions) resulting in 29 haplotypes. Analysis of the full-length gene showed that the nucleotide diversity was lower compared to its nearest ortholog pvtrap. Domain-wise analysis indicated that the proline/asparagine rich region had higher nucleotide diversity compared to the von Willebrand factor domain and the thrombospondin-type-1 domain. McDonald-Kreitman test identified that the ratio of the number of nonsynonymous to synonymous polymorphic sites within P. knowlesi was significantly higher than that of the number of nonsynonymous to synonymous fixed sites between P. knowlesi and P. vivax. The von Willebrand factor domain also indicated balancing selection using MK test, however, it did not give significant results when tested with P. coatneyi as an outgroup. Phylogenetic analysis of full-length genes identified three distinct sub-clusters of P. knowlesi, one originating from Peninsular Malaysia and two originating from Malaysian Borneo. High population differentiation values was observed within samples from Peninsular Malaysia and Malaysian Borneo. CONCLUSIONS: This study is the first to report on the genetic diversity and natural selection of full-length pktrap. Low level of genetic diversity was found across the full-length gene of pktrap. Balancing selection of the von Willebrand factor domain indicated that TRAP could be a target in inducing immune response against P. knowlesi infections. However, higher number of samples would be necessary to further confirm the findings.

Identification of immune-related biomarkers for glaucoma using gene expression profiling.

Introduction: Glaucoma, a principal cause of irreversible vision loss, is characterized by intricate optic neuropathy involving significant immune mechanisms. This study seeks to elucidate the molecular and immune complexities of glaucoma, aiming to improve our understanding of its pathogenesis. Methods: Gene expression profiles from glaucoma patients were analyzed to identify immune-related differentially expressed genes (DEGs). Techniques used were weighted gene co-expression network analysis (WGCNA) for network building, machine learning algorithms for biomarker identification, establishment of subclusters related to immune reactions, and single-sample gene set enrichment analysis (ssGSEA) to explore hub genes' relationships with immune cell infiltration and immune pathway activation. Validation was performed using an NMDA-induced excitotoxicity model and RT-qPCR for hub gene expression measurement. Results: The study identified 409 DEGs differentiating healthy individuals from glaucoma patients, highlighting the immune response's significance in disease progression. Immune cell infiltration analysis revealed elevated levels of activated dendritic cells, natural killer cells, monocytes, and immature dendritic cells in glaucoma samples. Three hub genes, CD40LG, TEK, and MDK, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the NMDA-induced excitotoxicity model. Discussion: The findings propose the three identified immune-related genes (IRGs) as novel diagnostic markers for glaucoma, offering new insights into the disease's pathogenesis and potential therapeutic targets. The strong correlation between these IRGs and immune responses underscores the intricate role of immunity in glaucoma, suggesting a shift in the approach to its diagnosis and treatment.

Identification of novel genera and subcluster classifications for mycobacteriophages.

Aim: To identify novel genera amongst mycobacteriophages (MP) and verify a hypothesised correlation between the taxonomy set by the International Committee on Taxonomy of Viruses (ICTV) and the National Centre for Biotechnology Information (NCBI) with that of the Actinobacteriophage Database, which may help formalise subcluster assignment. Methods: A dataset of 721 MP genomes was analysed using VIRIDIC, a nucleotide alignment-based software that predicts genus assignments. Potentially novel genera were analysed using Gegenees and VICTOR, respectively. These genera were then compared to the subclusters assigned by the Actinobacteriophage Database to verify a hypothesis that one genus can be assigned to one subcluster (i.e., the genus-subcluster hypothesis). Results: Initially, when comparing the current genus classifications of the 721 MP dataset to the Actinobacteriophage database subcluster assignments, 83.3% of subclusters supported the genus-subcluster hypothesis. Following the sequential VIRIDIC, Gegenees and VICTOR analyses, a total of 20 novel genera were identified based on a ≥ 70% and ~ 50% similarity threshold for VIRIDIC and Gegenees, respectively, and a monophyletic nature in the VICTOR output. Interestingly, these criteria also appear to support the creation of 13 novel subclusters, which would increase the support for the genus-subcluster hypothesis to 97.6%. Conclusion: The link between genus and subcluster classifications appears robust, as most subclusters can be assigned a single genus and vice versa. By relating the taxonomic and clustering classification systems, they can be easily kept up to date to best reflect MP diversity, which could aid the rapid selection of related (or diverse) phages for research, therapeutic and diagnostic purposes.

