Single-cell transcriptome and accessible chromatin dynamics during endocrine pancreas development.

Delineating gene regulatory networks that orchestrate cell-type specification is a continuing challenge for developmental biologists. Single-cell analyses offer opportunities to address these challenges and accelerate discovery of rare cell lineage relationships and mechanisms underlying hierarchical lineage decisions. Here, we describe the molecular analysis of mouse pancreatic endocrine cell differentiation using single-cell transcriptomics, chromatin accessibility assays coupled to genetic labeling, and cytometry-based cell purification. We uncover transcription factor networks that delineate ß-, α-, and δ-cell lineages. Through genomic footprint analysis, we identify transcription factor-regulatory DNA interactions governing pancreatic cell development at unprecedented resolution. Our analysis suggests that the transcription factor Neurog3 may act as a pioneer transcription factor to specify the pancreatic endocrine lineage. These findings could improve protocols to generate replacement endocrine cells from renewable sources, like stem cells, for diabetes therapy.

Burnout assessment among pharmacist-academic detailers at the US Veterans Health Administration.

BACKGROUND: Burnout among clinical pharmacist practitioners has been well established, but not among those who perform academic detailing. OBJECTIVES: To measure burnout among clinical pharmacist practitioners who perform academic detailing (pharmacist-academic detailers) at the United States Veterans Health Administration and compare the findings using 2 validated burnout instruments for healthcare professionals. METHODS: A cross-sectional study design was performed to measure burnout in VHA pharmacist-academic detailers across all VA regions between April 2023 and May 2023. Burnout was measured using the Oldenburg Burnout Inventory (OLBI) and a validated single-item burnout measure (SIMB). OLBI has 2 domains (exhaustion and disengagement) and categorizes burnout into Low, Moderate, and High based on scores above or below 1 standard deviation (SD) of the mean. The validated SIMB categorized burnout as having a score of 3 or greater (range: 1-5). Interrater reliability testing between the OLBI and the SIMB at detecting burnout among pharmacist-academic detailers was performed using the kappa test. Correlation between the 2 burnout instruments was assessed using the Spearman rho test. RESULTS: A total of 50 pharmacist-academic detailers completed the burnout survey. A large proportion of respondents had Moderate levels of burnout for the total (72%) burnout score, disengagement (64%) domain, and exhaustion (74%) domain. In total, 86% of pharmacist-academic detailers reported having Moderate to High levels of burnout on the total OLBI score. On the SIMB, a total of 14 (28%) pharmacist-academic detailers reported having one or more symptoms of burnout. Interrater reliability was considered poor/slight agreement between the OLBI and SIMB. Correlation between the 2 burnout instruments was considered moderately correlated (rho = 0.67, P < 0.001). CONCLUSION: This study provides an empirical analysis of burnout among pharmacist-academic detailers; however, the ability to detect burnout among pharmacist-academic detailers may be impacted by the selection of burnout instrument used.

GenomicDistributions: fast analysis of genomic intervals with Bioconductor.

BACKGROUND: Epigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data. RESULTS: GenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs. CONCLUSIONS: GenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html ).

Embeddings of genomic region sets capture rich biological associations in lower dimensions.

MOTIVATION: Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis. RESULTS: We propose a new method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. We compared our approach to two simpler methods based on interval unions or term frequency-inverse document frequency and evaluated the methods in three ways: First, by classifying the cell line, antibody or tissue type of the region set; second, by assessing whether similarity among embeddings can reflect simulated random perturbations of genomic regions; and third, by testing robustness of the proposed representations to different signal thresholds for calling peaks. Our word2vec-based region set embeddings reduce dimensionality from more than a hundred thousand to 100 without significant loss in classification performance. The vector representation could identify cell line, antibody and tissue type with over 90% accuracy. We also found that the vectors could quantitatively summarize simulated random perturbations to region sets and are more robust to subsampling the data derived from different peak calling thresholds. Our evaluations demonstrate that the vectors retain useful biological information in relatively lower-dimensional spaces. We propose that vector representation of region sets is a promising approach for efficient analysis of genomic region data. AVAILABILITY AND IMPLEMENTATION: https://github.com/databio/regionset-embedding. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pilot RCT Testing A Mailing About Sleeping Pills and Cognitive Behavioral Therapy for Insomnia: Impact on Benzodiazepines and Z-Drugs.

