Explore the vast range of titles available.

Molecular subtyping is an instrumental tool for foodborne illness surveillance and outbreak investigation. The term “molecular epidemiology‐ in the context of foodborne bacteria is usually applied to the subtyping of bacteria that cause foodborne disease and the ways in which such subtyping data contribute to understanding the transmission of those bacteria to humans. Molecular subtyping techniques can be applied to identifying the source of a particular outbreak or to a broader understanding of the role of certain foods or processes in outbreak‐related or sporadic infections. Advances in sequencing technology over the last two decades have made whole‐genome sequencing (WGS)‐based subtyping approaches the method of choice for many foodborne pathogens. Routine application of WGS in laboratory surveillance and monitoring of foodborne pathogens is transforming public health microbiology. With the increasing international trade of food and food animals, it is crucial that molecular subtyping methods for foodborne pathogens be harmonized worldwide to facilitate the rapid comparison of strains isolated in different countries. This method harmonization for comparison is best done in the framework of surveillance networks. The ongoing implementation of WGS provides an unprecedented opportunity to establish universal global standards for subtyping foodborne bacteria that will result in easily exchangeable data and global nomenclature.

Campylobacter is regarded as a leading cause of bacterial foodborne infection in many areas of the world. Campylobacter jejuni and, to a lesser extent, Campylobacter coli are important causes of human diarrheal illnesses, even surpassing Salmonella in importance in many countries. Although human illnesses are usually self‐limiting, the associated morbidity and cost are significant. In Europe, the burden of human campylobacteriosis is estimated to be between 8 and 100 times higher than the annually reported number of cases, which has approximated 200,000 in recent years. Such a high incidence of Campylobacter‐related diarrhea has significant socioeconomic impact, making this pathogen a priority for public health and food safety researchers. This chapter highlights the important bacteriological and epidemiologic features of contamination thermotolerant Campylobacter spp. (C. jejuni and C. coli) in the human food supply chain.

To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system’s capabilities for improving the accuracy of categorical prediction of cancer subtypes. We developed SyntheVAEiser, a variational autoencoder based tool that was trained and tested on over 8000 cancer samples. We have shown that this technique can be used to augment machine learning tasks and increase performance of recognition of underrepresented cohorts.

Background Facing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Results Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering . ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters. Conclusion We applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. Availability The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub: https://github.com/galadrielbriere/ClustOmics .

Background We explored potential predictive biomarkers of immunotherapy response in patients with extensive-stage small-cell lung cancer (ES-SCLC) treated with durvalumab (D) + tremelimumab (T) + etoposide-platinum (EP), D + EP, or EP in the randomized phase 3 CASPIAN trial. Methods 805 treatment-naïve patients with ES-SCLC were randomized (1:1:1) to receive D + T + EP, D + EP, or EP. The primary endpoint was overall survival (OS). Patients were required to provide an archived tumor tissue block (or ≥ 15 newly cut unstained slides) at screening, if these samples existed. After assessment for programmed cell death ligand-1 expression and tissue tumor mutational burden, residual tissue was used for additional molecular profiling including by RNA sequencing and immunohistochemistry. Results In 182 patients with transcriptional molecular subtyping, OS with D ± T + EP was numerically highest in the SCLC-inflamed subtype ( n  = 10, median 24.0 months). Patients derived benefit from immunotherapy across subtypes; thus, additional biomarkers were investigated. OS benefit with D ± T + EP versus EP was greater with high versus low CD8A expression/CD8 cell density by immunohistochemistry, but with no additional benefit with D + T + EP versus D + EP. OS benefit with D + T + EP versus D + EP was associated with high expression of CD4 (median 25.9 vs. 11.4 months) and antigen-presenting and processing machinery (25.9 vs. 14.6 months) and MHC I and II (23.6 vs. 17.3 months) gene signatures, and with higher MHC I expression by immunohistochemistry. Conclusions These findings demonstrate the tumor microenvironment is important in mediating better outcomes with D ± T + EP in ES-SCLC, with canonical immune markers associated with hypothesized immunotherapy mechanisms of action defining patient subsets that respond to D ± T. Trial registration ClinicalTrials.gov, NCT03043872.

