An exhaustive analysis was performed on more than 2000 microbiotas from French Protected Designation of Origin (PDO) cheeses, covering most cheese families produced throughout the world. Thanks to a complete and accurate set of associated metadata, we have carried out a deep analysis of the ecological drivers of microbial communities in milk and \"terroir\" cheeses. We show that bacterial and fungal microbiota from milk differed significantly across dairy species while sharing a core microbiome consisting of four microbial species. By contrast, no microbial species were detected in all ripened cheese samples. Our network analysis suggested that the cheese microbiota was organized into independent network modules. These network modules comprised mainly species with an overall relative abundance lower than 1%, showing that the most abundant species were not those with the most interactions. Species assemblages differed depending on human drivers, dairy species, and geographical area, thus demonstrating the contribution of regional know-how to shaping the cheese microbiota. Finally, an extensive analysis at the milk-to-cheese batch level showed that a high proportion of cheese taxa were derived from milk under the influence of the dairy species and protected designation of origin.

BACKGROUND: The occurrence and progression of hepatocellular carcinoma (HCC) are significantly affected by DNA damage response (DDR). Exploring DDR-related biomarkers can help predict the prognosis and immune characteristics of HCC.
METHODS: First, the single-cell RNA sequencing (scRNA-seq) dataset GSE242889 was processed and performed manual annotation. Then we found the marker genes of DDR-active subgroups based on \"AUCell\" algorithm. The \"Limma\" R package was used to identify differentially expressed genes (DEGs) between tumor and normal samples of HCC. The risk prognostic model was constructed by filtering genes using univariate Cox and LASSO regression analyses. Finally, the signatures were analyzed for immune infiltration, gene mutation, and drug sensitivity. Last but not least, KPNA2, which had the largest coefficient in our model was validated by experiments including western blot, MTT, colony formation and γ-H2AX assays.
RESULTS: We constructed a prognostic model based on 5 DDR marker genes including KIF2C, CDC20, KPNA2, UBE2S and ADH1B for HCC. We also proved that the model had an excellent performance in both training and validation cohorts. Patients in the high-risk group had a poorer prognosis, different immune features, gene mutation frequency, immunotherapy response and drug sensitivity compared with the low-risk group. Besides, our experimental results proved that KPNA2 was up-regulated in liver cancer cells than in hepatocytes. More importantly, the knockdown of KPNA2 significantly inhibited cell variability, proliferation and promoted DNA damage.
CONCLUSIONS: We innovatively integrated scRNA-seq and bulk RNA sequencing to construct the DDR-related prognostic model. Our model could effectively predict the prognosis, immune landscape and therapy response of HCC.

BACKGROUND: A key component of idiopathic pulmonary fibrosis (IPF) is the involvement of immune cells. However, the causal interaction between different immune cell signatures and IPF remain inconclusive.
OBJECTIVE: Based on publicly accessible data, our study utilized the Mendelian randomization (MR) approach to determine the causative relevance of complex immune cell phenotypes in IPF.
METHODS: We deployed a two-sample Mendelian randomization approach to evaluate the causal interaction between immune cell markers and IPF. All data regarding 731 immune signatures and IPF were acquired from two genome-wide association studies (GWAS) that are accessible to the public. The original study adopted the inverse variance weighted (IVW) method, followed by sensitivity analyses aimed at eliminating heterogeneity and pleiotropy. Additionally, Multivariate Mendelian randomization (MVMR) was utilized to identify the independent risk factors in our study.
RESULTS: The summary dataset for IPF was accessed from the Finnish Genetic Consortium R9, comprising 2018 patients and 373,064 controls. And the dataset for immune signatures was conducted in 3,757 Sardinian individuals. By conducting IVW and extensive sensitivity analyses, univariate Mendelian randomization (UVMR) identified one immunophenotype that remained causally associated with IPF after false discovery rate (FDR) correction: CD39 on CD39+ CD8+T cells (odd ratio [OR] = 0.850, 95 % confidence interval [CI] = 0.787-0.918, P = 3.68 × 10-5). The causal association with IPF was further validated using MVMR.
CONCLUSIONS: Based on rigorous MR analysis methods and FDR correction, our study demonstrated that CD39 on CD39+ CD8+T cells showed a protective effect against IPF, providing effective insights for preventing and diagnosing pulmonary fibrosis.

Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes-C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)-emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.

UNASSIGNED: Aberrant reactive oxygen species (ROS) production is one of the hallmarks of cancer. During their growth and dissemination, cancer cells control redox signaling to support protumorigenic pathways. As a consequence, cancer cells become reliant on major antioxidant systems to maintain a balanced redox tone, while avoiding excessive oxidative stress and cell death. This concept appears especially relevant in the context of glioblastoma multiforme (GBM), the most aggressive form of brain tumor characterized by significant heterogeneity, which contributes to treatment resistance and tumor recurrence. From this viewpoint, this study aims to investigate whether gene regulatory networks can effectively capture the diverse redox states associated with the primary phenotypes of GBM.
UNASSIGNED: In this study, we utilized publicly available GBM datasets along with proprietary bulk sequencing data. Employing computational analysis and bioinformatics tools, we stratified GBM based on their antioxidant capacities and evaluated the distinctive functionalities and prognostic values of distinct transcriptional networks in silico.
UNASSIGNED: We established three distinct transcriptional co-expression networks and signatures (termed clusters C1, C2, and C3) with distinct antioxidant potential in GBM cancer cells. Functional analysis of each cluster revealed that C1 exhibits strong antioxidant properties, C2 is marked with a discrepant inflammatory trait and C3 was identified as the cluster with the weakest antioxidant capacity. Intriguingly, C2 exhibited a strong correlation with the highly aggressive mesenchymal subtype of GBM. Furthermore, this cluster holds substantial prognostic importance: patients with higher gene set variation analysis (GSVA) scores of the C2 signature exhibited adverse outcomes in overall and progression-free survival.
UNASSIGNED: In summary, we provide a set of transcriptional signatures that unveil the antioxidant potential of GBM, offering a promising prognostic application and a guide for therapeutic strategies in GBM therapy.

