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Direct cellular reprogramming, converting one differentiated cell type directly into another, holds immense promise for regenerative medicine, developmental biology, and disease modeling. Identifying optimal transcription factor (TF) combinations to control this process remains complex and labor-intensive. Over the last decade, various computational tools emerged to infer TF sets for reprogramming. However, current methodologies possess critical limitations, and the absence of robust benchmarking standards makes it impossible to precisely validate and compare their performance. To address these challenges, we present a comprehensive analysis of existing computational methods for direct reprogramming and introduce a web application designed to support researchers in identifying and validating optimal TF sets. Our platform integrates predictions from established tools, incorporates a state-of-the-art Retrieval-Augmented Generation (RAG) system for efficient literature querying, and offers tools to further validate predictions. By providing a unified and interactive resource, our web application enhances the accessibility and efficiency of TF discovery for direct reprogramming. Furthermore, we discuss critical limitations shared by current methodologies and highlight the need for computational tools that can account for the complex regulatory dynamics of direct reprogramming. This work not only advances the toolkit available to researchers but also lays the groundwork for future innovations aimed at realizing the full potential of direct reprogramming.

The traditional nomenclature of enteroendocrine cells (EECs), established in 1977, applied the “one cell - one hormone” dogma, which distinguishes subpopulations based on the secretion of a specific hormone. These hormone-specific subpopulations included S cells for secretin (SCT), K cells for glucose-dependent insulinotropic polypeptide (GIP), N cells producing neurotensin (NTS), I cells producing cholecystokinin (CCK), D cells producing somatostatin (SST), and others. In the past 15 years, reinvestigations into murine and human organoid-derived EECs, however, strongly questioned this dogma and established that certain EECs coexpress multiple hormones. Using the Gut Cell Atlas, the largest available single-cell transcriptome dataset of human intestinal cells, this study consolidates that the original dogma is outdated not only for murine and human organoid-derived EECs, but also for primary human EECs, showing that the expression of certain hormones is not restricted to their designated cell type. Moreover, specific analyses into SCT-expressing cells reject the presence of any cell population that exhibits significantly elevated secretin expression compared to other cell populations, previously referred to as S cells. Instead, this investigation indicates that secretin production is realized jointly by other enteroendocrine subpopulations, validating corresponding observations in murine EECs also for human EECs. Furthermore, our findings corroborate that SCT expression peaks in mature EECs, in contrast, progenitor EECs exhibit markedly lower expression levels, supporting the hypothesis that SCT expression is a hallmark of EEC maturation.

The hypothalamus in the central nervous system (CNS) has important functions in controlling systemic metabolism. A calorie-rich diet triggers CNS immune activation, impairing metabolic control and promoting obesity and Type 2 Diabetes (T2D), but the mechanisms driving hypothalamic immune activation remain unclear. Here we identify regulatory T cells (Tregs) as key modulators of hypothalamic immune responses. In mice, calorie-rich environments activate hypothalamic CD4 + T cells, infiltrating macrophages and microglia while reducing hypothalamic Tregs. mRNA profiling of hypothalamic CD4 + T cells reveals a Th1-like activation state, with increased Tbx21 , Cxcr3 and Cd226 but decreased Ccr7 and S1pr1 . Importantly, results from Treg loss-of function and gain-of-function experiments show that Tregs limit hypothalamic immune activation and reverse metabolic impairments induced by hyper-caloric feeding. Our findings thus help refine the current model of Treg-centered immune-metabolic crosstalk in the brain and may contribute to the development of precision immune modulation for obesity and diabetes. Hypothalamus participates in systemic metabolic processes, while high calorie intake increases immune activation in the central nervous system. Here the authors show that reduced regulatory T cells in the hypothalamus contribute to elevated immune activation in a high calorie environment, thereby prompting a potential therapy target for metabolic diseases.

Human regulatory T (T reg ) cells are essential for immune homeostasis. The transcription factor FOXP3 maintains T reg cell identity, yet the complete set of key transcription factors that control T reg cell gene expression remains unknown. Here, we used pooled and arrayed Cas9 ribonucleoprotein screens to identify transcription factors that regulate critical proteins in primary human T reg cells under basal and proinflammatory conditions. We then generated 54,424 single-cell transcriptomes from T reg cells subjected to genetic perturbations and cytokine stimulation, which revealed distinct gene networks individually regulated by FOXP3 and PRDM1, in addition to a network coregulated by FOXO1 and IRF4. We also discovered that HIVEP2, to our knowledge not previously implicated in T reg cell function, coregulates another gene network with SATB1 and is important for T reg cell–mediated immunosuppression. By integrating CRISPR screens and single-cell RNA-sequencing profiling, we have uncovered transcriptional regulators and downstream gene networks in human T reg cells that could be targeted for immunotherapies. T reg cells are essential for immune homeostasis, but the transcription factors controlling their cellular identity are incompletely understood. Schumann and colleagues use pooled and arrayed CRISPR screens and scRNA-seq to describe key gene networks in human T reg cells.

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guIded Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on low-dimensional data, while methods implicitly handling missingness performed best on high-dimensional, multi-omics data. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.Competing Interest StatementThe authors have declared no competing interest.Footnotes* We have added a second dataset to do the performance evaluation of Lasso models dealing with missing data over a high-dimensional data* https://doi.org/10.5281/zenodo.7348113* https://github.com/cellmapslab/kimono

16S rRNA gene profiling is currently the most widely used technique in microbiome research and allows for studying microbial diversity, taxonomic profiling, phylogenetics, functional and network analysis. While a plethora of tools have been developed for the analysis of 16S rRNA gene data, only a few platforms offer a user-friendly interface and none comprehensively covers the whole analysis pipeline from raw data processing down to complex analysis. We introduce Namco, an R shiny application that offers a streamlined interface and serves as a one-stop solution for microbiome analysis. We demonstrate Namco's capabilities by studying the association between a rich fibre diet and the gut microbiota composition. Namco helped to prove the hypothesis that butyrate-producing bacteria are prompted by fibre-enriched intervention. Namco provides a broad range of features from raw data processing and basic statistics down to machine learning and network analysis, thus covering complex data analysis tasks that are not comprehensively covered elsewhere. Namco is freely available at https://exbio.wzw.tum.de/Namco/. Competing Interest Statement Dr. Markus List receives consulting fees from mbiomics GmbH outside this work. Footnotes * Newly added features in Namco manuscript: 1. Additional amplicon pipeline \"LotuS2\" in addition to DADA2. 2. Included decontam R package to identify and visualize contaminating DNA features, 3. Figure 1 revised accordingly 4. Beta diversity was updated with PCoA plot in addition to NMDS plot * https://exbio.wzw.tum.de/namco/

Banks have a process to refer complaints to their complaint- handling groups and from there to the banks' ombudsmen. If the matter is not ultimately resolved to the customer's satisfaction, she can bring the complaint to the independent Canadian Banking Ombudsman .

The main purpose of a steam trap is to discharge condensate without releasing steam. There are four main types of steam traps that accomplish this in different ways: 1. thermostatic, 2. float and thermostatic (F/T), 3. bucket, and 4. disk. Generally, thermostatic and F/T traps discharge continuously and bucket and disk traps discharge intermittently. Proper selection of a trap requires knowledge of normal pressure and flow. The trap has to be big enough to handle the flow and be rated for the maximum expected pressure. It is common practice to multiply the normal flow rate by a safety factor (which is often two, three, or even higher) in selecting a trap's flow capacity. Choose traps that have strengths that are consistent with local conditions. Size them properly and make sure they can be effectively tested and maintained. Install them as recommended by the manufacturer.