The novel subclusters based on cancer-associated fibroblast for pancreatic adenocarcinoma.

Introduction: Pancreatic adenocarcinoma (PAAD) is a fatal disease characterized by promoting connective tissue proliferation in the stroma. Activated cancer-associated fibroblasts (CAFs) play a key role in fibrogenesis in PAAD. CAF-based tumor typing of PAAD has not been explored. Methods: We extracted single-cell sequence transcriptomic data from GSE154778 and CRA001160 datasets from Gene Expression Omnibus or Tumor Immune Single-cell Hub to collect CAFs in PAAD. On the basis of Seurat packages and new algorithms in machine learning, CAF-related subtypes and their top genes for PAAD were analyzed and visualized. We used CellChat package to perform cell-cell communication analysis. In addition, we carried out functional enrichment analysis based on clusterProfiler package. Finally, we explored the prognostic and immunotherapeutic value of these CAF-related subtypes for PAAD. Results: CAFs were divided into five new subclusters (CAF-C0, CAF-C1, CAF-C2, CAF-C3, and CAF-C4) based on their marker genes. The five CAF subclusters exhibited distinct signaling patterns, immune status, metabolism features, and enrichment pathways and validated in the pan-cancer datasets. In addition, we found that both CAF-C2 and CAF-C4 subgroups were negatively correlated with prognosis. With their top genes of each subclusters, the sub-CAF2 had significantly relations to immunotherapy response in the patients with pan-cancer and immunotherapy. Discussion: We explored the heterogeneity of five subclusters based on CAF in signaling patterns, immune status, metabolism features, enrichment pathways, and prognosis for PAAD.

Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS.

Time-series single-cell RNA sequencing (scRNA-seq) provides a breakthrough in modern biology by enabling researchers to profile and study the dynamics of genes and cells based on samples obtained from multiple time points at an individual cell resolution. However, cell asynchrony and an additional dimension of multiple time points raises challenges in the effective use of time-series scRNA-seq data for identifying genes and cell subclusters that vary over time. However, no effective tools are available. Here, we propose scTITANS (https://github.com/ZJUFanLab/scTITANS), a method that takes full advantage of individual cells from all time points at the same time by correcting cell asynchrony using pseudotime from trajectory inference analysis. By introducing a time-dependent covariate based on time-series analysis method, scTITANS performed well in identifying differentially expressed genes and cell subclusters from time-series scRNA-seq data based on several example datasets. Compared to current attempts, scTITANS is more accurate, quantitative, and capable of dealing with heterogeneity among cells and making full use of the timing information hidden in biological processes. When extended to broader research areas, scTITANS will bring new breakthroughs in studies with time-series single cell RNA sequencing data.

Virioplankton distribution in the tropical western Pacific Ocean in the vicinity of a seamount.

The shallow Caroline Seamount is located in the tropical western Pacific Ocean. Its summit is 57 m below the surface and penetrates the euphotic zone. Therefore, it is ideal for the study of the influence of seamount on plankton distribution. Here, virioplankton abundance and distribution were investigated by flow cytometry (FCM) in the Caroline Seamount in August and September 2017. The total abundance of virus-like particles (VLP) was in the range of 0.64 × 106 -18.77 × 106  particles/ml and the average was 5.37 ± 3.75 × 106  particles/ml. Three to four distinct viral subclusters with similar side scatter but different green fluorescence intensities were identified. Above the deep chlorophyll maximum (DCM), two medium fluorescence virus (MFV) subclusters were discriminated. Between the DCM and the deeper layers, only one MFV subcluster was resolved. In general, low fluorescence viruses (LFV) comprised the most abundant subclusters. In the 75-150 m water column, however, the MFV abundance was higher than the LFV abundance. High fluorescence viruses (HFV) constituted the least abundant subcluster throughout the entire water column. Virioplankton abundance was significantly enhanced at the seamount stations. Environmental factors including water temperature and nitrate concentration were the most correlated with the variation in virioplankton abundance at the seamount stations. Interactions between shallow seamounts and local currents can support large virus standing stocks, causing a so-called indirect "seamount effect" on the virioplankton.