Salmonella enterica subsp. enterica serovars Typhimurium (S. Typhimurium) and Heidelberg (S. Heidelberg) are major causes of foodborne salmonellosis, accounting for a fifth of all annual salmonellosis cases in the United States. Rapid, efficient and accurate methods for identification are required for routine surveillance and to track specific strains during outbreaks. We used Pulsed-field Gel Electrophoresis (PFGE) and a recently developed molecular subtyping approach termed CRISPR-MVLST that exploits the hypervariable nature of virulence genes and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) to subtype clinical S. Typhimurium and S. Heidelberg isolates. We analyzed a broad set of 175 S. Heidelberg and S. Typhimurium isolates collected over a five-year period. We identified 21 Heidelberg Sequence Types (HSTs) and 37 Typhimurium STs (TSTs) that were represented by 27 and 45 PFGE pulsotypes, respectively, and determined the discriminatory power of each method. For S. Heidelberg, our data shows that combined typing by both CRISPR-MVLST and PFGE provided a discriminatory power of 0.9213. Importantly, CRISPR-MVLST was able to separate common PFGE patterns such as JF6X01.0022 into distinct STs, thus providing significantly greater discriminatory power. Conversely, we show that subtyping by either CRISPR-MVLST or PFGE independently provides a sufficient discriminatory power (0.9345 and 0.9456, respectively) for S. Typhimurium. Additionally, using isolates from two S. Typhimurium outbreaks, we demonstrate that CRISPR-MVLST provides excellent epidemiologic concordance.

Disease subtyping is essential for personalized medicine, enabling tailored treatment strategies based on disease heterogeneity. Advances in high-throughput technologies have led to the rapid accumulation of multi-omics data, driving the development of integration methods for comprehensive disease subtyping. However, existing approaches often lack explainability and fail to establish clear links between subtypes and clinical outcomes. To address these challenges, we developed EMitool, an explainable multi-omics integration tool that leverages a network-based fusion strategy to achieve biologically and clinically relevant disease subtyping without requiring prior clinical information. Using data from 31 cancer types in The Cancer Genome Atlas (TCGA), EMitool demonstrated superior subtyping accuracy compared to eight state-of-the-art methods. It also provides contribution scores for different omics data types, enhancing interpretability. EMitool-derived subtypes exhibited significant associations with the overall survival, pathological stage, tumor mutational burden, immune microenvironment characteristics, and therapeutic responses. Specifically, in kidney renal clear cell carcinoma (KIRC), EMitool identified three distinct subtypes with varying prognoses, immune cell compositions, and drug sensitivities. These findings highlight its potential for biomarker discovery and precision oncology.

As a highly heterogeneous and complex disease, the identification of cancer’s molecular subtypes is crucial for accurate diagnosis and personalized treatment. The integration of multi-omics data enables a comprehensive interpretation of the molecular characteristics of cancer at various biological levels. In recent years, an increasing number of multi-omics clustering algorithms for cancer molecular subtyping have been proposed. However, the absence of a definitive gold standard makes it challenging to evaluate and compare these methods effectively. In this study, we developed a general framework for the comprehensive evaluation of multi-omics clustering algorithms and introduced an innovative metric, the accuracy-weighted average index, which simultaneously considers both clustering performance and clinical relevance. Using this framework, we performed a thorough evaluation and comparison of 11 state-of-the-art multi-omics clustering algorithms, including deep learning-based methods. By integrating the accuracy-weighted average index with computational efficiency, our analysis reveals that PIntMF demonstrates the best overall performance, making it a promising tool for molecular subtyping across a wide range of cancers.

Purpose To evaluate diagnostic performance of four diagnostic methods for rapid determination of methicillin resistance in S. aureus positive blood cultures (BCs). Methods Clinical and spiked BCs were subjected to the evaluation of the following methods and protocols: a. Eazyplex ® MRSA Plus loop‐mediated isothermal amplification (LAMP) assay directly from BC fluid; b. MALDI-TOF MS subtyping on BC pellet extracted with Rapid Sepsityper ® protocol and on 4-h short-term subculture; c. Clearview™ Culture Colony PBP2a SA immunochromatography assay on BC pellet and on 4-h short-term subculture; d. EUCAST RAST cefoxitin screen test performed directly from BC and including reading times at 4-h, 6-h and 16–20-h. Results Eazyplex ® MRSA plus exhibited the best performance, showing 100% sensitivity, specificity, positive predictive value, and negative predictive value, followed by PBP2a SA Culture Colony Clearview assay and EUCAST RAST cefoxitin screen. MALDI-TOF MS subtyping showed the lowest diagnostic accuracy (59.8 and 65.7% directly from BC and from 4-h subculture, respectively). In detail, sensitivity and specificity ranged from 24.3% to 20.4% and from 88.9% to 98.3% for protocols performed from BC pellet and 4-h subculture, respectively. Conclusions The Eazyplex ® MRSA Plus and the immunochromatographic Clearview™ PBP2a SA Culture Colony methods can provide reliable results within 1 h from the start of positive BC processing. MALDI TOF MS subtyping showed unacceptable specificity by performing analysis from BC pellets, while its sensitivity depends on the prevalence of PSM-positive MRSA strains. The EUCAST RAST, based on disc diffusion, showed excellent performance with a time-to-result of at least 4 h.