OBJECTIVES: The aim is to pilot a low-touch program for reducing benzodiazepine receptor agonist (BZRA; benzodiazepines, z-drugs) prescriptions among older veterans. METHODS: Pilot randomized controlled trial consists of 2,009 veterans aged ≥ 65 years who received BZRA prescriptions from a Veterans Health Administration pharmacy (Colorado or Montana) during the prior 18 months. Active: Arm 1 was a mailed brochure about BZRA risks that also included information about a free, online cognitive behavioral therapy for the insomnia (CBTI) program. Arm 2 was a mailed brochure (same as arm 1) and telephone reinforcement call. Control: Arm 3 was a mailed brochure without insomnia treatment information. Active BZRA prescriptions at follow-up (6 and 12 months) were measured. RESULTS: In logistic regression analyses, the odds of BZRA prescription at 6- and 12-month follow-ups were not significantly different for arm 1 or 2 (active) versus arm 3 (control), including models adjusted for demographics and prescription characteristics (p-values >0.36). CONCLUSIONS: Although we observed no differences in active BZRA prescriptions, this pilot study provides guidance for conducting a future study, indicating a need for a more potent intervention. A full-scale trial testing an optimized program would provide conclusive results. CLINICAL IMPLICATIONS: Mailing information about BZRA risks and CBTI did not affect BZRA prescriptions.

Nervonic acid limits weight gain in a mouse model of diet-induced obesity.

Lipid perturbations contribute to detrimental outcomes in obesity. We previously demonstrated that nervonic acid, a C24:1 ω-9 fatty acid, predominantly acylated to sphingolipids, including ceramides, are selectively reduced in a mouse model of obesity. It is currently unknown if deficiency of nervonic acid-sphingolipid metabolites contribute to complications of obesity. Mice were fed a standard diet, a high fat diet, or these diets supplemented isocalorically with nervonic acid. The primary objective was to determine if dietary nervonic acid content alters the metabolic phenotype in mice fed a high fat diet. Furthermore, we investigated if nervonic acid alters markers of impaired fatty acid oxidation in the liver. We observed that a nervonic acid-enriched isocaloric diet reduced weight gain and adiposity in mice fed a high fat diet. The nervonic acid enrichment led to increased C24:1-ceramides and improved several metabolic parameters including blood glucose levels, and insulin and glucose tolerance. Mechanistically, nervonic acid supplementation increased PPARα and PGC1α expression and improved the acylcarnitine profile in liver. These alterations indicate improved energy metabolism through increased ß-oxidation of fatty acids. Taken together, increasing dietary nervonic acid improves metabolic parameters in mice fed a high fat diet. Strategies that prevent deficiency of, or restore, nervonic acid may represent an effective strategy to treat obesity and obesity-related complications.

Virtual academic detailing (e-Detailing): A vital tool during the COVID-19 pandemic.

As the coronavirus disease (COVID-19) pandemic continues its course in 2020, telehealth technology provides opportunities to connect patients and providers. Health policies have been amended to allow easy access to virtual health care, highlighting the field's dynamic ability to adapt to a public health crisis. Academic detailing, a peer-to-peer collaborative outreach designed to improve clinical decision-making, has traditionally relied on in-person encounters for effectiveness. A growth in the adoption of telehealth technology translates to increases in academic detailing reach for providers unable to meet with academic detailers in person. The U.S. Department of Veterans Affairs (VA) has used academic detailing to promote and reinforce evidence-based practices and has encouraged more virtual academic detailing (e-Detailing). Moreover, VA academic detailers are primarily clinical pharmacy specialists who provide clinical services and education and have made meaningful contributions to improving health care at VA. Amid the COVID-19 pandemic and physical isolation orders, VA academic detailers have continued to meet with providers to disseminate critical health care information in a timely fashion by using video-based telehealth. When working through the adoption of virtual technology for the delivery of medical care, providers may need time and nontraditional delivery of "evidence" before eliciting signals for change. Academic detailers are well suited for this role and can develop plans to help address provider discomfort surrounding the use of telehealth technology. By using e-Detailing as a method for both familiarizing and normalizing health professionals with video-based telehealth technology, pharmacists are uniquely poised to deliver consultation and direct-care services. Moreover, academic detailing pharmacists are ambassadors of change, serving an important role navigating the evolution of health care in response to emergent public health crises and helping define the norms of care delivery to follow.

