A versatile information retrieval framework for evaluating profile strength and similarity.

In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data.

Reproducible image-based profiling with Pycytominer.

Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images. These features represent valuable single-cell phenotypes that contain information about cell state and biological processes. The use of these features for biological discovery is known as image-based or morphological profiling. However, these raw features need processing before use and image-based profiling lacks scalable and reproducible open-source software. Inconsistent processing across studies makes it difficult to compare datasets and processing steps, further delaying the development of optimal pipelines, methods, and analyses. To address these issues, we present Pycytominer, an open-source software package with a vibrant community that establishes an image-based profiling standard. Pycytominer has a simple, user-friendly Application Programming Interface (API) that implements image-based profiling functions for processing high-dimensional morphological features extracted from microscopy images of cells. Establishing Pycytominer as a standard image-based profiling toolkit ensures consistent data processing pipelines with data provenance, therefore minimizing potential inconsistencies and enabling researchers to confidently derive accurate conclusions and discover novel insights from their data, thus driving progress in our field.

Development of enhanced conformational sampling methods to probe the activation landscape of GPCRs.

G protein-coupled receptors (GPCRs) make up the largest superfamily of integral membrane proteins and play critical signal transduction roles in many physiological processes. Developments in molecular biology, biophysical, biochemical, pharmacological, and computational techniques aimed at these important therapeutic targets are beginning to provide unprecedented details on the structural as well as functional basis of their pleiotropic signaling mediated by G proteins, ß arrestins, and other transducers. This pleiotropy presents a pharmacological challenge as the same ligand-receptor interaction can cause a therapeutic effect as well as an undesirable on-target side-effect through different downstream pathways. GPCRs don't function as simple binary on-off switches but as finely tuned shape-shifting machines described by conformational ensembles, where unique subsets of conformations may be responsible for specific signaling cascades. X-ray crystallography and more recently cryo-electron microscopy are providing snapshots of some of these functionally-important receptor conformations bound to ligands and/or transducers, which are being utilized by computational methods to describe the dynamic conformational energy landscape of GPCRs. In this chapter, we review the progress in computational conformational sampling methods based on molecular dynamics and discrete sampling approaches that have been successful in complementing biophysical and biochemical studies on these receptors in terms of their activation mechanisms, allosteric effects, actions of biased ligands, and effects of pathological mutations. Some of the sampled simulation time scales are beginning to approach receptor activation time scales. The list of conformational sampling methods and example uses discussed is not exhaustive but includes representative examples that have pushed the limits of classical molecular dynamics and discrete sampling methods to describe the activation energy landscape of GPCRs.

Utilization Efficiency of Human Milk Oligosaccharides by Human-Associated Akkermansia Is Strain Dependent.

Akkermansia muciniphila is a mucin-degrading bacterium found in the human gut and is often associated with positive human health. However, despite being detected by as early as 1 month of age, little is known about the role of Akkermansia in the infant gut. Human milk oligosaccharides (HMOs) are abundant components of human milk and are structurally similar to the oligosaccharides that comprise mucin, the preferred growth substrate of human-associated Akkermansia. A limited subset of intestinal bacteria has been shown to grow well on HMOs and mucin. We therefore examined the ability of genomically diverse strains of Akkermansia to grow on HMOs. First, we screened 85 genomes representing the four known Akkermansia phylogroups to examine their metabolic potential to degrade HMOs. Furthermore, we examined the ability of representative isolates to grow on individual HMOs in a mucin background and analyzed the resulting metabolites. All Akkermansia genomes were equipped with an array of glycoside hydrolases associated with HMO deconstruction. Representative strains were all able to grow on HMOs with various efficiencies and growth yields. Strain CSUN-19, belonging to the AmIV phylogroup, grew to the highest level in the presence of fucosylated and sialylated HMOs. This activity may be partially related to the increased copy numbers and/or the enzyme activities of the α-fucosidases, α-sialidases, and ß-galactosidases. This study examines the utilization of individual purified HMOs by Akkermansia strains representing all known phylogroups. Further studies are required to examine how HMO ingestion influences gut microbial ecology in infants harboring different Akkermansia phylogroups. IMPORTANCE Human milk oligosaccharides (HMOs) are the third most abundant component of breast milk and provide several benefits to developing infants, including the recruitment of beneficial bacteria to the human gut. Akkermansia strains are largely considered beneficial bacteria and have been detected in colostrum, breast milk, and young infants. A. muciniphila MucT, belonging to the AmI phylogroup, contributes to the HMO deconstruction capacity of the infant. Here, using phylogenomics, we examined the genomic capacities of four Akkermansia phylogroups to deconstruct HMOs. Indeed, each phylogroup contained differences in their genomic capacities to deconstruct HMOs, and representative strains of each phylogroup were able to grow using HMOs. These Akkermansia-HMO interactions potentially influence gut microbial ecology in early life, a critical time for the development of the gut microbiome and infant health.

Barriers to Nutrition Interventions in Army Dining Facilities: A Qualitative Study.

INTRODUCTION: Interventions that encourage good nutrition-related behaviors in the dining environment can potentially influence the health of large numbers of military personnel. Thus, the Army has studied the effectiveness of implementing nutrition education and dining facility (DFAC) changes that included healthier recipes, revised menus, and population-specific point-of-choice labeling, but successful intervention implementation largely depends on the foodservice employees' understanding, knowledge, and desire to sustain changes. This phenomenological, qualitative study aimed to better understand common barriers to the implementation and sustainment of DFAC-based nutrition interventions at two U.S. Army DFACs. MATERIALS AND METHODS: Focus group sessions (n = 168 participants) ranging from 60 to 90 minutes in length were conducted at two large DFACs on three separate occasions every 4 months from May 2015 to January 2016 among the foodservice staff during intervention implementation. Focus group transcripts were analyzed using NVivo 11 software. Researchers conducted multiple rounds of coding following an iterative process until four principal themes emerged. RESULTS: Principal themes related to the foodservice employees' experience during the nutrition intervention revealed barriers to a successful implementation related to (1) nutrition knowledge deficits, (2) inadequate culinary training, (3) poor management practices, and (4) low staff morale. CONCLUSION: A lack of foodservice staff training and education is a significant contributor to implementation barriers. Future interventions should increase engagement with foodservice employees during intervention planning and implementation phases with a structured and tailored nutrition education and culinary skill training program. Addressing these barriers may enhance staff morale and promote intervention adherence.

A systematic approach for using DICOM structured reports in clinical processes: focus on breast cancer.

This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.