Forensic document examiners are often confronted with questioned documents written with ballpoint pens. Depending on the force applied (or pressure) as well as the angle between the pen and the surface, sister lines running parallel to the inked strokes can be left by the lip of the housing ball. In a real case, sister lines were observed on the left side of inked strokes of a questioned signature. To assess whether the writer of that signature was a left-handed or a right-handed writer based on this result, an experimental study was carried out. Handwritten samples and signatures from 182 right-handed and 18 left-handed writers were collected with different ballpoint pens. For every writer, the presence or absence of sister lines was recorded. Sister lines were observed in 49% of the writers of the studied population (in 50% of the right-handed and ca. 39% of the left-handed writers). Most individuals who left sister lines showed a writing angle of 50°-55°. The location of sister lines was tabulated to inform probabilities of observing sister lines at a given location, if the writer is a right-handed or left-handed writer. In the case at hand, a likelihood ratio of 48 was obtained in support of the proposition of a left-handed writer, rather than a right-handed writer. Applying Bayes\' theorem, such value moves the prior probability of 15% that the writer of the questioned signature is left-handed to a posterior probability of 89%.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

BACKGROUND: Severe community-acquired pneumonia (SCAP) results in high mortality as well as massive economic burden worldwide, yet limited knowledge of the bio-signatures related to prognosis has hindered the improvement of clinical outcomes. Pathogen, microbes and host are three vital elements in inflammations and infections. This study aims to discover the specific and sensitive biomarkers to predict outcomes of SCAP patients.
METHODS: In this study, we applied a combined metagenomic and transcriptomic screening approach to clinical specimens gathered from 275 SCAP patients of a multicentre, prospective study.
RESULTS: We found that 30-day mortality might be independent of pathogen category or microbial diversity, while significant difference in host gene expression pattern presented between 30-day mortality group and the survival group. Twelve outcome-related clinical characteristics were identified in our study. The underlying host response was evaluated and enrichment of genes related to cell activation, immune modulation, inflammatory and metabolism were identified. Notably, omics data, clinical features and parameters were integrated to develop a model with six signatures for predicting 30-day mortality, showing an AUC of 0.953 (95% CI: 0.92-0.98).
CONCLUSIONS: In summary, our study linked clinical characteristics and underlying multi-omics bio-signatures to the differential outcomes of patients with SCAP. The establishment of a comprehensive predictive model will be helpful for future improvement of treatment strategies and prognosis with SCAP.
BACKGROUND: National Natural Science Foundation of China (No. 82161138018), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100).

Some evidence suggests a biphasic pattern of changes in cortical thickness wherein higher, rather than lower, thickness is associated with very early Alzheimer\'s disease (AD) pathology. We examined whether integrating information from AD brain signatures based on mean diffusivity (MD) can aid in the interpretation of cortical thickness/volume as a risk factor for future AD-related changes. Participants were 572 men in the Vietnam Era Twin Study of Aging who were cognitively unimpaired at baseline (mean age = 56 years; range = 51-60). Individuals with both high thickness/volume signatures and high MD signatures at baseline had lower cortical thickness/volume in AD signature regions and lower episodic memory performance 12 years later compared to those with high thickness/volume and low MD signatures at baseline. Groups did not differ in level of young adult cognitive reserve. Our findings are in line with a biphasic model in which increased cortical thickness may precede future decline and establish the value of examining cortical MD alongside cortical thickness to identify subgroups with differential risk for poorer brain and cognitive outcomes.

Non-follicular low-grade B-cell lymphomas (LGBCL) are biologically diverse entities that share clinical and histologic features that make definitive pathologic categorization challenging. While most patients with LGBCL have an indolent course, some experience aggressive disease, highlighting additional heterogeneity across these subtypes. To investigate the potential for shared biology across subtypes, we performed RNA sequencing and applied machine learning approaches that identified five clusters of patients that grouped independently of subtype. One cluster was characterized by inferior outcome, upregulation of cell cycle genes, and increased tumor immune cell content. Integration of whole exome sequencing identified novel LGBCL mutations and enrichment of TNFAIP3 and BCL2 alterations in the poor survival cluster. Building on this, we further refined a transcriptomic signature associated with early clinical failure in two independent cohorts. Taken together, this study identifies unique clusters of LGBCL defined by novel gene expression signatures and immune profiles associated with outcome across diagnostic subtypes.