A whole canopy gas exchange system for the targeted manipulation of grapevine source-sink relations using sub-ambient CO2.

BACKGROUND: Elucidating the effect of source-sink relations on berry composition is of interest for wine grape production as it represents a mechanistic link between yield, photosynthetic capacity and wine quality. However, the specific effects of carbohydrate supply on berry composition are difficult to study in isolation as leaf area or crop adjustments can also change fruit exposure, or lead to compensatory growth or photosynthetic responses. A new experimental system was therefore devised to slow berry sugar accumulation without changing canopy structure or yield. This consisted of six transparent 1.2 m3 chambers to enclose large pot-grown grapevines, and large soda-lime filled scrubbers that reduced carbon dioxide (CO2) concentration of day-time supply air by approximately 200 ppm below ambient. RESULTS: In the first full scale test of the system, the chambers were installed on mature Shiraz grapevines for 14 days from the onset of berry sugar accumulation. Three chambers were run at sub-ambient CO2 for 10 days before returning to ambient. Canopy gas exchange, and juice hexose concentrations were determined. Net CO2 exchange was reduced from 65.2 to 30 g vine- 1 day- 1, or 54%, by the sub-ambient treatment. At the end of the 10 day period, total sugar concentration was reduced from 95 to 77 g L- 1 from an average starting value of 23 g L- 1, representing a 25% reduction. Scaling to a per vine basis, it was estimated that 223 g of berry sugars accumulated under ambient supply compared to 166 g under sub-ambient, an amount equivalent to 50 and 72% of total C assimilated. CONCLUSIONS: Through supply of sub-ambient CO2 using whole canopy gas exchange chambers system, an effective method was developed for reducing photosynthesis and slowing the rate of berry sugar accumulation without modifying yield or leaf area. While in this case developed for further investigations of grape and wine composition, the system has broader applications for the manipulation and of study of grapevine source-sink relations.

Grape berry flavonoids: a review of their biochemical responses to high and extreme high temperatures.

Climate change scenarios predict an increase in average temperatures and in the frequency, intensity, and length of extreme temperature events in many wine regions around the world. In already warm and hot regions, such changes may compromise grape growing and the production of high quality wine as high temperature has been found to affect berry composition critically. Most recent studies focusing on the sole effect of temperature, separated from light and water, on grape berry composition found that high temperature affects a wide range of metabolites, and in particular flavonoids-key compounds for berry and wine quality. A decrease in total anthocyanins is reported in most cases, and appears to be directly associated with high temperature. Changes in anthocyanin composition, and flavonol and proanthocyanidin responses are however less consistent, and reflect the complexity of the underlying biosynthetic pathways and diversity of experimental treatments that have been used in these studies. This review examines the impact of high temperature on the biosynthesis, accumulation, and degradation of flavonoids, and attempts to reconcile the diversity of responses in relation to the latest understanding of flavonoid chemistry and molecular regulation.

Grape Berry Flavonoid Responses to High Bunch Temperatures Post Véraison: Effect of Intensity and Duration of Exposure.

Climate models predict an increase in the frequency and duration of heatwaves with an increase in intensity already strongly evident worldwide. The aim of this work was to evaluate the effect of two heatwave-related parameters (intensity and duration) during berry ripening and identify a threshold for berry survival and flavonoid accumulation. A Doehlert experimental design was used to test three temperature intensities (maxima of 35, 46, and 54 °C) and five durations (3 to 39 h), with treatments applied at the bunch level shortly after véraison. Berry skin and seeds were analysed by liquid chromatography-triple quadrupole-mass spectrometry (LC-QqQ-MS) for flavonoids (flavonols, anthocyanins, free flavan-3-ols, and tannins). Berries exposed to 46 °C showed little difference compared to 35 °C. However, berries reaching temperatures around 54 °C were completely desiccated, and all flavonoids were significantly decreased except for skin flavonols on a per berry basis and seed tannins in most cases. Some compounds, such as dihydroxylated flavonoids and galloylated flavan-3-ols (free and polymerised), were in higher proportion in damaged berries suggesting they were less degraded or more synthesised upon heating. Overall, irreversible berry damages and substantial compositional changes were observed and the berry survival threshold was estimated at around 50-53 °C for mid-ripe Shiraz berries, regardless of the duration of exposure.

Vitis vinifera berry metabolic composition during maturation: Implications of defoliation.

Leaves are an important contributor toward berry sugar and nitrogen (N) accumulation, and leaf area, therefore, affects fruit composition during grapevine (Vitis vinifera) berry ripening. The aim of this study was to investigate the impact of leaf presence on key berry quality attributes in conjunction with the accumulation of primary berry metabolites. Shortly after the start of véraison (berry ripening), potted grapevines were defoliated (total defoliation and 25% of the control), and the accumulation of berry soluble solids, N and anthocyanins were compared to that of a full leaf area control. An untargeted approach was undertaken to measure the content in primary metabolites by gas chromatography/mass spectrometry. Partial and full defoliation resulted in reduced berry sugar and anthocyanin accumulation, while total berry N content was unaffected. The juice yeast assimilable N (YAN), however, increased upon partial and full defoliation. Remobilized carbohydrate reserves allowed accumulation of the major berry sugars during the absence of leaf photoassimilation. Berry anthocyanin biosynthesis was strongly inhibited by defoliation, which could relate to the carbon (C) source limitation and/or increased bunch exposure. Arginine accumulation, likely resulting from reserve translocation, contributed to increased YAN upon defoliation. Furthermore, assessing the implications on various products of the shikimate pathway suggests the C flux through this pathway to be largely affected by leaf source limitation during fruit maturation. This study provides a novel investigation of impacts of leaf C and N source presence during berry maturation, on the development of key berry quality parameters as underlined by alterations in primary metabolism.

Vitis vinifera root and leaf metabolic composition during fruit maturation: implications of defoliation.

Grapevine (Vitis vinifera) roots and leaves represent major carbohydrate and nitrogen (N) sources, either as recent assimilates, or mobilized from labile or storage pools. This study examined the response of root and leaf primary metabolism following defoliation treatments applied to fruiting vines during ripening. The objective was to link alterations in root and leaf metabolism to carbohydrate and N source functioning under conditions of increased fruit sink demand. Potted grapevine leaf area was adjusted near the start of véraison to 25 primary leaves per vine compared to 100 leaves for the control. An additional group of vines were completely defoliated. Fruit sugar and N content development was assessed, and root and leaf starch and N concentrations determined. An untargeted GC/MS approach was undertaken to evaluate root and leaf primary metabolite concentrations. Partial and full defoliation increased root carbohydrate source contribution towards berry sugar accumulation, evident through starch remobilization. Furthermore, root myo-inositol metabolism played a distinct role during carbohydrate remobilization. Full defoliation induced shikimate pathway derived aromatic amino acid accumulation in roots, while arginine accumulated after full and partial defoliation. Likewise, various leaf amino acids accumulated after partial defoliation. These results suggest elevated root and leaf amino N source activity when leaf N availability is restricted during fruit ripening. Overall, this study provides novel information regarding the impact of leaf source restriction, on metabolic compositions of major carbohydrate and N sources during berry maturation. These results enhance the understanding of source organ carbon and N metabolism during fruit maturation.

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings.

Data from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) are now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. We implemented our approach in scEmbed, an unsupervised machine-learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, models pre-trained on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. scEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.

Whole-genome sequencing of a single proband together with linkage analysis identifies a Mendelian disease gene.

Although more than 2,400 genes have been shown to contain variants that cause Mendelian disease, there are still several thousand such diseases yet to be molecularly defined. The ability of new whole-genome sequencing technologies to rapidly indentify most of the genetic variants in any given genome opens an exciting opportunity to identify these disease genes. Here we sequenced the whole genome of a single patient with the dominant Mendelian disease, metachondromatosis (OMIM 156250), and used partial linkage data from her small family to focus our search for the responsible variant. In the proband, we identified an 11 bp deletion in exon four of PTPN11, which alters frame, results in premature translation termination, and co-segregates with the phenotype. In a second metachondromatosis family, we confirmed our result by identifying a nonsense mutation in exon 4 of PTPN11 that also co-segregates with the phenotype. Sequencing PTPN11 exon 4 in 469 controls showed no such protein truncating variants, supporting the pathogenicity of these two mutations. This combination of a new technology and a classical genetic approach provides a powerful strategy to discover the genes responsible for unexplained Mendelian disorders.

The characterization of twenty sequenced human genomes.

We present the analysis of twenty human genomes to evaluate the prospects for identifying rare functional variants that contribute to a phenotype of interest. We sequenced at high coverage ten "case" genomes from individuals with severe hemophilia A and ten "control" genomes. We summarize the number of genetic variants emerging from a study of this magnitude, and provide a proof of concept for the identification of rare and highly-penetrant functional variants by confirming that the cause of hemophilia A is easily recognizable in this data set. We also show that the number of novel single nucleotide variants (SNVs) discovered per genome seems to stabilize at about 144,000 new variants per genome, after the first 15 individuals have been sequenced. Finally, we find that, on average, each genome carries 165 homozygous protein-truncating or stop loss variants in genes representing a diverse set of pathways.

Determinants of renin cell differentiation: a single cell epi-transcriptomics approach.

Rationale: Renin cells are essential for survival. They control the morphogenesis of the kidney arterioles, and the composition and volume of our extracellular fluid, arterial blood pressure, tissue perfusion, and oxygen delivery. It is known that renin cells and associated arteriolar cells descend from FoxD1 + progenitor cells, yet renin cells remain challenging to study due in no small part to their rarity within the kidney. As such, the molecular mechanisms underlying the differentiation and maintenance of these cells remain insufficiently understood. Objective: We sought to comprehensively evaluate the chromatin states and transcription factors (TFs) that drive the differentiation of FoxD1 + progenitor cells into those that compose the kidney vasculature with a focus on renin cells. Methods and Results: We isolated single nuclei of FoxD1 + progenitor cells and their descendants from FoxD1 cre/+ ; R26R-mTmG mice at embryonic day 12 (E12) (n cells =1234), embryonic day 18 (E18) (n cells =3696), postnatal day 5 (P5) (n cells =1986), and postnatal day 30 (P30) (n cells =1196). Using integrated scRNA-seq and scATAC-seq we established the developmental trajectory that leads to the mosaic of cells that compose the kidney arterioles, and specifically identified the factors that determine the elusive, myo-endocrine adult renin-secreting juxtaglomerular (JG) cell. We confirm the role of Nfix in JG cell development and renin expression, and identified the myocyte enhancer factor-2 (MEF2) family of TFs as putative drivers of JG cell differentiation. Conclusions: We provide the first developmental trajectory of renin cell differentiation as they become JG cells in a single-cell atlas of kidney vascular open chromatin and highlighted novel factors important for their stage-specific differentiation. This improved understanding of the regulatory landscape of renin expressing JG cells is necessary to better learn the control and function of this rare cell population as overactivation or aberrant activity of the RAS is a key factor in cardiovascular and kidney pathologies.

Common genetic variation and the control of HIV-1 in humans.

To extend the understanding of host genetic determinants of HIV-1 control, we performed a genome-wide association study in a cohort of 2,554 infected Caucasian subjects. The study was powered to detect common genetic variants explaining down to 1.3% of the variability in viral load at set point. We provide overwhelming confirmation of three associations previously reported in a genome-wide study and show further independent effects of both common and rare variants in the Major Histocompatibility Complex region (MHC). We also examined the polymorphisms reported in previous candidate gene studies and fail to support a role for any variant outside of the MHC or the chemokine receptor cluster on chromosome 3. In addition, we evaluated functional variants, copy-number polymorphisms, epistatic interactions, and biological pathways. This study thus represents a comprehensive assessment of common human genetic variation in HIV-1 control in Caucasians.

An application of neighbourhoods in digraphs to the classification of binary dynamics.

A binary state on a graph means an assignment of binary values to its vertices. A time-dependent sequence of binary states is referred to as binary dynamics. We describe a method for the classification of binary dynamics of digraphs, using particular choices of closed neighbourhoods. Our motivation and application comes from neuroscience, where a directed graph is an abstraction of neurons and their connections, and where the simplification of large amounts of data is key to any computation. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. We consider existing and introduce new real-valued functions on closed neighbourhoods, comparing them by their ability to accurately classify different binary dynamics. We describe a classification algorithm that uses two parameters and sets up a machine learning pipeline. We demonstrate the effectiveness of the method on simulated activity on a digital reconstruction of cortical tissue of a rat, and on a nonbiological random graph with similar density.